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

Journal: Nucleic acids research
Published Date:

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/.

Authors

  • Xujun Zhang
    Injury Prevention Research Institute, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China.
  • Linlong Jiang
    College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Gaoqi Weng
    Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
  • Chao Shen
    Department of Epidemiology, School of Public Health, Soochow University, Suzhou 215123, China.
  • Odin Zhang
    Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.
  • Mingquan Liu
    College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, Hunan, China.
  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Shukai Gu
    College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
  • Jike Wang
    School of Computer Science, Wuhan University, Wuhan, Hubei 430072, China.
  • Xiaorui Wang
    Structural Biophysics Group, School of Optometry and Vision Sciences, Cardiff University, Cardiff, Wales, UK.
  • Hongyan Du
    Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
  • Hui Zhang
    Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Ke Zhang
    Center for Radiation Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou 310001, China.
  • Ercheng Wang
    Hangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China.
  • Tingjun Hou
    College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.