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

化学 共价键 非共价相互作用 结合能 选择性 受体-配体动力学 计算化学 分子 有机化学 生物化学 氢键 物理 核物理学 催化作用 受体
作者
Payal Chatterjee,Wesley M. Botello‐Smith,Han Zhang,Qian Li,Abdelaziz Alsamarah,David Kent,Jérôme J. Lacroix,Michel Baudry,Yun Luo
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
卷期号:139 (49): 17945-17952 被引量:56
标识
DOI:10.1021/jacs.7b08938
摘要

Reversible covalent inhibitors have many clinical advantages over noncovalent or irreversible 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 analogues relative to a common warhead scaffold, in both noncovalent and covalent binding states, and for two highly homologous proteases, calpain-1 and calpain-2. We conclude that covalent binding state 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 to combine the relative FEP calculations with a single QM/MM calculation of warhead to predict the binding affinity and binding kinetics. Our FEP calculations also revealed that covalent and noncovalent binding 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.
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