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833 lines
247 KiB
Plaintext
833 lines
247 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "725805fc",
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"metadata": {},
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"source": [
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"# [Tutorial](https://github.com/biohub/esm/tree/main/cookbook/tutorials): Which Layer Should You Use? A Layer Sweep for Enzyme Function Classification\n",
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"\n",
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"ESMC is a transformer model, which means protein representations are computed in stages: each transformer layer refines the model's understanding, building from low-level residue chemistry in the early layers to high-level functional context in the later ones. **The final layer is not always the best layer for your task.**\n",
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"\n",
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"This tutorial demonstrates how to systematically evaluate every layer of ESMC (Layer sweep) by asking a simple question: given a protein's sequence, can ESMC's embeddings tell us what broad type of chemical reaction that protein catalyzes?\n",
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"\n",
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"We use Enzyme Commission (EC) numbers as labels—a standardized system that classifies enzymes by reaction type in a four-level hierarchy. In this tutorial, we focus on third-level EC prefixes (e.g., 3.1.3), which group enzymes by closely related reaction types while keeping the problem tractable.\n",
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"\n",
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"You can think of this as sorting proteins into buckets: \n",
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"- kinases (transfer phosphate groups)\n",
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"- phosphatases (remove phosphate groups)\n",
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"- oxidoreductases (transfer electrons)\n",
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"- lyases (break or form bonds without hydrolysis). \n",
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"\n",
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"These classes are chemically distinct, but the proteins within each class can look very different in sequence and structure, making this a meaningful test of what the model has learned.\n",
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"\n",
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"We then ask: which layer’s embeddings give the best classification performance with a simple (linear) model?\n",
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"\n",
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"## Learning goals\n",
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"\n",
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"After completing this tutorial, you will be able to:\n",
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"\n",
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"- Extract embeddings from all ESMC layers and average across the sequence to obtain one vector per protein per layer\n",
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"- Build a layer sweep: train a linear probe on each layer and compare performance\n",
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"- Interpret the performance curve to identify which layer is best for your task\n",
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"- Apply this workflow to your own classification or regression problem\n",
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"\n",
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"## Background: Why do layers differ?\n",
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"\n",
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"Protein language models learn a hierarchy of representations as information flows through their layers:\n",
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"\n",
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"- Early layers tend to capture local sequence features such as amino acid identity and short-range chemical context.\n",
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"- Middle layers often encode higher-order structural or functional patterns, including motifs and evolutionary constraints.\n",
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"- The last layer is directly optimized for the model's training objective (e.g., masked token prediction) and may become more specialized to that objective.\n",
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"- Late layers are often the best for contact prediction [(Vig et al. 2020)](https://arxiv.org/abs/2006.15222).\n",
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"\n",
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"In practice, this means **the most useful layer depends on your task**. For example, enzyme function classification often peaks in intermediate layers, while other tasks (e.g., stability prediction) may favor different depths. The most reliable way to determine this is empirical: measure performance across layers.\n",
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"\n",
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"If, however, you don't have enough data to work with, we recommend the following guidelines:\n",
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"\n",
