Improving classification of correct and incorrect protein-protein docking models by augmenting the training set

Database-Driven Identification of Structurally Similar Protein-Protein Interfaces

Analyzing the similarity of protein interfaces in protein-protein interactions gives new insights into protein function and assists in discovering new drugs. Usually, tools that assess the similarity focus on the interactions between two protein interfaces, while sometimes we only have one predicted interface. Herein, we present PiMine, a database-driven protein interface similarity search. It compares interface residues of one or two interacting chains by calculating and searching tetrahedral geometric patterns of α-carbon atoms and calculating physicochemical and shape-based similarity. On a dedicated, tailor-made dataset, we show that PiMine outperforms commonly used comparison tools in terms of early enrichment when considering interfaces of sequentially and structurally unrelated proteins. In an application example, we demonstrate its usability for protein interaction partner prediction by comparing predicted interfaces to known protein-protein interfaces.