Hotspot Coevolution Is a Key Identifier of Near-Native Protein Complexes

Abstract

Protein−protein interactions play a key role in mediating numerous biological functions, with more than half the proteins in living organisms existing as either homo-or hetero-oligomeric assemblies. Protein subunits that form oligomers minimize the free energy of the complex, but exhaustive computational search-based docking methods have not comprehensively addressed the challenge of distinguishing a natively bound complex from non-native forms. Current protein docking approaches address this problem by sampling multiple binding modes in proteins and scoring each mode, with the lowest-energy (or highest scoring) binding mode being regarded as a near-native complex. However, high-scoring modes often match poorly with the true bound form, suggesting a need for improvement of the scoring function. In this study, we propose a scoring function, KFC-E, that accounts for both conservation and coevolution of putative binding hotspot residues at protein− protein interfaces. We tested KFC-E on four benchmark sets of unbound examples and two benchmark sets of bound examples, with the results demonstrating a clear improvement over scores that examine conservation and coevolution across the entire interface.

Iterative Knowledge-Based Scoring Function for Protein–Ligand Interactions by Considering Binding Affinity Information

“…The binding mode with the highest binding score is normally selected as the docking result of the predicted protein–ligand complex. Therefore, the reliability of molecular docking strongly depends on the accuracy of its scoring function. − In molecular docking for structure-based drug design, scoring functions are usually used in three major applications: , binding mode identification, , binding affinity prediction, , and virtual screening. , …”