AIMC Topic: Binding Sites

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Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.

Scientific reports Aug 7, 2020
Transcription factors (TFs) regulate the gene expression of their target genes by binding to the regulatory sequences of target genes (e.g., promoters and enhancers). To fully understand gene regulatory mechanisms, it is crucial to decipher the relat...
Neural Networks, Computer Forecasting Deep Learning Transcription Factors Gene Expression Regulation Base Sequence Binding Sites Protein Binding Computational Biology Models, Theoretical
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Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction.

Frontiers in immunology Aug 5, 2020
Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding ce...
Ligands Protein Binding Histocompatibility Antigens Class II High-Throughput Screening Assays Databases, Protein Humans Antigen Presentation CD4 Antigens Protein Array Analysis HLA Antigens Machine Learning Binding Sites Epitopes, T-Lymphocyte Protein Interaction Domains and Motifs
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Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Nature communications Jul 15, 2020
Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such d...
Phenotype Deep Learning Computational Biology Gene Knockout Techniques Genome, Bacterial Ribosomes Datasets as Topic Regulatory Sequences, Nucleic Acid Molecular Sequence Annotation Binding Sites Escherichia coli Sequence Analysis, DNA High-Throughput Nucleotide Sequencing
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Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model.

Analytical biochemistry Jul 2, 2020
Information embedded in ligand-binding residues (LBRs) of proteins is important for understanding protein functions. How to accurately identify the potential ligand-binding residues is still a challenging problem, especially only protein sequence is ...
Binding Sites Databases, Protein Amino Acid Sequence Ligands Support Vector Machine Proteins Protein Binding
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Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.

Molecular omics Jun 22, 2020
MOTIVATION: enhancers play an important role in the regulation of gene expression during spermatogenesis. The development of ChIP-Chip and ChIP-Seq sequencing technology has enabled researchers to focus on the relationship between enhancers and DNA s...
Support Vector Machine Databases, Genetic Internet Base Sequence Male Deep Learning Mice Enhancer Elements, Genetic Spermatogenesis Binding Sites Genome Neural Networks, Computer Animals
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Using machine learning to improve ensemble docking for drug discovery.

Proteins May 25, 2020
Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activ...
Ligands ErbB Receptors Protein Kinase Inhibitors Binding Sites Protein Structure, Secondary Protein Binding Support Vector Machine Bayes Theorem Molecular Docking Simulation Drug Discovery User-Computer Interface Humans
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GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains.

Cells May 20, 2020
Protein phosphorylation is essential for regulating cellular activities by modifying substrates at specific residues, which frequently interact with proteins containing phosphoprotein-binding domains (PPBDs) to propagate the phosphorylation signaling...
Databases, Protein Phosphorylation Protein Domains Binding Sites Phosphoproteins Statistics as Topic Protein Binding Proteome Signal Transduction Algorithms Animals Deep Learning Humans
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CNN-Peaks: ChIP-Seq peak detection pipeline using convolutional neural networks that imitate human visual inspection.

Scientific reports May 13, 2020
ChIP-seq is one of the core experimental resources available to understand genome-wide epigenetic interactions and identify the functional elements associated with diseases. The analysis of ChIP-seq data is important but poses a difficult computation...
Humans Histones Binding Sites Nucleotide Motifs Protein Binding Databases, Nucleic Acid Chromatin Immunoprecipitation Sequencing Neural Networks, Computer Computational Biology Transcription Initiation Site Epigenesis, Genetic Software Epigenomics Algorithms
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DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction.

BMC bioinformatics Apr 23, 2020
BACKGROUND: Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) a...
Proteins Lysine Binding Sites Succinates Deep Learning Citric Acid Cycle Protein Processing, Post-Translational
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Predicting protein-peptide binding sites with a deep convolutional neural network.

Journal of theoretical biology Apr 13, 2020
MOTIVATION: Interactions between proteins and peptides influence biological functions. Predicting such bio-molecular interactions can lead to faster disease prevention and help in drug discovery. Experimental methods for determining protein-peptide b...
Protein Binding Proteins Neural Networks, Computer Binding Sites Peptides
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