A combinatorial score to distinguish biological and nonbiological protein-protein interfaces.

With the large amount of protein-protein complex structural data available, to understand the key features governing the specificity of protein-protein recognition and to define a suitable scoring function for protein-protein interaction predictions, we have analyzed the protein interfaces from geometric and energetic points of view. Atom-based potential of mean force (PMFScore), packing density, contact size, and geometric complementarity are calculated for crystal contacts in 74 homodimers and 91 monomers, which include real biological interactions in dimers and nonbiological contacts in monomers and dimers. Simple cutoffs were developed for single and combinatorial parameters to distinguish biological and nonbiological contacts. The results show that PMFScore is a better discriminator between biological and nonbiological interfaces comparable in size. The combination of PMFScore and contact size is the most powerful pairwise discriminator. A combinatorial score (CFPScore) based on the four parameters was developed, which gives the success rate of the homodimer discrimination of 96.6% and error rate of the monomer discrimination of 6.0% and 19.8% according to Valdar's and our definition, respectively. Compared with other statistical learning models, the cutoffs for the four parameters and their combinations are directly based on physical models, simple, and can be easily applied to protein-protein interface analysis and docking studies.