Prediction of Binding Sites of Peptide Recognition Domains: An Application on Grb2 and SAP SH2 Domains

Determination of the binding motif and identification of interaction partners of the modular domains such as SH2 domains can enhance our understanding of the regulatory mechanism of protein–protein interactions. We propose here a new computational method to achieve this goal by integrating the orthogonal information obtained from binding free energy estimation and peptide sequence analysis. We performed a proof-of-concept study on the SH2 domains of SAP and Grb2 proteins. The method involves the following steps: (1) estimating the binding free energy of a set of randomly selected peptides along with a sample of known binders; (2) clustering all these peptides using sequence and energy characteristics; (3) extracting a sequence motif, which is represented by a hidden Markov model (HMM), from the cluster of peptides containing the sample of known binders; and (4) scanning the human proteome to identify binding sites of the domain. The binding motifs of the SAP and Grb2 SH2 domains derived by the method agree well with those determined through experimental studies. Using the derived binding motifs, we have predicted new possible interaction partners for the Grb2 and SAP SH2 domains as well as possible interaction sites for interaction partners already known. We also suggested novel roles for the proteins by reviewing their top interaction candidates.