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"- If you are predicting a functional property (thermostability, solubility, etc), we recommend using the **second to last layer** `[-2]`\n",
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"- If you are looking for high-level functional similarity (EC number, GO term, homology, etc), we recommend using a **layer ~3/4** of the way through the network.\n",
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"\n",
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"## What this notebook does\n",
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"\n",
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"We use a pre-built dataset of ~600 reviewed (SwissProt) enzymes spanning 4 EC classes. We embed them with ESMC and fit a logistic regression probe on each layer's mean-pooled representations. Performance is evaluated with 5-fold cross-validation, reporting MCC across layers to determine which layer works the best for the task. \n",
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"\n",
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"**Note on scope:** This tutorial uses the ESMC 600M model. The results are best interpreted as a comparison across layers rather than a rigorous benchmark. We find that the 6B model is best for a variety of protein representation learning tasks. \n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "55a2939e",
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"metadata": {},
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"source": [
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"## 1. Install and import dependencies\n",
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"\n",
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"Plus set up some utilities. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "38b5cb02",
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"metadata": {},
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"outputs": [],
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"source": [
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"# If you are working in colab, uncomment these lines to install dependencies\n",
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"# !pip install esm@git+https://github.com/Biohub/esm.git@c94ed8d"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a2d21a7b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from getpass import getpass\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib.ticker as ticker\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.metrics import matthews_corrcoef\n",
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"from sklearn.model_selection import StratifiedKFold\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from tqdm.auto import tqdm\n",
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"\n",
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"from esm.sdk import batch_executor, esmc_client\n",
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"from esm.sdk.api import ESMProtein, ESMProteinError, LogitsConfig"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3aadf22a",
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"metadata": {},
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"source": [
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"## 2. Set up Biohub client. \n",
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"First, we need to set up the Biohub client. Generate an API key on the [Biohub platform](https://biohub.ai) and add it to your account. This API key manages your access to credits and tokens, and the term API key/token is often used interchangeably within documentation.\n",
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"\n",
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"Please note that your API key is like a password for your account and you should take care to protect it. For this reason it is recommended to frequently create a new API key and delete old, unused ones. It is also recommended to paste the API key directly into an environment variable or use a utility like `getpass` as shown in the example below so keys are not accidentally shared or checked into code repositories.\n",
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"\n",
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"\n",
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"We use **ESMC 600M** here. It has 36 transformer layers, making the sweep fast enough to run interactively. The same workflow applies to the 300M and 6B models ."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b25a91f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"token = getpass(\"Biohub API token: \")\n",
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"\n",
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"model_name = \"esmc-600m-2024-12\"\n",
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"forge_client = esmc_client(model=model_name, url=\"https://biohub.ai\", token=token)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1adc64ab",
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"metadata": {},
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"source": [
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"## 3. Fetch the enzyme dataset from UniProt\n",
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"\n",
