Large-scale survey and database of high affinity ligands for peptide recognition modules.

Joan Teyra,Shobhit Jain,Philip M Kim,Jun Gu,Raghav Jajodia,Marius Sudol,Yogesh Hooda,Sachdev S Sidhu,Gary D Bader,Mohamed Helmy,Abdellali Kelil,Sandra E Nicholson,Akshay A D’Cruz,Jinrong Min

doi:10.15252/msb.20199310

Abstract

Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large‐scale peptide‐phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM‐ligand complexes, indicating that a large majority of the phage‐derived peptides are likely to target natural peptide‐binding sites and could thus act as inhibitors of natural protein–protein interactions. The complete dataset has been assembled in an online database (http://www.prm‐db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs.

Highlights

Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners
We added to our panel 39 domains from 25 families (Dataset EV3), each of which was grouped in a clan with a confirmed PRM family, with a clan being defined by Pfam as a group of families with an evolutionary relationship based on structure, function and sequence comparison (Finn et al, 2016)
Taken together with the results of the amino acid distributions, these results show that optimal peptide ligands for PRMs are characterized by much greater hydrophobicity than natural peptides in the disorderome, and the hydrophobicity is highest in specific positions that presumably make contacts with PRM surfaces

Summary

Introduction

Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM-ligand complexes, indicating that a large majority of the phage-derived peptides are likely to target natural peptide-binding sites and could act as inhibitors of natural protein–protein interactions. The complete dataset has been assembled in an online database (http://www.prm-db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs

Methods

Results

Conclusion