diff --git a/.gitignore b/.gitignore index 2e89dbf..3de1e1c 100644 --- a/.gitignore +++ b/.gitignore @@ -134,7 +134,7 @@ models/initial_values/* models/*retrained* models/*check* models/*handcrafted* -visualizations/*.data +#visualizations/*.data data/pdb/*.txt data/tmp/* predictions/.DS_Store @@ -143,3 +143,4 @@ datasets/.DS_Store .DS_Store datasets/.DS_Store utilities/paths_jerome.py +*test.ipynb diff --git a/Saliency analysis.ipynb b/Saliency analysis.ipynb deleted file mode 100644 index 456d828..0000000 --- a/Saliency analysis.ipynb +++ /dev/null @@ -1,1439 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "86e08fd6", - "metadata": {}, - "source": [ - "# Saliency analysis for explaining model prediction\n", - "## Installation:\n", - "For running this notebook, the following additional packages are required:\n", - "\n", - "pythreejs (https://pythreejs.readthedocs.io/en/stable/)\n", - "\n", - "ipywebrtc==0.5.0\n", - "\n", - "ipywidgets==7.5.1 \n", - "\n", - "Installation with pip should work with a recent jupyter version. Reboot the jupyter notebook afterwards.\n", - "\n", - "\n", - "\n", - "## Important: Cells must be executed one at time (rather than with Cell -> Run All)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8902f4f9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "Using TensorFlow backend.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cdd575e786834056b1524d5db209db44", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from utilities import wrappers,dataset_utils\n", - "from utilities.paths import model_folder,MSA_folder\n", - "from preprocessing import pipelines,PDBio,PDB_processing,protein_chemistry,sequence_utils\n", - "import tensorflow as tf\n", - "import keras.backend as K\n", - "import numpy as np\n", - "from visualizations import show_3d_filters,show_3d_neighborhoods,weight_logo_3d,ngl\n", - "import predict_features\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "%matplotlib inline\n", - "\n", - "def flatten_list(nested_list):\n", - " out = []\n", - " for list_ in nested_list:\n", - " out+= list_\n", - " return np.array(out)\n", - "\n", - "def get_gradient(model, input, residue_index, layer = 'SCAN_filter_activity_atom'):\n", - " gradient0 = K.gradients(\n", - " model.model.get_layer('attention_layer').output[0][:, residue_index, 0],\n", - " model.model.get_layer(layer).output\n", - " )\n", - "\n", - " gradient1 = K.gradients(\n", - " model.model.get_layer('attention_layer').output[0][:, residue_index, 1],\n", - " model.model.get_layer(layer).output\n", - " )\n", - " gradient = gradient0 + gradient1\n", - " activation = model.model.get_layer(layer).output\n", - "\n", - " sess = K.get_session()\n", - " output = sess.run(gradient +[activation], feed_dict=dict(\n", - " [(input_tensor,input_value[np.newaxis]) for input_tensor,input_value in zip(model.model.inputs, input) ]\n", - " )\n", - " )\n", - " test_gradient = output[1][0] - output[0][0]\n", - " test_activation = output[-1][0]\n", - " return test_gradient,test_activation\n", - "\n", - "\n", - "def get_saliency(model, inputs, \n", - " residue_index, \n", - " filter_specificities,sequence,atomids,layer='SCAN_filter_activity_atom'):\n", - " \n", - " gradient,activation = get_gradient(model, inputs, residue_index, \n", - " layer = layer\n", - " )\n", - " \n", - " \n", - " natoms = len(flatten_list(atomids))\n", - " nresidues = len(sequence)\n", - " print(natoms,nresidues)\n", - " \n", - " if layer == 'SCAN_filter_activity_atom':\n", - " baseline_entries = filter_specificities['atom_activity']['conditional_median2']\n", - " baseline_activation = np.concatenate([baseline_entries[protein_chemistry.list_aa.index(aa),atomid] for atomid,aa in zip(atomids,sequence)]) \n", - " elif layer == 'SCAN_filter_activity_aa':\n", - " baseline_entries = filter_specificities['aa_activity']['conditional_median']\n", - " baseline_activation =np.stack([baseline_entries[protein_chemistry.list_aa.index(aa)] for aa in sequence],axis=0)\n", - " elif layer == 'all_embedded_attributes_aa':\n", - " baseline_entries = filter_specificities['aggregated_atom_activity']['conditional_median']\n", - " baseline_activation =np.stack([baseline_entries[protein_chemistry.list_aa.index(aa)] for aa in sequence],axis=0) \n", - " elif layer == 'attributes_aa':\n", - " if activation.shape[-1] == 21: # PWM\n", - " baseline_activation = np.ones([nresidues,21])/21 # Uniform distribution.\n", - " else:\n", - " baseline_activation = np.zeros([nresidues,20])\n", - " baseline_activation[:,0] +=1 # Alanine\n", - " \n", - " if 'atom' in layer:\n", - " gradient = gradient[:natoms]\n", - " activation = activation[:natoms]\n", - " else:\n", - " gradient = gradient[:nresidues]\n", - " activation = activation[:nresidues]\n", - " saliency = gradient * (activation - baseline_activation)\n", - " return saliency,gradient,activation,baseline_activation\n", - "\n", - "sg = weight_logo_3d.make_sphere_geometry(30)\n", - "renderer = None" - ] - }, - { - "cell_type": "markdown", - "id": "9c249411", - "metadata": {}, - "source": [ - "# Calculate filter specificities (precomputed)\n", - "The first step is to calculate the filter specificities.\n", - "For the atomic filters, we must simply extract the neighborhood embedding layer parameters from the keras model object.\n", - "For the amino acid filters, since the amino-acid specificity of individual gaussian components is *non-linear*, we must first determine it on a set of test proteins. This is done as follows:\n", - "1. Calculate, for each gaussian component of each filter, the distribution of activities.\n", - "2. Identify the top-1/5\\% activating residues.\n", - "3. Determine their amino acid type, secondary structure and accessible surface area.\n", - "4. Determine the mean activity across the top-activating residues.\n", - "\n", - "For the trained networks, the specificities are precomputed. Otherwise, calculating the amino acid filter specificities takes about 1-2 hour on a laptop\n", - "To avoid this step and visualize only the atom filters, specify only_atom=True\n", - "\n", - "Then, for a given filter, display the gaussians with highest mean activity (threshold1 = 33\\% of the maximum mean activity), and show attribute specificity as inset.\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "72b28717", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/dataset_utils.py:71: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", - " list_labels = np.array(list_labels)\n", - "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/dataset_utils.py:72: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", - " list_resids = np.array(list_resids)\n" - ] - } - ], - "source": [ - "mode = 'interface' # Prediction mode: 'interface' (protein-protein binding sites), 'epitope' (B-cell epitopes), 'idp' (protein - intrinsically disordered proteins binding sites)\n", - "use_MSA = False # Whether to use evolutionary information or not.\n", - "\n", - "\n", - "if mode == 'interface':\n", - " top_percent = 5\n", - " if use_MSA:\n", - " model_name = 'ScanNet_PPI'\n", - " else:\n", - " model_name = 'ScanNet_PPI_noMSA'\n", - " dataset_name = 'PPBS_validation'\n", - " list_origins = np.concatenate([dataset_utils.read_labels('datasets/PPBS/labels_%s.txt'%dataset)[0]\n", - " for dataset in ['validation_70','validation_homology','validation_topology','validation_none']\n", - " ])\n", - " biounit = True\n", - " \n", - "elif mode == 'epitope':\n", - " top_percent = 5\n", - " if use_MSA:\n", - " model_name = 'ScanNet_PAI_0'\n", - " else:\n", - " model_name = 'ScanNet_PAI_noMSA_0'\n", - " dataset_name = 'BCE_fold1'\n", - " list_origins = dataset_utils.read_labels('datasets/BCE/labels_fold1.txt')[0]\n", - " biounit = False \n", - " \n", - "elif mode == 'idp':\n", - " top_percent = 5\n", - " if use_MSA:\n", - " model_name = 'ScanNet_PIDPI_0'\n", - " else:\n", - " model_name = 'ScanNet_PIDPI_noMSA_0'\n", - " dataset_name = 'PIDPBS_fold0'\n", - "# list_origins = dataset_utils.read_labels('datasets/PIDP/labels_fold0.txt')[0]\n", - " biounit = True \n", - " \n", - "\n", - "\n", - "filter_specificities = show_3d_filters.calculate_filter_specificities(\n", - " model_name,\n", - " dataset_name = dataset_name,\n", - " dataset_origins = list_origins,\n", - " biounit=False,\n", - " ncores=4,\n", - " only_atom=False,\n", - " top_percent = top_percent,\n", - " fresh = False,\n", - " Lmax = 1024\n", - "\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "04edbcd3", - "metadata": {}, - "source": [ - "# Define chain of interest and compute binding propensity profile\n", - "\n", - "Three examples are shown:\n", - "- The barstar enzyme inhibitor protein (protein-protein binding site)\n", - "- The spike protein receptor binding domain of SARS-CoV-2 (B-cell epitope)\n", - "- Calcineurin (Protein-intrinsically disordered protein binding sites)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "002d3ea1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parsing /Users/jerometubiana/PDB/pdb1brs.bioent\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", - "\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", - "\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", - "\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/embeddings.py:432: The name tf.squared_difference is deprecated. Please use tf.math.squared_difference instead.