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"We use a pre-built dataset of ~600 reviewed (SwissProt) enzymes spanning 4 EC classes, downloaded from Google Drive. This dataset was generated by querying the UniProt REST API for manually curated entries with high-confidence EC annotations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "e69d27e6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--2026-05-12 17:20:03-- https://drive.google.com/uc?export=download&id=1zi-vnJOfvZs2uv3PmSxeiW7EFtK05tvV\n",
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"Resolving drive.google.com (drive.google.com)... 2607:f8b0:4005:815::200e, 142.251.215.78\n",
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"Connecting to drive.google.com (drive.google.com)|2607:f8b0:4005:815::200e|:443... connected.\n",
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"HTTP request sent, awaiting response... 303 See Other\n",
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"Location: https://drive.usercontent.google.com/download?id=1zi-vnJOfvZs2uv3PmSxeiW7EFtK05tvV&export=download [following]\n",
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"--2026-05-12 17:20:03-- https://drive.usercontent.google.com/download?id=1zi-vnJOfvZs2uv3PmSxeiW7EFtK05tvV&export=download\n",
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"Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 2607:f8b0:4005:806::2001, 142.251.218.65\n",
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"Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|2607:f8b0:4005:806::2001|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 392654 (383K) [application/octet-stream]\n",
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"Saving to: ‘ec_layer_sweep_dataset.csv’\n",
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"\n",
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"ec_layer_sweep_data 100%[===================>] 383.45K --.-KB/s in 0.07s \n",
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"\n",
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"2026-05-12 17:20:04 (5.47 MB/s) - ‘ec_layer_sweep_dataset.csv’ saved [392654/392654]\n",
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"\n",
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"\n",
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"Class counts:\n",
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"ec_prefix\n",
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"1.1.1 150\n",
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"2.7.1 148\n",
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"3.1.3 150\n",
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"4.2.1 145\n",
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"Name: count, dtype: int64\n",
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"\n",
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"Final dataset: 593 proteins\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>accession</th>\n",
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" <th>entry_name</th>\n",
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" <th>protein_name</th>\n",
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" <th>organism</th>\n",
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" <th>ec</th>\n",
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" <th>length</th>\n",
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" <th>sequence</th>\n",
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" <th>ec_prefix</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>O15297</td>\n",
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" <td>PPM1D_HUMAN</td>\n",
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" <td>Protein phosphatase 1D (EC 3.1.3.16) (Protein ...</td>\n",
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" <td>Homo sapiens (Human)</td>\n",
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" <td>3.1.3.16</td>\n",
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" <td>605</td>\n",
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" <td>MAGLYSLGVSVFSDQGGRKYMEDVTQIVVEPEPTAEEKPSPRRSLS...</td>\n",
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" <td>3.1.3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>O60729</td>\n",
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" <td>CC14B_HUMAN</td>\n",
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" <td>Dual specificity protein phosphatase CDC14B (E...</td>\n",
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" <td>Homo sapiens (Human)</td>\n",
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" <td>3.1.3.16; 3.1.3.48</td>\n",
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" <td>498</td>\n",
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" <td>MKRKSERRSSWAAAPPCSRRCSSTSPGVKKIRSSTQQDPRRRDPQD...</td>\n",
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" <td>3.1.3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>O75319</td>\n",
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" <td>DUS11_HUMAN</td>\n",
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" <td>RNA/RNP complex-1-interacting phosphatase (EC ...</td>\n",
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" <td>Homo sapiens (Human)</td>\n",
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" <td>3.1.3.-</td>\n",
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" <td>330</td>\n",
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" <td>MSQWHHPRSGWGRRRDFSGRSSAKKKGGNHIPERWKDYLPVGQRMP...</td>\n",
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" <td>3.1.3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>O75365</td>\n",
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" <td>TP4A3_HUMAN</td>\n",
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" <td>Protein tyrosine phosphatase type IVA 3 (EC 3....</td>\n",
