AIMC Topic: Models, Molecular

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Improved protein structure refinement guided by deep learning based accuracy estimation.

Nature communications Feb 26, 2021
We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutio...
Proteins Models, Molecular Models, Biological Caspases Protein Conformation Software Algorithms Deep Learning Computational Biology
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DeepCys: Structure-based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains.

Proteins Feb 22, 2021
Cysteine (Cys) is the most reactive amino acid participating in a wide range of biological functions. In-silico predictions complement the experiments to meet the need of functional characterization. Multiple Cys function prediction algorithm is scar...
Electron Transport Complex IV Nitroso Compounds Sulfinic Acids Sulfonic Acids Cysteine Software Cations, Divalent Deep Learning Protein Structure, Secondary Protein Processing, Post-Translational Structure-Activity Relationship Sulfides Models, Molecular Glutathione Protein Domains Disulfides Amino Acid Sequence
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CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy.

Communications biology Feb 15, 2021
Particle identification and selection, which is a prerequisite for high-resolution structure determination of biological macromolecules via single-particle cryo-electron microscopy poses a major bottleneck for automating the steps of structure determ...
Cryoelectron Microscopy Models, Molecular Humans Deep Learning Animals Image Processing, Computer-Assisted Semantics Protein Conformation Proteins Single Molecule Imaging
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An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

Scientific reports Feb 5, 2021
The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical the...
COVID-19 Vaccines Spike Glycoprotein, Coronavirus Computational Biology Models, Molecular Molecular Docking Simulation Drug Design Deep Learning Solubility Immunogenicity, Vaccine T-Lymphocytes, Helper-Inducer Protein Conformation Mutation RNA, Viral Molecular Dynamics Simulation Allergens Humans COVID-19 SARS-CoV-2 Epitopes, T-Lymphocyte Codon Usage Epitopes, B-Lymphocyte Vaccines, Subunit T-Lymphocytes, Cytotoxic
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Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.

Molecular diversity Feb 3, 2021
Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 re...
Cheminformatics Leukotriene Antagonists Receptors, Leukotriene Drug Discovery Models, Molecular ROC Curve Machine Learning Protein Binding Algorithms Molecular Structure Reproducibility of Results Quantitative Structure-Activity Relationship Binding Sites
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Robust principal component analysis-based prediction of protein-protein interaction hot spots.

Proteins Feb 2, 2021
Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help des...
Humans Machine Learning ROC Curve Protein Binding Models, Molecular Binding Sites Protein Interaction Mapping Protein Interaction Domains and Motifs Computational Biology Proteins Databases, Protein Principal Component Analysis
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Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.

Chemical research in toxicology Jan 29, 2021
Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess a...
Models, Molecular Humans Pharmaceutical Preparations Machine Learning Quantitative Structure-Activity Relationship Databases, Factual Chemical and Drug Induced Liver Injury
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Classification and prediction of protein-protein interaction interface using machine learning algorithm.

Scientific reports Jan 19, 2021
Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therap...
Support Vector Machine Proteins Databases, Protein Models, Molecular Molecular Docking Simulation Binding Sites Protein Interaction Maps Thermodynamics Protein Binding Protein Interaction Mapping
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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...
Protein Kinases Protein Conformation Protein Kinase Inhibitors Machine Learning Protein Binding Humans Support Vector Machine Models, Molecular
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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...
Models, Molecular Neural Networks, Computer Machine Learning Rotation Proteins Ligands Protein Conformation
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