Multiscale modeling of solvation in chemical and biological nanosystems and in nanoporous materials

2013 ◽  
Vol 85 (1) ◽  
pp. 159-199 ◽  
Author(s):  
Andriy Kovalenko
Keyword(s):  
Density Functional ◽  
Molecular Mechanisms ◽  
Conformational Stability ◽  
Nanoporous Materials ◽  
Molecular Theory ◽  
Multiple Time ◽  
Time Step ◽  
Complete Active Space ◽  
Statistical Mechanical ◽  
Force Coordinate

Statistical–mechanical, 3D-RISM-KH molecular theory of solvation (3D reference interaction site model with the Kovalenko–Hirata closure) is promising as an essential part of multiscale methodology for chemical and biomolecular nanosystems in solution. 3D-RISM-KH explains the molecular mechanisms of self-assembly and conformational stability of synthetic organic rosette nanotubes (RNTs), aggregation of prion proteins and β-sheet amyloid oligomers, protein-ligand binding, and function-related solvation properties of complexes as large as the Gloeobacter violaceus pentameric ligand-gated ion channel (GLIC) and GroEL/ES chaperone. Molecular mechanics/Poisson–Boltzmann (generalized Born) surface area [MM/PB(GB)SA] post-processing of molecular dynamics (MD) trajectories involving SA empirical nonpolar terms is replaced with MM/3D-RISM-KH statistical–mechanical evaluation of the solvation thermodynamics. 3D-RISM-KH has been coupled with multiple time-step (MTS) MD of the solute biomolecule driven by effective solvation forces, which are obtained analytically by converging the 3D-RISM-KH integral equations at outer time-steps and are calculated in between by using solvation force coordinate extrapolation (SFCE) in the subspace of previous solutions to 3D-RISM-KH. The procedure is stabilized by the optimized isokinetic Nosé–Hoover (OIN) chain thermostatting, which enables gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties. The multiscale OIN/SFCE/3D-RISM-KH algorithm is implemented in the Amber package and illustrated on a fully flexible model of alanine dipeptide in aqueous solution, exhibiting the computational rate of solvent sampling 20 times faster than standard MD with explicit solvent. Further substantial acceleration can be achieved with 3D-RISM-KH efficiently sampling essential events with rare statistics such as exchange and localization of solvent, ions, and ligands at binding sites and pockets of the biomolecule. 3D-RISM-KH was coupled with ab initio complete active space self-consistent field (CASSCF) and orbital-free embedding (OFE) Kohn–Sham (KS) density functional theory (DFT) quantum chemistry methods in an SCF description of electronic structure, optimized geometry, and chemical reactions in solution. The (OFE)KS-DFT/3D-RISM-KH multi-scale method is implemented in the Amsterdam Density Functional (ADF) package and extensively validated against experiment for solvation thermochemistry, photochemistry, conformational equilibria, and activation barriers of various nanosystems in solvents and ionic liquids (ILs). Finally, the replica RISM-KH-VM molecular theory for the solvation structure, thermodynamics, and electrochemistry of electrolyte solutions sorbed in nanoporous materials reveals the molecular mechanisms of sorption and supercapacitance in nanoporous carbon electrodes, which is drastically different from a planar electrical double layer.

scholarly journals Comparative Transcriptome Analysis Reveals Genetic Mechanisms of Sugarcane Aphid Resistance in Grain Sorghum

2021 ◽  
Vol 22 (13) ◽  
pp. 7129
Author(s):  
Desalegn D. Serba ◽  
Xiaoxi Meng ◽  
James Schnable ◽  
Elfadil Bashir ◽  
J. P. Michaud ◽  
...  
Keyword(s):  
Differential Expression ◽  
Molecular Mechanisms ◽  
Grain Sorghum ◽  
Lipid Binding ◽  
Multiple Time ◽  
Melanaphis Sacchari ◽  
Resistant Genotype ◽  
Sugarcane Aphid ◽  
Genetic Mechanisms ◽  
Moderately Resistant