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" <td>Homo sapiens (Human)</td>\n",
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" <td>3.1.3.48</td>\n",
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" <td>173</td>\n",
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" <td>MARMNRPAPVEVSYKHMRFLITHNPTNATLSTFIEDLKKYGATTVV...</td>\n",
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" <td>3.1.3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>P09467</td>\n",
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" <td>F16P1_HUMAN</td>\n",
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" <td>Fructose-1,6-bisphosphatase 1 (FBPase 1) (EC 3...</td>\n",
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" <td>Homo sapiens (Human)</td>\n",
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" <td>3.1.3.11</td>\n",
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" <td>338</td>\n",
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||
" <td>MADQAPFDTDVNTLTRFVMEEGRKARGTGELTQLLNSLCTAVKAIS...</td>\n",
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" <td>3.1.3</td>\n",
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||
" </tr>\n",
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||
" </tbody>\n",
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||
"</table>\n",
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||
"</div>"
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],
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"text/plain": [
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" accession entry_name protein_name \\\n",
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"0 O15297 PPM1D_HUMAN Protein phosphatase 1D (EC 3.1.3.16) (Protein ... \n",
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"1 O60729 CC14B_HUMAN Dual specificity protein phosphatase CDC14B (E... \n",
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"2 O75319 DUS11_HUMAN RNA/RNP complex-1-interacting phosphatase (EC ... \n",
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"3 O75365 TP4A3_HUMAN Protein tyrosine phosphatase type IVA 3 (EC 3.... \n",
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"4 P09467 F16P1_HUMAN Fructose-1,6-bisphosphatase 1 (FBPase 1) (EC 3... \n",
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"\n",
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" organism ec length \\\n",
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"0 Homo sapiens (Human) 3.1.3.16 605 \n",
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"1 Homo sapiens (Human) 3.1.3.16; 3.1.3.48 498 \n",
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"2 Homo sapiens (Human) 3.1.3.- 330 \n",
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"3 Homo sapiens (Human) 3.1.3.48 173 \n",
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"4 Homo sapiens (Human) 3.1.3.11 338 \n",
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"\n",
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" sequence ec_prefix \n",
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"0 MAGLYSLGVSVFSDQGGRKYMEDVTQIVVEPEPTAEEKPSPRRSLS... 3.1.3 \n",
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"1 MKRKSERRSSWAAAPPCSRRCSSTSPGVKKIRSSTQQDPRRRDPQD... 3.1.3 \n",
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||
"2 MSQWHHPRSGWGRRRDFSGRSSAKKKGGNHIPERWKDYLPVGQRMP... 3.1.3 \n",
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||
"3 MARMNRPAPVEVSYKHMRFLITHNPTNATLSTFIEDLKKYGATTVV... 3.1.3 \n",
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"4 MADQAPFDTDVNTLTRFVMEEGRKARGTGELTQLLNSLCTAVKAIS... 3.1.3 "
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||
]
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||
},
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||
"execution_count": 3,
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||
"metadata": {},
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||
"output_type": "execute_result"
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}
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],
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"source": [
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"# Download the pre-built EC dataset from Google Drive\n",
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"!wget --no-check-certificate \"https://drive.google.com/uc?export=download&id=1zi-vnJOfvZs2uv3PmSxeiW7EFtK05tvV\" -O ec_layer_sweep_dataset.csv\n",
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"\n",
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"df = pd.read_csv(\"ec_layer_sweep_dataset.csv\")\n",
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"\n",
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"print(\"\\nClass counts:\")\n",
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"print(df[\"ec_prefix\"].value_counts().sort_index())\n",
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"print(f\"\\nFinal dataset: {len(df)} proteins\")\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4349ad13",
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||
"metadata": {},
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"source": [
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"**EC classes:** We use four third-level EC prefixes representing chemically distinct reaction types. Using the third level (e.g., `3.1.3`) gives us a manageable number of classes while still posing a meaningful discrimination challenge — these are mechanistically distinct but all involve nucleotide chemistry or redox reactions, so they are not trivially separable.\n",
