AIMC Topic: Plant Proteins

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Identification and characterization of antioxidative peptides derived from simulated in vitro gastrointestinal digestion of walnut meal proteins.

Food research international (Ottawa, Ont.)
The aim of this study was to isolate and identify antioxidant peptides from defatted walnut meal proteins hydrolysates (DWMPH) prepared by simulated gastrointestinal digestion, and to evaluate the protective effect of the selected antioxidant peptide...

ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning.

The New phytologist
The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate betwee...

Rama: a machine learning approach for ribosomal protein prediction in plants.

Scientific reports
Ribosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological pro...

DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants.

Computers in biology and medicine
Plant disease outbreak is increasing rapidly around the globe and is a major cause for crop loss worldwide. Plants, in turn, have developed diverse defense mechanisms to identify and evade different pathogenic microorganisms. Early identification of ...

NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction.

Bioinformatics (Oxford, England)
UNLABELLED: The nucleotide binding site leucine-rich repeats (NBSLRRs) belong to one of the largest known families of disease resistance genes that encode resistance proteins (R-protein) against the pathogens of plants. Various defence mechanisms hav...

DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.

Nucleic acids research
There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provi...

DPP-IV inhibitory peptides from highland barley via machine learning and multi-scale validation.

Food chemistry
Highland barley has shown potential in regulating blood glucose and may serve as a natural source of dipeptidyl peptidase-IV (DPP-IV) inhibitors. In this study, machine learning (Gradient Boosting Decision Trees) and virtual screening were employed t...

Antioxidant bioactivity of sunflower protein hydrolysates in Caco-2 cells and in silico structural properties.

Food chemistry
Sunflower protein hydrolysate (SPH), with 95 % reduced phenolic content, was studied for its protective effects against oxidative stress in intestinal cells (Caco-2). Produced via alcalase hydrolysis, SPH's molecular weight, amino acid composition, a...

Compositional analysis of alternative protein blends using near and mid-infrared spectroscopy coupled with conventional and machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The non-invasive real-time analysis of the composition of alternative, plant-based protein sources is important to control high moisture extrusion processes and ensure the quality and texture of the final extrudates used in the elaboration of meat an...

Why Protein Modifications Matter for Digestibility: The Case of Ara h 1 Peanut Allergen and Trypsin Cleavage.

Journal of agricultural and food chemistry
Trypsin is the principal intestinal endopeptidase and proteomics digestion tool, yet the impact of protein modifications (PMs) on digestibility and allergenicity remains underexplored. We employed a proteomic approach to assess trypsin cleavage effic...