BINDING SPECIFICITY OF PEPTIDE RECOGNITION DOMAINS

肽识别域的结合特异性

• 批准号: 7367782
• 负责人: WEI WANG
• 金额: $ 2.21万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7367782
• 关键词: BINDING; SPECIFICITY; PEPTIDE; RECOGNITION; DOMAINS

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.

该子项目是利用 NIH/NCRR 资助的中心拨款提供的资源的众多研究子项目之一。子项目和研究者 (PI) 可能已从另一个 NIH 来源获得主要资金，因此可以在其他 CRISP 条目中出现。列出的机构是中心的机构，不一定是研究者的机构。我们开发了一种称为虚拟诱变 (VM) 的计算方法来确定肽结合蛋白结构域（例如 SH3 和 SH2 结构域）识别的肽序列，并预测这些结构域的生理相互作用伙伴。在概念验证研究中，我们将此方法应用于人类蛋白 Abl 的 SH3 结构域。基于SH3结构域和结合肽的复杂结构，我们将模板肽的每个氨基酸突变为所有其他19个氨基酸，并计算模板和突变肽之间的结合自由能差异，这反映了模板肽的有利/不利程度氨基酸位于肽的每个位置。这种自由能差异用于生成位置特异性评分矩阵 (PSFM)，以筛选人类蛋白质组中的所有肽并识别 Abl SH3 结构域的假定相互作用伙伴。在前十个候选蛋白中，我们发现已知有四种蛋白质与 SH3 结构域相互作用，并且 VM 的性能比 Scansite（一种依赖于随机肽库实验生成的 PSFM 的方法）要好得多。