Ranking Reversible Covalent Drugs: From Free Energy Perturbation to Fragment Docking.

Abstract:

: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.

journal_name

J Chem Inf Model

authors

Zhang H,Jiang W,Chatterjee P,Luo Y

doi

10.1021/acs.jcim.8b00959

subject

Has Abstract

pub_date

2019-05-28 00:00:00

pages

2093-2102

issue

5

eissn

1549-9596

issn

1549-960X

journal_volume

59

pub_type

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