MSA clustering enhances AF-Multimer’s ability to predict conformational landscapes of protein–protein interactions

Abstract

Motivation Understanding the conformational landscape of protein-ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how MSA (multiple sequence alignment) clustering enhance AF-Multimer's ability to predict conformational landscapes, particularly for proteins with multiple conformational states. Results We verified this approach by predicting the conformational landscapes of chemokine (CXCR4) and glucagon (GCGR) receptors in the presence of their agonists and antagonists. In our experiments AF-Multimer predicted the structures of CXCR4 and GCGR predominantly in active state in the presence of agonists and in inactive state in the presence of antagonists. Moreover, we tested our approach with proteins known to switch between monomeric and dimeric states, such as lymphotactin, SH3 and thermonuclease. AFcluster-Multimer accurately predicted conformational states during oligomerization, which AFcluster with AlphaFold2 alone fails to achieve. In conclusion, MSA clustering enhances AF-Multimer's ability to predict protein conformational landscapes and mechanistic effects of ligand binding, offering a robust tool for understanding protein-ligand interactions. Availability and Implementation Code for running AFcluster-Multimer is available at https://github.com/KhondamirRustamov/AF-Multimer-cluster Supplementary information Supplementary figures are available at Bioinformatics Advances online.