AIMC Topic: Binding Sites

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Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.

Nucleic acids research Nov 8, 2021
The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility ...
Humans Neural Networks, Computer Machine Learning Transcription Factors Cell Communication Genome, Human Histones Software Chromatin Protein Binding Epigenesis, Genetic Chromatin Immunoprecipitation Binding Sites Eukaryotic Cells Cell Lineage Atlases as Topic
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Prediction of RBP binding sites on circRNAs using an LSTM-based deep sequence learning architecture.

Briefings in bioinformatics Nov 5, 2021
Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is ...
RNA, Circular Databases, Genetic Base Sequence Binding Sites Algorithms Reproducibility of Results Computational Biology RNA-Binding Proteins Deep Learning ROC Curve
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High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method.

Briefings in bioinformatics Nov 5, 2021
Transcription factors (TFs) are essential proteins in regulating the spatiotemporal expression of genes. It is crucial to infer the potential transcription factor binding sites (TFBSs) with high resolution to promote biology and realize precision med...
Deep Learning Protein Binding Computational Biology Chromatin Immunoprecipitation Transcription Factors Binding Sites
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Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

Briefings in bioinformatics Nov 5, 2021
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date...
Reproducibility of Results Deep Learning ROC Curve Protein Binding Machine Learning Software Databases, Factual Histocompatibility Antigens Class I Amino Acid Sequence Binding Sites Web Browser Computational Biology Carrier Proteins Epitopes Neural Networks, Computer
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Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2.

Proceedings of the National Academy of Sciences of the United States of America Oct 19, 2021
The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 ha...
Binding Sites Host-Pathogen Interactions Angiotensin-Converting Enzyme 2 Amino Acid Substitution SARS-CoV-2 Spike Glycoprotein, Coronavirus Molecular Dynamics Simulation Humans Neural Networks, Computer
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SAResNet: self-attention residual network for predicting DNA-protein binding.

Briefings in bioinformatics Sep 2, 2021
Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have a...
Internet DNA-Binding Proteins Binding Sites Neural Networks, Computer Algorithms Reproducibility of Results Computational Biology DNA Deep Learning Protein Binding Humans
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Explainability in transformer models for functional genomics.

Briefings in bioinformatics Sep 2, 2021
The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs fr...
Deep Learning Genomics DNA, Bacterial Promoter Regions, Genetic Binding Sites Genome, Bacterial Transcription Initiation Site Transcription Factors Escherichia coli Base Sequence
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DeepDTAF: a deep learning method to predict protein-ligand binding affinity.

Briefings in bioinformatics Sep 2, 2021
Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many comp...
Models, Molecular Amino Acid Sequence Ligands Binding Sites Protein Conformation, alpha-Helical Data Accuracy Hydrogen Bonding Deep Learning Protein Binding Proteins Software Reproducibility of Results Drug Discovery
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Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.

Briefings in bioinformatics Sep 2, 2021
Protein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein inte...
Binding Sites Protein Multimerization Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Deep Learning Protein Binding Proteins Datasets as Topic Humans Software Molecular Docking Simulation
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RNAProt: an efficient and feature-rich RNA binding protein binding site predictor.

GigaScience Aug 18, 2021
BACKGROUND: Cross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies...
RNA Binding Sites Neural Networks, Computer Protein Binding RNA-Binding Proteins
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