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261 lines
14 KiB
Markdown
261 lines
14 KiB
Markdown
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<p align="left">
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<img src="https://github.com/rdk/p2rank/blob/develop/misc/img/p2rank_logo.svg?raw=true" width="280" alt="P2Rank" title="logo">
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</p>
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Ligand-binding site prediction based on machine learning.
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<p align="center">
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<img src="https://github.com/rdk/p2rank/blob/develop/misc/img/p2rank_sas_points.png?raw=true" width="600" alt="P2Rank illustration">
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</p>
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<a href="https://github.com/rdk/p2rank/releases" title="go to the release page"><img src="https://img.shields.io/badge/release-2.5.1-green.svg"></img></a>
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[](https://github.com/rdk/p2rank/actions)
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[](/LICENSE.txt)
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### Description
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P2Rank is a stand-alone command-line program for fast and accurate prediction of ligand-binding sites from protein structures.
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It achieves high prediction success rates without relying on external software for computation of complex features or on a database of known protein-ligand templates.
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### ✨ What's new?
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* Version **2.5** brings speed optimizations (~2x faster prediction), ChimeraX visualizations, and improvements to rescoring (`fpocket-rescore` command).
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* Version **2.4.2** adds support for BinaryCIF (`.bcif`) input and rescoring of fpocket predictions in `.cif` format.
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* Version **2.4** adds support for mmCIF (`.cif`) input and contains a special profile for predictions on AlphaFold models and NMR/cryo-EM structures.
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### Requirements
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* Java 17 to 23
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* PyMOL/ChimeraX for viewing visualizations (optional)
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P2Rank is tested on Linux, macOS, and Windows.
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### Setup
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P2Rank requires no installation. Binary packages are available as GitHub Releases.
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* **Download**: https://github.com/rdk/p2rank/releases
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* Source code: https://github.com/rdk/p2rank
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* Datasets: https://github.com/rdk/p2rank-datasets
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### Usage
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<pre>
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<b>prank</b> predict -f test_data/1fbl.pdb # predict pockets on a single pdb file
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</pre>
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See more usage examples below...
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### Algorithm
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P2Rank makes predictions by scoring and clustering points on the protein's solvent accessible surface.
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Ligandability score of individual points is determined by a machine learning model trained on a dataset of known protein-ligand complexes.
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For more details, see the slides and publications.
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Presentation slides introducing the original version of the algorithm: [Slides (pdf)](https://bit.ly/p2rank-slides)
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### Publications
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If you use P2Rank, please cite relevant papers: <a href="/misc/citations.md" title="go to the BibTex citations" target="_blank"><img align="right" src="https://img.shields.io/badge/BibTeX-eee"></img></a>
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* [Software article](https://doi.org/10.1186/s13321-018-0285-8) about P2Rank pocket prediction tool
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Krivak R, Hoksza D. ***P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure.*** Journal of Cheminformatics. 2018 Aug.
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* [Latest web-server article](https://doi.org/10.1093/nar/gkaf421) about updates in P2Rank and [prankweb.cz](https://prankweb.cz)
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Polak L, Skoda P, Riedlova K, Krivak R, Novotny M, Hoksza D. ***PrankWeb 4: a modular web server for protein–ligand binding site prediction and downstream analysis.*** Nucleic Acids Research, 2025 May.
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* [Web-server article](https://doi.org/10.1093/nar/gkac389) about updates in [prankweb.cz](https://prankweb.cz)
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Jakubec D, Skoda P, Krivak R, Novotny M, Hoksza D. ***PrankWeb 3: accelerated ligand-binding site predictions for experimental and modelled protein structures.*** Nucleic Acids Research, Volume 50, Issue W1, 5 July 2022, Pages W593–W597
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* [Web-server article](https://doi.org/10.1093/nar/gkz424) introducing the web interface at [prankweb.cz](https://prankweb.cz)
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Jendele L, Krivak R, Skoda P, Novotny M, Hoksza D. ***PrankWeb: a web server for ligand binding site prediction and visualization.*** Nucleic Acids Research, Volume 47, Issue W1, 02 July 2019, Pages W345-W349
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* [Conference paper](https://doi.org/10.1007/978-3-319-21233-3_4) introducing P2Rank prediction algorithm
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Krivak R, Hoksza D. ***P2RANK: Knowledge-Based Ligand Binding Site Prediction Using Aggregated Local Features.*** International Conference on Algorithms for Computational Biology 2015 Aug 4 (pp. 41-52). Springer
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* [Research article](https://doi.org/10.1186/s13321-015-0059-5) about PRANK rescoring algorithm (now included in P2Rank)
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Krivak R, Hoksza D. ***Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features.*** Journal of Cheminformatics. 2015 Dec.
