AI Medical Compendium Topic:
Amino Acid Sequence

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Protein function prediction from protein-protein interaction network using gene ontology based neighborhood analysis and physico-chemical features.

Journal of bioinformatics and computational biology
Protein Function Prediction from Protein-Protein Interaction Network (PPIN) and physico-chemical features using the Gene Ontology (GO) classification are indeed very useful for assigning biological or biochemical functions to a protein. They also lea...

Extraction, identification and structure-activity relationship of antioxidant peptides from sesame (Sesamum indicum L.) protein hydrolysate.

Food research international (Ottawa, Ont.)
To elucidate the sequence, origin and structure-activity relationship of antioxidant peptides from sesame protein, sesame protein was hydrolysed by a dual-enzyme system comprised alcalase and trypsin, then this hydrolysate was fractionated by ultrafi...

Predicting improved protein conformations with a temporal deep recurrent neural network.

PloS one
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally re...

Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.

Journal of theoretical biology
The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this study, we propose a novel model called MACC-PSSM by integrating Moran autocorrelat...

Detecting Proline and Non-Proline Cis Isomers in Protein Structures from Sequences Using Deep Residual Ensemble Learning.

Journal of chemical information and modeling
It has been long established that cis conformations of amino acid residues play many biologically important roles despite their rare occurrence in protein structure. Because of this rarity, few methods have been developed for predicting cis isomers f...

Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning.

Analytical chemistry
The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this predict...

Antibacterial activity and its mechanisms of a recombinant Funme peptide against Cronobacter sakazakii in powdered infant formula.

Food research international (Ottawa, Ont.)
Cronobacter sakazakii (Cs) is a typical foodborne bacterium that infect powdered infant formula (PIF) worldwide. In this study, a recombinant antimicrobial peptide, branded as Funme peptide (FP)was applied to protect PIF from Cs contamination. The re...

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Journal of proteome research
Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to ...

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences.

Molecules (Basel, Switzerland)
Machine learning based predictions of protein⁻protein interactions (PPIs) could provide valuable insights into protein functions, disease occurrence, and therapy design on a large scale. The intensive feature engineering in most of these methods make...

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.

Frontiers in immunology
Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibact...