AIMC Topic: Protein Conformation

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Application of Machine-Learning Methods to Recognize mitoBK Channels from Different Cell Types Based on the Experimental Patch-Clamp Results.

International journal of molecular sciences Jan 15, 2021
(1) Background: In this work, we focus on the activity of large-conductance voltage- and Ca2+-activated potassium channels (BK) from the inner mitochondrial membrane (mitoBK). The characteristic electrophysiological features of the mitoBK channels ar...
Patch-Clamp Techniques Large-Conductance Calcium-Activated Potassium Channels Potassium Channels Endothelium Pregnancy, Animal Ion Channel Gating Rats, Wistar Rats Fibroblasts Hippocampus Protein Conformation Kinetics Mitochondria Algorithms Pregnancy Female Animals Neural Networks, Computer Artificial Intelligence Machine Learning
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Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets.

Scientific reports Jan 12, 2021
Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and b...
Humans Models, Molecular Protein Kinases Protein Conformation Machine Learning Protein Kinase Inhibitors Protein Binding Support Vector Machine
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Energy-dependent protein folding: modeling how a protein folding machine may work.

F1000Research Jan 5, 2021
Proteins fold robustly and reproducibly , but many cannot fold in isolation from cellular components. Despite the remarkable progress that has been achieved by the artificial intelligence approaches in predicting the protein native conformations, t...
Peptides Proteins Protein Conformation Protein Folding Artificial Intelligence
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Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes.

Proteins Dec 31, 2020
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to di...
Proteins Models, Molecular Rotation Ligands Protein Conformation Neural Networks, Computer Machine Learning
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A protein folding robot driven by a self-taught agent.

Bio Systems Dec 29, 2020
This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to sol...
Computer Simulation Systems Biology Robotics Protein Conformation Viral Proteins Models, Molecular Amino Acid Sequence Humans Neural Networks, Computer Machine Learning Algorithms Hemagglutinins, Viral Protein Folding Viral Fusion Proteins Systems Analysis Coronavirus
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Template-based prediction of protein structure with deep learning.

BMC genomics Dec 29, 2020
BACKGROUND: Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary struct...
Databases, Protein Protein Conformation Sequence Analysis, Protein Software Algorithms Deep Learning Proteins
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Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

Proteins Dec 5, 2020
To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by norm...
Binding Sites Coronavirus 3C Proteases Coronavirus Papain-Like Proteases Karyopherins Elasticity Protein Conformation Receptors, Cytoplasmic and Nuclear Protein Tyrosine Phosphatase, Non-Receptor Type 1 Exportin 1 Protein Models, Statistical Regression Analysis Area Under Curve Hepacivirus Ligands Antigens, CD Molecular Dynamics Simulation ROC Curve Computational Biology Antigens, Neoplasm Interleukin-2 Humans Algorithms Machine Learning Reproducibility of Results SARS-CoV-2
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Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants.

The Analyst Nov 19, 2020
Establishing standardized methods for a consistent analysis of spectral data remains a largely underexplored aspect in surface-enhanced Raman spectroscopy (SERS), particularly applied to biological and biomedical research. Here we propose an effectiv...
Machine Learning Spectrum Analysis, Raman Models, Molecular Silver Protein Conformation Nanowires
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OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods.

Journal of chemical information and modeling Nov 19, 2020
Side-chain modeling is critical for protein structure prediction since the uniqueness of the protein structure is largely determined by its side-chain packing conformation. In this paper, differing from most approaches that rely on rotamer library sa...
Neural Networks, Computer Gene Library Software Proteins Protein Conformation
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Spatiotemporal identification of druggable binding sites using deep learning.

Communications biology Oct 27, 2020
Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site id...
Models, Biological Adenosine Triphosphate Protein Binding Protein Conformation Receptors, Purinergic P2X3 Software Receptors, G-Protein-Coupled Binding Sites Drug Delivery Systems Models, Molecular Ion Channel Gating Deep Learning
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