AIMC Topic: Protein Folding

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State-of-the-art web services for de novo protein structure prediction.

Briefings in bioinformatics May 20, 2021
Residue coevolution estimations coupled to machine learning methods are revolutionizing the ability of protein structure prediction approaches to model proteins that lack clear homologous templates in the Protein Data Bank (PDB). This has been patent...
Software Animals Humans Machine Learning Databases, Protein Models, Molecular Computational Biology Proteins Sequence Analysis, Protein Protein Conformation Protein Folding
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ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm.

Briefings in bioinformatics May 20, 2021
As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artifici...
Algorithms Reproducibility of Results Neural Networks, Computer Artificial Intelligence Proteins Protein Folding Protein Interaction Maps Computational Biology
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A novel sequence alignment algorithm based on deep learning of the protein folding code.

Bioinformatics (Oxford, England) May 1, 2021
MOTIVATION: From evolutionary interference, function annotation to structural prediction, protein sequence comparison has provided crucial biological insights. While many sequence alignment algorithms have been developed, existing approaches often ca...
Algorithms Deep Learning Protein Folding Sequence Alignment Software
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GraphQA: protein model quality assessment using graph convolutional networks.

Bioinformatics (Oxford, England) Apr 20, 2021
MOTIVATION: Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein's structure can be time-consuming, prohibitively expensive and not always possible. Alt...
Neural Networks, Computer Proteins Protein Folding
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Protein sequence design by conformational landscape optimization.

Proceedings of the National Academy of Sciences of the United States of America Mar 16, 2021
The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowe...
Protein Folding Protein Conformation Thermodynamics Models, Molecular Neural Networks, Computer Proteins
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'The game has changed.' AI triumphs at protein folding.

Science (New York, N.Y.) Dec 4, 2020
Protein Folding Artificial Intelligence Protein Conformation Software
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Fold-LTR-TCP: protein fold recognition based on triadic closure principle.

Briefings in bioinformatics Dec 1, 2020
As an important task in protein structure and function studies, protein fold recognition has attracted more and more attention. The existing computational predictors in this field treat this task as a multi-classification problem, ignoring the relati...
Software Computational Biology Proteins Databases, Protein Protein Folding Artificial Intelligence
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MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks.

Briefings in bioinformatics Dec 1, 2020
Protein fold recognition is one of the most critical tasks to explore the structures and functions of the proteins based on their primary sequence information. The existing protein fold recognition approaches rely on features reflecting the character...
Proteins Protein Folding Algorithms Support Vector Machine Neural Networks, Computer
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DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks.

Briefings in bioinformatics Sep 25, 2020
Protein fold recognition is critical for studying the structures and functions of proteins. The existing protein fold recognition approaches failed to efficiently calculate the pairwise sequence similarity scores of the proteins in the same fold shar...
Deep Learning Support Vector Machine Algorithms Proteins Protein Folding
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Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13).

Proteins Dec 1, 2019
We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free-modeling (FM) methods which combine the predictions of three neural networks. All three systems were guide...
Proteins Protein Folding Algorithms Databases, Protein Neural Networks, Computer Models, Molecular Computational Biology Protein Conformation
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