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"\n",
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"| EC prefix | Reaction type |\n",
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"|-----------|---------------|\n",
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"| 3.1.3 | Phosphoric monoester hydrolases (phosphatases) |\n",
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"| 2.7.1 | Phosphotransferases with alcohol acceptor (kinases) |\n",
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"| 1.1.1 | Oxidoreductases acting on CH–OH with NAD+/NADP+ |\n",
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"| 4.2.1 | Hydro-lyases |\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fc821424",
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"metadata": {},
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"source": [
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"## 5. Embed proteins with ESMC\n",
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"\n",
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"For each protein, we extract representations from every layer of ESMC. Each layer produces a vector for every residue in the sequence, so we average across residues to obtain a single vector per protein per layer.\n",
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"\n",
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"This results in an embedding array of shape (n_proteins, n_layers, hidden_dim) — for example, (593, 37, 1152) for ESMC 600M."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "5572ae91",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Using return_mean_hidden_states=True is more memory efficient\n",
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"# than returning all hidden states and pooling locally.\n",
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"EMBEDDING_CONFIG = LogitsConfig(\n",
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" sequence=True,\n",
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" return_mean_hidden_states=True, # returns mean-pooled hidden states (n_layers, hidden_dim)\n",
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")\n",
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"\n",
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"\n",
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"def embed_sequence(model, sequence):\n",
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" protein = ESMProtein(sequence=sequence)\n",
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" protein_tensor = model.encode(protein)\n",
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" output = model.logits(protein_tensor, EMBEDDING_CONFIG)\n",
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" # We want to propagate errors to the batch executor.\n",
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" if isinstance(output, ESMProteinError):\n",
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" raise output\n",
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" return output.mean_hidden_state.squeeze().float().numpy()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "c077da35",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Embedding 593 proteins (this may take several minutes)...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Processing 100%|████████████████████████| 593/593 [Elapsed: 01:12 | Remaining: 00:00] , Success=593 Fail=0 Retry=216"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
|
||
"Embedding matrix shape: (593, 37, 1152) → (n_proteins, n_layers, hidden_dim)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"sequences = df[\"sequence\"].tolist()\n",
|
||
"labels = df[\"ec_prefix\"].tolist()\n",
|
||
"\n",
|
||
"print(f\"Embedding {len(sequences):,} proteins (this may take several minutes)...\")\n",
|
||
"with batch_executor() as executor:\n",
|
||
" outputs = executor.execute_batch(\n",
|
||
" user_func=embed_sequence, model=forge_client, sequence=sequences\n",
|
||
" )\n",
|
||
"\n",
|
||
"valid = [isinstance(o, np.ndarray) for o in outputs]\n",
|
||
"sequences = [s for s, v in zip(sequences, valid) if v]\n",
|
||
"labels = [l for l, v in zip(labels, valid) if v]\n",
|
||
"all_embeddings = np.stack([o for o, v in zip(outputs, valid) if v], axis=0)\n",
|
||
"\n",
|
||
"N_LAYERS = all_embeddings.shape[1]\n",
|
||
"HIDDEN_DIM = all_embeddings.shape[2]\n",
|
||
"print(\n",
|
||
" f\"\\nEmbedding matrix shape: {all_embeddings.shape} → (n_proteins, n_layers, hidden_dim)\"\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "51690794",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 6. Layer sweep: train a linear probe on each layer\n",
|
||
"\n",
|
||
"For each layer we:\n",
|
||
"1. Extract the mean-pooled embedding for that layer across all proteins\n",
|
||
"2. Train a **logistic regression classifier** with 5-fold cross-validation \n",
|
||
" - If performance is high at layer $\\ell$, it indicates that EC class information is already encoded in a form that can be extracted with a simple linear model—without requiring more complex nonlinear decoding.\n",
|
||
"3. Record **MCC [(Matthews Correlation Coefficient)](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html):** Ranges from −1 (complete disagreement) to +1 (perfect prediction). It accounts for class imbalance and is generally preferred over accuracy for multi-class problems.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "8ab0d619",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Running 5-fold CV across 37 layers...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/var/folders/nk/0860l6gs5vnfnn5jdnzbz9qw0000gq/T/ipykernel_74612/1798675473.py:4: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
