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 报告题目: Innate immunity, autoimmunity, and antimicrobial peptides meet machine learning
  
 开始时间: 2018-09-18 10:30:00
  
 报告地点: 实验一楼第一会议室
  
 报 告 人: Prof. Gerard C. L. Wong
  
 主办单位: 材料科学与工程学院
  
 备    注:

报告题目:Innate immunity, autoimmunity, and antimicrobial peptides meet machine learning

报告时间:9月18日(星期二)10:30-11:30

报告人:Prof. Gerard C. L. Wong

报告地点:实验一楼第一会议室

联系人:刘润辉

 

报告人介绍

Biography

1987      B.S., California Institute of Technology

1994      Ph.D., University of California at Berkeley

1996      Postdoctoral Fellow, FOM Inst. of Atomic & Molecular Physics

1999      Postdoctoral Fellow, University of California at Santa Barbara

2000      Assistant Professor, University of Illinois at Urban

2006      Associate Professor, University of Illinois at Urban

2009      Professor, University of California, Los Angeles

Honors

2001      Beckman Young Investigator Award

2013      Director, Center for Early Biofilm Studies, Human Frontiers Science Program

2016      Fellow of the American Academy of Microbiology


报告介绍

Abstract

Antimicrobial peptides (AMPs) which kill pathogens have long been known to be immunomodulatory, but the precise mechanisms are not clear. The recognition of “pathogen associated molecular patterns” (PAMPs) by immune receptors has been one of the paradigmatic examples of specific binding in molecular biology. This is exemplified by the recognition of pathogen nucleic acids by Toll-Like Receptors (TLRs) of innate immunity, the ‘first responders’ to infection. We show that TLRs recognize and respond not to just chemical patterns on individual ligand nucleic acids they are designed to detect, but also to crystalline inflammas ome-like complexes

in which AMPs organize nucleic acids into spatially-periodic arrays for optimal presentation. Since the immune receptors are detecting crystalline arrangements of dsDNA and dsRNA in a multivalent manner, the resultant immune response can be highly amplified, leading to a broad range of health outcomes, including inflammation induced by heart disease, neutrophil apoptosis, and autoimmune diseases. We will also discuss how machine learning can be used to map out the undiscovered sequence space of AMPs, and how this knowledge may be used to create new molecules against multi-drug resistant pathogens by renovating existing obsolete antibiotics.