Generative β-hairpin design using a residue-based physicochemical property landscape DOI Creative Commons
Vardhan Satalkar, Gemechis D. Degaga, Wei Li

и другие.

Biophysical Journal, Год журнала: 2024, Номер 123(17), С. 2790 - 2806

Опубликована: Фев. 1, 2024

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de are largely based on sequence homology can be restricted evolutionarily derived protein sequences lack physicochemical context essential folding. Generative machine learning promising way to synthesize theoretical data on, but unique from, observable universe. In this study, we created tested custom generative adversarial network intended fold into β-hairpin secondary structure. This deep neural model designed establish preliminary foundation of approach conformational properties 20 canonical amino acids, example, hydrophobicity residue volume, using extant structure-specific from PDB. The beta robustly distinguishes structures β hairpin α helix intrinsically disordered peptides with an accuracy up 96% generates artificial minimum identities around 31% 50% when compared against current NCBI PDB nonredundant databases, respectively. These results highlight specifically anchored by property features acids expand sequence-to-structure landscape proteins beyond evolutionary limits.

Язык: Английский

Production of bioactive peptides by high-voltage pulsed electric field: Protein extraction, mechanism, research status and collaborative application DOI

Z. C. Kang,

Zhicheng Wang, Jingjing Wang

и другие.

Food Chemistry, Год журнала: 2025, Номер unknown, С. 144139 - 144139

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Novel bioactive peptides from ginger rhizome: Integrating in silico and in vitro analysis with mechanistic insights through molecular docking DOI Creative Commons
Kruttika Purohit, Rachana Pathak,

E. Russell Hayes

и другие.

Food Chemistry, Год журнала: 2025, Номер unknown, С. 144432 - 144432

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Systematic Evaluation and Identification of Anti-Inflammatory and Anti-Aging Ginseng Peptides for Skincare Applications DOI Creative Commons

Z. C. Xia,

Wei Liu,

Fang Zeng

и другие.

Cosmetics, Год журнала: 2025, Номер 12(2), С. 85 - 85

Опубликована: Апрель 21, 2025

This study explores the potential of ginseng-derived peptides (GPs) as multifunctional bioactive agents for skincare. Unlike previous research into ginseng saponins and polysaccharides, we identified that extracts containing water-soluble small molecules polypeptides exhibit potent antioxidant, anti-inflammatory, anti-aging properties. In vitro assays revealed peptide extract (GPE) reduced reactive oxygen species (ROS) inflammatory cytokines (IL-6, TNF-α, IL-1β) in RAW264.7 macrophages while enhancing collagen synthesis human skin fibroblasts (HSFs). Validation using 3D epidermal dermal models further confirmed GPE’s ability to mitigate UV-induced damage, restore barrier proteins (filaggrin, loricrin), increase content. addition, screened 19 candidate from machine learning prioritized their interaction with aging inflammation-related targets. Three (QEGIYPNNDLYRPK, VDCPTDDATDDYRLK, ADEVVHHPLDKSSEVE) demonstrated significant collagen-promoting, anti-inflammatory effects cellular models. These findings highlight efficacy computational approaches identifying natural ingredients, positioning promising candidates innovative cosmeceutical formulations targeting inflammaging rejuvenation.

Язык: Английский

Процитировано

0
Progress and future of the computational design of antimicrobial peptides (AMPs): bio-inspired functional molecules DOI Creative Commons
Miroslava Nedyalkova, Andrew S. Paluch,

Diana Potes Vecini

и другие.

Digital Discovery, Год журнала: 2023, Номер 3(1), С. 9 - 22

Опубликована: Ноя. 29, 2023

The effectiveness of antibiotics is greatly enhanced by their ability to target invasive organisms involved in the ancient evolutionary battle between hosts and pathogens.

Язык: Английский

Процитировано

7
Generative β-hairpin design using a residue-based physicochemical property landscape DOI Creative Commons
Vardhan Satalkar, Gemechis D. Degaga, Wei Li

и другие.

Biophysical Journal, Год журнала: 2024, Номер 123(17), С. 2790 - 2806

Опубликована: Фев. 1, 2024

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de are largely based on sequence homology can be restricted evolutionarily derived protein sequences lack physicochemical context essential folding. Generative machine learning promising way to synthesize theoretical data on, but unique from, observable universe. In this study, we created tested custom generative adversarial network intended fold into β-hairpin secondary structure. This deep neural model designed establish preliminary foundation of approach conformational properties 20 canonical amino acids, example, hydrophobicity residue volume, using extant structure-specific from PDB. The beta robustly distinguishes structures β hairpin α helix intrinsically disordered peptides with an accuracy up 96% generates artificial minimum identities around 31% 50% when compared against current NCBI PDB nonredundant databases, respectively. These results highlight specifically anchored by property features acids expand sequence-to-structure landscape proteins beyond evolutionary limits.

Язык: Английский

Процитировано

2