This page provide you links to software and software manuals of the computational structural biology group.
- HADDOCK
- HADDOCKING GitHub repository
- 3D-DART DNA modelling
- Bioinformatics interface predictors
- Deep learning protein interactions
- Benchmarks and datasets
HADDOCK
Software package for integrative modelling of biomolecular complexes
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HADDOCK best practice guide - A must read when starting to use our software!
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HADDOCK2.4 software - Official 2.4 production version
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HADDOCK2.4 web server - production version
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HADDOCK3 software - A new, very experimental BioExcel redesign of HADDOCK in a modular code. Use it at your own risk!
HADDOCKING GitHub repository
The GitHub repository for HADDOCK and its associated tools
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Binding_affinity: PRODIGY: A collection of Python scripts to predict the binding affinity in protein-protein complexes.
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DisVis: A Python package and command-line tool to quantify and visualize the accessible interaction space of distance-restrained biomolecular complexes.
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Fraction of common contact clustering: Clustering of biomolecular complexes based on the fraction of common contacts
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HADDOCK-tools: A collection of useful scripts related to HADDOCK
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PDB-tools: A collection of Python scripts for the manipulation (renumbering, changing chain and segIDs…) of PDB files. For documentation refer to https://www.bonvinlab.org/pdb-tools/. And now also available as web portal!
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PowerFit: PowerFit is a Python package and simple command-line program to automatically fit high-resolution atomic structures in cryo-EM densities.
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Samplex: Samplex is an automatic and unbiased method to distinguish perturbed and unperturbed regions in a protein existing in two distinct states (folded/partially unfolded, bound/unbound). Samplex takes as input a set of data and the corresponding three-dimensional structure and returns the confidence for each residue to be in a perturbed or unperturbed state.
3D-DART DNA modelling
3D-DART provides a convenient means of generating custom structural models of DNA. Our server is no longer in operation because of security issues, but you can run it yourself from a docker container. Visit for this our GitHub repo below.
Bioinformatics interface predictors
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WHISCY WHISCY is a program to predict protein-protein interfaces. It is primarily based on conservation, but it also takes into account structural information.
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CPORT CPORT is an algorithm for the prediction of protein-protein interface residues. It combines six interface prediction methods into a consensus predictor
Deep learning protein interactions
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DeepRank DeepRank is a general, configurable deep learning framework for data mining protein-protein interactions (PPIs) using 3D convolutional neural networks (CNNs).
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DeepRank-GNN DeepRank-GNN is a general, configurable deep learning framework for data mining protein-protein interactions (PPIs) using graph convolutional neural networks (CNNs).
Benchmarks and datasets
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Docking benchmark of membrane protein complexes (GitHub) and associated decoy dataset https://doi.org/10.15785/SBGRID/618
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Cleaned Docking Benchmark 5 dataset, HADDOCK-ready, with unbound and bound structures matched: https://github.com/haddocking/BM5-clean
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HADDOCK docking decoys for the new entries (55) of the protein-protein Docking Benchmark5: https://data.sbgrid.org/dataset/131/
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Docking models for Docking Benchmark 4, 5 and CAPRI score_set: https://doi.org/10.15785/SBGRID/684
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HADDOCK refined models for the biological/crystallographic interfaces collected in the DC and MANY datasets: https://doi.org/10.15785/SBGRID/566
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HADDOCK models of mutant protein complexes: https://doi.org/10.15785/SBGRID/651
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Protein-cyclic peptide docking benchmark and associated models dataset https://data.sbgrid.org/dataset/912
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All-atom and Coarse-grained HADDOCK docking models for Protein-DNA complexes: https://zenodo.org/record/3941636