The coronavirus SARS-CoV-2 main protease, M pro , is conserved among coronaviruses with no human homolog and has therefore attracted significant attention as an enzyme drug target for COVID-19. The number of studies targeting M pro for in silico screening has grown rapidly, and it would be of great interest to know in advance how well docking methods can reproduce the correct ligand binding modes and rank these correctly. Clearly, current attempts at designing drugs targeting M pro with the aid of computational docking would benefit from a priori knowledge of the ability of docking programs to predict correct binding modes and score these correctly. In the current work, we tested the ability of several leading docking programs, namely, Glide, DOCK, AutoDock, AutoDock Vina, FRED, and EnzyDock, to correctly identify and score the binding mode of M pro ligands in 193 crystal structures. None of the codes were able to correctly identify the crystal structure binding mode (lowest energy pose with root-mean-square deviation < 2 Å) in more than 26% of the cases for noncovalently bound ligands (Glide: top performer), whereas for covalently bound ligands the top score was 45% (EnzyDock). These results suggest that one should perform in silico campaigns of M pro with care and that more comprehensive strategies including ligand free energy perturbation might be necessary in conjunction with virtual screening and docking.

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Terpene synthases generate terpenes employing diversified carbocation chemistry, including highly specific ring formations, proton and hydride transfers, and methyl as well as methylene migrations, followed by reaction quenching. In this enzyme family, the main catalytic challenge is not rate enhancement, but rather structural and reactive control of intrinsically unstable carbocations in order to guide the resulting product distribution.Here we employ multiscale modeling within classical and quantum dynamics frameworks to investigate the reaction mechanism in the diterpene synthase CotB2, commencing with the substrate geranyl geranyl diphosphate and terminating with the carbocation precursor to the final product cyclooctat-9en-7-ol. The 11-step in-enzyme carbocation cascade is compared with the same reaction in the absence of the enzyme. Remarkably, the free energy profiles in gas phase and in CotB2 are surprisingly similar. This similarity contrasts the multitude of strong π−cation, dipole−cation, and ion-pair interactions between all intermediates in the reaction cascade and the enzyme, suggesting a remarkable balance of interactions in CotB2. We ascribe this balance to the similar magnitude of the interactions between the carbocations along the reaction coordinate and the enzyme environment. The effect of CotB2 mutations is studied using multiscale mechanistic docking, machine learning, and X-ray crystallography, pointing the way for future terpene synthase design.

Enzymes play a pivotal role in all biological systems. These biomachines are the most effective catalysts known, dramatically enhancing the rate of reactions by more than 10 orders of magnitude relative to the uncatalyzed reactions in solution. Predicting the correct, mechanistically appropriate binding modes for substrate and product, as well as all reaction intermediates and transition states, along a reaction pathway is immensely challenging and remains an unsolved problem. In the present work, we developed an effective methodology for identifying probable binding modes of multiple ligand states along a reaction coordinate in an enzyme active site. The program is called EnzyDock and is a CHARMM-based multistate consensus docking program that includes a series of protocols to predict the chemically relevant orientation of substrate, reaction intermediates, transition states, product, and inhibitors. EnzyDock is based on simulated annealing molecular dynamics and Monte Carlo sampling and allows ligand, as well as enzyme side-chain and backbone flexibility. The program can employ many user-defined constraints and restraints and classical force field potentials, as well as a range of hybrid quantum mechanics-molecular mechanics potentials. Herein, we apply EnzyDock to several different kinds of problems. First, we study two terpene synthase reactions, namely bornyl diphosphate synthase and the bacterial diterpene synthase CotB2. Second, we use EnzyDock to predict reaction coordinate states in a pair of Diels–Alder reactions in the enzymes spirotetronate AbyU and LepI. Third, we study a couple of racemases: the cofactor-dependent serine racemase and the cofactor independent proline racemase. Finally, we study several cases of covalent docking involving the Michael addition reaction. For all systems we predict binding modes that are consistent with available experimental observations, as well as with theoretical modeling studies from the literature. EnzyDock provides a platform for generating mechanistic insight into enzyme reactions, useful and reliable starting points for in-depth multiscale modeling projects, and rational design of noncovalent and covalent enzyme inhibitors.

We analyze, with density functional theory calculations, the adsorption energies of Li2O2, Na2O2, and NaO2 on clean and oxygen-passivated TiC (111) surfaces. We show that after deposition of two molecular layers of alkali metal oxides, the initial state of the TiC surface becomes unimportant for the adsorption energy and that all adsorption energies approach their native crystal values. The structure of the adsorbed molecular layers is analyzed and compared with their native oxide crystal structure. Finally, we discuss the similarities and differences of Li peroxide and Na oxides adsorption at the electrode surface.