AIMC Topic: Protein Conformation

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A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors.

International journal of molecular sciences Jun 14, 2024
G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them cruci...
Receptors, G-Protein-Coupled Molecular Dynamics Simulation Receptors, Adrenergic, beta-2 Protein Conformation Humans Deep Learning
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EGG: Accuracy Estimation of Individual Multimeric Protein Models Using Deep Energy-Based Models and Graph Neural Networks.

International journal of molecular sciences Jun 6, 2024
Reliable and accurate methods of estimating the accuracy of predicted protein models are vital to understanding their respective utility. Discerning how the quaternary structure conforms can significantly improve our collective understanding of cell ...
Protein Multimerization Proteins Models, Molecular Protein Conformation Neural Networks, Computer Computational Biology
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Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.

Scientific data Jun 6, 2024
Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins...
Protein Folding Models, Molecular Protein Conformation Humans Proteome Databases, Protein Machine Learning
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Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

Molecules (Basel, Switzerland) Jun 5, 2024
CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (...
Principal Component Analysis Molecular Dynamics Simulation Protein Conformation Deep Learning Protein Binding Thermodynamics Normal Distribution Cyclin-Dependent Kinase 6 Protein Kinase Inhibitors Binding Sites Humans
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RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.

Journal of chemical information and modeling Jun 3, 2024
Microbial rhodopsins (MRs) are a diverse and abundant family of photoactive membrane proteins that serve as model systems for biophysical techniques. Optogenetics utilizes genetic engineering to insert specialized proteins into specific neurons or br...
Deep Learning Models, Molecular Optogenetics Rhodopsin Rhodopsins, Microbial Amino Acid Sequence Protein Conformation
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NPEX: Never give up protein exploration with deep reinforcement learning.

Journal of molecular graphics & modelling May 25, 2024
Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain ...
Dipeptides Proteins Protein Conformation Molecular Dynamics Simulation Markov Chains Monte Carlo Method Algorithms Deep Learning
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Integrative modeling meets deep learning: Recent advances in modeling protein assemblies.

Current opinion in structural biology May 24, 2024
Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the succe...
Proteins Models, Molecular Protein Conformation Protein Binding Computational Biology Humans Deep Learning
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Transferable deep generative modeling of intrinsically disordered protein conformations.

PLoS computational biology May 23, 2024
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. ...
Machine Learning Algorithms Computational Biology Databases, Protein Deep Learning Neural Networks, Computer Protein Conformation Models, Molecular Intrinsically Disordered Proteins
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Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Journal of chemical information and modeling May 15, 2024
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand ...
Phosphotransferases Molecular Docking Simulation Protein Conformation Drug Discovery Protein Kinase Inhibitors Protein Kinases Ligands Neural Networks, Computer Protein Binding Machine Learning
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Molecular Mechanism of Phosphorylation-Mediated Impacts on the Conformation Dynamics of GTP-Bound KRAS Probed by GaMD Trajectory-Based Deep Learning.

Molecules (Basel, Switzerland) May 15, 2024
The phosphorylation of different sites produces a significant effect on the conformational dynamics of KRAS. Gaussian accelerated molecular dynamics (GaMD) simulations were combined with deep learning (DL) to explore the molecular mechanism of the ph...
Protein Binding Proto-Oncogene Proteins p21(ras) Phosphorylation Molecular Dynamics Simulation Humans Guanosine Triphosphate Principal Component Analysis Protein Conformation Deep Learning
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