Thermodynamically Optimized Machine-learned Reaction Coordinates for Hydrophobic Ligand Dissociation

Ligand unbinding is mediated by the free energy change, which has intertwined contributions from both energy and entropy. It is important but not easy to quantify their individual contributions. We model hydrophobic ligand unbinding for two systems, a methane particle and a C60 fullerene, both unbinding from hydrophobic pockets in all-atom water. By using a modified deep learning framework, we learn a thermodynamically optimized reaction coordinate to describe hydrophobic ligand dissociation for both systems. Interpretation of these reaction coordinates reveals the roles of entropic and enthalpic forces as ligand and pocket sizes change. Irrespective of the contrasting roles of energy and entropy, we also find that for both the systems the transition from the bound to unbound states is driven primarily by solvation of the pocket and ligand, independent of ligand size. Our framework thus gives useful thermodynamic insight into hydrophobic ligand dissociation problems that are otherwise difficult to glean.Comment: 27 pages; 5 figure

Recent advances in describing and driving crystal nucleation using machine learning and artificial intelligence

With the advent of faster computer processors and especially graphics processing units (GPUs) over the last few decades, the use of data-intensive machine learning (ML) and artificial intelligence (AI) has increased greatly, and the study of crystal nucleation has been one of the beneficiaries. In this review, we outline how ML and AI have been applied to address four outstanding difficulties of crystal nucleation: how to discover better reaction coordinates (RCs) for describing accurately non-classical nucleation situations; the development of more accurate force fields for describing the nucleation of multiple polymorphs or phases for a single system; more robust identification methods for determining crystal phases and structures; and as a method to yield improved course-grained models for studying nucleation.Comment: 15 pages; 1 figur

Driving and characterizing nucleation of urea and glycine polymorphs in water

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights to the molecular details of nucleation, some form of molecular dynamics simulations are typically performed, which are, in turn, limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here we employ the machine learning augmented molecular dynamics framework ``Reweighted Autoencoded Variational Bayes for enhanced sampling (RAVE)". We study the two molecular systems, urea and glycine in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe back-and-forth liquid-solid transitions of different polymorphs, correctly ranking polymorph stabilities as A- $>$ I- $>$ B-urea and $\gamma$- $>$ $\beta$- $\ge$ $\alpha$-glycine. Finally, the machine learning based reaction coordinates allow for an in-depth analysis of the nucleation mechanism for both molecules, providing clear evidence of nonclassical two-step nucleation for both urea and glycine nucleation in water.Comment: 11 pages, 7 figure

Dinucleotides as simple models of the base stacking-unstacking component of DNA 'breathing' mechanisms

14 pagesRegulatory protein access to the DNA duplex 'interior' depends on local DNA 'breathing' fluctuations, and the most fundamental of these are thermally-driven base stacking-unstacking interactions. The smallest DNA unit that can undergo such transitions is the dinucleotide, whose structural and dynamic properties are dominated by stacking, while the ion condensation, cooperative stacking and inter-base hydrogen-bonding present in duplex DNA are not involved. We use dApdA to study stacking-unstacking at the dinucleotide level because the fluctuations observed are likely to resemble those of larger DNA molecules, but in the absence of constraints introduced by cooperativity are likely to be more pronounced, and thus more accessible to measurement. We study these fluctuations with a combination of Molecular Dynamics simulations on the microsecond timescale and Markov State Model analyses, and validate our results by calculations of circular dichroism (CD) spectra, with results that agree well with the experimental spectra. Our analyses show that the CD spectrum of dApdA is defined by two distinct chiral conformations that correspond, respectively, to a Watson-Crick form and a hybrid form with one base in a Hoogsteen configuration. We find also that ionic structure and water orientation around dApdA play important roles in controlling its breathing fluctuations.This research was supported by a grant from the National Institute of Child Health and Human Development (5R01HD081 362-05) awarded to L.S. and N.B.A. The funding sources had no role in the study design, data collection and analysis, or submission process

Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar

Extensions of the Langevin Equation for Protein Dynamics for Modelling Equilibrium Fluctuations of Proteins

Proteins are not static structures; they must undergo conformational fluctuations about their folded state to function. Typically, the slow, near-equilibrium conformational dynamics of proteins encode the functional motions; an accurate description of these dynamics is useful for elucidating the functional motions of proteins. Use of molecular dynamics (MD) simulations gives a physical model of proteins' motions, but the dynamics are too high dimensional and coupled to determine the functional motions purely from observation of the MD trajectory; thus, methods to effciently extract the slow conformational dynamics of proteins from atomistic models are valuable. This dissertation advances the Langevin equation for protein dynamics (LE4PD), a diffusive, coarse-grained equation of motion for modeling protein dynamics adapted from the field of polymer physics. The LE4PD is solved by an eigenvalue decomposition into a set of normal mode coordinates, each of which encodes dynamics on a specific time- and lengthscale. A discrete-state master equation approach, Markov state modeling, is used to precisely determine the dynamics and kinetics of conformational dynamics described by the slow LE4PD modes by analyzing a 1- microsecond, folded simulation of the protein ubiquitin. The approach is able to extract slow dynamics in important binding regions of ubiquitin. In chapter III, Markov state models are used to determine the contributions of metastable states to the circular dichroism spectrum of a dinucleotide system. Because protein dynamics is inherently anisotropic, we develop an anisotropic version of the LE4PD. When both hydrodynamic effects and free-energy barriers are neglected, the model reduces to a principal component analysis of the alpha-carbon coordinates; including both these effects are important for quantitatively modelling the decay of simulated autocorrelation functions. Finally, we compare the LE4PD predictions from the ubiquitin simulation to the slow modes extracted by a time-lagged independent component analysis of the trajectory. We nd both methods are able to extract the slow dynamics of the protein, but the tICA compresses the information into a smaller number of modes; however, for ubiquitin, the tICA modes cannot model the simulated autocorrelation functions as effectively as the anisotropic LE4PD model. This dissertation includes previously published and unpublished co-authored material

Thermodynamically Optimized Machine-Learned Reaction Coordinates for Hydrophobic Ligand Dissociation

Ligand unbinding is mediated by its free energy change, which has intertwined contributions from both energy and entropy. It is important, but not easy, to quantify their individual contributions to the free energy profile. We model hydrophobic ligand unbinding for two systems, a methane particle and a C60 fullerene, both unbinding from hydrophobic pockets in all-atom water. Using a modified deep learning framework, we learn a thermodynamically optimized reaction coordinate to describe the hydrophobic ligand dissociation for both systems. Interpretation of these reaction coordinates reveals the roles of entropic and enthalpic forces as the ligand and pocket sizes change. In both cases, we observe that the free-energy barrier to unbinding is dominated by entropy considerations. Furthermore, the process of methane unbinding is driven by methane solvation, while fullerene unbinding is driven first by pocket wetting and then fullerene wetting. For both solutes, the direct importance of the distance from the binding pocket to the learned reaction coordinate is present, but low. Our framework and subsequent feature important analysis thus give useful thermodynamic insight into hydrophobic ligand dissociation problems that are otherwise difficult to glean