<분자과학기술세미나> 안내 드립니다. 

 ♠ 주제 : Deciphering mechanism of action of antimicrobials using genomic profiling with machine learning approaches and chemical biology 
♠ 연사 : 이원식 교수님 (성균관대학교)         
♠ 일시 : 2021년 10월 12일 17:00~18:00  
♠ 초청 : 이평천교수님(내선 2461)  

Abstract 

 Identifying targets of antibacterial compounds remains a challenging step in the development of antibiotics. We have developed a two-pronged functional genomics approach to predict mechanism of action that uses mutant fitness data from antibiotictreated transposon libraries containing both upregulation and inactivation mutants. We treated a Staphylococcus aureus transposon library containing 690,000 unique insertions with 32 antibiotics. Upregulation signatures identified from directional biases in insertions revealed known molecular targets and resistance mechanisms for the majority of these. Because single-gene upregulation does not always confer resistance, we used a complementary machine-learning approach to predict the mechanism from inactivation mutant fitness profiles. This approach suggested the cell wall precursor Lipid II as the molecular target of the lysocins, a mechanism we have confirmed. We conclude that docking to membrane-anchored Lipid II precedes the selective bacteriolysis that distinguishes these lytic natural products, showing the utility of our approach for nominating the antibiotic mechanism of action. Using the chemical biology approaches, we also established the mechanism of Lysobactin which is a potent antibacterial natural product with in vivo efficacy against S. aureus. We show that lysobactin forms 1:1 complexes with Lipid I, Lipid II, and Lipid II(A)(WTA), substrates in the PG and wall teichoic acid (WTA) biosynthetic pathways. Therefore, lysobactin, like ramoplanin and teixobactin, recognizes the reducing end of lipid-linked cell wall precursors. 

♠ ZOOM LINK  

Topic: MST6016_ 분자과학기술세미나Ⅱ(O1413-1) [이원식교수님] 
Time: Oct 12, 2021 05:00 PM Seoul 

https://ajou-ac-kr.zoom.us/j/89130159878?pwd=TldZUHVPSzY4WHBWN1QyUmt6cUkrQT09 

Meeting ID: 891 3015 9878 
Passcode: 295649