Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Summary

We describe a methodology based on sequence diversification to estimate the amino acid preferences of multispecific binding sites in protein-protein interactions (PPIs). In this strategy, thousands of potential peptide ligands are generated and screened in silico, thus overcoming some limitations of available experimental methods.

Abstract

Many protein-protein interactions involve the binding of short protein segments to peptide-binding domains. Usually, such interactions require the recognition of linear motifs with variable conservation. The combination of highly conserved and more variable regions in the same ligands often contributes to the multispecificity of binding, a common property of enzymes and cell signaling proteins. Characterization of amino acid preferences of peptide-binding domains is important for the design of mediators of protein-protein interactions (PPIs). Computational methods are an efficient alternative to the often costly and cumbersome experimental techniques, enabling the design of potential mediators that can be later validated in downstream experiments. Here, we described a methodology using the Pepspec application of the Rosetta molecular modeling package to predict the amino acid preferences of peptide-binding domains. This methodology is useful when the structure of the receptor protein and the nature of the peptide ligand are both known or can be inferred. The methodology starts with a well-characterized anchor from the ligand, which is extended by randomly adding amino acid residues. The binding affinity of peptides generated this way is then evaluated by flexible-backbone peptide docking in order to select the peptides with the best predicted binding scores. These peptides are then used to calculate amino acid preferences and to optionally compute a position-weight matrix (PWM) that can be used in further studies. To illustrate the application of this methodology, we used the interaction between subunits of human interferon regulatory factor 5 (IRF5), previously known to be multispecific but globally guided by a short conserved motif called pLxIS. The estimated amino acid preferences were consistent with previous knowledge about the IRF5 binding surface. Positions occupied by phosphorylatable serine residues exhibited a high frequency of aspartate and glutamate, likely because their negatively charged side chains are similar to phosphoserine.

Introduction

Interaction between two proteins often involves the binding of short segments of amino acids to peptide-binding domains, resembling protein-peptide interfaces. Receptor proteins involved in such protein-protein interactions (PPI) often have the ability to recognize a certain set of overlapping but divergent ligand sequences, a property known as multispecificity^1,2. Multispecific recognition is a feature of many cellular proteins, but it is particularly remarkable in enzymes and cell signaling proteins³. Proteins interacting with multispecific binding sites often have a combination of mo....

Protocol

1. Initial preparation of the protein-peptide interface

1. Downloading the structure of the protein-protein complex
   1. Navigate to the Protein Data Bank (PDB) homepage (https://www.rcsb.org/) and type the PDB ID for the structure of the protein-protein complex in the main search box (Figure 2A). The PDB ID for the structure of the IRF5 dimer, used as an example in this work, is 3DSH¹⁹.
   2. In the main page for the desired.......

Discussion

The present article describes a protocol to estimate the amino acid preferences of potentially multispecific binding sites based on in silico sequence diversification. Few computational tools have been developed to estimate amino acid preferences of protein-peptide interfaces^14,25,26. These tools have a predictive nature, but they differ in the computational algorithms used to perform their predictions and the corrections they i.......

Materials

Name	Company	Catalog Number	Comments
BUDE Alanine Scan Server	University of Edinburgh	https://pragmaticproteindesign.bio.ed.ac.uk/balas/	doi: 10.1021/acschembio.9b00560
Rosetta Modeling Software	Rosetta Commons	https://www.rosettacommons.org/software	doi: 10.1002/prot.22851
UCSF Chimera	University of California San Francisco	https://www.cgl.ucsf.edu/chimera/	doi: 10.1002/jcc.20084