首页|Data on Antioxidants Reported by Abdolvahab Seif and Colleagues (De novo antioxi dant peptide design via machine learning and DFT studies)

Data on Antioxidants Reported by Abdolvahab Seif and Colleagues (De novo antioxi dant peptide design via machine learning and DFT studies)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Drugs and Therapies-Antioxidants is the subject of a report.According to news reporting originating in Padua, Italy, by NewsRx journalists, research stated, "Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their sign ificant role in human function. This study introduces a novel approach to identi fying robust AOPs using a deep generative model based on sequence representation ." The news reporters obtained a quote from the research, "Through filtration with a deep-learning classification model and subsequent clustering via the Butina cl uster algorithm, twelve peptides (GP1-GP12) with potential antioxidant capacity were predicted. Density functional theory (DFT) calculations guided the selectio n of six peptides for synthesis and biological experiments. Molecular orbital re presentations revealed that the HOMO for these peptides is primarily localized o n the indole segment, underscoring its pivotal role in antioxidant activity. All six synthesized peptides exhibited antioxidant activity in the DPPH assay, whil e the hydroxyl radical test showed suboptimal results. A hemolysis assay confirm ed the nonhemolytic nature of the generated peptides. Additionally, an in silic o investigation explored the potential inhibitory interaction between the peptid es and the Keap1 protein. Analysis revealed that ligands GP3, GP4, and GP12 indu ced significant structural changes in proteins, affecting their stability and fl exibility." According to the news reporters, the research concluded: "These findings highlig ht the capability of machine learning approaches in generating novel antioxidant peptides."

PaduaItalyEuropeAntioxidantsCybo rgsDrugs and TherapiesEmerging TechnologiesMachine LearningPeptidesPep tides and ProteinsProtective AgentsProteinsProteomics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Apr.2)