BINDING SPECIFICITY OF PEPTIDE RECOGNITION DOMAINS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We have developed a computational method called Virtual Mutagenesis (VM) to determine the peptide sequences recognized by peptide binding protein domains, such as SH3 and SH2 domains, and predict their physiological interacting partners of these domains. In the proof of concept study, we applied this method to SH3 domain of human protein Abl. Based on a complex structure of the SH3 domain and a binding peptide, we mutated every amino acid of the template peptide to all other 19 amino acids and calculated the binding free energy difference between the template and mutated peptides, which reflects how favorable/unfavorable an amino acid is at each position of the peptide. This free energy difference is used to generate a position specific scoring matrix (PSFM) to screen all peptides in the human proteome and identify the putative interacting partners of the Abl SH3 domain. In the top ten candidates, we found four proteins were known to interact with the SH3 domain and the performance of VM was much better than Scansite, a method relying on the PSFM generated from random peptide library experiments.