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Protein Binding

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Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.

Experimental biology and medicine (Maywood, N.J.) 40177220
Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overd...
Analgesics, Opioid Deep Learning Humans Ligands Machine Learning Protein Binding Receptors, Opioid, mu Structure-Activity Relationship Support Vector Machine
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OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction.

Frontiers in immunology 40092998
The key roles of Major Histocompatibility Complex (MHC) Class I and II molecules in the immune system are well established. This study aims to develop a novel machine learning framework for predicting antigen peptide presentation by MHC Class I and I...
Antigen Presentation Antigens, Neoplasm Cancer Vaccines Endometrial Neoplasms Female Histocompatibility Antigens Class I Histocompatibility Antigens Class II Humans Machine Learning Peptides Protein Binding Vaccine Development
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Binding mechanism of inhibitors to DFG-in and DFG-out P38α deciphered using multiple independent Gaussian accelerated molecular dynamics simulations and deep learning.

SAR and QSAR in environmental research 40110797
P38α has been identified as a key target for drug design to treat a wide range of diseases. In this study, multiple independent Gaussian accelerated molecular dynamics (GaMD) simulations, deep learning (DL), and the molecular mechanics generalized Bo...
Deep Learning Drug Design Mitogen-Activated Protein Kinase 14 Molecular Dynamics Simulation Protein Binding Quantitative Structure-Activity Relationship
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Atomic context-conditioned protein sequence design using LigandMPNN.

Nature methods 40155723
Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme and small-molecule binder and sensor design, but current state-of-the-art deep-learning-based sequence design methods are unable to model nonprote...
Amino Acid Sequence Crystallography, X-Ray Deep Learning Ligands Models, Molecular Protein Binding Protein Conformation Proteins
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Leveraging Transfer Learning for Predicting Protein-Small-Molecule Interaction Predictions.

Journal of chemical information and modeling 40127309
A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼10 proteins in a typical human cell offer ...
Humans Ligands Machine Learning Neural Networks, Computer Protein Binding Proteins Small Molecule Libraries
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Mechanistic Study of Protein Interaction with Natto Inhibitory Peptides Targeting Xanthine Oxidase: Insights from Machine Learning and Molecular Dynamics Simulations.

Journal of chemical information and modeling 40125929
Bioactive peptides from food sources offer a safe and biocompatible approach to enzyme inhibition, with potential applications in managing metabolic disorders such as hyperuricemia and gout, conditions linked to excessive xanthine oxidase activity. U...
Bacillus subtilis Enzyme Inhibitors Humans Machine Learning Molecular Dynamics Simulation Peptides Protein Binding Xanthine Oxidase
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Deep-ProBind: binding protein prediction with transformer-based deep learning model.

BMC bioinformatics 40121399
Binding proteins play a crucial role in biological systems by selectively interacting with specific molecules, such as DNA, RNA, or peptides, to regulate various cellular processes. Their ability to recognize and bind target molecules with high speci...
Algorithms Binding Sites Computational Biology Databases, Protein Deep Learning Neural Networks, Computer Protein Binding Proteins Sequence Analysis, Protein
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Computational discovery of novel PI3KC2α inhibitors using structure-based pharmacophore modeling, machine learning and molecular dynamic simulation.

Journal of molecular graphics & modelling 40112531
PI3KC2α is a lipid kinase associated with cancer metastasis and thrombosis. In this study, we present a novel computational workflow integrating structure-based pharmacophore modeling, machine learning (ML), and molecular dynamics (MD) simulations to...
Drug Discovery Humans Ligands Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Pharmacophore Phosphatidylinositol 3-Kinases Phosphoinositide-3 Kinase Inhibitors Protein Binding Protein Kinase Inhibitors
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CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BMC genomics 40102719
BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent yea...
Algorithms Binding Sites Chromatin Immunoprecipitation Sequencing Computational Biology Convolutional Neural Networks DNA Neural Networks, Computer Protein Binding Software Transcription Factors
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Interpreting regulatory mechanisms of Hippo signaling through a deep learning sequence model.

Cell genomics 40174587
Signaling pathway components are well studied, but how they mediate cell-type-specific transcription responses is an unresolved problem. Using the Hippo pathway in mouse trophoblast stem cells as a model, we show that the DNA binding of signaling eff...
Adaptor Proteins, Signal Transducing Animals Deep Learning DNA-Binding Proteins Enhancer Elements, Genetic Hippo Signaling Pathway Mice Molecular Dynamics Simulation Muscle Proteins Phosphoproteins Protein Binding Protein Serine-Threonine Kinases Signal Transduction TEA Domain Transcription Factors Transcription Factors Trophoblasts YAP-Signaling Proteins
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