SiteLight: Binding-site prediction using phage display libraries

Inbal Halperin, Haim Wolfson, Ruth Nussinov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1344-1359
Number of pages16
JournalProtein Science
Issue number7
StatePublished - 1 Jul 2003


FundersFunder number
National Cancer InstituteZ01BC010441


    • Active site prediction
    • Artificial evolution
    • Binding-site
    • Graphi algorithms
    • Phage display library


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