The sugarcane aphid, Melanaphis sacchari (Zehntner) (Hemiptera: Aphididae) (SCA), has become a major pest of grain sorghum since its appearance in the USA. Several grain sorghum parental lines are moderately resistant to the SCA. However, the molecular and genetic mechanisms underlying this resistance are poorly understood, which has constrained breeding for improved resistance. RNA-Seq was used to conduct transcriptomics analysis on a moderately resistant genotype (TAM428) and a susceptible genotype (Tx2737) to elucidate the molecular mechanisms underlying resistance. Differential expression analysis revealed differences in transcriptomic profile between the two genotypes at multiple time points after infestation by SCA. Six gene clusters had differential expression during SCA infestation. Gene ontology enrichment and cluster analysis of genes differentially expressed after SCA infestation revealed consistent upregulation of genes controlling protein and lipid binding, cellular catabolic processes, transcription initiation, and autophagy in the resistant genotype. Genes regulating responses to external stimuli and stress, cell communication, and transferase activities, were all upregulated in later stages of infestation. On the other hand, expression of genes controlling cell cycle and nuclear division were reduced after SCA infestation in the resistant genotype. These results indicate that different classes of genes, including stress response genes and transcription factors, are responsible for countering the physiological effects of SCA infestation in resistant sorghum plants.

scholarly journals Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3517 ◽  
Author(s):  
Anh Ngoc-Lan Huynh ◽  
Ravinesh C. Deo ◽  
Duc-Anh An-Vo ◽  
Mumtaz Ali ◽  
Nawin Raj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Radiation ◽  
Short Term Memory ◽  
Global Solar Radiation ◽  
Multiple Time ◽  
Short Term ◽  
Time Step ◽  
Term Memory ◽  
Multiple Time Step ◽  
Long Short Term Memory

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

scholarly journals Molecular basis for two stereoselective Diels-Alderases that produce decalin skeletons

2021 ◽  
Author(s):  
Keisuke Fujiyama ◽  
Naoki Kato ◽  
Suyong Re ◽  
Kiyomi Kinugasa ◽  
Kohei Watanabe ◽  
...  
Keyword(s):  
Natural Products ◽  
Crystal Structures ◽  
Molecular Mechanism ◽  
Density Functional ◽  
Molecular Mechanisms ◽  
Site Directed Mutagenesis ◽  
Bioactive Molecules ◽  
Docking Simulations ◽  
Primary And Secondary Metabolites ◽  
Binding Models

SummaryMolecular chirality, discovered by Louis Pasteur in the middle of the 19th century1, is found in most primary and secondary metabolites. Particularly, the so-called natural products are rich in chiral centres2. The stereochemistry of natural products is strictly recognized in living organisms, and is thus closely related to their biological functions. The Diels–Alder (DA) reaction, which forms a six-membered ring with up to four chiral centres, is a fundamental practical reaction for C–C bond formation in synthetic chemistry3. Nature has also adopted this reaction to elaborate the complex structures of natural products using enzymes derived from various progenitor proteins4-7. Although enzymes catalysing the DA reaction, Diels–Alderases (DAases), have attracted increasing attention, little is known about the molecular mechanism by which they control the stereochemistry and perform catalysis. Here, we solved the X-ray crystal structures of a pair of decalin synthases, Fsa2 and Phm7, that catalyse intramolecular DA reactions to form enantiomeric decalin scaffolds during biosynthesis of the HIV-1 integrase inhibitor equisetin and its stereochemical opposite, phomasetin8,9. Based on the crystal structures, docking simulations followed by all-atom molecular dynamics simulations provided dynamic binding models demonstrating the folding of linear polyenoyl tetramic acid substrates in the binding pocket of these enzymes, explaining the stereoselectivity in the construction of decalin scaffolds. Site-directed mutagenesis studies verified the binding models and, in combination with density functional theory calculations, clarified how hydrophilic amino acid residues in the Phm7 pocket regulate and catalyse the stereoselective DA reaction. This study highlights the distinct molecular mechanisms of the enzymatic DA reaction and its stereoselectivity experimentally and computationally. We anticipate that clarified molecular mechanism herein provides not only the basic understanding how these important enzymes work but also the guiding principle to create artificial enzymes that produce designer bioactive molecules.

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