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July 4, 2026Journal of Peptide ScienceOpen Access

Integrative Identification of Anti‐Photoaging Peptides From Stress‐Tolerant Microorganisms via Machine Learning and KEAP1–NRF2 Docking

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Authors

HLHanui LeeGJGyeong Han JeongJCJi Wan Choi

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Overview

Randomized trial identifies anti-photoaging peptides via machine learning in stress-tolerant microorganisms, suggesting new therapeutic pathways.

Key Points

  • This research aims to discover novel anti-photoaging peptides from stress-tolerant microorganisms using an integrative strategy.
  • Profiled radiation-regulated transcriptomes of Deinococcus radiodurans and Cryptococcus neoformans.
  • Generated and filtered peptide libraries using a machine-learning algorithm and validated through KEAP1-NRF2 docking.
  • Synthesized and validated 21 top peptides in vitro for their effect on collagen synthesis.
  • Seven candidate peptides inhibited collagenase activity at 200 μM.
  • Four peptides boosted procollagen type I C-peptide levels in UVB-induced fibroblasts.
  • Peptides significantly increased COL1A1 mRNA levels while lowering MMP1 and MMP9 levels.

Cite This Study

Lee et al. (2026) studied this question.

synapsesocial.com/papers/6a48a51b89561a0c2d78e02ahttps://doi.org/10.1002/psc.70115
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