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Protein Conformation

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Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure.

Genomics Dec 11, 2018
The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning method...
Databases, Protein Receptors, G-Protein-Coupled Protein Conformation Machine Learning Receptors, Cytoplasmic and Nuclear Software Ion Channels Drug Development Position-Specific Scoring Matrices
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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 ...
Animals Water Models, Theoretical Humans Neural Networks, Computer Temperature Freezing Protein Conformation Thermodynamics Antifreeze Proteins Kinetics Molecular Dynamics Simulation Crystallization
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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...
Neural Networks, Computer Ion Transport Thermodynamics Ion Channels Least-Squares Analysis Molecular Dynamics Simulation Protein Conformation
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TopScore: Using Deep Neural Networks and Large Diverse Data Sets for Accurate Protein Model Quality Assessment.

Journal of chemical theory and computation Oct 9, 2018
The value of protein models obtained with automated protein structure prediction depends primarily on their accuracy. Protein model quality assessment is thus critical to select the model that can best answer biologically relevant questions from an e...
Neural Networks, Computer Proteins Databases, Protein Models, Molecular Protein Conformation
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Predicting improved protein conformations with a temporal deep recurrent neural network.

PloS one Sep 4, 2018
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally re...
Molecular Dynamics Simulation Neural Networks, Computer Amino Acid Sequence Protein Folding Protein Conformation Software Sequence Analysis, Protein Algorithms Deep Learning
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Integration of virtual screening and susceptibility test to discover active-site subpocket-specific biogenic inhibitors of Helicobacter pylori shikimate dehydrogenase.

International microbiology : the official journal of the Spanish Society for Microbiology Aug 24, 2018
Shikimate dehydrogenase (HpSDH) (EC 1.1.1.25) is a key enzyme in the shikimate pathway of Helicobacter pylori (H. pylori), which catalyzes the NADPH-dependent reversible reduction of 3-dehydroshikimate to shikimate. Targeting HpSDH has been recognize...
Enzyme Inhibitors Computational Biology Anti-Bacterial Agents Crystallography, X-Ray Helicobacter pylori Molecular Docking Simulation Catalytic Domain Protein Conformation Drug Evaluation, Preclinical Microbial Sensitivity Tests Alcohol Oxidoreductases
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Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Journal of computer-aided molecular design Aug 16, 2018
Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affin...
Cathepsins Receptors, Cytoplasmic and Nuclear Binding Sites Thermodynamics Protein Binding Databases, Protein Protein Kinases Ligands Crystallography, X-Ray Protein Conformation Computer-Aided Design Drug Design Deep Learning Molecular Docking Simulation
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Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme.

Chemical research in toxicology Jul 30, 2018
Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacop...
Computer Simulation Reproducibility of Results Protein Binding Support Vector Machine Models, Molecular Ligands Humans Machine Learning Estrogen Receptor alpha Quantitative Structure-Activity Relationship Crystallography, X-Ray Protein Conformation
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Convolutional neural network scoring and minimization in the D3R 2017 community challenge.

Journal of computer-aided molecular design Jul 10, 2018
We assess the ability of our convolutional neural network (CNN)-based scoring functions to perform several common tasks in the domain of drug discovery. These include correctly identifying ligand poses near and far from the true binding mode when giv...
Molecular Docking Simulation Drug Discovery Protein Binding Ligands Protein Conformation Binding Sites Databases, Protein Neural Networks, Computer Cathepsins Structure-Activity Relationship Algorithms
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Redefining the Protein Kinase Conformational Space with Machine Learning.

Cell chemical biology May 31, 2018
Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the...
Small Molecule Libraries Ligands Machine Learning Protein Conformation Humans Models, Molecular Protein Kinase Inhibitors Protein Kinases
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