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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HADDOCK2.4/5 software - Official 2.4/5 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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Arctic-3D: ARCTIC-3D is a software for data-mining and clustering of protein interface information. It allows you to retrieve all the existing interface information for your desired protein from the PDBE graph database, grouping similar interfaces in interacting surfaces.
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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).
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DeepRank-GNN-esm DeepRank-GNN-esm is a general, configurable deep learning framework for data mining protein-protein interactions (PPIs) using graph convolutional neural networks (CNNs) and including language model features.
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
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Dataset of modelled protein-cyclic peptide complexes obtained with HADDOCK corresponding to the optimal protocol described in Charitou et al. JCTC 2022