| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Bio-Nano Interfaces and Engineering Applications
|
| Presentation Title |
H-12: Machine Learning-guided Identification of Antimicrobial Peptides Targeting Oral Health |
| Author(s) |
Kalea Chu, Aya Kirahm Cloyd, Nur Ceren, Kyle Boone, Candan Tamerler |
| On-Site Speaker (Planned) |
Kalea Chu |
| Abstract Scope |
Nearly 3.5 billion people around the globe suffer from oral diseases including dental caries, periodontal disease, and edentulism. These diseases chronically impact individuals’ livelihood and overall health and create economic and systemic burdens. Oral diseases are characterized by pathogenic bacterial communities that encourage infection and further dybiosis in the oral cavity. Current treatment includes mainly systemic antibiotics, which are not specific to dental diseases, and raise concerns due to resistance. Targeted antimicrobial therapies aiming to restore balance of the oral microbiome is a critical need in restoring oral health. Our lab addresses this need by designing antimicrobial peptides (AMPs) with a hybrid machine learning approach to specifically target pathogens and establish explicit definitions between antimicrobial activities. The peptides were then assessed for antimicrobial ability and specificity in vitro. They demonstrate remarkable promise in the fight against oral pathogens and the development of generative machine learning approaches for target-specific AMPs. |
| Proceedings Inclusion? |
Planned: |