AI Medical Compendium Topic

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Sequence Analysis, Protein

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CoABind: a novel algorithm for Coenzyme A (CoA)- and CoA derivatives-binding residues prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Coenzyme A (CoA)-protein binding plays an important role in various cellular functions and metabolic pathways. However, no computational methods can be employed for CoA-binding residues prediction.

SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification.

Bioinformatics (Oxford, England)
SUMMARY: Artificial intelligence tools are gaining more and more ground each year in bioinformatics. Learning algorithms can be taught for specific tasks by using the existing enormous biological databases, and the resulting models can be used for th...

Protein threading using residue co-variation and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Template-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain...

DeepFam: deep learning based alignment-free method for protein family modeling and prediction.

Bioinformatics (Oxford, England)
MOTIVATION: A large number of newly sequenced proteins are generated by the next-generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to cha...

DeepSig: deep learning improves signal peptide detection in proteins.

Bioinformatics (Oxford, England)
MOTIVATION: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.

Protein classification using modified n-grams and skip-grams.

Bioinformatics (Oxford, England)
MOTIVATION: Classification by supervised machine learning greatly facilitates the annotation of protein characteristics from their primary sequence. However, the feature generation step in this process requires detailed knowledge of attributes used t...

DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Significant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the ...

pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.

Bioinformatics (Oxford, England)
MOTIVATION: For in-depth understanding the functions of proteins in a cell, the knowledge of their subcellular localization is indispensable. The current study is focused on human protein subcellular location prediction based on the sequence informat...

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...

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

Bioinformatics (Oxford, England)
MOTIVATION: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often ...