Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol-1 Å-1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol-1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.

Marine Microbial-Derived Resource Exploration: Uncovering the Hidden Potential of Marine Carotenoids

Microbes in marine ecosystems are known to produce secondary metabolites. One of which are carotenoids, which have numerous industrial applications, hence their demand will continue to grow. This review highlights the recent research on natural carotenoids produced by marine microorganisms. We discuss the most recent screening approaches for discovering carotenoids, using in vitro methods such as culture-dependent and culture-independent screening, as well as in silico methods, using secondary metabolite Biosynthetic Gene Clusters (smBGCs), which involves the use of various rule-based and machine-learning-based bioinformatics tools. Following that, various carotenoids are addressed, along with their biological activities and metabolic processes involved in carotenoids biosynthesis. Finally, we cover the application of carotenoids in health and pharmaceutical industries, current carotenoids production system, and potential use of synthetic biology in carotenoids production.

Ensembles Are Required to Handle Aleatoric and Parametric Uncertainty in Molecular Dynamics Simulation

Classical molecular dynamics is a computer simulation technique that is in widespread use across many areas of science, from physics and chemistry to materials, biology, and medicine. The method continues to attract criticism due its oft-reported lack of reproducibility which is in part due to a failure to submit it to reliable uncertainty quantification (UQ). Here we show that the uncertainty arises from a combination of (i) the input parameters and (ii) the intrinsic stochasticity of the method controlled by the random seeds. To illustrate the situation, we make a systematic UQ analysis of a widely used molecular dynamics code (NAMD), applied to estimate binding free energy of a ligand-bound to a protein. In particular, we replace the usually fixed input parameters with random variables, systematically distributed about their mean values, and study the resulting distribution of the simulation output. We also perform a sensitivity analysis, which reveals that, out of a total of 175 parameters, just six dominate the variance in the code output. Furthermore, we show that binding energy calculations dampen the input uncertainty, in the sense that the variation around the mean output free energy is less than the variation around the mean of the assumed input distributions, if the output is ensemble-averaged over the random seeds. Without such ensemble averaging, the predicted free energy is five times more uncertain. The distribution of the predicted properties is thus strongly dependent upon the random seed. Owing to this substantial uncertainty, robust statistical measures of uncertainty in molecular dynamics simulation require the use of ensembles in all contexts.

Uncertainty Quantification and Sensitivity Analysis of Partial Charges on Macroscopic Solvent Properties in Molecular Dynamics Simulations with a Machine Learning Model

The molecular dynamics (MD) simulation technique is among the most broadly used computational methods to investigate atomistic phenomena in a variety of chemical and biological systems. One of the most common (and most uncertain) parametrization steps in MD simulations of soft materials is the assignment of partial charges to atoms. Here, we apply uncertainty quantification and sensitivity analysis calculations to assess the uncertainty associated with partial charge assignment in the context of MD simulations of an organic solvent. Our results indicate that the effect of partial charge variance on bulk properties, such as solubility parameters, diffusivity, dipole moment, and density, measured from MD simulations is significant; however, measured properties are observed to be less sensitive to partial charges of less accessible (or buried) atoms. Diffusivity, for example, exhibits a global sensitivity of up to 22 × 10^-5 cm²/s per electron charge on some acetonitrile atoms. We then demonstrate that machine learning techniques, such as Gaussian process regression (GPR), can be effective and rapid tools for uncertainty quantification of MD simulations. We show that the formulation and application of an efficient GPR surrogate model for the prediction of responses effectively reduces the computational time of additional sample points from hours to milliseconds. This study provides a much-needed context for the effect that partial charge uncertainty has on MD-derived material properties to illustrate the benefit of considering partial charges as distributions rather than point-values. To aid in this treatment, this work then demonstrates methods for rapid characterization of resulting sensitivity in MD simulations.

Atomic Charges in Highly Ionic Diatomic Molecules

Atomic charges were investigated as functions of detectable atomic and molecular constants at equilibrium structures. It was found based upon the variation idea that atomic charges in highly ionic molecules can be expressed as a function of molecular dipole moments, polarizabilities of free cations, and polarizabilities of free neutral atoms of the corresponding anions. The function can be given in the form of classical Rittner's relationship (J. Chem. Phys. 1951, 19, 1030). For the ground states of alkali halide molecules, the predicted atomic charges are close to an elementary charge e and the predicted dipole moments are in good agreement with the observed values; for spin-restricted high-ionic systems such as the lowest ⁹Σ electronic states of BN, AlN, GaN, BP, AlP, GaP, BAs, AlAs, and GaAs molecules, the predicted atomic charges are also near 1e and in good agreement with the results of natural population analysis at MRCI/cc-pvqz and HF/6-311+G(3df) levels. Polarizabilities for the lowest quintet states of B^-, Al^-, Ga^-, N⁺, P⁺, and As⁺ ions were also obtained based upon high-level ab initio computations. Atomic charges from other related methods are also investigated for comparison. The results demonstrate that high-quality atomic charges can be obtained with detectable variables, such as molecular dipole moment, vibrational frequency, as well as polarizabilities of the related free atoms and ions.

Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method

We report an evaluation of a semi-empirical quantum chemical method PM7 from the perspective of uncertainty quantification. Specifically, we apply Bound-to-Bound Data Collaboration, an uncertainty quantification framework, to characterize (a) variability of PM7 model parameter values consistent with the uncertainty in the training data and (b) uncertainty propagation from the training data to the model predictions. Experimental heats of formation of a homologous series of linear alkanes are used as the property of interest. The training data are chemically accurate, i.e., they have very low uncertainty by the standards of computational chemistry. The analysis does not find evidence of PM7 consistency with the entire data set considered as no single set of parameter values is found that captures the experimental uncertainties of all training data. A set of parameter values for PM7 was able to capture the training data within ±1 kcal/mol, but not to the smaller level of uncertainty in the reported data. Nevertheless, PM7 was found to be consistent for subsets of the training data. In such cases, uncertainty propagation from the chemically accurate training data to the predicted values preserves error within bounds of chemical accuracy if predictions are made for the molecules of comparable size. Otherwise, the error grows linearly with the relative size of the molecules.

Molecular Dynamics Simulation of Cyclooxygenase-2 Complexes with Indomethacin closo-Carborane Analogs

Molecular dynamics simulation of carborane-containing ligands in complex with target enzymes is a challenging task due to the unique structure and properties of the carborane substituents and relative lack of appropriate experimental data to help assess the quality of carborane force field parameters. Here, we report results from energy minimization calculations for a series of carborane-amino acid complexes using carborane force field parameters published previously in the literature and adapted for use with the AMBER ff99SB and ff14SB potential functions. These molecular mechanics results agree well with quantum mechanical geometry optimization calculations obtained using dispersion-corrected density functional theory methods, suggesting that the carborane force field parameters should be suitable for more detailed calculations. We then performed molecular dynamics simulations for the 1,2-, 1,7-, and 1,12-dicarba- closo-dodecaborane(12) derivatives of indomethacin methyl ester bound with cyclooxygenase-2. The simulation results suggest that only the ortho-carborane derivative forms a stable complex, in agreement with experimental findings, and provide insight into the possible molecular basis for isomer binding selectivity.