Receptor–Ligand Interactions in Biological Systems

Abstract

Despite the significant research that has been invested in understanding molecular recognition in biological systems, accurate prediction of macroscopic properties based on microscopic interactions remains elusive, which makes it difficult to identify systematically tight binding inhibitors in computational drug design. In the past, most ligand design efforts have centered on descriptors derived mainly from structure, neglecting entropic effects that develop from receptor flexibility. The lack of explicit incorporation of receptor motion has meant that compensatory effects between enthalpy and entropy, which are essential for the accurate estimation of free energy, have also been neglected. In addition, cooperative effects, which develop from alteration in the motion of the receptor because of ligand binding, are not captured. These effects pose a challenge for the design of small molecules that allosterically modulate protein‐protein and protein‐small‐molecule interactions. Here, we describe macroscopic properties of receptor‐ligand interactions, which are followed by a discussion of how these properties are predicted with microscopic interactions. A discussion then ensues on some fundamental aspects of the molecular recognition process that have attracted renewed attention, such as conformational selection versus induced‐fit binding, entropy‐enthalpy compensation, and allostery.

Support Vector Regression Scoring of Receptor–Ligand Complexes for Rank-Ordering and Virtual Screening of Chemical Libraries

“…A combination of faster computers, access to an ever increasing set of three-dimensional structures, and more robust computational methods has led to a constant stream of studies attempting to predict the affinity of ligands to their target. Today, it is established that one can reproduce experimental binding affinities with high fidelity, using methodologies such as free energy perturbation, thermodynamic integration, and other methods. − The implications of the accurate calculations of the free energy are profound in drug discovery, since the efficacy of a therapeutic is, after all, completely dictated by its binding profile in the human proteome. Unfortunately, the large computational resources required to accurately reproduce the free energy of binding have prevented the use of high-end methods in high-throughput screening efforts, where >10 5 compounds are typically screened.…”

BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome

“…Receptor–ligand interactions are driven by a complex array of forces that include polar, nonpolar and entropy ( 39 ). A requirement for optimal binding is the presence of a cavity within the receptor that can accommodate a small molecule ligand.…”

“…A requirement for optimal binding is the presence of a cavity within the receptor that can accommodate a small molecule ligand. For example, most enzymes possess well-defined cavities that have been fine-tuned during evolution to exquisitely accommodate a substrate ( 39 ). But when the ligand is a protein, these cavities are no longer prerequisite, since the protein–protein interaction occurs over a large surface that can be shielded from solvent to promote favorable nonpolar interactions ( 40 , 41 ).…”