Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy

Abstract

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.

Convergence of Domain Architecture, Structure, and Ligand Affinity in Animal and Plant RNA-Binding Proteins

“…We also note that there are some differences between computationally predicted and experimentally determined p K d estimates (figs. 2 and4); this is expected, given that the statistical prediction algorithm was trained across a wide variety of protein–RNA and protein–DNA complexes (Dias and Kolazckowski 2015), and the RNA crystalized with TARBP2 and DCL1 templates is short and may not engage the entire potential RNA-binding region (Ryter and Schultz 1998; Yang et al 2010). Particularities of the experimental conditions can also have a large effect on affinity measurements (Svec et al 1980; Reverberi and Reverberi 2007).…”

Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies

“…Molecular docking is a method which predicts the preferred orientation of one molecule (generally small) on a macromolecule to form a stable complex . Knowledge of the preferred orientation may be used to predict the binding affinity of a small drug molecule with its protein target, or protein–protein or a DNA–protein binding affinities .…”

“…Molecular docking is a method which predicts the preferred orientation of one molecule (generally small) on a macromolecule to form a stable complex . Knowledge of the preferred orientation may be used to predict the binding affinity of a small drug molecule with its protein target, or protein–protein or a DNA–protein binding affinities . Quantitative structure–activity relationship (QSAR) studies have become quite useful in the evaluation of new designed drugs because they summarize a supposed relationship between chemical structures and biologic activity within a dataset of chemicals .…”

“…[18] Knowledge of the preferred orientation may be used to predict the binding affinity of a small drug molecule with its protein target, or protein-protein or a DNA-protein binding affinities. [18] Quantitative structure-activity relationship (QSAR) studies have become quite useful in the evaluation of new designed drugs because they summarize a supposed relationship between chemical structures and biologic activity within a dataset of chemicals. [19] Particularly, QSAR is a computational modeling technique that helps in capturing the chemical and biologic knowledge from the experimental data and speeds the drug discovery process.…”

Resurrecting ancestral structural dynamics of an antiviral immune receptor: adaptive binding pocket reorganization repeatedly shifts RNA preference

“…However, the long-term molecular evolution of macromolecular interactions remains under-studied. Structural properties important for the evolution of macromolecular interactions may be different from those driving protein-small ligand interactions, and the extent to which results obtained in one case can be generalized to the other is not clear [41, 42]. …”