\n", - "\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", - "\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/attention.py:68: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "keep_dims is deprecated, use keepdims instead\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/attention.py:72: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "keep_dims is deprecated, use keepdims instead\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", - "\n", - "Model: \"model_1\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 957, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 783, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 957, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 783, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 783) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 783, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 783, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 783, 16, 3), 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 783, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 783, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 783, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 87, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 783, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 783, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 87, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 783, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 783, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 783, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 87, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 87, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 87, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 87, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 87, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 87, 64), (No 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 175, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 87, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 87, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 87, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 175, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 87, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 87, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 87, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 87, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 87, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 87, 16, 3), 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 87, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 87, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 87, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 87, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 87, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 87, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 87, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 87, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 87, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 87, 32, 5), 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 87, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 87, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 87, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 87, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 87, 2), (Non 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 87, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/typedlist.py:66: NumbaPendingDeprecationWarning: \n", - "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'item' of function 'impl_append..impl'.\n", - "\n", - "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", - "\n", - "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/listobject.py\", line 597:\n", - "\n", - " def impl(l, item):\n", - " ^\n", - "\n", - " l.append(item)\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", - "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'item' of function '_append'.\n", - "\n", - "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", - "\n", - "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/typedlist.py\", line 65:\n", - "@njit\n", - "def _append(l, item):\n", - "^\n", - "\n", - " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", - "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lst' of function 'in_seq..seq_contains_impl'.\n", - "\n", - "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", - "\n", - "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/cpython/listobj.py\", line 660:\n", - "def in_seq(context, builder, sig, args):\n", - " def seq_contains_impl(lst, value):\n", - " ^\n", - "\n", - " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", - "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lst' of function 'list_index..list_index_impl'.\n", - "\n", - "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", - "\n", - "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/cpython/listobj.py\", line 911:\n", - "\n", - " def list_index_impl(lst, value):\n", - " ^\n", - "\n", - " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Predicting binding sites from pdb structures with ScanNet_interface\n", - "Parsing /Users/jerometubiana/PDB/pdb1brs.ent\n", - "List of inputs:\n", - "['1brs_0_D']\n", - "Loading model ScanNet_interface\n", - "Model: \"model_2\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 957, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 783, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 957, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 783, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 783) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 783, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 783, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 783, 16, 3), 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 783, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 783, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 783, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 87, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 783, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 783, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 87, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 783, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 783, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 783, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 87, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 87, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 87, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 87, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 87, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 87, 64), (No 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 175, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 87, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 87, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 87, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 175, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 87, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 87, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 87, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 87, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 87, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 87, 16, 3), 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 87, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 87, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 87, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 87, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 87, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 87, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 87, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 87, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 87, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 87, 32, 5), 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 87, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 87, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 87, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 87, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 87, 2), (Non 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 87, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating groups...