AIMC Topic: Protein Conformation

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Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.

Journal of computer-aided molecular design Nov 15, 2019
Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for...
Humans Protein Conformation Binding Sites Machine Learning Thermodynamics Cathepsins Molecular Docking Simulation Computer-Aided Design Protein Binding Databases, Protein Ligands Crystallography, X-Ray
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ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance.

Molecular informatics Nov 8, 2019
Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all-against-all ensemble docking). Rec...
Molecular Docking Simulation Machine Learning Proteins Algorithms Protein Conformation
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Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.

Journal of computer-aided molecular design Oct 18, 2019
In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibito...
Ligands Protein Conformation Small Molecule Libraries Sequence Homology Proteins Models, Molecular Amino Acid Sequence Protein Binding Computational Biology Machine Learning Humans Software Artificial Intelligence Drug Design
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Graph Convolutional Neural Networks for Predicting Drug-Target Interactions.

Journal of chemical information and modeling Oct 16, 2019
Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-aut...
Neural Networks, Computer Drug Discovery Binding Sites Ligands Databases, Chemical Protein Conformation Protein Binding Drug Development Proteins
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Recent developments in deep learning applied to protein structure prediction.

Proteins Oct 14, 2019
Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted considerable interest in recent years. Methods employing DNNs have had a significant impact in recent...
Neural Networks, Computer Deep Learning Computational Biology Structural Homology, Protein Proteins Models, Molecular Protein Conformation
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Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.

Biomolecules Oct 14, 2019
The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, se...
Supervised Machine Learning Protein Conformation Thermodynamics Proteins Databases, Protein
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Investigation of machine learning techniques on proteomics: A comprehensive survey.

Progress in biophysics and molecular biology Sep 27, 2019
Proteomics is the extensive investigation of proteins which has empowered the recognizable proof of consistently expanding quantities of protein. Proteins are necessary part of living life form, with numerous capacities. The proteome is the complete ...
Proteins Torsion, Mechanical Computational Biology Machine Learning Databases, Protein Protein Conformation Proteomics Computer Simulation Proteome
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Analysis of distance-based protein structure prediction by deep learning in CASP13.

Proteins Sep 13, 2019
This paper reports the CASP13 results of distance-based contact prediction, threading, and folding methods implemented in three RaptorX servers, which are built upon the powerful deep convolutional residual neural network (ResNet) method initiated by...
Proteins Protein Conformation Machine Learning Models, Molecular Protein Folding Sequence Analysis, Protein Software Crystallography, X-Ray Sequence Alignment Algorithms Deep Learning Computational Biology
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Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.

Journal of chemical information and modeling Sep 6, 2019
We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extr...
Neural Networks, Computer Algorithms Computer Graphics Ligands Computational Biology Molecular Targeted Therapy Protein Conformation Proteins Models, Molecular
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Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints.

Nature communications Sep 4, 2019
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even f...
Computational Biology Proteomics Proteome Reproducibility of Results Humans Animals Algorithms Models, Molecular Deep Learning Protein Conformation Binding Sites
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