|
||
" classes = sorted(pd.unique(labels))\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "1a0bb854d3354fc6b9f9703c41b77b9b",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Layer sweep: 0%| | 0/37 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Best layer by MCC: layer 25 (MCC = 0.935)\n",
|
||
"Last layer: layer 37 (MCC = 0.843)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"N_SPLITS = 5 # number of cross-validation folds\n",
|
||
"RANDOM_STATE = 44\n",
|
||
"\n",
|
||
"classes = sorted(pd.unique(labels))\n",
|
||
"y = np.array([classes.index(l) for l in labels])\n",
|
||
"\n",
|
||
"skf = StratifiedKFold(n_splits=N_SPLITS, shuffle=True, random_state=RANDOM_STATE)\n",
|
||
"\n",
|
||
"mcc_scores = np.zeros((N_LAYERS, N_SPLITS))\n",
|
||
"\n",
|
||
"print(f\"Running {N_SPLITS}-fold CV across {N_LAYERS} layers...\")\n",
|
||
"\n",
|
||
"for layer_idx in tqdm(range(N_LAYERS), desc=\"Layer sweep\"):\n",
|
||
" X_layer = all_embeddings[:, layer_idx, :] # (n_proteins, hidden_dim)\n",
|
||
"\n",
|
||
" pipe = Pipeline(\n",
|
||
" [\n",
|
||
" (\"scaler\", StandardScaler()),\n",
|
||
" (\n",
|
||
" \"clf\",\n",
|
||
" LogisticRegression(\n",
|
||
" max_iter=1000, random_state=RANDOM_STATE, solver=\"lbfgs\", C=0.1\n",
|
||
" ),\n",
|
||
" ),\n",
|
||
" ]\n",
|
||
" )\n",
|
||
"\n",
|
||
" for fold_idx, (train_idx, test_idx) in enumerate(skf.split(X_layer, y)):\n",
|
||
" X_train, X_test = X_layer[train_idx], X_layer[test_idx]\n",
|
||
" y_train, y_test = y[train_idx], y[test_idx]\n",
|
||
"\n",
|
||
" pipe.fit(X_train, y_train)\n",
|
||
" y_pred = pipe.predict(X_test)\n",
|
||
"\n",
|
||
" mcc_scores[layer_idx, fold_idx] = matthews_corrcoef(y_test, y_pred)\n",
|
||
"\n",
|
||
"\n",
|
||
"mcc_mean, mcc_std = mcc_scores.mean(axis=1), mcc_scores.std(axis=1)\n",
|
||
"\n",
|
||
"best_mcc_layer = int(mcc_mean.argmax())\n",
|
||
"last_layer = N_LAYERS - 1\n",
|
||
"\n",
|
||
"print(\n",
|
||
" f\"\\nBest layer by MCC: layer {best_mcc_layer + 1} (MCC = {mcc_mean[best_mcc_layer]:.3f})\"\n",
|
||
")\n",
|
||
"print(\n",
|
||
" f\"Last layer: layer {last_layer + 1} (MCC = {mcc_mean[last_layer]:.3f})\"\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c3e85d50",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 7. Visualize the layer sweep\n",
|
||
"\n",
|
||
"Next we will visualize how classification performance varies across transformer layers. This plot shows how well a simple linear model can extract enzyme function from each layer’s embeddings, using MCC as an evaluation metric. By examining performance as a function of depth, we can identify where in the network function-relevant information is most accessible."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "77950c88",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_layer_sweep(\n",
|
||
" layers,\n",
|
||
" mcc_mean,\n",
|
||
" mcc_std,\n",
|
||
" best_mcc_layer,\n",
|
||
" last_layer,\n",
|
||
" save_path=\"ec_layer_sweep.png\",\n",
|
||
"):\n",
|
||
" fig, ax = plt.subplots(figsize=(12, 5))\n",
|
||
" ax.plot(layers, mcc_mean, \"o-\", linewidth=2, label=\"MCC\", color=\"steelblue\")\n",
|
||
" ax.fill_between(\n",
|
||
" layers,\n",
|
||
" mcc_mean - mcc_std,\n",
|
||
" mcc_mean + mcc_std,\n",
|
||
" alpha=0.2,\n",
|
||
" color=\"steelblue\",\n",
|
||
" label=\"±1 std\",\n",
|
||
" )\n",
|
||
" ax.axvline(\n",
|
||
" best_mcc_layer + 1,\n",
|
||
" color=\"steelblue\",\n",
|
||
" linestyle=\":\",\n",
|
||
" linewidth=1.5,\n",
|
||
" label=f\"Best MCC layer = {best_mcc_layer + 1}\",\n",
|
||
" )\n",
|
||
" ax.axvline(\n",
|
||
" last_layer + 1,\n",
|
||
" color=\"gray\",\n",
|
||
" linestyle=\":\",\n",
|
||
" linewidth=1.5,\n",
|
||
" label=f\"Last layer = {last_layer + 1}\",\n",
|
||
" )\n",
|
||
" ax.set_xlabel(\"Transformer layer\", fontsize=12)\n",
|
||
" ax.set_ylabel(\"CV performance\", fontsize=12)\n",
|
||
" ax.set_title(\n",
|
||
" \"EC classification performance by ESMC layer\\n\"\n",
|
||
" \"(mean-pooled embeddings + logistic regression, 5-fold CV)\",\n",
|
||
" fontsize=13,\n",
|
||
" )\n",
|
||
" ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
|
||
" ax.set_xlim(0.5, last_layer + 1.5)\n",
|
||
" ax.grid(axis=\"y\", alpha=0.3)\n",
|
||
" ax.legend(fontsize=10)\n",
|
||
" plt.tight_layout()\n",
|
||
" if save_path:\n",
|
||
" plt.savefig(save_path, dpi=150, bbox_inches=\"tight\")\n",
|
||
" print(f\"Figure saved to {save_path}\")\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "5737302d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_layer_sweep(\n",
|
||
" np.arange(1, N_LAYERS + 1),\n",
|
||
" mcc_mean,\n",
|
||
" mcc_std,\n",
|
||
" best_mcc_layer,\n",
|
||
" last_layer,\n",
|
||
" save_path=None,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "acc8a222",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Interpreting the performance curve\n",
|
||
"\n",
|
||
"We see the EC number is captured well by middle layers of the model, which captures information about functional similarity. We also observe a drop in performance at the last layer. This may appear because the final layer is most specialized for the model's training objective (next-token/masked-token prediction) rather than higher level representation. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "72e52af2",
|
||
"metadata": {},
|
||
"source": [
|
||
"### What do the embeddings look like?\n",
|
||
"\n",
|
||