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👨💻 Usage Examples
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--------------
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Following commands can be executed in the installation directory.
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### Help and version
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~~~ruby
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prank help # print help for main commands and parameters
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prank -v # print version and some system info
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~~~
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### Predict ligand binding sites (P2Rank algorithm)
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~~~ruby
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prank predict test.ds # run on dataset containing a list of pdb/cif files
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prank predict -f test_data/1fbl.pdb # run on a single pdb file
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prank predict -f test_data/1fbl.cif # run on a single mmCIF file
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prank predict -f test_data/1fbl.bcif # run on a single BinaryCIF file
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prank predict -f test_data/1fbl.pdb.gz # run on a single gzipped pdb file (other formats can be compressed too)
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prank predict -f test_data/1fbl.cif.zst # run on a single cif file compressed with Zstandard
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prank predict -threads 8 test.ds # specify num. of working threads for parallel dataset processing
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prank predict -o output_here test.ds # explicitly specify output directory
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prank predict -c alphafold test.ds # use alphafold config and model (config/alphafold.groovy)
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# this profile is recommended for AlphaFold models, NMR and cryo-EM
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# structures since it doesn't depend on b-factor as a feature
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~~~
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### Prediction output
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For each structure file `{struct_file}` in the dataset, P2Rank generates several output files:
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* `{struct_file}_predictions.csv`: lists **predicted pockets** in order of score, including each pocket's score, center coordinates, adjacent residues, adjacent protein surface atoms, and a calibrated probability of being a ligand-binding site.
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* `{struct_file}_residues.csv`: lists **all residues** from the input protein along with their scores, mapping to predicted pockets, and a calibrated probability of being a ligand-binding residue.
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* **PyMol and ChimeraX visualizations**: `.pml` and `.cxc` scripts in `visualizations/` directory with additional files in `data/`.
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* Optional settings:
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* Use `-visualizations 0` to disable visualization generation.
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* Use `-vis_renderers 'pymol,chimerax'` to toggle specific renderers on/off.
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* Use `-vis_copy_proteins 0` to prevent copying protein structures to the visualizations directory (faster, but visualizations won't be portable).
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* **SAS points data**: coordinates and ligandability scores for solvent-accessible surface (SAS) points are saved in `visualizations/data/{struct_file}_points.pdb.gz`. Here:
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* Residue sequence number (position 23-26) represents the pocket rank (0 indicates no pocket).
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* B-factor column contains predicted ligandability score.
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### Configuration
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You can override the default parameter values in a custom config file:
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~~~ruby
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prank predict -c config/example.groovy test.ds
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prank predict -c example test.ds # same effect, config/ is default location and .groovy implicit extension
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~~~
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It is also possible to override parameters on the command line using their full name after `-` (not `--`).
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~~~ruby
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prank predict -visualizations 0 -threads 8 test.ds # turn off visualizations and set the number of threads
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prank predict -c example.groovy -visualizations 0 -threads 8 test.ds # overrides defaults as well as values from example.groovy
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~~~
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P2Rank has many configurable parameters.
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To see the list of standard parameters look into `config/default.groovy` and other example config files in this directory.
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To see the complete commented list of all (including undocumented)
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parameters see [Params.groovy](https://github.com/rdk/p2rank/blob/develop/src/main/groovy/cz/siret/prank/program/params/Params.groovy) in the source code.
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### Rescoring (PRANK algorithm)
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In addition to predicting new ligand binding sites,
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P2Rank is also able to rescore pockets predicted by other methods
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(Fpocket,
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ConCavity,
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SiteHound,
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MetaPocket2,
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LISE,
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DeepSite,
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and PUResNetV2.0
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are supported at the moment).
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Rescoring output:
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* `{struct_file}_rescored.csv`: list of pockets sorted by the new score
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* `{struct_file}_predictions.csv`: same as with `prank predict` (since 2.5)
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* Note: probability column is calibrated for rescoring fpocket predictions
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* visualizations
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~~~ruby
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prank rescore fpocket.ds
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prank rescore fpocket.ds -o output_here # explicitly specify output directory
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prank rescore fpocket.ds -c rescore_2024 # use new experimental rescoring model (recommended for alphafold models)
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~~~
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For rescoring, the dataset file needs to have a specific 2-column format. See examples in `test_data/`: `fpocket.ds`, `concavity.ds`, `puresnet.ds`.
