UMass Chan Medical School Faculty Publications


Binding interface prediction by combining protein-protein docking results

UMMS Affiliation

Program in Bioinformatics and Integrative Biology

Publication Date


Document Type



*Algorithms; Area Under Curve; *Models, Molecular; Protein Binding; *Protein Interaction Maps; Support Vector Machines; ran GTP-Binding Protein


Biochemistry | Bioinformatics


We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein-protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision-recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta-PPISP (AUC = 0.43), which is one of the best monomer-based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta-PPISP and another monomer-based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta-PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues.

DOI of Published Version



Proteins. 2014 Jan;82(1):57-66. doi: 10.1002/prot.24354. Epub 2013 Aug 31. Link to article on publisher's site

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