AIMC Topic: Binding Sites

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A novel end-to-end method to predict RNA secondary structure profile based on bidirectional LSTM and residual neural network.

BMC bioinformatics Mar 31, 2021
BACKGROUND: Studies have shown that RNA secondary structure, a planar structure formed by paired bases, plays diverse vital roles in fundamental life activities and complex diseases. RNA secondary structure profile can record whether each base is pai...
Research Design RNA Binding Sites Protein Structure, Secondary Neural Networks, Computer
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Machine learning predicts nucleosome binding modes of transcription factors.

BMC bioinformatics Mar 30, 2021
BACKGROUND: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding...
Amino Acid Sequence Binding Sites Nucleosomes Machine Learning Protein Binding Transcription Factors
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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 Deep Learning Protein Binding Alleles Datasets as Topic Binding Sites HLA-A Antigens Databases, Protein Area Under Curve Benchmarking
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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...
Humans Hydrogen Bonding Binding Sites Dipeptidyl-Peptidase IV Inhibitors Drug Discovery Algorithms Neural Networks, Computer Protein Binding Molecular Docking Simulation Artificial Intelligence Molecular Structure Molecular Dynamics Simulation Drug Design Machine Learning Hypoglycemic Agents Workflow Quantitative Structure-Activity Relationship
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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 Sites Protein Binding Transcriptome RNA-Binding Proteins Humans Deep Learning
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Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.

PLoS computational biology Feb 18, 2021
N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing ...
Deep Learning Databases, Nucleic Acid Computational Biology DNA, Plant DNA Methylation Oryza Fragaria Adenine Neural Networks, Computer Arabidopsis DNA Base Sequence Rosa Binding Sites Species Specificity
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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...
Deep Learning Protein Binding Computational Biology Benchmarking Protein Conformation Proteins Databases, Protein Humans Protein Interaction Domains and Motifs Software Research Design Binding Sites Sequence Alignment Sequence Analysis, Protein
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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 Machine Learning ROC Curve Quantitative Structure-Activity Relationship Algorithms Models, Molecular Protein Binding Binding Sites Reproducibility of Results Molecular Structure
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An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2021
Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary D...
Histone Code Chromatin Immunoprecipitation Sequencing Deep Learning Epigenesis, Genetic Protein Binding Transcription Factors Computational Biology 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...
Protein Interaction Mapping Protein Interaction Domains and Motifs Protein Binding Computational Biology Proteins Humans Models, Molecular Machine Learning Binding Sites ROC Curve Databases, Protein Principal Component Analysis
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