⋅The Suaeda glauca is widely recognized for its robust physiological resilience, with previous studies confirming the potent pharmacological effects of its crude extracts. While these extracts demonstrate clear antioxidant and antimicrobial properties, the specific peptide-level drivers of such activities remain largely uncharacterized. In this study, we implemented a bioinformatics workflow starting with de novo transcriptome assembly.

To prioritize bioactive sequences, we focused on the physiochemical features of peptides. Each candidate was characterized by its intrinsic molecular properties, including isoelectric point, 2nd structure, and aggregation. We then utilized supervised learning algorithms to map these biochemical signatures to functional potential, assigning bioactivity scores to the top-tier candidates. To ensure biological relevance, we incorporated a pan-genomic framework to filter for evolutionary conservation and analyzed DEGs to pinpoint peptides actively synthesized between tissues.

The integration of computational modeling with genomic validation successfully filtered the extensive sequence space down to a select group of sequence-verified peptide candidates. This methodology demonstrates an efficient strategy for converting the broad biological value of S. glaucainto specific molecular assets. These newly identified peptides represent promising leads for the development of innovative ingredients in the pharmaceutical and high-end skincare sectors.

⋅Funding: This work was supported by a grant from the Honam National Institute of Biological Resources (HNIBR), funded by the Ministry of Climate, Energy and Environment (MCEE), Republic of Korea (HNIBR2026-B-1-12), and by the Korea Environment Industry & Technology Institute (KEITI) through the project Advancement of Multi-ministerial National Biological Research Resources, funded by the Ministry of Climate, Energy and Environment (MCEE) (RS-2023-00230404).