AIMC Topic: 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...
Databases, Protein Models, Molecular Protein Conformation Neural Networks, Computer Proteins
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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...
Algorithms Deep Learning Molecular Dynamics Simulation Neural Networks, Computer Software Sequence Analysis, Protein Amino Acid Sequence Protein Conformation Protein Folding
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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...
Microbial Sensitivity Tests Anti-Bacterial Agents Alcohol Oxidoreductases Computational Biology Molecular Docking Simulation Drug Evaluation, Preclinical Helicobacter pylori Crystallography, X-Ray Protein Conformation Enzyme Inhibitors Catalytic Domain
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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...
Receptors, Cytoplasmic and Nuclear Binding Sites Thermodynamics Computer-Aided Design Ligands Drug Design Crystallography, X-Ray Molecular Docking Simulation Deep Learning Protein Conformation Protein Binding Databases, Protein Protein Kinases Cathepsins
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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...
Protein Conformation Estrogen Receptor alpha Quantitative Structure-Activity Relationship Crystallography, X-Ray Computer Simulation Reproducibility of Results Protein Binding Support Vector Machine Models, Molecular Ligands Humans Machine Learning
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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...
Neural Networks, Computer Protein Binding Databases, Protein Structure-Activity Relationship Algorithms Molecular Docking Simulation Drug Discovery Binding Sites Cathepsins Ligands Protein Conformation
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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 Humans Machine Learning Ligands Models, Molecular Protein Conformation Protein Kinases Protein Kinase Inhibitors
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Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

Biomolecules May 25, 2018
Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep lear...
Humans Animals Deep Learning Solubility Hydrophobic and Hydrophilic Interactions Protein Conformation Sequence Analysis, Protein
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Sequentially distant but structurally similar proteins exhibit fold specific patterns based on their biophysical properties.

Computational biology and chemistry May 16, 2018
The Three-dimensional structure of a protein depends on the interaction between their amino acid residues. These interactions are in turn influenced by various biophysical properties of the amino acids. There are several examples of proteins that sha...
Protein Conformation Protein Folding Proteins Support Vector Machine Models, Molecular Bayes Theorem
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Linking of single-molecule experiments with molecular dynamics simulations by machine learning.

eLife May 3, 2018
Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide data, such as donor-acceptor distances, whereas the latter give atomistic information, ...
Protein Folding Protein Conformation Single Molecule Imaging Machine Learning Carrier Proteins Molecular Dynamics Simulation Fluorescence Resonance Energy Transfer
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