Exploring the role of receptor flexibility in structure-based drug discovery

Abstract

The proper understanding of biomolecular recognition mechanisms that take place in a drug target is of paramount importance to improve the efficiency of drug discovery and development. The intrinsic dynamic character of proteins has a strong influence on biomolecular recognition mechanisms and models such as conformational selection have been widely used to account for this dynamic association process. However, conformational changes occurring in the receptor prior and upon association with other molecules are diverse and not obvious to predict when only a few structures of the receptor are available. In view of the prominent role of protein flexibility in ligand binding and its implications for drug discovery, it is of great interest to identify receptor conformations that play a major role in biomolecular recognition before starting rational drug design efforts. In this review, we discuss a number of recent advances in computer-aided drug discovery techniques that have been proposed to incorporate receptor flexibility into structure-based drug design. The allowance for receptor flexibility provided by computational techniques such as molecular dynamics simulations or enhanced sampling techniques helps to improve the accuracy of methods used to estimate binding affinities and, thus, such methods can contribute to the discovery of novel drug leads.

Keywords: Accelerated molecular dynamics; Allostery; Computer-aided drug design; Conformational selection; Molecular dynamics; Receptor flexibility.

Fig. 1

Schematic pathways of biomolecular recognition. Conformational selection and induced fit mechanisms are depicted in dashed and solid lines respectively.

Fig. 2

Accounting for receptor flexibility in structure-based virtual screening. Examples of methods used at each step that are discussed in the present review.

Fig. 3

Accelerated Molecular Dynamics. Equations to calculate boost energy, V*(ṙ), and boost potential, ΔV(ṙ). The true potential energy function is shown as a solid black line, ΔV(ṙ). A series of modified potential energy functions are represented in different colors for various values of α as shown in the plot while E was always fixed at 60 (black dashed line).

Fig. 4

Principal Component Analysis. PCA build with E. coli UPPS crystal structures. Substrate bound structures in yellow are closed; apo (PDB ID code 3QAS) and non-bisphosphonate inhibitors in red are in slightly open conformation; bisphosphonate inhibitors in blue are crystallized in open conformations.

Fig. 5

Binding sites of UPPS. Site 1 (substrate site) and Site 2–4 are binding sites where inhibitors can bind. Bisphosphonates can bind to all sites as shown in PDB ID code 2E98. All of these sites were predicted by FTMAP to be druggable.