HawkDock version 2: an updated web server to predict and analyze the structures of protein–protein complexes

Protein-protein interactions (PPIs) are fundamental to cellular functions, yet predicting and analyzing their 3D structures remains a critical and computationally demanding challenge. To address this, the HawkDock web server was developed as an integrated computational platform for predicting and analyzing protein-protein complexes. Over the past 6 years, HawkDock has successfully processed >234 000 computational tasks. In this study, an updated version of HawkDock was developed with the following advancements: (1) a deep learning-based flexible docking method, GeoDock, has been integrated to improve docking accuracy, particularly for apo-protein structures; (2) the VD-MM/GBSA method, which outperforms conventional MM/GBSA approaches in predicting binding affinities, has been implemented; (3) a new Mutation Analysis Module has been added to systematically evaluate the energetic impacts of amino acid mutations on protein-protein binding; (4) the server has been migrated to a high-performance cluster with Amber upgraded to version 24. Here, we describe the general protocol of HawkDock2, with a particular focus on its new features related to flexible docking, VD-MM/GBSA affinity prediction, and amino acid residue mutations. Comprehensive validation studies have demonstrated the reliability and effectiveness of these new features. HawkDock2 will remain freely accessible to all users at http://cadd.zju.edu.cn/hawkdock/.

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Publication Details

Journal
Nucleic Acids Research
Published
2025-05-06
DOI
https://doi.org/10.1093/nar/gkaf379
Citations
22
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
17.73

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article

HawkDock version 2: an updated web server to predict and analyze the structures of protein–protein complexes

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22 citations
Nucleic Acids Research
Protein Structure and Dynamics
17.73
article

HawkDock version 2: an updated web server to predict and analyze the structures of protein–protein complexes

Ke Zhang, Gaoqi Weng, Tingjun Hou, Ercheng Wang, Hongyan Du, Shukai Gu, Chao Shen, Xiaorui Wang, Chen Zhang, Jike Wang, Xujun Zhang, Mingquan Liu, Hui Zhang, Linlong Jiang, Odin Zhang
article en
22 citations

Abstract

Protein-protein interactions (PPIs) are fundamental to cellular functions, yet predicting and analyzing their 3D structures remains a critical and computationally demanding challenge. To address this, the HawkDock web server was developed as an integrated computational platform for predicting and analyzing protein-protein complexes. Over the past 6 years, HawkDock has successfully processed >234 000 computational tasks. In this study, an updated version of HawkDock was developed with the following advancements: (1) a deep learning-based flexible docking method, GeoDock, has been integrated to improve docking accuracy, particularly for apo-protein structures; (2) the VD-MM/GBSA method, which outperforms conventional MM/GBSA approaches in predicting binding affinities, has been implemented; (3) a new Mutation Analysis Module has been added to systematically evaluate the energetic impacts of amino acid mutations on protein-protein binding; (4) the server has been migrated to a high-performance cluster with Amber upgraded to version 24. Here, we describe the general protocol of HawkDock2, with a particular focus on its new features related to flexible docking, VD-MM/GBSA affinity prediction, and amino acid residue mutations. Comprehensive validation studies have demonstrated the reliability and effectiveness of these new features. HawkDock2 will remain freely accessible to all users at http://cadd.zju.edu.cn/hawkdock/.

Nucleic Acids ResearchVol. 53(W1)
Oregon Health & Science University (US), Hunan University (CN), Vollum Institute (US), Zhejiang Lab (CN), First Affiliated Hospital Zhejiang University (CN), Zhejiang University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 1%
Protein Structure and Dynamics
17.73
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