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 α-ketoamide warhead. More importantly, our results revealed that the chemical modification away from warhead changes 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 SILCS FragMaps 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 leadoptimization, the FEP approach described 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.

Helix breaking transition in the S4 of HCN channel is critical for hyperpolarization-dependent gating

“…Although we were unable to observe the effect of the S272L mutation on HCN1 experimentally, we can assess its influence on channel gating in silico using free energy perturbation (FEP) (Rodinger and Pomès, 2005; Zhang et al, 2019). The free energy difference between the activation of HCN1 and its S272L mutant ∆∆G=∆ G r → a ( mut )−∆ G r → a ( HCN 1) were calculated using FEP (Zwanzig, 1954) (Figure 4).…”

Exploring the Mechanism of Covalent Inhibition: Simulating the Binding Free Energy of α-Ketoamide Inhibitors of the Main Protease of SARS-CoV-2

“…While in most of these works the noncovalent and covalent states were considered, the authors of ref ( 17 ) used only the covalent state in their calculations. As pointed out in ref ( 19 ), the choice of considering just the covalent state is reasonable only when the contribution of the covalent state to the total binding free energy is at least −5.5 kcal/mol greater than that of the noncovalent state. Unfortunately, knowing a priori the contributions from the covalent and noncovalent states is not possible.…”

“…The situation gets even further complicated when one tries to calculate the absolute covalent binding free energies, because in this case the possibility of error cancelation (as we might expect in relative free energy calculations) is negligible. One might still be able to calculate the absolute binding free energies by considering one inhibitor as a reference and following the thermodynamic cycle used in ref ( 19 ) to avoid the expensive quantum mechanics (QM)-based calculations. On the contrary, a complete mechanistic understanding of the covalent inhibition process is not possible from such analysis.…”

Quantum Chemical Methods for Modeling Covalent Modification of Biological Thiols

“…Computational modeling of covalent modification can provide insights into the binding affinity, selectivity, and kinetics of these drugs . In two recent papers, Luo and coworkers have shown that the relative efficacy of covalent inhibitors can be related to the relative stabilities of the covalently bound adduct . The relative binding affinities of the covalent‐modifier drugs can be estimated using free‐energy perturbation so, in these cases, simulating the mechanism of covalent bond formation was not necessary.…”