AIMC Topic: Protein Folding

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Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds.

PLoS computational biology Nov 18, 2024
Peptides are an emerging modality for developing therapeutics that can either agonize or antagonize cellular pathways associated with disease, yet peptides often suffer from poor chemical and physical stability, which limits their potential. However,...
Machine Learning Peptides Peptide Library Amino Acid Sequence Disulfides Protein Folding Computational Biology Models, Molecular
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Ab initio characterization of protein molecular dynamics with AIBMD.

Nature Nov 6, 2024
Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods...
Protein Unfolding Nuclear Magnetic Resonance, Biomolecular Artificial Intelligence Molecular Dynamics Simulation Thermodynamics Time Factors Quantum Theory Protein Folding Machine Learning Proteins Protein Conformation Peptides Density Functional Theory
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Exploring structural diversity across the protein universe with The Encyclopedia of Domains.

Science (New York, N.Y.) Nov 1, 2024
The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts. Identifying domains can enabl...
Databases, Protein Deep Learning Protein Domains Proteins Protein Folding
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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...
Proteins Protein Conformation Spectrophotometry, Infrared Machine Learning Protein Structure, Secondary Protein Folding
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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...
Cryoelectron Microscopy Small Molecule Libraries Protein Folding Ligands Receptor, Serotonin, 5-HT2A Protein Conformation Serotonin 5-HT2 Receptor Agonists Serotonin 5-HT2 Receptor Antagonists Receptors, sigma Humans Molecular Docking Simulation Drug Design Drug Discovery Deep Learning
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Computational design of soluble and functional membrane protein analogues.

Nature Jun 19, 2024
De novo design of complex protein folds using solely computational means remains a substantial challenge. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topolog...
Humans Solubility Protein Stability Proof of Concept Study Protein Folding Amino Acid Motifs Membrane Proteins Computer-Aided Design Receptors, G-Protein-Coupled Deep Learning Models, Molecular Proteome
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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 Databases, Protein Models, Molecular Protein Conformation Machine Learning Humans Proteome
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Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.

Proteins May 29, 2024
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed ...
Artificial Intelligence Proteins Databases, Protein Machine Learning Amino Acid Sequence Intrinsically Disordered Proteins Protein Folding Algorithms Sequence Analysis, Protein Fourier Analysis Computational Biology
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One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates.

The journal of physical chemistry. B Apr 3, 2024
Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sa...
Thermodynamics Protein Folding Discriminant Analysis Molecular Dynamics Simulation Machine Learning Peptides
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Recent Progress of Protein Tertiary Structure Prediction.

Molecules (Basel, Switzerland) Feb 13, 2024
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI)...
Databases, Protein Models, Molecular Algorithms Protein Conformation Artificial Intelligence Computational Biology Protein Folding Software Proteins
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