Protein Conformation

Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model.

Proceedings of the National Academy of Sciences of the United States of America Jun 25, 2024

Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral charact...

Machine Learning Spectrophotometry, Infrared Protein Structure, Secondary Protein Folding Proteins Protein Conformation

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Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling.

PLoS computational biology Jun 24, 2024

As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in pro...

Databases, Protein Models, Molecular Protein Conformation Complementarity Determining Regions Algorithms Antibodies Deep Learning Computational Biology Humans Neural Networks, Computer

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AlphaFold2 structures guide prospective ligand discovery.

Science (New York, N.Y.) Jun 21, 2024

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules a...

Ligands Receptor, Serotonin, 5-HT2A Protein Conformation Drug Discovery Deep Learning Molecular Docking Simulation Serotonin 5-HT2 Receptor Agonists Drug Design Humans Serotonin 5-HT2 Receptor Antagonists Small Molecule Libraries Cryoelectron Microscopy Receptors, sigma Protein Folding

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Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Journal of chemical theory and computation Jun 18, 2024

Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This st...

Thermodynamics Protein Conformation Static Electricity Neural Networks, Computer Molecular Dynamics Simulation Dipeptides Proteins

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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...

Humans Deep Learning Receptors, Adrenergic, beta-2 Receptors, G-Protein-Coupled Protein Conformation Molecular Dynamics Simulation

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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...

Humans Machine Learning Models, Molecular Protein Conformation Protein Folding Proteome Databases, Protein

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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 (...

Protein Kinase Inhibitors Binding Sites Protein Conformation Normal Distribution Cyclin-Dependent Kinase 6 Protein Binding Humans Thermodynamics Principal Component Analysis Deep Learning Molecular Dynamics Simulation

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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...

Rhodopsin Rhodopsins, Microbial Amino Acid Sequence Deep Learning Protein Conformation Models, Molecular Optogenetics

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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 Algorithms Monte Carlo Method Protein Conformation Molecular Dynamics Simulation Deep Learning Markov Chains

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Explore the latest research on artificial intelligence and machine learning in medicine.

Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model.

Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling.

AlphaFold2 structures guide prospective ligand discovery.

Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

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.

EGG: Accuracy Estimation of Individual Multimeric Protein Models Using Deep Energy-Based Models and Graph Neural Networks.

Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.

Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.

NPEX: Never give up protein exploration with deep reinforcement learning.

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