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DeepRank2

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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What DeepRank2 can do for you

DeepRank2

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Overview

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 CNNs (for 3D-grids) or GNNs (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 here
  • Predefined target types
    • binary class, CAPRI categories, DockQ, RMSD, and FNAT
    • Detailed docking scores documentation is available here
  • Flexible definition of both new features and targets
  • Features generation for both graphs and 3D-grids
  • Efficient data storage in HDF5 format
  • Support for both classification and regression (based on PyTorch and PyTorch Geometric)

📚 Documentation

📣 Discussions

Acknowledgments

DeepRank2 software has been developed within the 3D-Vac project, funded by the Netherlands eScience Center (NLESC.OEC.2021.008). In addition to this core budget, we have obtained two fundings specific for software sustainability (SS). The first one was dedicated to improving the user experience of DeepRank2 by introducing a more adaptable interface intended for a wider audience. It also aimed to consolidate the original DeepRank, DeepRank-GNN and DeepRank-Mut packages into DeepRank2, intended to become the standard for developers and users. The second SS budget is still ongoing, and aims at expanding the current DeepRank2 package for handling highly diverse 3D molecular complexes composed of nucleic acids (e.g., DNA, RNA), proteins, and inorganic molecules.

Participating organisations

Life Sciences
Life Sciences
Radboud University Medical Center
Netherlands eScience Center

Reference papers

Mentions

Contributors

Giulia Crocioni
Giulia Crocioni
Sven van der Burg
Sven van der Burg
Netherlands eScience Center
DB
Dani Bodor
eScience Research Engineer
Netherlands eScience Center
DLB
Dani L. Bodor
CB
Coos Baakman
FMP
Farzaneh M. Parizi
DTR
Daniel T. Rademaker
GR
Gayatri Ramakrishnan
DFM
Dario F. Marzella
LCX
Li C. Xue

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