Presentation Information
[P03-301]Machine learning-guided signal peptide design for human type I collagen fragment production and its tissue engineering applications.
○Jigyeong Son1,2, SooJung Chae3, GeunHyung Kim3, Han Min Woo1,2,4 (1. Department of Food Science and Biotechnology, Sungkyunkwan University (SKKU) (Korea), 2. Biofoundry Research Center, Sungkyunkwan university (SKKU) (Korea), 3. Department of Precision Medicine, Sungkyunkwan University (SKKU) (Korea), 4. Department of MetaBioHealth, Sungkyunkwan University (SKKU) (Korea))
Keywords:
: human collagen,biofoundry,machine learning,ESM-2,strain development
[Purpose]Collagen is widely used as a biomaterial in tissue engineering and regenerative medicine. However, growing concerns over animal-derived sources have motivated development of synthetic human collagen to minimize associated risks.
[Method]
Here, we present a synthetic biology strategy for microbial production of human type I collagen (hCOL1) fragment, integrating machine learning with biofoundry-driven workflows. Using an ESM-2 protein language model combined with biofoundry-based strain development, context-dependent synthetic signal peptides (SP) were designed and evaluated.
[Results]
This approach achieved the production and secretion of hCOL1 fragments in Corynebacterium glutamicum as an industrial host at 24.3 ± 3 mg/L after the third round of strain engineering, representing a 4.9-fold increase compared with the native SP in the first round. Next, scalable hCOL1 fragment production was demonstrated in 5-L fermenters, achieving titers of 857 ± 75 mg/L in fed-batch cultivations using glucose. To validate its potential as a tissue engineering biomaterial, the collagen-based biocomposite scaffolds were fabricated and used to culture human adipose-derived stem cells. The engineered matrices significantly enhanced proliferation and stemness marker expression, surpassing conventional animal-derived collagens.
[Conclusion]
Active learning for context- and host-dependent SP design can facilitate the secretion of not only collagen but also other proteins of interest.
[Method]
Here, we present a synthetic biology strategy for microbial production of human type I collagen (hCOL1) fragment, integrating machine learning with biofoundry-driven workflows. Using an ESM-2 protein language model combined with biofoundry-based strain development, context-dependent synthetic signal peptides (SP) were designed and evaluated.
[Results]
This approach achieved the production and secretion of hCOL1 fragments in Corynebacterium glutamicum as an industrial host at 24.3 ± 3 mg/L after the third round of strain engineering, representing a 4.9-fold increase compared with the native SP in the first round. Next, scalable hCOL1 fragment production was demonstrated in 5-L fermenters, achieving titers of 857 ± 75 mg/L in fed-batch cultivations using glucose. To validate its potential as a tissue engineering biomaterial, the collagen-based biocomposite scaffolds were fabricated and used to culture human adipose-derived stem cells. The engineered matrices significantly enhanced proliferation and stemness marker expression, surpassing conventional animal-derived collagens.
[Conclusion]
Active learning for context- and host-dependent SP design can facilitate the secretion of not only collagen but also other proteins of interest.
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