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HADDOCK3:用于生物分子复合物整合建模的模块化通用平台。

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

作者信息

Giulini Marco, Reys Victor, Teixeira João M C, Jiménez-García Brian, V Honorato Rodrigo, Kravchenko Anna, Xu Xiaotong, Versini Raphaëlle, Engel Anna, Verhoeven Stefan, Bonvin Alexandre M J J

机构信息

Bijvoet Centre for Biomolecular Research, Faculty of Science─Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.

Zymvol Biomodeling, Carrer de Pau Claris 94, 3B, 08010 Barcelona, Spain.

出版信息

J Chem Inf Model. 2025 Jul 14;65(13):7315-7324. doi: 10.1021/acs.jcim.5c00969. Epub 2025 Jun 17.

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.

摘要

HADDOCK是一种广泛用于各种生物分子复合物整合建模的资源,它能够在复合物预测、优化、评分和分析过程中将实验知识纳入基于物理的计算中。在此,我们介绍HADDOCK3,该程序的新模块化版本,其中最初虽可参数化但较为僵化的流程首先被分解为一系列独立模块,然后通过强大的分析工具和第三方整合进行了扩充。得益于这种增强的灵活性,HADDOCK3现在可以处理多种整合建模场景,提供了一种有价值的、基于物理的工具,以丰富和补充后AlphaFold时代机器学习算法所做的预测。我们展示了HADDOCK3的成功应用示例,这些示例在前几个版本的HADDOCK中是不可行的,突出了其扩展的功能。HADDOCK3软件的源代码可从GitHub仓库(https://github.com/haddocking/haddock3)免费获取,并配有在线用户指南(www.bonvinlab.org/haddock3-user-manual)。本手稿中描述的所有示例数据可在https://github.com/haddocking/haddock3-paper-data获取。

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