\n", - "Grouped 1 examples in 1 groups\n", - "Grouping and padding...\n", - "Performing prediction...\n", - "1/1 [==============================] - 1s 873ms/step\n", - "Ungrouping and unpadding...\n", - "prediction done!\n", - "Predicting binding sites from pdb structures with ScanNet_interface\n", - "Parsing /Users/jerometubiana/PDB/pdb1brs.ent\n", - "List of inputs:\n", - "['1brs_0_D']\n", - "Loading model ScanNet_interface\n", - "Model: \"model_3\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 957, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 783, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 957, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 783, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 783) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 783, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 783, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 783, 16, 3), 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 783, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 783, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 783, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 87, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 783, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 783, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 87, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 783, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 783, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 783, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 87, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 87, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 87, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 87, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 87, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 87, 64), (No 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 175, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 87, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 87, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 87, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 175, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 87, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 87, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 87, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 87, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 87, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 87, 16, 3), 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 87, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 87, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 87, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 87, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 87, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 87, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 87, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 87, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 87, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 87, 32, 5), 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 87, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 87, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 87, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 87, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 87, 2), (Non 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 87, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating groups...\n", - "Grouped 1 examples in 1 groups\n", - "Grouping and padding...\n", - "Performing prediction...\n", - "1/1 [==============================] - 1s 937ms/step\n", - "Ungrouping and unpadding...\n", - "prediction done!\n", - "Warning, attention coeffs are flipped\n" - ] - } - ], - "source": [ - "if mode == 'interface':\n", - " pdbid = '1brs_D' # Barnase-barstar complex (enzyme-inhibitor)\n", - " biounit = True\n", - "elif mode == 'epitope':\n", - "# pdbid = '7jvb_A' # Spike protein RBD\n", - " pdbid = '6lzg_B' # Spike protein RBD\n", - " biounit = False\n", - "elif mode == 'idp':\n", - " pdbid = '2p6b_AB' # Calcineurin\n", - "\n", - "\n", - "file,chainid = PDBio.getPDB(pdbid,biounit=biounit)\n", - "\n", - "structure,chain = PDBio.load_chains(file=file,chain_ids=chainid)\n", - "resnumber = list(PDB_processing.get_PDB_indices(chain) )\n", - "sequence,_,_,atomids,_ = PDB_processing.process_chain(chain)\n", - "nresidues = len(resnumber)\n", - "natoms = len(flatten_list(atomids))\n", - "\n", - "\n", - "\n", - "model = wrappers.load_model('%s/%s'%(model_folder,model_name),Lmax=nresidues)\n", - "pipeline = pipelines.ScanNetPipeline(aa_features = 'pwm' if use_MSA else 'sequence', Lmax_aa=nresidues,padded=True)\n", - "\n", - "\n", - "\n", - "if use_MSA:\n", - " MSA_file= [sequence_utils.call_hhblits(sequence, MSA_folder+'MSA_%s_%s_%s.fasta'%(pdbid,chainid_[0],chainid_[1]) ) for chainid_ in chainid]\n", - "else:\n", - " MSA_file = None\n", - "\n", - "inputs = pipeline.process_example(chain_obj=chain,MSA_file=MSA_file)[0]\n", - "\n", - "dictionary_binding = predict_features.predict_features(pdbid,model=model_name,\n", - " layer=None )\n", - "dictionary_attention = predict_features.predict_features(pdbid,model=model_name,\n", - " layer='attention_layer' )\n", - "\n", - "\n", - "vector_binding = np.array(list(dictionary_binding.values()))\n", - "vector_attention = np.array(list(dictionary_attention.values()))" - ] - }, - { - "cell_type": "markdown", - "id": "0f0964fa", - "metadata": {}, - "source": [ - "# Visualize binding propensity and attention profile and identify residue of interest" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e3ddca92", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fig, ax = plt.subplots(figsize=(16, 8))\n", - "ax.plot(vector_binding,label='Binding site probability')\n", - "\n", - "if mode == 'interface':\n", - " ax.set_ylim([-0.02,0.9])\n", - "else:\n", - " ax.set_ylim([-0.02,0.52])\n", - "ax2 = ax.twinx()\n", - "ax2.plot(vector_attention, c='gray',label='Attention coefficient')\n", - "ax.set_xticks(np.arange(nresidues)[::4])\n", - "ax.set_xticklabels(resnumber[::4], rotation=90)\n", - "ax.grid()\n", - "ax.legend(fontsize=14,loc='upper left')\n", - "ax2.legend(fontsize=14,loc='center left')\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c560b31a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "75237533b90d4dfc957081bc24171227", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "NGLWidget()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "if mode == 'interface':\n", - " mini = 0\n", - " maxi = 0.7\n", - "else:\n", - " mini = 0\n", - " maxi = 0.35\n", - "\n", - "view = ngl.display(chain[0],dictionary_binding,\n", - " representation = 'cartoon',\n", - " mini=mini,maxi=maxi)\n", - "view" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "77100451", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6c1be6b92bb94b92a3614570aa116e93", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "NGLWidget()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "view = ngl.display(chain[0],dictionary_attention,\n", - " representation = 'cartoon',\n", - " mini=0.5,maxi=2.5)\n", - "view" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b81e6b58", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Residue: 76E, binding: 0.195, attention: 0.917\n" - ] - } - ], - "source": [ - "modelid = 0\n", - "chainid = 'D'\n", - "residue = 76\n", - "\n", - "print ('Residue: %s%s, binding: %.3f, attention: %.3f'%(\n", - "residue,\n", - "sequence[resnumber.index(residue)],\n", - "dictionary_binding[(modelid,chainid,residue)],\n", - "dictionary_attention[(modelid,chainid,residue)], \n", - ") \n", - " )\n", - "\n", - "\n", - "residue_index = resnumber.index(residue)" - ] - }, - { - "cell_type": "markdown", - "id": "64543df6", - "metadata": {}, - "source": [ - "# Explain model prediction based on atomic filters activity" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "959a0a1f", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "693 87\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "75 NH2 64 0.25846660173831637\n", - "76 OE2 1 0.15021078750814354\n", - "86 O 26 0.12160452707477898\n", - "75 O 1 0.11820768766286682\n", - "75 CA 40 0.10785494422112363\n", - "80 O 24 0.10462371799592773\n", - "66 CA 34 -0.10060152807395895\n", - "66 N 67 -0.17137757620199068\n" - ] - } - ], - "source": [ - "saliency,gradient,activation,baseline_activation = get_saliency(model, \n", - " inputs,residue_index, \n", - " filter_specificities,sequence,atomids,layer='SCAN_filter_activity_atom')\n", - "\n", - "\n", - "\n", - "plt.matshow(saliency.T, aspect='auto',\n", - " cmap='coolwarm', vmax=0.8 * np.abs(saliency).max(), vmin=- 0.8 * np.abs(saliency).max())\n", - "plt.title('Saliency map')\n", - "plt.xlabel('Atom index')\n", - "plt.ylabel('Filter index')\n", - "plt.show()\n", - "\n", - "\n", - "fig, ax = plt.subplots(ncols=2,figsize=(8,4))\n", - "\n", - "ax[0].hist(saliency.flatten(), bins=100, log=True)\n", - "atom_importance = np.sqrt((saliency ** 2).sum(-1))\n", - "ax[0].set_title('(Atom,Filter) importance',fontsize=14)\n", - "ax[1].hist(atom_importance, bins=100, log=True)\n", - "ax[1].set_title('Atom importance',fontsize=14)\n", - "plt.show()\n", - "\n", - "\n", - "explaining_atom_pairs = np.nonzero(np.abs(saliency) >= 0.1)\n", - "explanation_values = saliency[explaining_atom_pairs[0], explaining_atom_pairs[1]]\n", - "\n", - "order = np.argsort(explanation_values)[::-1]\n", - "\n", - "explaining_resids = [resnumber[x] for x in inputs[6][explaining_atom_pairs[0], 0]]\n", - "atomids_flat = flatten_list(atomids)\n", - "explaining_atomids = [protein_chemistry.list_atoms[x] for x in atomids_flat[explaining_atom_pairs[0]]]\n", - "\n", - "nexplanations = len(explaining_resids)\n", - "\n", - "for n in order:\n", - " print(explaining_resids[n], explaining_atomids[n], explaining_atom_pairs[1][n], explanation_values[n])\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "bb0d17f5", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parsing /Users/jerometubiana/PDB/pdb1brs.bioent\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a7ae1bcef94b494e93a4b7da267ad5dd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Renderer(camera=PerspectiveCamera(position=(8.0, 5.0, 8.0), projectionMatrix=(1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "explaining_residue = 75\n", - "explaining_atom = 'NH2'\n", - "explaining_filter =64\n", - "\n", - "if renderer is not None: # Clear previous objects\n", - " renderer.close()\n", - "\n", - "atom_positions,atom_types,atom_bonds = show_3d_neighborhoods.get_neighborhood(\n", - " pdb = pdbid[:4],\n", - " model = modelid,\n", - " chain = chainid,\n", - " resnumber = explaining_residue,\n", - " atom = explaining_atom,\n", - " assembly=False,\n", - "# assembly = [(0,'A'),(0,'D')], \n", - " biounit=biounit,\n", - ")\n", - "\n", - "\n", - "list_objects = show_3d_neighborhoods.show_atoms(atom_positions,atom_types,atom_bonds,render=False,\n", - " radius_scale = 0.15)\n", - "\n", - "renderer = show_3d_filters.plot_atomic_filter(filter_specificities,\n", - " explaining_filter,\n", - " y_offset = 0.25,\n", - " sg=sg,\n", - " list_additional_objects=list_objects,\n", - " threshold1=0.33);\n", - "display(renderer)" - ] - }, - { - "cell_type": "markdown", - "id": "7f53d055", - "metadata": {}, - "source": [ - "# Explain model prediction based on amino acid filters activity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b45a1e7", - "metadata": {}, - "outputs": [], - "source": [ - "saliency,gradient,activation,baseline_activation = get_saliency(model, \n", - " inputs,residue_index, \n", - " filter_specificities,sequence,atomids,layer='SCAN_filter_activity_aa')\n", - "\n", - "\n", - "\n", - "plt.matshow(saliency.T, aspect='auto',\n", - " cmap='coolwarm', vmax=0.8 * np.abs(saliency).max(), vmin=- 0.8 * np.abs(saliency).max())\n", - "plt.title('Saliency map')\n", - "plt.xlabel('Atom index')\n", - "plt.ylabel('Filter index')\n", - "plt.show()\n", - "\n", - "\n", - "fig, ax = plt.subplots(ncols=2,figsize=(8,4))\n", - "\n", - "ax[0].hist(saliency.flatten(), bins=100, log=True)\n", - "residue_importance = np.sqrt((saliency ** 2).sum(-1))\n", - "ax[0].set_title('(Residue,Filter) importance',fontsize=14)\n", - "ax[1].hist(residue_importance, bins=100, log=True)\n", - "ax[1].set_title('Residue importance',fontsize=14)\n", - "plt.show()\n", - "\n", - "\n", - "explaining_residue_pairs = np.nonzero(np.abs(saliency) >= 0.1)\n", - "explanation_saliencies = saliency[explaining_residue_pairs[0], explaining_residue_pairs[1]]\n", - "explanation_values = activation[explaining_residue_pairs[0], explaining_residue_pairs[1]]\n", - "\n", - "order = np.argsort(explanation_saliencies)[::-1]\n", - "\n", - "explaining_resids = [resnumber[x] for x in explaining_residue_pairs[0] ]\n", - "explaining_residue = [sequence[x] for x in explaining_residue_pairs[0] ]\n", - "\n", - "nexplanations = len(explaining_resids)\n", - "\n", - "for n in order:\n", - " print('RESID: %s %s Filter: %s, Activity: %.2f, Saliency %.2f ' % (explaining_resids[n], explaining_residue[n], explaining_residue_pairs[1][n], \n", - " explanation_values[n], explanation_saliencies[n]) )\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f16b14e2", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "explaining_residue = 35\n", - "explaining_filter = 111\n", - "\n", - "if renderer is not None: # Clear previous objects\n", - " renderer.close()\n", - "\n", - "\n", - "aa_positions,aa_types,aa_peptide_bonds = show_3d_neighborhoods.get_neighborhood_aa(\n", - " pdb = pdbid[:4],\n", - " model = modelid,\n", - " chain = chainid,\n", - " resnumber = explaining_residue,\n", - " assembly=False,\n", - " biounit=biounit,\n", - " Kmax=16\n", - ")\n", - "\n", - "list_objects = show_3d_neighborhoods.show_aminoacids(aa_positions,aa_types,aa_peptide_bonds,sg=sg,render=False);\n", - "\n", - "renderer = show_3d_filters.plot_aminoacid_filter(filter_specificities,\n", - " explaining_filter,\n", - " sg=sg,\n", - " list_additional_objects=list_objects,\n", - " threshold1=0.33,scale=3.0);\n", - "display(renderer)" - ] - }, - { - "cell_type": "markdown", - "id": "c7ddbb4b", - "metadata": {}, - "source": [ - "# Explain model prediction based on sequence/PSSM profile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "40c7b635", - "metadata": {}, - "outputs": [], - "source": [ - "# gradient,activation = get_gradient(model, inputs, residue_index, \n", - "# layer = 'attributes_aa'\n", - "# )\n", - "-\n", - "seqnum = sequence_utils.seq2num(sequence)[0]\n", - "\n", - "if use_MSA:\n", - " baseline_activation = np.zeros([1,21])/21\n", - " saliency = (gradient * (activation - baseline_activation ) )\n", - " plt.matshow(activation.T,aspect='auto',cmap='coolwarm'); plt.show()\n", - " \n", - " plt.matshow(saliency.T, aspect='auto',\n", - " cmap='coolwarm', vmax=0.8 * np.abs(saliency).max(), vmin=- 0.8 * np.abs(saliency).max())\n", - "\n", - " \n", - "else:\n", - " baseline_activation = np.array([1] + 19)[np.newaxis]\n", - " saliency = (gradient * (activation - baseline_activation ) ).sum(-1)\n", - " plt.plot(saliency)\n", - " plt.title('Saliency map')\n", - " plt.xlabel('Residue index')\n", - " plt.ylabel('Saliency')\n", - " plt.show()\n", - "\n", - "\n", - "# fig, ax = plt.subplots(ncols=2,figsize=(8,4))\n", - "\n", - "# ax[0].hist(saliency.flatten(), bins=100, log=True)\n", - "# residue_importance = np.sqrt((saliency ** 2).sum(-1))\n", - "# ax[0].set_title('(Residue,Filter) importance',fontsize=14)\n", - "# ax[1].hist(residue_importance, bins=100, log=True)\n", - "# ax[1].set_title('Residue importance',fontsize=14)\n", - "# plt.show()\n", - "\n", - "\n", - "# explaining_residues = np.abs(saliency)>0.2\n", - "# explanation_saliencies = saliency[explaining_residues]\n", - "\n", - "# order = np.argsort(explanation_saliencies)[::-1]\n", - "\n", - "# explaining_resids = [resnumber[x] for x in explaining_residue_pairs[0] ]\n", - "# explaining_residue = [sequence[x] for x in explaining_residue_pairs[0] ]\n", - "\n", - "# nexplanations = len(explaining_resids)\n", - "\n", - "# for n in order:\n", - "# print('RESID: %s %s Saliency %.2f ' % (explaining_resids[n], explaining_residue[n], explanation_saliencies[n]) )\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "046b7789", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "\n", - "aa_positions,aa_types,aa_peptide_bonds = show_3d_neighborhoods.get_neighborhood_aa(\n", - " pdb = pdbid[:4],\n", - " model = modelid,\n", - " chain = chainid,\n", - " resnumber = explaining_residue,\n", - " assembly=False,\n", - " biounit=biounit,\n", - " Kmax=16\n", - ")\n", - "\n", - "list_objects = show_3d_neighborhoods.show_aminoacids(aa_positions,aa_types,aa_peptide_bonds,sg=sg,render=False)\n", - "\n", - "renderer = show_3d_filters.plot_aminoacid_filter(filter_specificities,\n", - " explaining_filter,\n", - " sg=sg,\n", - " list_additional_objects=list_objects,\n", - " threshold1=0.33,scale=3.0);\n", - "display(renderer)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "448a8cfe", - "metadata": {}, - "outputs": [], - "source": [ - "def virtual_mutagenesis(model, inputs,residue_index, \n", - " filter_specificities,sequence,atomids):\n", - " saliency_atom,gradient_atom,activation_atom,baseline_activation_atom = get_saliency(model, inputs, \n", - " residue_index, \n", - " filter_specificities,sequence,atomids,layer='all_embedded_attributes_aa')\n", - "\n", - " saliency_sequence,gradient_sequence,activation_sequence,baseline_activation_sequence = get_saliency(model, inputs, \n", - " residue_index, \n", - " filter_specificities,sequence,atomids,layer='attributes_aa')\n", - " \n", - " atom_term = (gradient_atom[:,:,np.newaxis] * (baseline_activation_atom[:,:,np.newaxis] - filter_specificities['aggregated_atom_activity']['conditional_median'].T[np.newaxis] ) ).sum(1)\n", - " sequence_term = (gradient_sequence[:,:,np.newaxis] * (activation_sequence[:,:,np.newaxis] - np.eye(20)[np.newaxis] ) ).sum(1)\n", - " return atom_term + sequence_term\n", - "\n", - "\n", - "output = virtual_mutagenesis(model, inputs,residue_index, \n", - " filter_specificities,sequence,atomids)\n", - "\n", - "\n", - "plt.matshow(output.T,vmin=-0.5,vmax=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f28ad0c2", - "metadata": {}, - "outputs": [], - "source": [ - "plt.plot(output[:,0]); plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8c55e5f6", - "metadata": {}, - "outputs": [], - "source": [ - "order = np.argsort(np.abs(output[:,0]))[::-1]\n", - "\n", - "for u in order[:10]:\n", - " print('%s%s: contribution %.2f'%(resnumber[u],sequence[u], output[u,0]) )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3ffa1f3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Visualize superimposed neighborhood and filter.ipynb b/Visualize superimposed neighborhood and filter.ipynb deleted file mode 100644 index dbe193f..0000000 --- a/Visualize superimposed neighborhood and filter.ipynb +++ /dev/null @@ -1,1300 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0b17df01", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", - "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/dataset_utils.py:71: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", - " list_labels = np.array(list_labels)\n", - "/Users/jerometubiana/Documents/GitHub/ScanNet/utilities/dataset_utils.py:72: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n", - " list_resids = np.array(list_resids)\n" - ] - } - ], - "source": [ - "import os\n", - "from visualizations import show_3d_filters,show_3d_neighborhoods,weight_logo_3d\n", - "from utilities import dataset_utils\n", - "import predict_features\n", - "import numpy as np\n", - "model_name = 'ScanNet_PAI_noMSA_0'\n", - "dataset_name = 'BCE_fold1'\n", - "list_origins = dataset_utils.read_labels('datasets/BCE/labels_fold1.txt')[0]\n", - "\n", - "filter_specificities = show_3d_filters.calculate_filter_specificities(\n", - " model_name,\n", - " dataset_name = dataset_name,\n", - " dataset_origins = list_origins,\n", - " biounit=False,\n", - " ncores=4,\n", - " only_atom=False\n", - "\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0bf51963", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Predicting binding sites from pdb structures with ScanNet_interface\n", - "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n", - "List of inputs:\n", - "['7jvb_0_A']\n", - "Loading model ScanNet_interface\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n", - "\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n", - "\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n", - "\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/embeddings.py:432: The name tf.squared_difference is deprecated. Please use tf.math.squared_difference instead.\n", - "\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n", - "\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/attention.py:68: calling reduce_max_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "keep_dims is deprecated, use keepdims instead\n", - "WARNING:tensorflow:From /Users/jerometubiana/Documents/GitHub/ScanNet/network/attention.py:72: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "keep_dims is deprecated, use keepdims instead\n", - "WARNING:tensorflow:From /opt/anaconda3/envs/py36/lib/python3.6/site-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n", - "\n", - "Model: \"model_1\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 2134, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 1746, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 2134, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 1746, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 1746) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 1746, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 1746, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 1746, 16, 3) 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 1746, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 1746, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 1746, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 194, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 1746, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 1746, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 194, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 1746, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 194, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 194, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 194, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 194, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 194, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 194, 64), (N 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 389, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 194, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 194, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 194, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 389, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 194, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 194, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 194, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 194, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 194, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 194, 16, 3), 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 194, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 194, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 194, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 194, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 194, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 194, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 194, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 194, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 194, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 194, 32, 5), 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 194, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 194, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 194, 2), (No 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 194, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/typedlist.py:66: NumbaPendingDeprecationWarning: \n", - "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'item' of function 'impl_append..impl'.\n", - "\n", - "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", - "\n", - "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/listobject.py\", line 597:\n", - "\n", - " def impl(l, item):\n", - " ^\n", - "\n", - " l.append(item)\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", - "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'item' of function '_append'.