Predicting Peptide Binding Sites on Protein Surfaces by Clustering Chemical Interactions
Predicting Peptide Binding Sites on Protein Surfaces by Clustering Chemical Interactions
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DOI：
10.1002/jcc.23771
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发表时间：
2015-01-05
影响因子：
3
通讯作者：
Zou, Xiaoqin
中科院分区：
文献类型：
--
作者：
Yan, Chengfei;Zou, Xiaoqin
短肽在信号转导、免疫应答和转录调控等细胞过程中发挥着重要作用。正确识别给定蛋白质表面的肽结合位点不仅对这些生物过程的机制研究非常重要，而且对治疗开发也非常重要。在这项研究中，我们开发了一种新的计算方法，称为ACCLUSTER，用于预测蛋白质表面上的肽结合位点。具体来说，我们使用20个标准氨基酸作为探针来全局扫描蛋白质表面。识别与蛋白质形成良好化学相互作用的姿态，然后使用基于密度的空间聚类应用噪声技术进行聚类。最后，根据它们的大小对这些集群进行排名。预测大小最大的簇作为假定的结合位点。ACCLUSTER的评估是在251个非冗余蛋白肽复合物的不同测试集上进行的。结果与POCASA（一种用于预测配体结合位点的口袋检测方法）的性能进行了比较。Peptidb是另一个包含结合结构和非结合结构或同源结构的蛋白质肽数据库，用于测试ACCLUSTER的稳健性。ACCLUSTER的性能还与PepSite2和PeptiMap进行了比较，PepSite2和PeptiMap是最近开发的两种用于识别肽结合位点的方法。结果表明，ACCLUSTER是一种很有前途的肽结合位点预测方法。此外，ACCLUSTER也被证明适用于非肽配体结合位点预测。(c) 2014 Wiley期刊公司
Short peptides play important roles in cellular processes including signal transduction, immune response, and transcription regulation. Correct identification of the peptide binding site on a given protein surface is of great importance not only for mechanistic investigation of these biological processes but also for therapeutic development. In this study, we developed a novel computational approach, referred to as ACCLUSTER, for predicting the peptide binding sites on protein surfaces. Specifically, we use the 20 standard amino acids as probes to globally scan the protein surface. The poses forming good chemical interactions with the protein are identified, followed by clustering with the density-based spatial clustering of applications with noise technique. Finally, these clusters are ranked based on their sizes. The cluster with the largest size is predicted as the putative binding site. Assessment of ACCLUSTER was performed on a diverse test set of 251 nonredundant protein-peptide complexes. The results were compared with the performance of POCASA, a pocket detection method for ligand binding site prediction. Peptidb, another protein-peptide database that contains both bound structures and unbound or homologous structures was used to test the robustness of ACCLUSTER. The performance of ACCLUSTER was also compared with PepSite2 and PeptiMap, two recently developed methods developed for identifying peptide binding sites. The results showed that ACCLUSTER is a promising method for peptide binding site prediction. Additionally, ACCLUSTER was also shown to be applicable to nonpeptide ligand binding site prediction. (c) 2014 Wiley Periodicals, Inc.