Computational evaluation of protein-small molecule binding

Abstract

Determining protein-small molecule binding affinity is a key component of present-day rational drug discovery. To circumvent the time, labor, and materials costs associated with experimental protein-small molecule binding assays, a variety of structure-based computational methods have been developed for determining protein-small molecule binding affinities. These methods can be placed in one of two classes: accurate but slow (Class 1), and fast but approximate (Class 2). Class 1 methods, which explicitly take into account protein flexibility and include an atomic-level description of solvation, are capable of quantitatively reproducing experimental protein-small molecule absolute binding free energies. However, Class 1 computational requirements make screening thousands to millions of small molecules against a protein, as required for rational drug design, infeasible for the foreseeable future. Class 2 methods, on the contrary, are sufficiently fast to perform such inhibitor screening, yet they suffer from limited descriptions of protein flexibility and solvation, which in turn limit their ability to select and rank-order small molecules by computed binding affinities. This review presents an overview of Class 1 and Class 2 methods, and avenues of research in Class 2 methods aimed at bringing them closer to Class 1 accuracy.

Figure 1

Schematic view of Class 1 (a) annihilation and (b) separation simulations. In both simulations, all atoms in the system, including water molecules (“H₂O”) are explicitly represented. The protein is in purple and the small molecule in white. A dashed outline of the small molecule indicates interactions of the small molecule with the rest of the system (protein + water) have been decoupled, such that the small molecule is effectively “invisible” to the rest of the system.