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DeepRank

Scoring 3D protein-protein interaction models using deep learning

Image by: NIH Image Gallery

Interactions between biomolecules control all cellular processes. Understanding those interactions requires adding a three dimensional structural dimension. Next to experimental structural biology techniques, this can be done by docking, a complementary and high-throughput computational method allowing to model complexes from their known components.

A challenge in docking is scoring – the identification of correct (near-native) models from a large pool of docked models – due to our still limited knowledge of interaction rules. We will tackle this challenge by training deep networks (dNNs) to learn complex interaction patterns from the huge amount of experimental data in the Protein Data Bank (a valuable source of information not yet fully exploited). Our innovative strategy is to treat this problem as a 3D image classification problem: The interfaces of docked models will be represented as 3D images and dNNs will be trained to classify whether they are near-native or not. Unlike other machine learning techniques, dNNs are now able to learn from millions of data without reaching a performance plateau quickly, which is computationally tractable by harvesting GPU and Hadoop technologies.

The resulting scoring function, DeepRank, will markedly enhance our capability to reliably model biomolecular complexes, assisting the scientific community to gain insights into macromolecular aspects of life. It will be implemented in our HADDOCK modelling platform and freely distributed through GitHub and eStep repositories, ensuring a wide dissemination. The impact will be broad since 3D image-based dNNs have applications in many other domains, such as medical diagnoses (MRI), cryo-electron microscopy and computer vision.

Participating organisations

Netherlands eScience Center
Utrecht University
Life Sciences
Life Sciences

Impact

Output

Team

AB
Alexandre M.J.J. Bonvin
Principal investigator
Utrecht University
Cunliang Geng
Research Software Engineer
Netherlands eScience Center
Lars Ridder
Lars Ridder
Research Software Engineer
Netherlands eScience Center
LX
Li Xue
Principal investigator
Radboud University Medical Center
MR
Manon Réau
Research Software Engineer
Utrecht University
Nicolas Renaud
Nicolas Renaud
eScience Research Engineer
Netherlands eScience Center
Sonja Georgievska
Sonja Georgievska
eScience Research Engineer
Netherlands eScience Center

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Related software

DeepRank

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Deep learning framework for data mining protein-protein interactions using CNN

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DeepRank2 is an open-source deep learning framework for data mining of protein-protein interfaces or single-residue missense variants. This package is an improved and unified version of three previously developed packages: DeepRank, DeepRank-GNN and DeepRank-Mut.

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DeepRank GNN

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DeepRank-GNN is the graph neural network of our DeepRank package. DeepRank GNN allows to train graph neural networks to classify protein-protein interface

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A framework and predictor based on support vector machine and random walk graph kernel for scoring protein-protein interfaces.

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pdb2sql

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Fast and versatile Python package that leverages SQL queries to parse, manipulate and process biomolecular structure files. The structure files should be in the PDB format and are available on www.rcsb.org.

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PSSMGen

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Generates consistent PSSM and PDB files for protein-protein complexes

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