자료유형  

 학위논문

Control Number  

0017162422

International Standard Book Number  

9798384465928

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Chain, Connor.

Publication, Distribution, etc. (Imprint  

[S.l.] : Princeton University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

114 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

General Note  

Advisor: Gitai, Zemer.

Dissertation Note  

Thesis (Ph.D.)--Princeton University, 2024.

Summary, Etc.  

요약The discovery of new antibiotics is quickly being outpaced by the development of antibiotic resistance, leading towards a future in which bacterial infections are untreatable. Here we develop three unconventional strategies for the improvement of antibiotic discovery.First, we have discovered and characterized fluorofolin, the first narrow-spectrum antibiotic to be described for one of the most important human pathogens, P. aeruginosa. The method by which we developed this narrow spectrum antibiotic is fundamentally novel: all other narrow spectrum antibiotics target bacterial pathways specifically present in the pathogen of interest. But here we show that it is possible to instead target pathways that are specifically absent in the pathogen of choice. This dramatically expands the possible targets for future narrow-spectrum antibiotic development, which will have a significant impact on the field beyond the efficacy of our specific candidate compound.Next, we describe an innovative screening strategy to compare susceptibility to small molecule antibiotics across chemically rich and minimally defined media to elucidate antibiotics with novel targets. This approach led to the serendipitous discovery of M-789 2225, a novel antibiotic compound targeting the glycolytic pathway which only has antibiotic activity in the presence of carbohydrates. We hope that other molecules identified using this strategy will also yield novel antibiotic targets in the future.Finally, we developed a novel in silico pipeline to improve the success rate of finding candidate small molecule antibiotics. This approach improves on similar effort using machine learning by incorporating toxicity, novelty in mechanism, and desirable drug-like properties to our pipeline. Using this approach, we were able to identify novel small molecule antibiotics from in silico databases.Together these three efforts demonstrate that novel approaches to small molecule antibiotic discovery can lead us to uncovering previously unappreciated or missed targets. Hopefully these new approaches will inspire others to think outside of the box allowing us to discover novel antibiotic targets to combat the development and spread of antimicrobial resistant infections.

Subject Added Entry-Topical Term  

Molecular biology.

Subject Added Entry-Topical Term  

Cellular biology.

Subject Added Entry-Topical Term  

Biochemistry.

Index Term-Uncontrolled  

Antibiotic resistance

Index Term-Uncontrolled  

Fluorofolin

Index Term-Uncontrolled  

Screening strategy

Added Entry-Corporate Name  

Princeton University Molecular Biology

Host Item Entry  

Dissertations Abstracts International. 86-04B.

Electronic Location and Access  

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Control Number  

joongbu:657193