AIMC Topic: Binding Sites

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DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.

Genomics, proteomics & bioinformatics Mar 12, 2022
Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcript...
Binding Sites Regulatory Sequences, Nucleic Acid Chromatin Assembly and Disassembly Chromatin Protein Binding Transcription Factors Genome, Human Humans Deep Learning
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Data Mining Meets Machine Learning: A Novel ANN-based Multi-body Interaction Docking Scoring Function (MBI-score) Based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-ligand Complexes.

Molecular informatics Mar 9, 2022
Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. I...
Data Mining Ligands Binding Sites Artificial Intelligence Protein Binding Proteins Machine Learning
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Base-resolution prediction of transcription factor binding signals by a deep learning framework.

PLoS computational biology Mar 9, 2022
Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framewo...
Binding Sites Deep Learning Chromatin Immunoprecipitation Sequencing Protein Binding Transcription Factors
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Inferring RNA-binding protein target preferences using adversarial domain adaptation.

PLoS computational biology Feb 24, 2022
Precise identification of target sites of RNA-binding proteins (RBP) is important to understand their biochemical and cellular functions. A large amount of experimental data is generated by in vivo and in vitro approaches. The binding preferences det...
Nucleotides Acclimatization Binding Sites Neural Networks, Computer RNA-Binding Proteins
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Using Steady-State Kinetics to Quantitate Substrate Selectivity and Specificity: A Case Study with Two Human Transaminases.

Molecules (Basel, Switzerland) Feb 18, 2022
We examined the ability of two human cytosolic transaminases, aspartate aminotransferase (GOT1) and alanine aminotransferase (GPT), to transform their preferred substrates whilst discriminating against similar metabolites. This offers an opportunity ...
Transaminases Alkyl and Aryl Transferases Amino Acids Cattle Substrate Specificity Humans Kinetics Structure-Activity Relationship Transferases (Other Substituted Phosphate Groups) Enzyme Activation Protein Conformation Animals Models, Molecular Binding Sites
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Machine Learning Approaches for Metalloproteins.

Molecules (Basel, Switzerland) Feb 14, 2022
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts...
Proteolysis Binding Sites Metalloproteins Amino Acid Sequence Protein Stability Machine Learning Drug Design Structure-Activity Relationship Models, Molecular Protein Binding
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Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.

PLoS computational biology Jan 28, 2022
Machine learning with multi-layered artificial neural networks, also known as "deep learning," is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neur...
Sequence Analysis, Protein Macaca mulatta Amino Acid Sequence Humans Binding Sites Deep Learning Animals Computational Biology Peptides Models, Biological
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Predicting Ca and Mg ligand binding sites by deep neural network algorithm.

BMC bioinformatics Jan 20, 2022
BACKGROUND: Alkaline earth metal ions are important protein binding ligands in human body, and it is of great significance to predict their binding residues.
Algorithms Protein Binding Humans Neural Networks, Computer Binding Sites Ligands
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CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.

PLoS computational biology Jan 20, 2022
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role ...
Machine Learning Algorithms Computational Biology RNA-Binding Proteins Animals Neural Networks, Computer RNA Splicing Models, Biological RNA, Circular Binding Sites
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Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.

Genome research Jan 18, 2022
The intrinsic DNA sequence preferences and cell type-specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence model...
Transcription Factors Binding Sites Chromatin Immunoprecipitation Sequencing Protein Binding Computational Biology Neural Networks, Computer
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