Energy Centrality Relationship Reduces False Positive Prediction in Protein Docking

Abstract

Interacting protein networks are responsible for a multitude of biological functions. These Functionally-Linked Interacting Proteins (FLIPs) occur at specific interfaces. The structures of most FLIPs are identified via X-ray crystallography, which can also reveal contacts that are functionally uncorrelated (FunCs) and the result of aggregation during crystallization. We hypothesize the Energy Centrality Relationship (ECR) concept that evolutionary pressure to maintain FLIPs will generate signature characteristics that can discriminate between aggregation and association. In our ECR analysis, we assess the amino acid energetic variation upon computational alanine scanning as a function of distance from the centroid of the interface. Here, we show that positional and energetic correlation patterns can discriminate FLIP/FunC and when clustered can differentiate between proteins belonging to different FLIP/FunC sub-categories. In addition, by generating docking decoys for structures of representatives of protein functional categories, we demonstrate ECR also greatly reduces false positives in quaternary structure prediction.