Parallelized Natural Extension Reference Frame: Parallelized Conversion from Internal to Cartesian Coordinates

The conversion of polymer parameterization from internal coordinates (bond lengths, angles, and torsions) to Cartesian coordinates is a fundamental task in molecular modeling, often performed using the natural extension reference frame (NeRF) algorithm. NeRF can be parallelized to process multiple polymers simultaneously, but is not parallelizable along the length of a single polymer. A mathematically equivalent algorithm, pNeRF, has been derived that is parallelizable along a polymer's length. Empirical analysis demonstrates an order-of-magnitude speed up using modern GPUs and CPUs. In machine learning-based workflows, in which partial derivatives are backpropagated through NeRF equations and neural network primitives, switching to pNeRF can reduce the fractional computational cost of coordinate conversion from over two-thirds to around 10%. An optimized TensorFlow-based implementation of pNeRF is available on GitHub at https://github.com/aqlaboratory/pnerf © 2018 Wiley Periodicals, Inc.     

Development of a new parameter optimization scheme for a reactive force field based on a machine learning approach

Reactive molecular dynamics (MD) simulation is performed using a reactive force field (ReaxFF). To this end, we developed a new method to optimize the ReaxFF parameters based on a machine learning approach. This approach combines the k-nearest neighbor and random forest regressor algorithm to efficiently locate several possible ReaxFF parameter sets. As a pilot test of the developed approach, the optimized ReaxFF parameter set was applied to perform chemical vapor deposition (CVD) of an α-Al₂O₃ crystal. The crystal structure of α-Al₂O₃ was reasonably reproduced even at a relatively high temperature (2000 K). The reactive MD simulation suggests that the (110) surface grows faster than the (0001) surface, indicating that the developed parameter optimization technique could be used for understanding the chemical reaction in the CVD process. © 2019 Wiley Periodicals, Inc.     

Reference-Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory

For the first time, we report calculations of the free energies of activation of cracking and isomerization reactions of alkenes that combine several different electronic structure methods with molecular dynamics simulations. We demonstrate that the use of a high level of theory (here Random Phase Approximation—RPA) is necessary to bridge the gap between experimental and computed values. These transformations, catalyzed by zeolites and proceeding via cationic intermediates and transition states, are building blocks of many chemical transformations for valorization of long chain paraffins originating, e.g., from plastic waste, vegetable oils, Fischer–Tropsch waxes or crude oils. Compared with the free energy barriers computed at the PBE+D2 production level of theory via constrained ab initio molecular dynamics, the barriers computed at the RPA level by the application of Machine Learning thermodynamic Perturbation Theory (MLPT) show a significant decrease for isomerization reaction and an increase of a similar magnitude for cracking, yielding an unprecedented agreement with the results obtained by experiments and kinetic modeling.     

Designing Molecular Spanners to Throw in the Protein Networks

Proteins govern most aspects of cellular life and, through specific interfaces, are typically involved in intricate protein–protein interaction (PPI) networks and signaling pathways. Subtle up- or downregulation of key protein functions and PPIs results in disease; still, the preferred option to contrast the role of a protein in disease and healthy conditions alike remains its outright shutdown through orthosteric ligands that block its active site. Here, we explore subtler alternatives to modulate proteins and PPIs. Driven by a view of proteins as dynamic entities, we discuss ways to identify allosteric binding sites, which, when targeted by tailored ligands, can induce significant changes in the active site of a protein, and lead to agonistic or antagonistic effects. We also summarize the selective regulation of specific PPIs—either direct or allosteric—and show that effects can be stabilizing as well as destabilizing, depending on how the conformational equilibrium of a protein is shifted.     

Developing Efficient Small Molecule Acceptors with sp2-Hybridized Nitrogen at Different Positions by Density Functional Theory Calculations,Molecular Dynamics Simulations and Machine Learning

Chemical structure of small molecule acceptors determines their performance in organic solar cells. Multiscale simulations are necessary to avoid trial-and-error based design, ultimately to save time and resources. In current study, the effect of sp²-hybridized nitrogen substitution at the inner or the outmost position of central core, side chain, and terminal group of small molecule acceptors is investigated using multiscale computational modelling. Quantum chemical analysis is used to study the electronic behavior. Nitrogen substitution at end-capping has significantly decreased the electron-reorganization energy. No big change is observed in transfer integral and excited state behavior. However, nitrogen substitution at terminal group position is good way to improve electron-mobility. Power conversion efficiency (PCE) of newly designed acceptors is predicted using machine learning. Molecular dynamics simulations are also performed to explore the dynamics of acceptor and their blends with PBDB-T polymer donor. Florgy-Huggins parameter is calculated to study the mixing of designed small molecule acceptors with PBDB-T. Radial distribution function has indicated that PBDB-T has a closer packing with N3 and N4. From all analysis, it is found that nitrogen substitution at end-capping group is a better strategy to design efficient small molecule acceptors.     

