Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy. Issue 11 (23rd September 2015)

• Record Type: Journal Article
• Authors: Dias, Raquel; Kolazckowski, Bryan
• Is Part Of: Proteins. Volume 83:Issue 11(2015)
• Page Start: 2100
• Page End: 2114
• Publication Date: 2015-09-23
• Subjects: Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.24928
• Languages: English
• ISSNs: 0887-3585

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein−ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein−protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.</p> </abstract>