AIMC Topic: Protein Folding

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Deep convolutional networks for quality assessment of protein folds.

Bioinformatics (Oxford, England)
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...

DeepSF: deep convolutional neural network for mapping protein sequences to folds.

Bioinformatics (Oxford, England)
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...

NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers.

Bioinformatics (Oxford, England)
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...

An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier.

Bioinformatics (Oxford, England)
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...

[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
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 ...