Can Relative Binding Free Energy Predict Selectivity of Reversible Covalent Inhibitors?

Abstract

Reversible covalent inhibitors have many clinical advantages over noncovalent or covalent drugs. However, apart from selecting a warhead, substantial efforts in design and synthesis are needed to optimize noncovalent interactions to improve target-selective binding. Computational prediction of binding affinity for reversible covalent inhibitors presents a unique challenge since the binding process consists of multiple steps, which are not necessarily independent of each other. In this study, we lay out the relation between relative binding free energy and the overall reversible covalent binding affinity using a two-state binding model. To prove the concept, we employed free energy perturbation (FEP) coupled with λ-exchange molecular dynamics method to calculate the binding free energy of a series of α-ketoamide analogs relative to a common warhead scaffold, in both noncovalent and covalent bond states, and for two highly homologous proteases, calpain-1 and calpain-2. We conclude that covalent binding affinity alone, in general, can be used to predict reversible covalent binding selectivity. However, exceptions may exist. Therefore, we also discuss the conditions under which the noncovalent binding step is no longer negligible and propose a novel approach that combines the relative FEP calculations with a single QM/MM calculation of warhead to predict the binding affinity and binding kinetics for a large number of reversible covalent inhibitors. Our FEP calculations also revealed that covalent and noncovalent states of an inhibitor do not necessarily exhibit the same selectivity. Thus, investigating both binding states, as well as the kinetics will provide extremely useful information for optimizing reversible covalent inhibitors.

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors

“…CovalentInDB would provide valuable guidelines to the structure optimization and warhead selection for medicinal chemists and facilitate covalent drug discovery. Moreover, some computational algorithms for covalent drug design have been developed, such as covalent docking ( 13 ), binding free energy calculation ( 14 , 15 ), warhead reactivity prediction ( 16 ), covalent binding site prediction ( 17 ), etc. CovalentInDB can provide high-quality data to evaluate and develop computational algorithms for covalent drug design and screening.…”

Calpain-1 and Calpain-2 in the Brain: Dr. Jekill and Mr Hyde?

“…A ketoamide ligand is shown bound to the catalytic site. From[56]. (The color version of the figure is available in the electronic copy of the article).…”

Overview of Recent Strategic Advances in Medicinal Chemistry

“…The free energy perturbation combined with λ-exchange molecular dynamics technique provided particular advantages in predicting binding selectivity among protein isoforms, which is a major challenge in covalent inhibitor design. 227 We envision that Figure 31A). 233 In 2017, on the basis of the structure of the catalytic pocket of DNMT ( Figure 31B), the bisubstrate analogues-based inhibitors were designed, by mimicking each substrate of DNA methyltransferases (DNMT3A and DNMT1), the S-adenosyl-l-methionine (190,SAM) and the deoxycytidine, and linking them together, which resulted in quinazoline-quinoline-derived DNMT3A and DNMT1 inhibitors 191 and 192, some showing certain isoform selectivity.…”