\n", - "\n", - "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", - "\n", - "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/typed/typedlist.py\", line 65:\n", - "@njit\n", - "def _append(l, item):\n", - "^\n", - "\n", - " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", - "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lst' of function 'in_seq..seq_contains_impl'.\n", - "\n", - "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", - "\n", - "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/cpython/listobj.py\", line 660:\n", - "def in_seq(context, builder, sig, args):\n", - " def seq_contains_impl(lst, value):\n", - " ^\n", - "\n", - " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n", - "/opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/core/ir_utils.py:2067: NumbaPendingDeprecationWarning: \n", - "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lst' of function 'list_index..list_index_impl'.\n", - "\n", - "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n", - "\n", - "File \"../../../../../opt/anaconda3/envs/py36/lib/python3.6/site-packages/numba/cpython/listobj.py\", line 911:\n", - "\n", - " def list_index_impl(lst, value):\n", - " ^\n", - "\n", - " warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating groups...\n", - "Grouped 1 examples in 1 groups\n", - "Grouping and padding...\n", - "Performing prediction...\n", - "1/1 [==============================] - 1s 629ms/step\n", - "Ungrouping and unpadding...\n", - "prediction done!\n", - "Predicting binding sites from pdb structures with ScanNet_interface\n", - "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n", - "List of inputs:\n", - "['7jvb_0_A']\n", - "Loading model ScanNet_interface\n", - "Model: \"model_3\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 2134, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 1746, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 2134, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 1746, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 1746) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 1746, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 1746, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 1746, 16, 3) 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 1746, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 1746, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 1746, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 194, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 1746, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 1746, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 194, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 1746, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 194, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 194, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 194, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 194, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 194, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 194, 64), (N 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 389, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 194, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 194, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 194, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 389, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 194, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 194, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 194, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 194, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 194, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 194, 16, 3), 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 194, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 194, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 194, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 194, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 194, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 194, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 194, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 194, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 194, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 194, 32, 5), 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 194, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 194, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 194, 2), (No 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 194, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating groups...\n", - "Grouped 1 examples in 1 groups\n", - "Grouping and padding...\n", - "Performing prediction...\n", - "1/1 [==============================] - 1s 796ms/step\n", - "Ungrouping and unpadding...\n", - "prediction done!\n", - "Predicting binding sites from pdb structures with ScanNet_interface\n", - "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n", - "List of inputs:\n", - "['7jvb_0_A']\n", - "Loading model ScanNet_interface\n", - "Model: \"model_4\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 2134, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 1746, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 2134, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 1746, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 1746) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 1746, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 1746, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 1746, 16, 3) 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 1746, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 1746, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 1746, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 194, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 1746, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 1746, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 194, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 1746, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 194, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 194, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 194, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 194, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 194, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 194, 64), (N 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 389, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 194, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 194, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 194, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 389, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 194, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 194, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 194, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 194, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 194, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 194, 16, 3), 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 194, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 194, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 194, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 194, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 194, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 194, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 194, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 194, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 194, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 194, 32, 5), 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 194, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 194, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 194, 2), (No 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 194, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating groups...\n", - "Grouped 1 examples in 1 groups\n", - "Grouping and padding...\n", - "Performing prediction...\n", - "1/1 [==============================] - 1s 975ms/step\n", - "Ungrouping and unpadding...\n", - "prediction done!