AIMC Topic: Protein Folding

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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...
Algorithms Proteins Protein Folding Support Vector Machine Deep Learning
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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...
Protein Folding Protein Conformation Proteins Computational Biology Models, Molecular Databases, Protein Neural Networks, Computer Algorithms
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Deep convolutional networks for quality assessment of protein folds.

Bioinformatics (Oxford, England) Dec 1, 2018
MOTIVATION: The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of protein models. Most assessment methods rank candidate models using heavily engineered structural features, define...
Computational Biology Proteins Algorithms Neural Networks, Computer Protein Folding
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DeepSF: deep convolutional neural network for mapping protein sequences to folds.

Bioinformatics (Oxford, England) Apr 15, 2018
MOTIVATION: Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a te...
Sequence Alignment Sequence Analysis, Protein Protein Folding Humans Neural Networks, Computer Models, Molecular Computational Biology Software Animals
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NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers.

Bioinformatics (Oxford, England) Aug 1, 2017
MOTIVATION: Recent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact pr...
Protein Folding Models, Molecular Computational Biology Protein Conformation Bayes Theorem Neural Networks, Computer Machine Learning
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An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier.

Bioinformatics (Oxford, England) Mar 15, 2017
MOTIVATION: Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learn...
Computational Biology Algorithms Proteins Support Vector Machine Software Protein Folding
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[Enhancement of Coprinus cinereus peroxidase in Pichia pastoris by co-expression chaperone PDI and Ero1].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology Dec 1, 2015
The 1,095 bp gene encoding peroxidase from Coprinus cinereus was synthesized and integrated into the genome of Pichia pastoris with a highly inducible alcohol oxidase. The recombinant CiP (rCiP) fused with the a-mating factor per-pro leader sequence ...
Pichia Cytoplasm Saccharomyces cerevisiae Oxidoreductases Acting on Sulfur Group Donors Protein Disulfide-Isomerases Mating Factor Coprinus Culture Media Mass Spectrometry Peptides Peroxidases Glycoproteins Fermentation Saccharomyces cerevisiae Proteins Protein Folding
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