ArticleScientific reports2024
De novo antioxidant peptide design via machine learning and DFT studies.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- Proteome-Scale Discovery and Multilevel Validation of Antioxidant Short Peptides fromAntioxidants (Basel, Switzerland) · 2026Article
- Pharmacological potential of the marine peptide cyclo(L-phenylalanyl-L-prolyl) against both multidrug-resistant, gram-negative Acinetobacter baumannii and gram-positive Staphylococcus aureus: structure - activity relationship, computational and experimental studies.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2026Article
- RLAnOxPeptide: an integrated framework combining transformer and reinforcement learning for efficient antioxidant peptide prediction and innovative design.Bioinformatics (Oxford, England) · 2026Article
- Comparative Biological and Functional Profiling of Single-Position Cysteine Substitutions in the HNP-1-Derived Peptide Pep-H AgainstAntibiotics (Basel, Switzerland) · 2026Article
- Artificial intelligence-driven discovery of bioactive peptides: Computational approaches and future perspectives.aBIOTECH · 2026Review
- Computational examination of magainin-I-KL mediated disruption of E. coli biofilms via CsgA amyloid interference.Archives of microbiology · 2026Article
- Structural Insights into the Receptor-Binding Domain of Bat Coronavirus HKU5-CoV-2: Implications for Zoonotic Transmission via ACE2.Animals : an open access journal from MDPI · 2026Article
- Facile Synthesis of Non-Proteinogenic Sulfur-Containing Cyclic Dipeptides and Their Structure-Antioxidant Activity Relationship.Chemistry, an Asian journal · 2026Article
- Antioxidant activity at the molecular level: exploring ways of action and computational tools to investigate them.Chemical science · 2025Review
- In Silico Screening and Identification of Functional Peptides from Yak Bone Collagen Hydrolysates: Antioxidant and Osteoblastic Activities.International journal of molecular sciences · 2025Article
- Correlation of physicochemical properties with antioxidant activity in phenol and thiophenol analogues.Scientific reports · 2025Article
- AOP-DRL: A deep representation learning framework for the computational prediction of antioxidant peptides.Computational and structural biotechnology journal · 2025Article
- RoseTTAFold diffusion-guided short peptide design: a case study of binders against Keap1/Nrf2.Computational and structural biotechnology journal · 2025Article
- Peptide nanozymes: An emerging direction for functional enzyme mimics.Bioactive materials · 2024Review
- Intracellular bactericidal activity and action mechanism of MDP1 antimicrobial peptide against VRSA and MRSA in human endothelial cells.Frontiers in microbiology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antioxidant peptides (AOPs) are highly valued in food and pharmaceutical industries due to their significant role in human function. This study introduces a novel approach to identifying robust AOPs using a deep generative model based on sequence representation. Through filtration with a deep-learning classification model and subsequent clustering via the Butina cluster algorithm, twelve peptides (GP1-GP12) with potential antioxidant capacity were predicted. Density functional theory (DFT) calculations guided the selection of six peptides for synthesis and biological experiments. Molecular orbital representations revealed that the HOMO for these peptides is primarily localized on the indole segment, underscoring its pivotal role in antioxidant activity. All six synthesized peptides exhibited antioxidant activity in the DPPH assay, while the hydroxyl radical test showed suboptimal results. A hemolysis assay confirmed the non-hemolytic nature of the generated peptides. Additionally, an in silico investigation explored the potential inhibitory interaction between the peptides and the Keap1 protein. Analysis revealed that ligands GP3, GP4, and GP12 induced significant structural changes in proteins, affecting their stability and flexibility. These findings highlight the capability of machine learning approaches in generating novel antioxidant peptides.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.