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Bioinformatics Methods for Natural P...

Mohimani, Hosein.

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Bioinformatics Methods for Natural Product Discovery.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bioinformatics Methods for Natural Product Discovery./
作者:	Mohimani, Hosein.
面頁冊數:	92 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
Contained By:	Dissertation Abstracts International75-02B(E).
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3598505
ISBN:	9781303471506

Bioinformatics Methods for Natural Product Discovery.
Mohimani, Hosein.

Bioinformatics Methods for Natural Product Discovery. - 92 p.

Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2013.

Most of new chemical entities introduced as antibacterials over the last decades are derivated from natural products produced by living organisms. Some of the most effective antibiotics are peptidic natural products. The traditional process of natural products discovery is to elucidate strcuture of the compound of interest by chemical assays such as Nuclear Magnetic Resonance and Crystallography, and association of the chemical compound to its biosynthetic gene cluster by genome manipulation. This process is long, laborious, and requires large amounts of highly purified material. Recent advances in mass spectrometry has enabled natural product discovery from picograms of material. In this thesis we propose various computational techniques to aid natural product discovery by computational mass spectrometry.

ISBN: 9781303471506Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Bioinformatics Methods for Natural Product Discovery.
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500 $a Source: Dissertation Abstracts International, Volume: 75-02(E), Section: B.
500 $a Advisers: Pavel A. Pavezner; Alexander Vardy.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2013.
520 $a Most of new chemical entities introduced as antibacterials over the last decades are derivated from natural products produced by living organisms. Some of the most effective antibiotics are peptidic natural products. The traditional process of natural products discovery is to elucidate strcuture of the compound of interest by chemical assays such as Nuclear Magnetic Resonance and Crystallography, and association of the chemical compound to its biosynthetic gene cluster by genome manipulation. This process is long, laborious, and requires large amounts of highly purified material. Recent advances in mass spectrometry has enabled natural product discovery from picograms of material. In this thesis we propose various computational techniques to aid natural product discovery by computational mass spectrometry.
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