Novel method for prioritizing protein binding sites using pocket analysis and MD simulations

Successful virtual screening of therapeutically relevant proteins depends on selecting optimal protein structures and accurately identifying druggable binding sites. This study presents a novel methodology that combines pocket analysis, molecular docking, and molecular dynamics simulations to prioritize protein binding sites, using SARS-CoV-2 spike protein as a case study. The methodology begins by collecting a comprehensive dataset of resolved spike protein structures and known inhibitors. A pocket search identified potential binding sites across various protein conformations, followed by docking simulations to evaluate ligand-binding affinities. We introduce a novel algorithm COMPASS: COMputational Pocket Analysis and Scoring System includes the calculation of Pocket Frequency Score, which assesses pocket relevance based on the frequency of key residues, was introduced to refine pocket selection. This scoring system was combined with traditional pocket and docking scores to produce a Global Score, enabling the ranking of pockets. The top-ranked pockets underwent molecular dynamics simulations and free energy calculations to assess their stability and druggability. Six out of the ten best-ranked pockets demonstrated stable interactions with all tested inhibitors, highlighting their potential as drug targets. The study found that the selected pockets not only showed significant structural uniqueness but also correlated well with experimentally validated binding sites, confirming the method's effectiveness. In conclusion, the proposed algorithm and the method enhance the accuracy of structure-based drug discovery by enabling the rational selection of protein-binding pockets. It shows potential for enhancing virtual screening efforts, especially for proteins with numerous available experimental structures, where selecting an optimal structure is critical.