AI Medical Compendium Topic:
Protein Binding

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Sequence-Based Prediction of Fuzzy Protein Interactions.

Journal of molecular biology Feb 27, 2020
It is becoming increasingly recognised that disordered proteins may be fuzzy, in that they can exhibit a wide variety of binding modes. In addition to the well-known process of folding upon binding (disorder-to-order transition), many examples are em...
Humans Algorithms Models, Molecular Protein Domains Amino Acid Sequence Intrinsically Disordered Proteins Protein Binding Fuzzy Logic Databases, Protein Protein Interaction Domains and Motifs Sequence Homology Protein Conformation Protein Folding Sequence Analysis, Protein
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Computational Identification and Analysis of Ubiquinone-Binding Proteins.

Cells Feb 24, 2020
Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will...
Protein Interaction Domains and Motifs Position-Specific Scoring Matrices Gene Ontology Binding Sites Sequence Analysis, Protein Machine Learning Protein Binding Computational Biology Humans Membrane Proteins Amino Acid Sequence Carrier Proteins Ubiquinone
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A novel artificial intelligence protocol for finding potential inhibitors of acute myeloid leukemia.

Journal of materials chemistry. B Feb 18, 2020
There is currently no effective treatment for acute myeloid leukemia, and surgery is also ineffective as an important treatment for most tumors. Rapidly developing artificial intelligence technology can be applied to different aspects of drug develop...
Databases, Chemical Molecular Dynamics Simulation STAT3 Transcription Factor Ligands Humans Artificial Intelligence Drug Discovery Protein Binding Machine Learning Leukemia, Myeloid, Acute
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Machine learning and ligand binding predictions: A review of data, methods, and obstacles.

Biochimica et biophysica acta. General subjects Feb 10, 2020
Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data ...
Humans Big Data Machine Learning Protein Binding Algorithms Computational Biology Drug Discovery Ligands
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Deep neural networks for interpreting RNA-binding protein target preferences.

Genome research Jan 28, 2020
Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpreta...
Binding Sites Deep Learning Humans Nucleotide Motifs Neural Networks, Computer Protein Binding Algorithms RNA-Binding Proteins
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Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.

Biochimica et biophysica acta. General subjects Jan 16, 2020
Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding...
Amino Acid Sequence Mice Machine Learning Protein Conformation Alleles Peptides Algorithms Histocompatibility Antigens Class I Protein Binding Animals
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Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation Jan 16, 2020
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...
Monte Carlo Method HIV Protease Inhibitors Hydrogen Bonding Static Electricity Ligands Humans Drug Resistance, Viral Molecular Dynamics Simulation Machine Learning HIV Protease HIV-1 Protein Binding Mutation
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Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets.

International journal of molecular sciences Jan 11, 2020
Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the dev...
Humans Neural Networks, Computer Binding Sites Protein Binding Protein Interaction Mapping Reproducibility of Results Datasets as Topic Animals
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Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4.

Journal of computer-aided molecular design Jan 8, 2020
Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secret...
Enzyme Inhibitors Amyloid Precursor Protein Secretases Aspartic Acid Endopeptidases Drug Design Macrocyclic Compounds Humans Protein Binding Binding Sites Neural Networks, Computer Ligands Molecular Docking Simulation
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Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Nature methods Jan 6, 2020
In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 ...
Humans Machine Learning src Homology Domains Reproducibility of Results Signal Transduction Proteins Peptides Biophysical Phenomena Protein Binding
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