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Published on: January 12, 2009
Integrative Identification of Anti-Photoaging Peptides From Stress-Tolerant Microorganisms via Machine Learning and
Hanui Lee1, Gyeong Han Jeong1, Ji Wan Choi1,2
1Research Division for Biotechnology, Advanced Radiation Technology Institute (ARTI), Korea Atomic Energy Research Institute (KAERI), Jeongeup, Republic of Korea.
Abstract:
Excess reactive oxygen species generated by ultraviolet exposure cause photoaging by degrading collagen and inhibiting its synthesis. This study presents a comprehensive strategy connecting the biological stress responses of γ-irradiated microorganisms to the discovery of novel anti-photoaging peptides. We profiled the radiation-regulated transcriptomes of Deinococcus radiodurans and Cryptococcus neoformans, focusing on DNA repair and oxidative stress responses. From these datasets, peptide libraries were generated in silico, filtered for biochemical properties, and prioritized using a seven-classifier machine-learning algorithm. Structural validity was established using Rosetta FlexPepDocking against the KEAP1-NRF2 pocket, which identified 48 docking-positive sequences. We then synthesized the top 21 peptides and subjected them to in vitro validation. Seven of these candidate peptides inhibited collagenase activity at 200 μM. Among them, four peptides dose-dependently increased the procollagen type I C-peptide level in ultraviolet B-induced fibroblasts. Furthermore, these peptides significantly elevated COL1A1 mRNA levels while simultaneously reducing MMP1 and MMP9 transcript and protein levels. In summary, this study provides an integrated strategy that combines omics, machine learning, and docking to discover promising peptide candidates, which were validated through in vitro assessments. This approach offers promising anti-photoaging candidates that can be applied to other oxidative stress pathways and biological resources.
Insights
Researchers discovered new anti-photoaging peptides by studying stress responses in irradiated microbes. This innovative approach identified peptides that protect collagen and combat skin aging caused by UV radiation.
Area of Science:
- Biochemistry
- Molecular Biology
- Bioinformatics
Background:
- Ultraviolet (UV) radiation exposure generates excess reactive oxygen species (ROS), leading to photoaging through collagen degradation and inhibited synthesis.
- Understanding biological stress responses can uncover novel therapeutic targets for skin aging.
Purpose of the Study:
- To develop a comprehensive strategy for discovering novel anti-photoaging peptides.
- To leverage the stress response mechanisms of γ-irradiated microorganisms for peptide discovery.
- To validate the efficacy of identified peptides in preventing UV-induced skin damage.
Main Methods:
- Transcriptome profiling of γ-irradiated Deinococcus radiodurans and Cryptococcus neoformans, focusing on DNA repair and oxidative stress.
- In silico generation and filtering of peptide libraries, prioritized using a machine-learning algorithm.
- Structural validation via Rosetta FlexPepDocking and in vitro assays for collagenase inhibition and procollagen synthesis.
Main Results:
- Identified 48 potential peptide candidates through docking simulations.
- Synthesized and validated 21 peptides in vitro, with seven inhibiting collagenase activity.
- Four peptides demonstrated dose-dependent increases in procollagen type I C-peptide levels in UVB-induced fibroblasts.
- These peptides also elevated COL1A1 mRNA and reduced MMP1/MMP9 expression.
Conclusions:
- An integrated omics, machine learning, and docking strategy effectively discovers anti-photoaging peptides.
- Validated peptides show promise in protecting collagen and mitigating UV-induced skin aging.
- This approach can be extended to identify candidates for other oxidative stress-related conditions.