HawkDock: a web server to predict and analyze the protein–protein complex based on computational docking and MM/GBSA
By Gaoqi Weng, Ercheng Wang, Zhe Wang, Hui Liu, Feng Zhu, Dan Li, Tingjun Hou. Nucleic Acids Res., 2019
565 citations recorded by the source corpus.
Abstract Protein–protein interactions (PPIs) play an important role in the different functions of cells, but accurate prediction of the three-dimensional structures for PPIs is still a notoriously difficult task. In this study, HawkDock, a free and open accessed web server, was developed to predict and analyze the structures of PPIs. In the HawkDock server, the ATTRACT docking algorithm, the HawkRank scoring function developed in our group and the MM/GBSA free energy decomposition analysis were seamlessly integrated into a multi-functional platform. The structures of PPIs were predicted by combining the ATTRACT docking and the HawkRank re-scoring, and the key residues for PPIs were highlighted by the MM/GBSA free energy decomposition. The molecular visualization was supported by 3Dmol.js. For the structural modeling of PPIs, HawkDock could achieve a better performance than ZDOCK 3.0.2 in the benchmark testing. For the prediction of key residues, the important residues that play an essential role in PPIs could be identified in the top 10 residues for ∼81.4% predicted models and ∼95.4% crystal structures in the benchmark dataset. To sum up, the HawkDock server is a powerful tool to predict the binding structures and identify the key residues of PPIs. The HawkDock server is accessible free of charge at http://cadd.zju.edu.cn/hawkdock/.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/eea7aaeb8c3afcac7ed07fcd98303022f0c8fd19
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Citations in the corpus
- The HDOCK server for integrated protein–protein docking (2020, methodology): “ATTRACT 47 and SWARMDOCK 43 use normal modes to consider large conformational changes of backbone.”
- Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021, methodology): “…methods (Chen et al., 2003; Venkatraman et al., 2009; De Vries et al., 2010; Biesiada et al., 2011; Torchala et al., 2013; Schindler et al., 2017; Weng et al., 2019; Sunny and Jayaraj, 2021; Christoffer et al., 2021; Yan et al., 2020) typically run several steps: first, they sample thousands or…”