DeepRank2 3.1.0 documentation

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

As input, DeepRank2 takes PDB-formatted atomic structures, and map them to graphs, where nodes can represent either residues or atoms, as chosen by the user, and edges represent the interactions between them. DeepRank2 has the option to choose between two types of queries as input for the featurization phase:

  • PPIs, for mining interaction patterns within protein-protein complexes, implemented by the ProteinProteinInterfaceQuery class;

  • SRVs, for mining mutation phenotypes within protein structures, implemented by the SingleResidueVariantQuery class.

The physico-chemical and geometrical features are then computed and assigned to each node and edge. The user can choose which features to generate from several pre-existing options defined in the package, or define custom features modules, as explained in the documentation. The graphs can then be mapped to 3D-grids as well. The generated data can be used for training neural networks. DeepRank2 also offers a pre-implemented training pipeline, using either convolutional neural networks (for 3D-grids) or graph neural networks (for graphs), as well as output exporters for evaluating performances.

Main features:

  • Predefined atom-level and residue-level feature types (e.g. atom/residue type, charge, size, potential energy, all features’ documentation is available under Features notes)

  • Predefined target types (binary class, CAPRI categories, DockQ, RMSD, and FNAT, detailed docking scores documentation is available under Docking scores notes)

  • Flexible definition of both new features and targets

  • Features generation for both graphs and 3D-grids

  • Efficient data storage in HDF5 format

  • Support both classification and regression (based on PyTorch and PyTorch Geometric)

Getting started

Installation

Get DeepRank2 installed on your computer.

Get Started

Understand how to use DeepRank2 and how it can help you.

Notes

Features

Get a detailed overview about nodes’ and edges’ features implemented in the package.

Docking Scores

Get a detailed overview about PPIs’ docking metrics implemented in the package.

Package reference

deeprank2 main modules

This section documents the main DeepRank2 modules.

deeprank2.molstruct submodules

This section documents the molecular structures submodules.

deeprank2.neuralnets

This section documents the pre-implemented neural networks architectures.

deeprank2.tools

This section documents the pre-implemented targets.

Contributing

Contributing guidelines

Learn how to contribute to DeepRank2.