Chad W. Hopkins

Previous studies have shown that the method of hydrogen mass repartitioning (HMR) is a potentially useful tool for accelerating molecular dynamics (MD) simulations. By repartitioning the mass of heavy atoms into the bonded hydrogen atoms, it is possible to slow the highest-frequency motions of the macromolecule under study, thus allowing the time step of the simulation to be increased by up to a factor of 2. In this communication, we investigate further how this mass repartitioning allows the simulation time step to be increased in a stable fashion without significantly increasing discretization error. To this end, we ran a set of simulations with different time steps and mass distributions on a three-residue peptide to get a comprehensive view of the effect of mass repartitioning and time step increase on a system whose accessible phase space is fully explored in a relatively short amount of time. We next studied a 129-residue protein, hen egg white lysozyme (HEWL), to verify that the observed behavior extends to a larger, more-realistic, system. Results for the protein include structural comparisons from MD trajectories, as well as comparisons of pKa calculations via constant-pH MD. We also calculated a potential of mean force (PMF) of a dihedral rotation for the MTS [(1-oxyl-2,2,5,5-tetramethyl-pyrroline-3-methyl)methanethiosulfonate] spin label via umbrella sampling with a set of regular MD trajectories, as well as a set of mass-repartitioned trajectories with a time step of 4 fs. Since no significant difference in kinetics or thermodynamics is observed by the use of fast HMR trajectories, further evidence is provided that long-time-step HMR MD simulations are a viable tool for accelerating MD simulations for molecules of biochemical interest.

The derivation and optimization of most energy terms in modern force fields are aided by automated computational tools. It is therefore important to have algorithms to rapidly and precisely train large numbers of interconnected parameters to allow investigators to make better decisions about the content of molecular models. In particular, the traditional approach to deriving dihedral parameters has been a least-squares fit to target conformational energies through variational optimization strategies. We present a computational approach for simultaneously fitting force field dihedral amplitudes and phase constants which is analytic within the scope of the data set. This approach completes the optimal molecular mechanics representation of a quantum mechanical potential energy surface in a single linear least-squares fit by recasting the dihedral potential into a linear function in the parameters. We compare the resulting method to a genetic algorithm in terms of computational time and quality of fit for two simple molecules. As suggested in previous studies, arbitrary dihedral phases are only necessary when modeling chiral molecules, which include more than half of drugs currently in use, so we also examined a dihedral parametrization case for the drug amoxicillin and one of its stereoisomers where the target dihedral includes a chiral center. Asymmetric dihedral phases are needed in these types of cases to properly represent the quantum mechanical energy surface and to differentiate between stereoisomers about the chiral center.

Understanding enzymatic reactions with atomic resolution has proven in recent years to be of tremendous interest for biochemical research, and thus, the use of QM/MM methods for the study of reaction mechanisms is experiencing a continuous growth. Glycosyltransferases (GTs) catalyze the formation of glycosidic bonds, and are important for many biotechnological purposes, including drug targeting. Their reaction product may result with only one of the two possible stereochemical outcomes for the reacting anomeric center, and therefore, they are classified as either inverting or retaining GTs. While the inverting GT reaction mechanism has been widely studied, the retaining GT mechanism has always been controversial and several questions remain open to this day. In this work, we take advantage of our recent GPU implementation of a pure QM(DFT-PBE)/MM approach to explore the reaction and inhibition mechanism of MshA, a key retaining GT responsible for the first step of mycothiol biosynthesis, a low weight thiol compound found in pathogens like Mycobacterium tuberculosis that is essential for its survival under oxidative stress conditions. Our results show that the reaction proceeds via a front-side SNi-like concerted reaction mechanism (DNAN in IUPAC nomenclature) and has a 17.5 kcal/mol free energy barrier, which is in remarkable agreement with experimental data. Detailed analysis shows that the key reaction step is the diphosphate leaving group dissociation, leading to an oxocarbenium-ion-like transition state. In contrast, fluorinated substrate analogues increase the reaction barrier significantly, rendering the enzyme effectively inactive. Detailed analysis of the electronic structure along the reaction suggests that this particular inhibition mechanism is associated with fluorine’s high electronegative nature, which hinders phosphate release and proper stabilization of the transition state.