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Peptides

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pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

Analytical chemistry
In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides ap...

Purification and characterization of a protease from the visceral mass of Mytella charruana and its evaluation to obtain antimicrobial peptides.

Food chemistry
This work describes purification of a protease from the visceral mass of the mussel Mytella charruana as well as evaluation of its ability to hydrolyze milk casein to generate antimicrobial peptides. The enzyme showed pI of 4.1 and a single polypepti...

A novel aspartic protease from Rhizomucor miehei expressed in Pichia pastoris and its application on meat tenderization and preparation of turtle peptides.

Food chemistry
A novel aspartic protease gene (RmproA) was cloned from the thermophilic fungus Rhizomucor miehei CAU432 and expressed in Pichia pastoris. The RmproA was successfully expressed in P. pastoris as an active extracellular protease. High protease activit...

Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass Spectrometry.

Analytical chemistry
Emerging peptide array technologies are able to profile molecular activities within cell lysates. However, the structural diversity of peptides leads to inherent differences in peptide signal-to-noise ratios (S/N). These complex effects can lead to p...

Prediction of lysine propionylation sites using biased SVM and incorporating four different sequence features into Chou's PseAAC.

Journal of molecular graphics & modelling
Lysine propionylation is an important and common protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of propionylation, it is important to identify propionylated substrates and their corresp...

Machine learning-enabled discovery and design of membrane-active peptides.

Bioorganic & medicinal chemistry
Antimicrobial peptides are a class of membrane-active peptides that form a critical component of innate host immunity and possess a diversity of sequence and structure. Machine learning approaches have been profitably employed to efficiently screen s...

Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

Immunology
MHC class II molecules play a fundamental role in the cellular immune system: they load short peptide fragments derived from extracellular proteins and present them on the cell surface. It is currently thought that the peptide binds lying more or les...

FFLUX: Transferability of polarizable machine-learned electrostatics in peptide chains.

Journal of computational chemistry
The fully polarizable, multipolar, and atomistic force field protein FFLUX is being built from machine learning (i.e., kriging) models, each of which predicts an atomic property. Each atom of a given protein geometry needs to be assigned such a krigi...

Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides.

BMC bioinformatics
BACKGROUND: Several methods have been developed to predict the pathogenicity of missense mutations but none has been specifically designed for classification of variants in mtDNA-encoded polypeptides. Moreover, there is not available curated dataset ...