Allosteric Recognition of Homomeric and Heteromeric Pairs of Monosaccharides by a Foldamer Capsule

The recognition of either homomeric or heteromeric pairs of pentoses in an aromatic oligoamide double helical foldamer capsule was evidenced by circular dichroism (CD), NMR spectroscopy, and X‐ray crystallography. The cavity of the host was predicted to be large enough to accommodate simultaneously two xylose molecules and to form a 1:2 complex (one container, two saccharides). Solution and solid‐state data revealed the selective recognition of the α‐⁴C₁‐d ‐xylopyranose tautomer, which is bound at two identical sites in the foldamer cavity. A step further was achieved by sequestering a heteromeric pair of pentoses, that is, one molecule of α‐⁴C₁‐d ‐xylopyranose and one molecule of β‐¹C₄‐d ‐arabinopyranose despite the symmetrical nature of the host and despite the similarity of the guests. Subtle induced‐fit and allosteric effects are responsible for the outstanding selectivities observed.     

Energy exchange network of inter‐residue interactions within a thermally fluctuating protein molecule: A computational study

Protein function is regulated not only by the structure but also by physical dynamics and thermal fluctuations. We have developed the computer program, CURrent calculation for proteins (CURP), for the flow analysis of physical quantities within thermally fluctuating protein media. The CURP program was used to calculate the energy flow within the third PDZ domain of the neuronal protein PSD‐95, and the results were used to illustrate the energy exchange network of inter‐residue interactions based on atomistic molecular dynamics simulations. The removal of the α3 helix is known to decrease ligand affinity by 21‐fold without changing the overall protein structure; nevertheless, we demonstrated that the helix constitutes an essential part of the network graph. © 2015 Wiley Periodicals, Inc.     

Machine learning for atomic forces in a crystalline solid: Transferability to various temperatures

Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum‐mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of atoms, we trained a machine‐learning model on a crystalline silicon system to directly predict the atomic forces at a wide range of temperatures. Our idea is to construct a machine‐learning model using a quantum‐mechanical dataset taken from canonical‐ensemble simulations at a higher temperature, or an upper bound of the temperature range. With our model, the force prediction errors were about 2% or smaller with respect to the corresponding force ranges, in the temperature region between 300 K and 1650 K. We also verified the applicability to a larger system, ensuring the transferability with respect to system size.     

Influence of temperature, friction, and random forces on folding of the B-domain of staphylococcal protein A: all-atom molecular dynamics in implicit solvent

The influences of temperature, friction, and random forces on the folding of protein A have been analyzed. A series of all-atom molecular dynamics folding simulations with the Amber ff99 potential and Generalized Born solvation, starting from the fully extended chain, were carried out for temperatures from 300 to 500 K, using (a) the Berendsen thermostat (with no explicit friction or random forces) and (b) Langevin dynamics (with friction and stochastic forces explicitly present in the system). The simulation temperature influences the relative time scale of the major events on the folding pathways of protein A. At lower temperatures, helix 2 folds significantly later than helices 1 and 3. However, with increasing temperature, the folding time of helix 2 approaches the folding times of helices 1 and 3. At lower temperatures, the complete formation of secondary and tertiary structure is significantly separated in time whereas, at higher temperatures, they occur simultaneously. These results suggest that some earlier experimental and theoretical observations of folding events, e.g., the order of helix formation, could depend on the temperature used in those studies. Therefore, the differences in temperature used could be one of the reasons for the discrepancies among published experimental and computational studies of the folding of protein A. Friction and random forces do not change the folding pathway that was observed in the simulations with the Berendsen thermostat, but their explicit presence in the system extends the folding time of protein A.     

Rotamer decomposition and protein dynamics: Efficiently analyzing dihedral populations from molecular dynamics

Molecular mechanics methods have matured into powerful methods to understand the dynamics and flexibility of macromolecules and especially proteins. As multinanosecond to microsecond length molecular dynamics (MD) simulations become commonplace, advanced analysis tools are required to generate scientifically useful information from large amounts of data. Some of the key degrees of freedom to understand protein flexibility and dynamics are the amino acid residue side chain dihedral angles. In this work, we present an easily automated way to summarize and understand the relevant dihedral populations. A tremendous reduction in complexity is achieved by describing dihedral timeseries in terms of histograms decomposed into Gaussians. Using the familiar and widely studied protein lysozyme, it is demonstrated that our approach captures essential properties of protein structure and dynamics. A simple classification scheme is proposed that indicates the rotational state population for each dihedral angle of interest and allows a decision if a given side chain or peptide backbone fragment remains rigid during the course of an MD simulation, adopts a converged distribution between conformational substates or has not reached convergence yet. © 2012 Wiley Periodicals, Inc.     

Machine Learning Approaches for Metalloproteins

Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts to predict the structural and functional roles of metalloproteins. Ultimately, having a comprehensive understanding of metalloproteins will lead to tangible applications, such as designing potent inhibitors in drug discovery. Recently, there has been an acceleration in the number of studies applying machine learning to predict metalloprotein properties, primarily driven by the advent of more sophisticated machine learning algorithms. This review covers how machine learning tools have consolidated and expanded our comprehension of various aspects of metalloproteins (structure, function, stability, ligand-binding interactions, and inhibitors). Future avenues of exploration are also discussed.     

Functional concerted motions in the bovine serum retinol-binding protein

The large concerted motions in the apo/holo bovine serum retinol- binding protein were studied using molecular dynamics simulation and 'essential dynamics' analysis. Initially, concerted motions were calculated from conformational differences between various crystal structures. The dynamic behaviour of the protein in the configurational space directions, described by these concerted motions, is analysed. This reveals that the large backbone dynamics of the protein is not influenced by the presence of retinol. Study of free retinol dynamics and retinol in the retinol binding site reveals that the protein binds retinol in a favourable conformation, as opposed to what has been previously described for the bovine cellular retinol-binding protein.     

麋鹿朊蛋白结构稳定性的分子动力学研究

朊蛋白病是一种致命且具有高度传染性的神经退行性疾病.糜鹿是目前已经报道的哺乳类动物中较易发生朊蛋白病的物种之一.作者使用分子动力学和操控式分子动力学模拟相结合的方法对糜鹿正常朊蛋白的结构稳定性进行了研究.发现了麋鹿朊蛋白结构中的不稳定结构域分布以及热动力学性质,揭示了糜鹿朊蛋白稳定性的分子结构基础以及力学特征.     

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates

The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacological efficacy and toxicity. Therefore, efficient means of predicting the interactions of small organic molecules with CYPs are of high importance to a host of different industries. In this work, we present a new set of machine learning models for the classification of xenobiotics into substrates and non-substrates of nine human CYP isozymes: CYPs 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4. The models are trained on an extended, high-quality collection of known substrates and non-substrates and have been subjected to thorough validation. Our results show that the models yield competitive performance and are favorable for the detection of CYP substrates. In particular, a new consensus model reached high performance, with Matthews correlation coefficients (MCCs) between 0.45 (CYP2C8) and 0.85 (CYP3A4), although at the cost of coverage. The best models presented in this work are accessible free of charge via the “CYPstrate” module of the New E-Resource for Drug Discovery (NERDD).     

基于机器学习和高通量计算筛选金属有机框架的甲烷/乙烷/丙烷分离性能

针对天然气中的甲烷、乙烷、丙烷(C₁、C₂、C₃)气体分离困难的问题,本工作采用高通量计算了137953种假设的金属有机框架(Metal-organicframework,MOF)对这三种混合气体的吸附分离吸能.为了避免水蒸气的竞争吸附,首先,筛选出31399种疏水性MOF.然后,单变量分析了这些MOF的最大孔径(LCD)、孔隙率(Φ)、体积比表面积(VSA)、亨利系数(K)、吸附热(Q_st)、密度(ρ)共六种MOF结构/能量描述符与MOF对C₁、C₂、C₃的选择性、吸附量及两者权衡值(Trade-off between S_i/j and N_i, TSN)的关系,发现了吸附量和选择性"第二峰值"的存在;尤其对于C₁、C₂的分离,所有最优MOF都分布在第二峰值区间.随后采用决策树、随机森林(Random forest, RF)、支持向量机和反向传播神...