| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Bio-Nano Interfaces and Engineering Applications
|
| Presentation Title |
Advancement of Targeted Antimicrobial Peptides Through Machine Learning: Toward Microbial Balance and Species Specificity |
| Author(s) |
Kalea Chu, Aya Cloyd, Kyle Boone, Candan Tamerler |
| On-Site Speaker (Planned) |
Kalea Chu |
| Abstract Scope |
Nearly 45% of the global population suffers from oral diseases including caries and periodontal disease. Chronic recurrence of these diseases, caused by pathogenic biofilms, induces a cycle of need for treatment and reduced patient quality of life. Current treatment consists of antibiotics, many of which lack specificity and are of high risk for raising bacterial resistance in the oral cavity. Therefore, targeted antimicrobial therapies are needed to combat such biofilms and restore microbial balance. With a hybridized machine learning approach, our lab previously designed antimicrobial peptides (AMPs) that demonstrated target specificity between bacterial species. Now, we begin a new iteration, focusing on select physiochemical features to create AMPs with heightened antimicrobial ability and specificity between oral pathogens. These AMPs signify steps toward personalized therapeutics that restore microbial balance without risk of inducing resistance in patients, as well as the potential machine learning approaches hold in creating such treatments. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Nanotechnology, Biomaterials |