Western University of Health Sciences , College of Pharmacy , Pomona , California 91766 , United States.
J Chem Inf Model. 2019 May 28;59(5):2093-2102. doi: 10.1021/acs.jcim.8b00959. Epub 2019 Feb 27.
Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions. We have previously used a simplistic two-state model for predicting the relative binding selectivity of reversible covalent inhibitors ( J. Am. Chem. Soc. 2017, 139 , 17945 ). Here we go beyond binding selectivity and demonstrate that it is possible to use free energy perturbation (FEP) molecular dynamics (MD) to calculate the overall reversible covalent binding using a specially designed thermodynamic cycle. We show that FEP can predict the varying binding free energies of the analogs sharing a common warhead. More importantly, our results revealed that the chemical modification away from warhead alters the binding affinity at both noncovalent and covalent binding states, and the computational prediction can be improved by considering the binding free energy of both states. Furthermore, we explored the possibility of using a more rapid computational method, site-identification by ligand competitive saturation (SILCS), to rank the same set of reversible covalent inhibitors. We found that the fragment docking to a set of precomputed fragment maps produces a reasonable ranking. In conclusion, two independent approaches provided consistent results that the covalent binding state is suitable for the initial ranking of the reversible covalent drug candidates. For lead-optimization, the FEP approach designed here can provide more rigorous and detailed information regarding how much the covalent and noncovalent binding states are contributing to the overall binding affinity, thus offering a new avenue for fine-tuning the noncovalent interactions for optimizing reversible covalent drugs.
可逆共价抑制剂在药物设计中引起了越来越多的关注，因为它们可能比非共价抑制剂更有效，比共价抑制剂毒性更小。尽管有这些优势，但预测可逆共价结合仍然是一个艰巨的挑战，因为结合过程包括多个步骤，需要量子力学（QM）水平的计算来估计共价键结合自由能。已经表明，即使带有相似弹头，由于非共价相互作用，离解速率和平衡离解常数也会有很大差异。我们之前使用一种简单的两态模型来预测可逆共价抑制剂的相对结合选择性（ J. Am. Chem. Soc. 2017, 139, 17945 ）。在这里，我们超越了结合选择性，并证明使用专门设计的热力学循环，使用自由能扰动（FEP）分子动力学（MD）计算整体可逆共价结合是可行的。我们表明，FEP 可以预测具有共同弹头的类似物的变化结合自由能。更重要的是，我们的结果表明，从弹头化学修饰会改变非共价和共价结合状态的结合亲和力，并且通过考虑两个状态的结合自由能可以提高计算预测。此外，我们探索了使用更快速的计算方法，配体竞争饱和的位点识别（SILCS），对同一组可逆共价抑制剂进行排序的可能性。我们发现，将片段对接一组预先计算的片段图产生了合理的排序。总之，两种独立的方法提供了一致的结果，即共价结合状态适合于最初对可逆共价药物候选物进行排名。对于先导优化，这里设计的 FEP 方法可以提供更严格和详细的信息，说明共价和非共价结合状态对整体结合亲和力的贡献程度，从而为精细调整非共价相互作用以优化可逆共价药物提供了新途径。
