AIMC Topic: Protein Folding

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An end-to-end deep learning method for protein side-chain packing and inverse folding.

Proceedings of the National Academy of Sciences of the United States of America May 30, 2023
Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to ...
Deep Learning Molecular Conformation Protein Conformation Protein Folding Proteins Amino Acids
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Personal Precise Force Field for Intrinsically Disordered and Ordered Proteins Based on Deep Learning.

Journal of chemical information and modeling Dec 19, 2022
Intrinsically disordered proteins (IDPs) are proteins without a fixed three-dimensional (3D) structure under physiological conditions and are associated with Parkinson's disease, Alzheimer's disease, cancer, cardiovascular disease, amyloidosis, diabe...
Protein Conformation Protein Folding Alzheimer Disease Intrinsically Disordered Proteins Deep Learning Humans Molecular Dynamics Simulation
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Fast and accurate Ab Initio Protein structure prediction using deep learning potentials.

PLoS computational biology Sep 16, 2022
Despite the immense progress recently witnessed in protein structure prediction, the modeling accuracy for proteins that lack sequence and/or structure homologs remains to be improved. We developed an open-source program, DeepFold, which integrates s...
Software Algorithms Deep Learning Protein Conformation Protein Folding Databases, Protein Computational Biology Models, Molecular Proteins
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AlphaFold, Artificial Intelligence (AI), and Allostery.

The journal of physical chemistry. B Aug 17, 2022
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises a...
Intrinsically Disordered Proteins Protein Conformation Artificial Intelligence Molecular Dynamics Simulation Protein Folding Allosteric Regulation Humans
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Scaffolding protein functional sites using deep learning.

Science (New York, N.Y.) Jul 21, 2022
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without n...
Binding Sites Protein Engineering Protein Folding Protein Structure, Secondary Proteins Deep Learning Catalysis Protein Binding
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Learning Proteome Domain Folding Using LSTMs in an Empirical Kernel Space.

Journal of molecular biology Jun 15, 2022
The recognition of protein structural folds is the starting point for protein function inference and for many structural prediction tools. We previously introduced the idea of using empirical comparisons to create a data-augmented feature space calle...
Neural Networks, Computer Algorithms Protein Domains Protein Folding Proteome
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Protein sequence design with a learned potential.

Nature communications Feb 8, 2022
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network mo...
Protein Domains Amino Acid Sequence Protein Engineering Protein Folding Crystallography, X-Ray Deep Learning Models, Molecular Computer Simulation
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Deep learning methods for 3D structural proteome and interactome modeling.

Current opinion in structural biology Feb 6, 2022
Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in...
Protein Folding Computational Biology Deep Learning Proteome Protein Conformation
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Has DeepMind's AlphaFold solved the protein folding problem?

BioTechniques Feb 4, 2022
DeepMind released AlphaFold 2.0 in 2020, an artificial intelligence model to predict the structure of proteins, which could mean that proteins can be characterized without the need for tedious and costly lab analysis.
Models, Molecular Artificial Intelligence Proteins Protein Conformation Protein Folding
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Harnessing protein folding neural networks for peptide-protein docking.

Nature communications Jan 10, 2022
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...
Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Protein Binding Protein Interaction Mapping Protein Folding Proteins Protein Conformation, beta-Strand Peptides Neural Networks, Computer Amino Acid Sequence Models, Molecular Molecular Docking Simulation Software Binding Sites
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