"So far, we’ve evaluated each layer quantitatively using a linear probe. To build intuition, we can visualize the representations with PCA projections of the embeddings, to see how proteins cluster by EC class in a low-dimensional space. We compare the best layer to the last layer. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "534d45f4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_pca_projections(\n",
|
||
" all_embeddings, labels, classes, best_mcc_layer, last_layer, random_state=42\n",
|
||
"):\n",
|
||
" fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n",
|
||
" projections = []\n",
|
||
" for layer_idx in [best_mcc_layer, last_layer]:\n",
|
||
" X = all_embeddings[:, layer_idx, :]\n",
|
||
" X_2d = PCA(n_components=2, random_state=random_state).fit_transform(\n",
|
||
" StandardScaler().fit_transform(X)\n",
|
||
" )\n",
|
||
" projections.append(X_2d)\n",
|
||
" all_coords = np.concatenate(projections, axis=0)\n",
|
||
" lim = np.abs(all_coords).max() * 1.05\n",
|
||
" titles = [\n",
|
||
" f\"Best layer (layer {best_mcc_layer + 1})\",\n",
|
||
" f\"Last layer (layer {last_layer + 1})\",\n",
|
||
" ]\n",
|
||
" for ax, X_2d, title in zip(axes, projections, titles):\n",
|
||
" for label, color in zip(\n",
|
||
" classes, plt.cm.Set1(np.linspace(0, 0.8, len(classes)))\n",
|
||
" ):\n",
|
||
" mask = np.array(labels) == label\n",
|
||
" ax.scatter(\n",
|
||
" X_2d[mask, 0],\n",
|
||
" X_2d[mask, 1],\n",
|
||
" label=f\"EC {label}\",\n",
|
||
" color=color,\n",
|
||
" s=20,\n",
|
||
" alpha=0.6,\n",
|
||
" )\n",
|
||
" ax.set_title(title)\n",
|
||
" ax.set_xlabel(\"PC 1\")\n",
|
||
" ax.set_ylabel(\"PC 2\")\n",
|
||
" ax.set_xlim(-lim, lim)\n",
|
||
" ax.set_ylim(-lim, lim)\n",
|
||
" ax.legend(fontsize=8)\n",
|
||
" plt.suptitle(\"PCA of mean-pooled ESMC embeddings by EC class\", y=1.02)\n",
|
||
" plt.tight_layout()\n",
|
||
" plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "cedcb5ba",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1100x400 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot_pca_projections(\n",
|
||
" all_embeddings,\n",
|
||
" labels,\n",
|
||
" classes,\n",
|
||
" best_mcc_layer,\n",
|
||
" last_layer,\n",
|
||
" random_state=RANDOM_STATE,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b6acb880",
|
||
"metadata": {},
|
||
"source": [
|
||
"At the best-performing layer, proteins roughly cluster by EC class, though there is still some overlap between classes. In contrast, the final layer shows substantial mixing between classes, suggesting that this separability is partially lost."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5dca0c4e",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 8. Summary table and takeaways\n",
|
||
"\n",
|
||
"Print a summary of the best layers and the performance gap between the best and last layer."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "cf9b1e11",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Top 5 layers by MCC:\n",
|
||
" Layer MCC mean MCC std\n",
|
||
" 25 0.935 0.018\n",
|
||
" 26 0.935 0.018\n",
|
||
" 29 0.933 0.025\n",
|
||
" 27 0.931 0.016\n",
|
||
" 31 0.931 0.030\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"summary = pd.DataFrame(\n",
|
||
" {\n",
|
||
" \"Layer\": np.arange(1, N_LAYERS + 1),\n",
|
||
" \"MCC mean\": mcc_mean.round(3),\n",
|
||
" \"MCC std\": mcc_std.round(3),\n",
|
||
" }\n",
|
||
")\n",
|
||
"\n",
|
||
"# Show top 5 layers\n",
|
||
"top5 = summary.nlargest(5, \"MCC mean\")[[\"Layer\", \"MCC mean\", \"MCC std\"]]\n",
|
||
"print(\"\\nTop 5 layers by MCC:\")\n",
|
||
"print(top5.to_string(index=False))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bd46cb23",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Next steps\n",
|
||
"\n",
|
||
"**Apply this to your own task.** \n",
|
||
"- The layer sweep approach can generalize to any labeled protein problem. You can replace the dataset and labels in Section 4 with your own task (e.g., stability, binding affinity, localization), then run the same workflow. \n",
|
||
"- The *optimal layer does not necessarily transfer across tasks*. Different biological properties are encoded at different depths, so the best-performing layer for function classification may differ from that for structure prediction or regression tasks. \n",
|
||
"- In some cases, you may also need to adjust the probe (e.g., regression instead of classification) or evaluation metric to match your task. The key idea is to treat layer choice as a hyperparameter and determine it empirically.\n",
|
||
"\n",
|
||
"\n",
|
||
"**Try different probes.** \n",
|
||
"- We use logistic regression as a simple linear probe, but other models can extract different signals from the same embeddings. For example:\n",
|
||
"\n",
|
||
" - **Ridge regression** for continuous outputs (e.g., thermostability)\n",
|
||
" - **SVMs or tree-based models** for more complex decision boundaries \n",
|
||
"\n",
|
||
"- If a more expressive model improves performance, it may indicate that useful information is present but not linearly separable.\n",
|
||
"\n",
|
||
"\n",
|
||
"**Explore other ESMC models.** \n",
|
||
"- In this tutorial, we use the **600M model** for speed. Other models (e.g., 6B) follow the same workflow but may have richer representations for your task.\n",
|
||
"\n",
|
||
"\n",
|
||
"**For more investigation on the latent space of ESMC, see the preprint!**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "945619d3",
|
||
"metadata": {},
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "esm-pixi",
|
||
"language": "python",
|
||
"name": "esm-pixi"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|