AIMC Topic: Molecular Dynamics Simulation

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Automated discovery of GPCR bioactive ligands.

Current opinion in structural biology Mar 23, 2019
While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiolog...
Humans Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Ligands Protein Conformation Drug Discovery Receptors, G-Protein-Coupled Drug Design
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Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning.

Proceedings of the National Academy of Sciences of the United States of America Feb 26, 2019
The lysosomal enzyme glucocerebrosidase-1 (GCase) catalyzes the cleavage of a major glycolipid glucosylceramide into glucose and ceramide. The absence of fully functional GCase leads to the accumulation of its lipid substrates in lysosomes, causing G...
Humans Molecular Dynamics Simulation Catalytic Domain Hydrogen Bonding Protein Interaction Maps Gaucher Disease Deep Learning Mutant Proteins Enzyme Activation Glucosylceramidase Protein Structure, Secondary Saposins
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Machine Learning Identifies Chemical Characteristics That Promote Enzyme Catalysis.

Journal of the American Chemical Society Feb 21, 2019
Despite tremendous progress in understanding and engineering enzymes, knowledge of how enzyme structures and their dynamics induce observed catalytic properties is incomplete, and capabilities to engineer enzymes fall far short of industrial needs. H...
Substrate Specificity Ketol-Acid Reductoisomerase Protein Conformation Machine Learning Monte Carlo Method Molecular Dynamics Simulation Biocatalysis
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Solvation Free Energy Calculations with Quantum Mechanics/Molecular Mechanics and Machine Learning Models.

The journal of physical chemistry. B Jan 15, 2019
For exploration of chemical and biological systems, the combined quantum mechanics and molecular mechanics (QM/MM) and machine learning (ML) models have been developed recently to achieve high accuracy and efficiency for molecular dynamics (MD) simul...
Solvents Thermodynamics Molecular Dynamics Simulation Quantum Theory Machine Learning
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Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.

Journal of chemical theory and computation Jan 7, 2019
Accurate force fields are crucial for molecular dynamics investigation of complex biological systems. Building accurate protein force fields from quantum mechanical (QM) calculations is challenging due to the complexity of proteins and high computati...
Static Electricity Thermodynamics Quantum Theory Dipeptides Peptides Proteins Amino Acids Molecular Dynamics Simulation Models, Chemical Neural Networks, Computer
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Physicochemical property based computational scheme for classifying DNA sequence elements of Saccharomyces cerevisiae.

Computational biology and chemistry Dec 26, 2018
GenerationE of huge "omics" data necessitates the development and application of computational methods to annotate the data in terms of biological features. In the context of DNA sequence, it is important to unravel the hidden physicochemical signatu...
Hydrogen Bonding DNA Computational Biology Saccharomyces cerevisiae Molecular Dynamics Simulation Sequence Analysis, DNA Chemistry, Physical
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Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE.

Journal of chemical theory and computation Dec 24, 2018
In this work, we demonstrate how to leverage our recent iterative deep learning-all atom molecular dynamics (MD) technique "Reweighted autoencoded variational Bayes for enhanced sampling (RAVE)" (Ribeiro, Bravo, Wang, Tiwary, J. Chem. Phys. 2018, 149...
Proteins Molecular Dynamics Simulation Protein Binding Thermodynamics Ligands Deep Learning
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Combined molecular dynamics and neural network method for predicting protein antifreeze activity.

Proceedings of the National Academy of Sciences of the United States of America Dec 7, 2018
Antifreeze proteins (AFPs) are a diverse class of proteins that depress the kinetically observable freezing point of water. AFPs have been of scientific interest for decades, but the lack of an accurate model for predicting AFP activity has hindered ...
Water Models, Theoretical Molecular Dynamics Simulation Crystallization Kinetics Neural Networks, Computer Temperature Thermodynamics Humans Animals Freezing Antifreeze Proteins Protein Conformation
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RNA3DCNN: Local and global quality assessments of RNA 3D structures using 3D deep convolutional neural networks.

PLoS computational biology Nov 27, 2018
Quality assessment is essential for the computational prediction and design of RNA tertiary structures. To date, several knowledge-based statistical potentials have been proposed and proved to be effective in identifying native and near-native RNA st...
Monte Carlo Method Neural Networks, Computer Datasets as Topic Hot Temperature Molecular Dynamics Simulation Nucleic Acid Conformation RNA
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Thermodynamic integration network approach to ion transport through protein channels: Perspectives and limits.

Journal of computational chemistry Oct 11, 2018
We present a molecular dynamics simulation study of alkali metal cation transport through the double-helical and the head-to-head conformers of the gramicidin ion channel. Our approach is based on a thermodynamic integration network, which consists o...
Thermodynamics Ion Channels Molecular Dynamics Simulation Ion Transport Neural Networks, Computer Protein Conformation Least-Squares Analysis
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