\n", - "Warning, attention coeffs are flipped\n", - "Predicting binding sites from pdb structures with ScanNet_interface\n", - "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n", - "List of inputs:\n", - "['7jvb_0_A']\n", - "Loading model ScanNet_interface\n", - "Model: \"model_6\"\n", - "__________________________________________________________________________________________________\n", - "Layer (type) Output Shape Param # Connected to \n", - "==================================================================================================\n", - "point_clouds_atom (InputLayer) (None, 2134, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_atom (InputLayer) (None, 1746, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_atom (Maski (None, 2134, 3) 0 point_clouds_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_atom (Mask (None, 1746, 3) 0 frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_atom (InputLayer) (None, 1746) 0 \n", - "__________________________________________________________________________________________________\n", - "frames_atom (FrameBuilder) (None, 1746, 4, 3) 0 masked_point_clouds_atom[0][0] \n", - " masked_frame_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_atom (Embed (None, 1746, 12) 156 attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_atom (LocalNeighbo [(None, 1746, 16, 3) 0 frames_atom[0][0] \n", - " embedded_attributes_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_atom (None, 1746, 16, 32) 384 neighborhood_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_atom (OuterPr (None, 1746, 128) 53248 embedded_local_coordinates_atom[0\n", - " neighborhood_atom[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom_norma (None, 1746, 128) 384 SCAN_filter_input_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_aa (InputLayer (None, 194, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "sequence_indices_atom (InputLay (None, 1746, 1) 0 \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_atom (Time (None, 1746, 128) 0 SCAN_filter_activity_atom_normali\n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_aa (Mas (None, 194, 1) 0 sequence_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_sequence_indices_atom (M (None, 1746, 1) 0 sequence_indices_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_coefficients_ (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attention_pooling_features_atom (None, 1746, 64) 8192 SCAN_filter_activity_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "attributes_aa (InputLayer) (None, 194, 20) 0 \n", - "__________________________________________________________________________________________________\n", - "pooling_intermediate_atom (Loca [(None, 194, 14, 1), 0 masked_sequence_indices_aa[0][0] \n", - " masked_sequence_indices_atom[0][0\n", - " attention_pooling_coefficients_at\n", - " attention_pooling_features_atom[0\n", - "__________________________________________________________________________________________________\n", - "masked_attributes_aa (Masking) (None, 194, 20) 0 attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "pooling_edges_atom (Lambda) (None, 194, 14, 1) 0 pooling_intermediate_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_projecti (None, 194, 32) 640 masked_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_in [(None, 194, 64), (N 0 pooling_intermediate_atom[0][1] \n", - " pooling_intermediate_atom[0][2] \n", - " pooling_edges_atom[0][0] \n", - "__________________________________________________________________________________________________\n", - "point_clouds_aa (InputLayer) (None, 389, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "frame_indices_aa (InputLayer) (None, 194, 3) 0 \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa_normaliz (None, 194, 32) 96 embedded_attributes_aa_projection\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 194, 64) 192 SCAN_filters_atom_aggregated_inpu\n", - "__________________________________________________________________________________________________\n", - "masked_point_clouds_aa (Masking (None, 389, 3) 0 point_clouds_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "masked_frame_indices_aa (Maskin (None, 194, 3) 0 frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_attributes_aa (TimeDis (None, 194, 32) 0 embedded_attributes_aa_normalizat\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_atom_aggregated_ac (None, 194, 64) 0 SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "frames_aa (FrameBuilder) (None, 194, 4, 3) 0 masked_point_clouds_aa[0][0] \n", - " masked_frame_indices_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "all_embedded_attributes_aa (Con (None, 194, 96) 0 embedded_attributes_aa[0][0] \n", - " SCAN_filters_atom_aggregated_acti\n", - "__________________________________________________________________________________________________\n", - "neighborhood_aa (LocalNeighborh [(None, 194, 16, 3), 0 frames_aa[0][0] \n", - " all_embedded_attributes_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_aa ( (None, 194, 16, 32) 384 neighborhood_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_input_aa (OuterProd (None, 194, 128) 397312 embedded_local_coordinates_aa[0][\n", - " neighborhood_aa[0][1] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa_normali (None, 194, 128) 384 SCAN_filter_input_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filter_activity_aa (TimeDi (None, 194, 128) 0 SCAN_filter_activity_aa_normaliza\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_proj (None, 194, 32) 4096 SCAN_filter_activity_aa[0][0] \n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1_norm (None, 194, 32) 96 SCAN_filters_aa_embedded_1_projec\n", - "__________________________________________________________________________________________________\n", - "SCAN_filters_aa_embedded_1 (Tim (None, 194, 32) 0 SCAN_filters_aa_embedded_1_normal\n", - "__________________________________________________________________________________________________\n", - "cross_attention (TimeDistribute (None, 194, 1) 32 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "node_output (TimeDistributed) (None, 194, 2) 66 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "neighborhood_graph (LocalNeighb [(None, 194, 32, 5), 0 frames_aa[0][0] \n", - " masked_sequence_indices_aa[0][0] \n", - " cross_attention[0][0] \n", - " node_output[0][0] \n", - "__________________________________________________________________________________________________\n", - "embedded_local_coordinates_grap (None, 194, 32, 32) 960 neighborhood_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "beta (TimeDistributed) (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "self_attention (TimeDistributed (None, 194, 1) 33 SCAN_filters_aa_embedded_1[0][0] \n", - "__________________________________________________________________________________________________\n", - "edges_graph (TimeDistributed) (None, 194, 32, 1) 32 embedded_local_coordinates_graph[\n", - "__________________________________________________________________________________________________\n", - "attention_layer (AttentionLayer [(None, 194, 2), (No 0 beta[0][0] \n", - " self_attention[0][0] \n", - " neighborhood_graph[0][1] \n", - " neighborhood_graph[0][2] \n", - " edges_graph[0][0] \n", - "__________________________________________________________________________________________________\n", - "classifier_output (TimeDistribu (None, 194, 2) 0 attention_layer[0][0] \n", - "==================================================================================================\n", - "Total params: 474,912\n", - "Trainable params: 473,988\n", - "Non-trainable params: 924\n", - "__________________________________________________________________________________________________\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generating groups...\n", - "Grouped 1 examples in 1 groups\n", - "Grouping and padding...\n", - "Performing prediction...\n", - "1/1 [==============================] - 1s 800ms/step\n", - "Ungrouping and unpadding...\n", - "prediction done!