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New experimental rescoring model `-c rescore_2024` shows promising result but hasn't been fully evaluated yet. It is recommended for AlphaFold models, NMR and cryo-EM structures since it doesn't depend on b-factor as a feature.
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#### Run fpocket and rescore in one command
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You can use `fpocket-rescore` command to run [Fpocket](https://github.com/Discngine/fpocket) and then rescore its predictions automatically.
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~~~ruby
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prank fpocket-rescore test.ds # expects 'fpocket' command in PATH
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prank fpocket-rescore test.ds -fpocket_command "/bin/fpocket -w m" # specify custom fpocket command (optionally with arguments)
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prank fpocket-rescore test.ds -fpocket_keep_output 0 # delete fpocket output files
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~~~
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In this case, the dataset file can be a simple list of pdb/cif files since Fpocket predictions will be calculated ad-hoc.
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`prank fpocket-rescore` will produce `predictions.csv` as well, so it can be used as an in-place replacement for `prank predict` in most scenarios.
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Note: if you use `fpocket-rescore`, please cite Fpocket as well.
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### Evaluate prediction and rescoring models
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Use following commands to calculate prediction metrics (prediction success rates using DCA, DCC, ...) on structure files, where the ligands are present.
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~~~ruby
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prank eval-predict -f test_data/1fbl.pdb # evaluate default prediction model on a single file
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prank eval-predict test.ds # evaluate default prediction model on a dataset with known ligands
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prank eval-predict -c alphafold test.ds # evaluate specific prediction model on a dataset with known ligands
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prank eval-rescore fpocket.ds # evaluate default rescoring model on a dataset with known ligands
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prank eval-rescore -c rescore_2024 fpocket.ds # evaluate specific rescoring model on a dataset with known ligands
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~~~
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## 🏗️ Build from sources
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This project uses [Gradle](https://gradle.org/) build system via included Gradle wrapper.
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On Windows, use `bash` to run build commands (installed by default with [Git for Windows](https://git-scm.com/download/win)).
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```ruby
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git clone https://github.com/rdk/p2rank.git && cd p2rank
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./make.sh
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./unit-tests.sh # optionally you can run tests to check everything works fine on your machine
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./tests.sh quick # runs further tests
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```
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Now you can run the program via:
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```ruby
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distro/prank # standard mode that is run in production
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./prank.sh # development/training mode
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```
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To use `./prank.sh` (development/training mode) first you need to copy and edit `misc/locval-env.sh` into repo root directory (see [training tutorial](https://github.com/rdk/p2rank/blob/develop/misc/tutorials/training-tutorial.md#preparing-the-environment)).
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## ⚖️ Comparison with Fpocket
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[Fpocket](https://github.com/Discngine/fpocket) is a widely used open source ligand binding site prediction program.
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It is fast, easy to use and well documented. As such, it served as a great inspiration for this project.
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Some practical differences:
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* **Fpocket**
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- has a much smaller memory footprint
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- runs faster when executed on a single protein
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- produces a high number of less relevant pockets (and since the default scoring function isn't very effective, the most relevant pockets often don't get to the top)
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- contains MDpocket algorithm for pocket predictions from molecular trajectories
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- still better documented
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* **P2Rank**
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- achieves significantly higher identification success rates when considering top-ranked pockets
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- produces a smaller number of more relevant pockets
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- speed:
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+ slower when running on a single protein (due to JVM startup cost)
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+ approximately as fast on average running on a big dataset on a single core
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+ due to parallel implementation potentially much faster on multi-core machines
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- higher memory footprint (~1G but doesn't grow much with more parallel threads)
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Both Fpocket and P2Rank have many configurable parameters that influence behaviour of the algorithm and can be tweaked to achieve better results for particular requirements.
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## 🙌 Thanks
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This program builds upon software written by other people, either through library dependencies or through code included in its source tree (where no library builds were available). Notably:
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* FastRandomForest by Fran Supek (https://code.google.com/archive/p/fast-random-forest/)
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* FastRandomForest 2.0 (https://github.com/GenomeDataScience/FastRandomForest)
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* KDTree by Rednaxela (http://robowiki.net/wiki/User:Rednaxela/kD-Tree)
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* BioJava (https://github.com/biojava)
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* Chemistry Development Kit (https://github.com/cdk)
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## 🤝 Contributing
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We welcome any bug reports, enhancement requests, and other contributions. To submit a bug report or enhancement request, please use the [GitHub issues tracker](https://github.com/rdk/p2rank/issues). For more substantial contributions, please fork this repo, push your changes to your fork, and submit a pull request with a good commit message.
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