AIMC Topic: Protein Binding

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Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.

Journal of chemical information and modeling Mar 23, 2021
Predicting accurate protein-ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite th...
Software Protein Binding Proteins Ligands Neural Networks, Computer
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Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.

PLoS computational biology Mar 19, 2021
Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson's disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antago...
Molecular Docking Simulation Drug Discovery Humans Receptor, Adenosine A2A Deep Learning Adenosine A2 Receptor Antagonists Adenosine A1 Receptor Antagonists Protein Binding Parkinson Disease
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Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism.

Proteins Mar 18, 2021
Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan-specific prediction algorithms can achieve better predict...
Humans Peptides Alleles Databases, Protein Deep Learning Datasets as Topic Binding Sites Area Under Curve HLA-A Antigens Benchmarking Protein Binding
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Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Mar 16, 2021
Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC (OPRC), for the molecular featurization and feature engineering,...
Protein Binding Proteins Databases, Protein Machine Learning Ligands
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A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Molecular diversity Mar 9, 2021
Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potentia...
Algorithms Molecular Docking Simulation Drug Design Drug Discovery Neural Networks, Computer Artificial Intelligence Machine Learning Workflow Molecular Dynamics Simulation Quantitative Structure-Activity Relationship Hydrogen Bonding Protein Binding Hypoglycemic Agents Molecular Structure Humans Binding Sites Dipeptidyl-Peptidase IV Inhibitors
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Screening of a novel free fatty acid receptor 1 (FFAR1) agonist peptide by phage display and machine learning based-amino acid substitution.

Biochemical and biophysical research communications Mar 9, 2021
Free fatty acid receptor 1 (FFAR1 or GPR40) has attracted attention for the treatment of type 2 diabetes mellitus, and various small-molecule agonists have been developed. However, most FFAR1 agonists as well as endogenous ligands, such as linoleic a...
Drug Evaluation, Preclinical Amino Acid Sequence HEK293 Cells Transforming Growth Factor alpha Cloning, Molecular Amino Acid Substitution Cell Line Regression Analysis Receptors, G-Protein-Coupled Glucose Insulin Protein Binding Peptide Library Machine Learning Humans Peptides
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Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Journal of the American Chemical Society Mar 8, 2021
Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-li...
Structure-Activity Relationship Amino Acid Motifs Cyclization Machine Learning Crystallography, X-Ray Humans Fluorescence Polarization NF-E2-Related Factor 2 Protein Binding Hydrogen Bonding Peptides Nuclear Magnetic Resonance, Biomolecular Kelch-Like ECH-Associated Protein 1 Mutagenesis, Site-Directed Recombinant Proteins
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Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features.

Molecules (Basel, Switzerland) Feb 26, 2021
Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain developm...
Protein Binding Logistic Models Rats Receptors, Androgen Proteins Humans Neural Networks, Computer Ligands Algorithms Software Animals Deep Learning
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Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures.

Cell research Feb 23, 2021
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP-RNA interactions employ RNA sequence and/o...
RNA-Binding Proteins RNA Humans Binding Sites Transcriptome Deep Learning Protein Binding
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Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.

Proteins Feb 16, 2021
Deep learning has emerged as a revolutionary technology for protein residue-residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning-based contact predictions have been achie...
Sequence Alignment Sequence Analysis, Protein Protein Interaction Domains and Motifs Humans Benchmarking Protein Conformation Protein Binding Binding Sites Software Computational Biology Research Design Proteins Deep Learning Databases, Protein
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