AIMC Topic: Protein Conformation

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Deep learning for protein structure prediction and design-progress and applications.

Molecular systems biology Jan 30, 2024
Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms th...
Protein Conformation Deep Learning Proteins
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Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure.

Biophysical journal Jan 23, 2024
Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achie...
Machine Learning Macromolecular Substances Protein Conformation Proteins Models, Molecular Cryoelectron Microscopy
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Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations.

Journal of chemical theory and computation Jan 12, 2024
Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equili...
Molecular Dynamics Simulation Protein Conformation Thermodynamics Artificial Intelligence Proteins
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Prediction of protein structure and AI.

Journal of human genetics Jan 4, 2024
AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-functi...
Computational Biology Proteins Artificial Intelligence Deep Learning Mutation, Missense Models, Molecular Humans Protein Folding Protein Conformation
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DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score.

Journal of structural biology Dec 30, 2023
Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality ...
Protein Conformation Cryoelectron Microscopy Models, Molecular Microscopy, Electron Deep Learning Algorithms
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Harnessing deep learning for enhanced ligand docking.

Trends in pharmacological sciences Dec 30, 2023
Ligand docking (LD), a technology for predicting protein-ligand (PL)-binding conformations and strengths, plays key roles in virtual screening (VS). However, the accuracy and speed of current LD methodologies remain suboptimal. Here, we discuss how d...
Humans Molecular Docking Simulation Protein Conformation Proteins Ligands Deep Learning Protein Binding
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Interpreting forces as deep learning gradients improves quality of predicted protein structures.

Biophysical journal Dec 15, 2023
Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a c...
Protein Conformation Deep Learning Proteins Molecular Dynamics Simulation
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DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction.

Nature methods Dec 8, 2023
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, the...
Models, Molecular Microscopy, Electron Protein Conformation Cryoelectron Microscopy Deep Learning Proteins
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AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination.

Nature methods Nov 30, 2023
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other...
Crystallography Protein Conformation Mental Processes Artificial Intelligence
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Predicting multiple conformations via sequence clustering and AlphaFold2.

Nature Nov 13, 2023
AlphaFold2 (ref. ) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates, and disease-causing point mutations often ca...
Mutation Machine Learning Rhodobacter sphaeroides Protein Conformation Bacterial Proteins Cluster Analysis Protein Folding Sequence Alignment Proteins
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