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

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• Protein-protein interactions (PPIs) are crucial for cellular processes.
• Predicting and analyzing the 3D structures of protein complexes is computationally challenging.
• The HawkDock web server has previously supported PPI analysis.

Purpose of the Study:

• To introduce HawkDock2, an upgraded web server for predicting and analyzing protein-protein complexes.
• To enhance docking accuracy, binding affinity prediction, and mutation impact analysis for PPIs.
• To provide a reliable and accessible computational platform for researchers.

Main Methods:

• Integration of GeoDock, a deep learning-based flexible docking method.
• Implementation of the VD-MM/GBSA method for superior binding affinity prediction.
• Addition of a Mutation Analysis Module to assess effects of amino acid mutations on binding.
• Migration to a high-performance computing cluster with updated Amber software.

Main Results:

• Improved accuracy in predicting protein-protein complex structures, especially for apo-protein targets.
• More reliable prediction of binding affinities compared to conventional methods.
• Systematic evaluation of mutation impacts on protein-protein binding energetics.
• Demonstrated reliability and effectiveness of new features through comprehensive validation.

Conclusions:

• HawkDock2 offers significant advancements in computational prediction and analysis of protein-protein interactions.
• The new features provide researchers with powerful tools for structural and energetic analysis of PPIs.
• HawkDock2 is freely accessible, supporting broader research in molecular and structural biology.