Polarizable Water Potential Derived from a Model Electron Density.

A new empirical potential for efficient, large scale molecular dynamics simulation of water is presented. The HIPPO (Hydrogen-like Intermolecular Polarizable POtential) force field is based upon the model electron density of a hydrogen-like atom. This framework is used to derive and parametrize individual terms describing charge penetration damped permanent electrostatics, damped polarization, charge transfer, anisotropic Pauli repulsion, and damped dispersion interactions. Initial parameter values were fit to Symmetry Adapted Perturbation Theory (SAPT) energy components for ten water dimer configurations, as well as the radial and angular dependence of the canonical dimer. The SAPT-based parameters were then systematically refined to extend the treatment to water bulk phases. The final HIPPO water model provides a balanced representation of a wide variety of properties of gas phase clusters, liquid water, and ice polymorphs, across a range of temperatures and pressures. This water potential yields a rationalization of water structure, dynamics, and thermodynamics explicitly correlated with an ab initio energy decomposition, while providing a level of accuracy comparable or superior to previous polarizable atomic multipole force fields. The HIPPO water model serves as a cornerstone around which similarly detailed physics-based models can be developed for additional molecular species.

Choice of Adaptive Sampling Strategy Impacts State Discovery, Transition Probabilities, and the Apparent Mechanism of Conformational Changes.

Interest in atomically detailed simulations has grown significantly with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder their widespread adoption. Namely, how do alternative sampling strategies explore conformational space and how might this influence predictions generated from the data? Here, we seek to answer these questions for four commonly used sampling methods: (1) a single long simulation, (2) many short simulations run in parallel, (3) adaptive sampling, and (4) our recently developed goal-oriented sampling algorithm, FAST. We first develop a theoretical framework for analytically calculating the probability of discovering select states on simple landscapes, where we uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize the probability of discovering particular states and transition pathways for each of the four methods. Consistently, we find that FAST simulations discover each target state with the highest probability, while traversing realistic pathways. Furthermore, we uncover the potential pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate. We refer to this pathology as "pathway tunneling". To protect against this phenomenon when using adaptive-sampling and FAST simulations, we introduce the FAST-string method. This method enhances sampling along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare the performance of a variety of MSM estimators in describing accurate thermodynamics and kinetics. For adaptive sampling, we recommend simply normalizing the transition counts out of each state after adding small pseudocounts to avoid creating sources or sinks. Lastly, we evaluate whether our insights from simple landscapes hold for all-atom molecular dynamics simulations of the folding of the λ-repressor protein. Remarkably, we find that FAST-contacts predicts the same folding pathway as a set of long simulations but with orders of magnitude less simulation time.

Identification, Characterization and Molecular Modelling Studies of Schistosoma mansoni Dihydrofolate Reductase Inhibitors: From Assay Development to Hit Identification.

INTRODUCTION: Schistosoma mansoni is responsible for virtually all reported cases of schistosomiasis in Latin America and the emergence of praziquantel- and oxaminiquine-resistant strains makes it urgent to develop new schistosomicide agents. Dihydrofolate reductases (DHFR) from bacteria and protozoan parasites are considered validated macromolecular targets for this goal, but S. mansoni DHFR (SmDHFR) has been largely overlooked. To fill this gap in knowledge, the present work describes optimized conditions to carry out thermal shift assays with SmDHFR, as well as a balanced kinetic assay that supports 2,4-diaminopyrimidine derivatives as SmDHFR inhibitors. The most potent inhibitor (2a) shows a large shift of the melting temperature (ΔTm = + 8 ± 0,21 ºC) and a low micromolar IC50 value (12 ± 2,3 µM). Both thermal shift and classical kinectic assay suggest that 2a binds to the substrate binding site (competitive inhibition mechanism). This information guided docking and molecular dynamics studies that probed 2a interaction profile towards SmDHFR. CONCLUSION: In conclusion, this work not only provides standardized assay conditions to identify SmDHFR inhibitors, but also describes the binding profile of the first low micromolar inhibitor of this macromolecular target.