HADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes

HADDOCK is a widely used resource for integrative modeling of a variety of biomolecular complexes that is able to incorporate experimental knowledge into physics-based calculations during complex prediction, refinement, scoring and analysis. Here we introduce HADDOCK3, the new modular version of the program, in which the original, parametrizable albeit rigid pipeline has been first broken down into a catalogue of independent modules and then enriched with powerful analysis tools and third-party integrations. Thanks to this increased flexibility, HADDOCK3 can now handle multiple integrative modeling scenarios, providing a valuable, physics-based tool to enrich and complement the predictions made by machine learning algorithms in the post-AlphaFold era. We present examples of successful applications of HADDOCK3 that were not feasible with the previous versions of HADDOCK, highlighting its expanded capabilities. The HADDOCK3 software source code is freely available from the GitHub repository (https://github.com/haddocking/haddock3) and comes with an online user guide (www.bonvinlab.org/haddock3-user-manual). All example data described in this manuscript are available at https://github.com/haddocking/haddock3-paper-data.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2025-06-17
DOI
https://doi.org/10.1021/acs.jcim.5c00969
Citations
17
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
12.90

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article

HADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes

Brian Jiménez‐García, Marco Giulini, Stefan Verhoeven, João M. C. Teixeira et al.
17 citations
Journal of Chemical Information and Modeling
Protein Structure and Dynamics
12.90
article

HADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes

Brian Jiménez‐García, Marco Giulini, Stefan Verhoeven, João M. C. Teixeira, Alexandre M. J. J. Bonvin, Victor Reys, Rodrigo V. Honorato, Anna Kravchenko, Anna Engel, Raphaëlle Versini, Xiaotong Xu
article en
17 citations

Abstract

HADDOCK is a widely used resource for integrative modeling of a variety of biomolecular complexes that is able to incorporate experimental knowledge into physics-based calculations during complex prediction, refinement, scoring and analysis. Here we introduce HADDOCK3, the new modular version of the program, in which the original, parametrizable albeit rigid pipeline has been first broken down into a catalogue of independent modules and then enriched with powerful analysis tools and third-party integrations. Thanks to this increased flexibility, HADDOCK3 can now handle multiple integrative modeling scenarios, providing a valuable, physics-based tool to enrich and complement the predictions made by machine learning algorithms in the post-AlphaFold era. We present examples of successful applications of HADDOCK3 that were not feasible with the previous versions of HADDOCK, highlighting its expanded capabilities. The HADDOCK3 software source code is freely available from the GitHub repository (https://github.com/haddocking/haddock3) and comes with an online user guide (www.bonvinlab.org/haddock3-user-manual). All example data described in this manuscript are available at https://github.com/haddocking/haddock3-paper-data.

Journal of Chemical Information and ModelingVol. 65(13)
Utrecht University (NL), Netherlands eScience Center (NL)
Netherlands eScience Center, HORIZON EUROPE European Innovation Council
Openalex Percentile: Top 1%
Protein Structure and Dynamics
12.90
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