| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Bio-Nano Interfaces and Engineering Applications
|
| Presentation Title |
Machine Learning Guided Biomimetic Peptide Design for Heterogenous Interfaces |
| Author(s) |
Candan Tamerler |
| On-Site Speaker (Planned) |
Candan Tamerler |
| Abstract Scope |
Biological systems offer inspiration for design strategies where molecular building blocks can be coupled into functional interfaces and material design. Small peptides can mimic the functions of proteins, be an integral component of an hybrid system, generate biomimetic platforms and modulate microenvironment. Biomimetic peptides and peptide hybrids have the ability to present tremendous structural and functional diversity. Our group has been exploring peptide design from single to multi-functional domains to expand their bioactivity and harness engineered peptides in different applications. By merging machine learning integrated computational approaches with the experimental design, we search for targeted properties of synthetic and protein derived short peptides, explore their self-organization and validate design conditions over the multitude of interactions. This presentation will summarize our approach in combining the self-assembly with bioactivity targeting heterogenous tissue interfaces as well as develop hybrid systems to modulate the environment through their metal chelation and catalysis properties. |
| Proceedings Inclusion? |
Planned: |