\n" - ] - } - ], - "source": [ - "pdb = '7jvb'\n", - "model = 0\n", - "chain = 'A'\n", - "\n", - "\n", - "dictionary_features = predict_features.predict_features('%s_%s'%(pdb,chain),model=model_name)\n", - "\n", - "dictionary_epitopes = predict_features.predict_features('%s_%s'%(pdb,chain),model=model_name,\n", - " layer=None )\n", - "\n", - "dictionary_attention = predict_features.predict_features('%s_%s'%(pdb,chain),model=model_name,\n", - " layer='attention_layer' )\n", - "\n", - "dictionary_epitopes_preaverage = predict_features.predict_features('%s_%s'%(pdb,chain),model=model_name,\n", - " layer='node_output' )\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fe512064", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "\n", - "%matplotlib inline\n", - "matrix_features = np.stack(list(dictionary_features.values()),axis=0)\n", - "matrix_percentile_features = show_3d_filters.activity2percentile(matrix_features, filter_specificities['filter_activity']['percentiles'] )\n", - "\n", - "\n", - "vector_epitopes = np.array(list(dictionary_epitopes.values()))\n", - "vector_attention = np.array(list(dictionary_attention.values()))\n", - "vector_preaverage_epitopes = np.array(list(dictionary_epitopes_preaverage.values()))\n", - "vector_preaverage_epitopes = -(vector_preaverage_epitopes[:,1] - vector_preaverage_epitopes[:,0] )\n", - "\n", - "resids = [key[2] for key in dictionary_features.keys() ]\n", - "L = len(resids)\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,8))\n", - "sns.heatmap( matrix_features.T ,ax = ax); \n", - "plt.show()\n", - "\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,8))\n", - "sns.heatmap( matrix_percentile_features.T ,ax = ax,vmin=95,vmax=100); \n", - "plt.show()\n", - "\n", - "\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,8))\n", - "ax.plot( vector_epitopes )\n", - "ax2 = ax.twinx()\n", - "ax2.plot(vector_attention,c='gray')\n", - "ax.set_xticks( np.arange(L)[::4] )\n", - "ax.set_xticklabels( resids[::4],rotation=90 )\n", - "ax.grid()\n", - "plt.show()\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "a = 1.0\n", - "b = -0.5\n", - "vector_preaverage_epitopes2 = 1/(1+np.exp(- a*vector_preaverage_epitopes - b ) )\n", - " \n", - "\n", - "\n", - "fig, ax = plt.subplots(figsize=(16,8))\n", - "ax.plot( vector_epitopes )\n", - "ax2 = ax.twinx()\n", - "ax2.plot(vector_preaverage_epitopes2,c='gray')\n", - "ax.set_xticks( np.arange(L)[::4] )\n", - "ax.set_xticklabels( resids[::4],rotation=90 )\n", - "ax.grid()\n", - "plt.show()\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b570ad87", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "374 74\n", - "381 104\n", - "402 107\n", - "404 29\n", - "409 16\n", - "432 8\n", - "438 55\n", - "499 117\n", - "515 74\n", - "527 2\n" - ] - } - ], - "source": [ - "salient_residues, salient_features = np.nonzero(matrix_percentile_features>99.4)\n", - "salient_residues = np.array(resids)[salient_residues]\n", - "\n", - "for sr,sf in zip(salient_residues,salient_features):\n", - " print(sr,sf)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "bc5d788d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "22 1.302515 96.10252\n", - "70 0.8990701 94.43268\n", - "36 0.5780388 93.403015\n", - "4 0.7472925 91.83968\n", - "76 1.0113704 90.58128\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "resnumber = 455\n", - "normalized = True\n", - "\n", - "\n", - "filter_activity = dictionary_features[(model,chain,resnumber)]\n", - "filter_activity_percentile = show_3d_filters.activity2percentile(filter_activity, filter_specificities['filter_activity']['percentiles'] )\n", - "\n", - "if normalized:\n", - " top_filters = np.argsort(filter_activity_percentile )[::-1][:5] \n", - "else:\n", - " top_filters = np.argsort(filter_activity )[::-1][:5]\n", - " \n", - "for top_filter in top_filters:\n", - " print(top_filter,filter_activity[top_filter],filter_activity_percentile[top_filter])\n", - "\n", - " \n", - "plt.plot(filter_activity); plt.show()\n", - "plt.plot(filter_activity_percentile); plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "2f2f8b95", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n" - ] - } - ], - "source": [ - "\n", - "atom_positions,atom_types,atom_bonds = show_3d_neighborhoods.get_neighborhood(\n", - " pdb = pdb,\n", - " model = model,\n", - " chain = chain,\n", - " resnumber = resnumber,\n", - " assembly=False,\n", - " biounit=False,\n", - " Kmax = 9*16\n", - ")\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "5b2192db", - "metadata": {}, - "outputs": [], - "source": [ - "sg = weight_logo_3d.make_sphere_geometry(30)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "0e0437f4", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2c86653ae12a4f9db540c8776ba43ab5", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Renderer(camera=PerspectiveCamera(position=(19.200000000000003, 12.0, 19.200000000000003), projectionMatrix=(1…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "list_objects = show_3d_neighborhoods.show_atoms(atom_positions,atom_types,atom_bonds,render=False)\n", - "\n", - "\n", - "renderer = show_3d_filters.plot_aminoacid_filter(filter_specificities,\n", - " top_filters[0],\n", - " sg=sg,\n", - " list_additional_objects=list_objects,\n", - " show_asa=True,\n", - " scale=2.0,\n", - " threshold1=0.33);\n", - "display(renderer)" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "id": "e9076ac8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import importlib\n", - "importlib.reload(show_3d_neighborhoods)\n", - "importlib.reload(show_3d_filters)\n", - "from network import neighborhoods\n", - "importlib.reload(neighborhoods)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "id": "7f9a5500", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parsing /Users/jerometubiana/PDB/pdb7jvb.ent\n" - ] - } - ], - "source": [ - "\n", - "atom_positions,atom_types,atom_bonds = show_3d_neighborhoods.get_neighborhood(\n", - " pdb = pdb,\n", - " model = model,\n", - " chain = chain,\n", - " resnumber = 468,\n", - " atom = 'CG1',\n", - " assembly=False,\n", - " biounit=False,\n", - ")\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "id": "6b31c2ac", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1aef14ff7b0940a586a7a301377e6b4f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Renderer(camera=PerspectiveCamera(position=(8.0, 5.0, 8.0), projectionMatrix=(1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "list_objects = show_3d_neighborhoods.show_atoms(atom_positions,atom_types,atom_bonds,render=False,\n", - " radius_scale = 0.15)\n", - "\n", - "\n", - "renderer = show_3d_filters.plot_atomic_filter(filter_specificities,\n", - " 46,\n", - " y_offset = 0.25,\n", - " sg=sg,\n", - " list_additional_objects=list_objects,\n", - " threshold1=0.33);\n", - "display(renderer)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f72269a8", - "metadata": {}, - "outputs": [], - "source": [ - "# from utilities import wrappers\n", - "# model = wrappers.load_model('models/ScanNet_PAI_noMSA_0')\n", - "\n", - "\n", - "\n", - "w1,w2 = model.model.get_layer('SCAN_filter_input_aa').get_weights()\n", - "w3 = model.model.get_layer('attention_pooling_features_atom').get_weights()[0]\n", - "\n", - "plt.matshow(w1[:,:,top_filters[0]]); plt.colorbar()\n", - "plt.show()\n", - "\n", - "\n", - "index = np.argmax(w1[0,:,top_filters[0]]) - 32\n", - "plt.plot(w3[:,index]); plt.show()\n", - "\n", - "\n", - "print( np.argsort( np.abs( w3[:,index]))[::-1][:5] )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2f5679bc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebaae6d1", - "metadata": {}, - "outputs": [], - "source": [ - "plt.matshow(filter_specificities['aa_specificity']['value'].T)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/visualizations/filter_specificities_ScanNet_PAI_0.data b/visualizations/filter_specificities_ScanNet_PAI_0.data new file mode 100644 index 0000000..9012505 Binary files /dev/null and b/visualizations/filter_specificities_ScanNet_PAI_0.data differ diff --git a/visualizations/filter_specificities_ScanNet_PAI_noMSA_0.data b/visualizations/filter_specificities_ScanNet_PAI_noMSA_0.data new file mode 100644 index 0000000..e5b3212 Binary files /dev/null and b/visualizations/filter_specificities_ScanNet_PAI_noMSA_0.data differ diff --git a/visualizations/filter_specificities_ScanNet_PIDPI_0.data b/visualizations/filter_specificities_ScanNet_PIDPI_0.data new file mode 100644 index 0000000..30a7b4e Binary files /dev/null and b/visualizations/filter_specificities_ScanNet_PIDPI_0.data differ diff --git a/visualizations/filter_specificities_ScanNet_PIDPI_noMSA_0.data b/visualizations/filter_specificities_ScanNet_PIDPI_noMSA_0.data new file mode 100644 index 0000000..f8683d8 Binary files /dev/null and b/visualizations/filter_specificities_ScanNet_PIDPI_noMSA_0.data differ diff --git a/visualizations/filter_specificities_ScanNet_PPI.data b/visualizations/filter_specificities_ScanNet_PPI.data new file mode 100644 index 0000000..c19fa67 Binary files /dev/null and b/visualizations/filter_specificities_ScanNet_PPI.data differ diff --git a/visualizations/filter_specificities_ScanNet_PPI_noMSA.data b/visualizations/filter_specificities_ScanNet_PPI_noMSA.data new file mode 100644 index 0000000..205a240 Binary files /dev/null and b/visualizations/filter_specificities_ScanNet_PPI_noMSA.data differ