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.

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.

Explicit treatment of electronic polarizability in empirical force fields (FFs) represents an extension over a traditional additive or pairwise FF and provides a more realistic model of the variations in electronic structure in condensed phase, macromolecular simulations. To facilitate utilization of the polarizable FF based on the classical Drude oscillator model, Drude Prepper has been developed in CHARMM‐GUI. Drude Prepper ingests additive CHARMM protein structures file (PSF) and pre‐equilibrated coordinates in CHARMM, PDB, or NAMD format, from which the molecular components of the system are identified. These include all residues and patches connecting those residues along with water, ions, and other solute molecules. This information is then used to construct the Drude FF‐based PSF using molecular generation capabilities in CHARMM, followed by minimization and equilibration. In addition, inputs are generated for molecular dynamics (MD) simulations using CHARMM, GROMACS, NAMD, and OpenMM. Validation of the Drude Prepper protocol and inputs is performed through conversion and MD simulations of various heterogeneous systems that include proteins, nucleic acids, lipids, polysaccharides, and atomic ions using the aforementioned simulation packages. Stable simulations are obtained in all studied systems, including 5 μs simulation of ubiquitin, verifying the integrity of the generated Drude PSFs. In addition, the ability of the Drude FF to model variations in electronic structure is shown through dipole moment analysis in selected systems. The capabilities and availability of Drude Prepper in CHARMM‐GUI is anticipated to greatly facilitate the application of the Drude FF to a range of condensed phase, macromolecular systems.

The Drude polarizable force field (FF) captures electronic polarization effects via auxiliary Drude particles that are attached to non-hydrogen atoms, distinguishing it from commonly used additive FFs that rely on fixed charges. The Drude FF currently includes parameters for biomolecules such as proteins, nucleic acids, lipids, and carbohydrates and small-molecule representative of those classes of molecules as well as a range of atomic ions. Extension of the Drude FF to novel small druglike molecules is challenging as it requires the assignment of partial charges, atomic polarizabilities, and Thole scaling factors. In the present article, deep neural network (DNN) models are trained on quantum mechanical (QM)-based partial charges and atomic polarizabilities along with Thole scale factors trained to target QM molecular dipole moments and polarizabilities. Training of the DNN model used a collection of 39 421 molecules with molecular weights up to 200 Da and containing H, C, N, O, P, S, F, Cl, Br, or I atoms. The DNN model utilizes bond connectivity, including 1,2, 1,3, 1,4, and 1,5 terms and distances of Drude FF atom types as the feature vector to build the model, allowing it to capture both local and nonlocal effects in the molecules. Novel methods have been developed to determine restrained electrostatic potential (RESP) charges on atoms and external points representing lone pairs and to determine Thole scale factors, which have no QM analogue. A penalty scheme is devised as a performance predictor of the trained model. Validation studies show that these DNN models can precisely predict molecular dipole and polarizabilities of Food and Drug Administration (FDA)-approved drugs compared to reference MP2 calculations. The availability of the DNN model allowing for the rapid estimation of the Drude electrostatic parameters will facilitate its applicability to a wider range of molecular species.

Resveratrol, a natural compound found in red wine and various vegetables, has drawn increasing interest due to its reported benefit in cardiovascular protection, neurodegenerative disorders, and cancer therapy. The mechanism by which resveratrol exerts such pleiotropic effects remains unclear. It remains as one of the most discussed polyphenol compounds in the debating "French Paradox". In this study, using molecular dynamics simulations of dipalmitoyl phosphatidylcholine (DPPC) bilayer with resveratrol, we generated a free energy map of resveratrol’s location and orientation of inside the lipid bilayer. We found that resveratrol increases the surface area per lipid and decreases membrane thickness, which is the opposite effect of the well-studied cholesterol on liquid phase DPPC. Most importantly, based on the simulation observation that resveratrol has a high probability of forming hydrogen bonds with sn-1 and sn-2 ester groups, we discovered a new mechanism using experimental approach, in which resveratrol protects both sn-1 and sn-2 ester bonds of DPPC and distearoyl phosphatidylcholine (DSPC) from phospholipase A1 (PLA1) and phospholipase A2 (PLA2) cleavage. Our study elucidates the new molecular mechanism of potential health benefits of resveratrol and possibly other similar polyphenols and provides a new paradigm for drug design based on resveratrol and its analogs.