AIMC Topic: Binding Sites

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Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning.

Genes May 23, 2024
Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well a...
DNA-Binding Proteins Binding Sites Protein Binding Computational Biology DNA Machine Learning Algorithms
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ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Proteins May 9, 2024
Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the...
Databases, Protein Binding Sites Thermodynamics Neural Networks, Computer Protein Binding Protein Interaction Mapping Algorithms Computational Biology Proteins
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Prediction of Transcription Factor Binding Sites on Cell-Free DNA Based on Deep Learning.

Journal of chemical information and modeling May 9, 2024
Transcription factors (TFs) are important regulatory elements for vital cellular activities, and the identification of transcription factor binding sites (TFBS) can help to explore gene regulatory mechanisms. Research studies have proved that cfDNA (...
DNA Binding Sites Humans Deep Learning Transcription Factors Cell-Free Nucleic Acids
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Predicting Transcription Factor Binding Sites with Deep Learning.

International journal of molecular sciences May 3, 2024
Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with diff...
Humans Deep Learning Protein Binding HeLa Cells Binding Sites Chromatin Immunoprecipitation Sequencing Computational Biology Transcription Factors Cell Line
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Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation.

International journal of biological macromolecules Apr 30, 2024
GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibi...
Thermodynamics Pharmacophore G-Protein-Coupled Receptor Kinase 5 Chronic Disease Quantitative Structure-Activity Relationship Humans Molecular Docking Simulation Machine Learning Protein Binding Molecular Dynamics Simulation Protein Kinase Inhibitors Ligands Binding Sites
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Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.

Scientific reports Apr 24, 2024
Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigatin...
Binding Sites Humans Machine Learning Bayes Theorem Molecular Docking Simulation Molecular Dynamics Simulation Protein Binding Ligands Receptors, G-Protein-Coupled
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Prospective de novo drug design with deep interactome learning.

Nature communications Apr 22, 2024
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of dru...
Ligands PPAR gamma Drug Design Humans Binding Sites Deep Learning Protein Binding
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DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes.

Scientific reports Apr 21, 2024
Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. ...
Eukaryotic Cells Neural Networks, Computer Eukaryota Transcription Factors Deep Learning Binding Sites Computational Biology Genome Prokaryotic Cells
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Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BMC bioinformatics Apr 20, 2024
BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online ...
Drug Delivery Systems Drug Interactions Deep Learning Drug Evaluation, Preclinical Binding Sites
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Binding Mechanism of Inhibitors to BRD4 and BRD9 Decoded by Multiple Independent Molecular Dynamics Simulations and Deep Learning.

Molecules (Basel, Switzerland) Apr 19, 2024
Bromodomain 4 and 9 (BRD4 and BRD9) have been regarded as important targets of drug designs in regard to the treatment of multiple diseases. In our current study, molecular dynamics (MD) simulations, deep learning (DL) and binding free energy calcula...
Bromodomain Containing Proteins Thermodynamics Azepines Transcription Factors Deep Learning Triazoles Humans Molecular Dynamics Simulation Protein Binding Cell Cycle Proteins Nuclear Proteins Molecular Docking Simulation Binding Sites
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