Halperin Inbal, Wolfson Haim, Nussinov Ruth
Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
Protein Sci. 2003 Jul;12(7):1344-59. doi: 10.1110/ps.0237103.
Phage display enables the presentation of a large number of peptides on the surface of phage particles. Such libraries can be tested for binding to target molecules of interest by means of affinity selection. Here we present SiteLight, a novel computational tool for binding site prediction using phage display libraries. SiteLight is an algorithm that maps the 1D peptide library onto a three-dimensional (3D) protein surface. It is applicable to complexes made up of a protein Template and any type of molecule termed Target. Given the three-dimensional structure of a Template and a collection of sequences derived from biopanning against the Target, the Template interaction site with the Target is predicted. We have created a large diverse data set for assessing the ability of SiteLight to correctly predict binding sites. SiteLight predictive mapping enables discrimination between the binding and nonbinding parts of the surface. This prediction can be used to effectively reduce the surface by 75% without excluding the binding site. In 63% of the cases we have tested, there is at least one binding site prediction that overlaps the interface by at least 50%. These results suggest the applicability of phage display libraries for automated binding site prediction on three-dimensional structures. For most effective binding site prediction we propose using a random phage display library twice, to scan both binding partners of a given complex. The derived peptides are mapped to the other binding partner (now used as a Template). Here, the surface of each partner is reduced by 75%, focusing their relative positions with respect to each other significantly. Such information can be utilized to improve docking algorithms and scoring functions.
噬菌体展示能够在噬菌体颗粒表面呈现大量肽段。通过亲和选择,可以对这类文库进行与感兴趣的靶分子结合的测试。在此,我们介绍SiteLight,一种利用噬菌体展示文库进行结合位点预测的新型计算工具。SiteLight是一种将一维肽文库映射到三维(3D)蛋白质表面的算法。它适用于由蛋白质模板和任何类型的被称为靶标的分子组成的复合物。给定模板的三维结构以及通过对靶标进行生物淘选获得的一系列序列,就可以预测模板与靶标的相互作用位点。我们创建了一个多样化的大型数据集,用于评估SiteLight正确预测结合位点的能力。SiteLight预测性映射能够区分表面的结合部分和非结合部分。这种预测可有效减少75%的表面区域,而不排除结合位点。在我们测试的63%的案例中,至少有一个结合位点预测与界面的重叠率至少为50%。这些结果表明噬菌体展示文库在三维结构上自动进行结合位点预测的适用性。为了实现最有效的结合位点预测,我们建议使用随机噬菌体展示文库两次,以扫描给定复合物的两个结合伴侣。将得到的肽段映射到另一个结合伴侣(现在用作模板)上。在此,每个伴侣的表面减少75%,显著聚焦它们彼此之间的相对位置。这类信息可用于改进对接算法和评分函数。