日期：112年12月21日(四) 10:00~11:00

地點：工EV4024演講廳

演講者：Kim Donghyuk教授(韓國蔚山科學技術研究院UNIST)

演講題目：Deep Learning-based Analysis of Antimicrobial Resistance for Bacterial Pathogens

摘要：

Antimicrobial resistance (AMR) in pathogenic bacteria poses a significant threat to public health, yet there is still a need for development in the tools to deeply understand AMR genes based on genetic or structural information. In this study, we present an interactive web database named Blanket Overarching Antimicrobial-Resistance gene Database with Structural information (BOARDS, sbml.unist.ac.kr), a database that comprehensively includes 3,943 reported AMR gene information for 1,997 extended-spectrum beta-lactamase (ESBL) and 1,946 other genes as well as a total of 27,395 predicted protein structures. These structures, which include both wild-type AMR genes and their mutants were derived from 80,094 publicly available whole genome sequences. In addition, we developed the Rapid Analysis and Detection tool of Antimicrobial-Resistance (RADAR), a one-stop analysis pipeline to detect AMR genes across WGSs. By integrating BOARDS and RADAR, the AMR prevalence landscape for eight multi-drug resistant pathogens was reconstructed, leading to unexpected findings such as the pre-existence of the MCR genes before their official reports. Enzymatic structure prediction-based analysis revealed that the occurrence of mutations found in some ESBL genes was found to be closely related to the binding affinities with their antibiotic substrates. Overall, BOARDS can play a significant role in performing in-depth analysis on AMR.