Protein-ligand (un)binding kinetics as a new paradigm for drug discovery at the crossroad between experiments and modelling

Abstract

In the last three decades, protein and nucleic acid structure determination and comprehension of the mechanisms, leading to their physiological and pathological functions, have become a cornerstone of biomedical sciences. A deep understanding of the principles governing the fates of cells and tissue at the molecular level has been gained over the years, offering a solid basis for the rational design of drugs aimed at the pharmacological treatment of numerous diseases. Historically, affinity indicators (i.e. K_d and IC₅₀/EC₅₀) have been assumed to be valid indicators of the in vivo efficacy of a drug. However, recent studies pointed out that the kinetics of the drug-receptor binding process could be as important or even more important than affinity in determining the drug efficacy. This eventually led to a growing interest in the characterisation and prediction of the rate constants of protein-ligand association and dissociation. For instance, a drug with a longer residence time can kinetically select a given receptor over another, even if the affinity for both receptors is comparable, thus increasing its therapeutic index. Therefore, understanding the molecular features underlying binding and unbinding processes is of central interest towards the rational control of drug binding kinetics. In this review, we report the theoretical framework behind protein-ligand association and highlight the latest advances in the experimental and computational approaches exploited to investigate the binding kinetics.

Fig. 1. A scheme of the energetic landscape of the complex (PL) formation between a protein (P) and a ligand (L). TS represents the transition state, E_a is the activation energy of the process, ΔG_d is the difference between the free energies of the reactants and of the product, the free energy difference between the reactants and TS, and the free energy difference between the product and the TS.

Fig. 2. The three different binding models of proteins and ligands, i.e. the lock and key (a), the induced fit (b), and the conformational selection (c). The protein is represented in blue and the ligand is represented in red.

Fig. 3. (a) Grp78 ligand prioritisation via Scaled MD. The ligand's scaffold is reported together with single ligand names and their substituents in positions R1 and R2. The scaled MD based vs. experimental normalized residence times are represented in the right part of the figure, as reported by Mollica et al. (the dashed line is the regression line of the four points represented in the graph). In the inset, the absolute values of residence time are reported. (b) GK1 ligands prioritised according to the scaled MD-based methodology described in the text are reported and grouped according to their molecular shape and computational residence times. Ligands 2, 6a and 7a (enclosed in the blue line) possess a linear shape and have a short computational residence time (respectively 29, 26 and 25 ns; the last two values are relative to a racemic mixture, whereas in the figure only the R entantiomer is represented for simplicity). Ligands 1, 3, 4 and 5 (enclosed in the red line) are T-shaped and have a longer computational residence time than the ones in the first group (105, 39, 93 and 99 ns, respectively).