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Selectivity in de novo drug design.

McClellan, Richard G., Jr.

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Selectivity in de novo drug design.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Selectivity in de novo drug design./
作者:	McClellan, Richard G., Jr.
面頁冊數:	289 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-11, Section: B, page: 5537.
Contained By:	Dissertation Abstracts International64-11B.
標題:	Chemistry, Pharmaceutical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3113457

Selectivity in de novo drug design.
McClellan, Richard G., Jr.

Selectivity in de novo drug design. - 289 p.

Source: Dissertation Abstracts International, Volume: 64-11, Section: B, page: 5537.

Thesis (Ph.D.)--University of California, Santa Cruz, 2003.

Methodologies for the de novo design of selective drugs is presented. The design process includes the identification of steric and electrostatic differences between the target and non-target proteins. The differences are then used as to invent molecules that make use of the differences to obtain selectivity. The methodology is amenable to multiple target and non-target proteins.Subjects--Topical Terms:

550957
Chemistry, Pharmaceutical.

Selectivity in de novo drug design.
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245 1 0 $a Selectivity in de novo drug design.
300 $a 289 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-11, Section: B, page: 5537.
500 $a Chair: Ilan Benjamin.
502 $a Thesis (Ph.D.)--University of California, Santa Cruz, 2003.
520 $a Methodologies for the de novo design of selective drugs is presented. The design process includes the identification of steric and electrostatic differences between the target and non-target proteins. The differences are then used as to invent molecules that make use of the differences to obtain selectivity. The methodology is amenable to multiple target and non-target proteins.
520 $a Looking for differences between target and non-target proteins begins with the automatic detection of the protein active sites using the software program ASITE. The proteins are then aligned on active site residues. Next, the active sites are compared to identify differences. A steric difference can be either a volume or a surface unique to the target proteins. Electrostatic differences are obtained by comparing the molecular electrostatic potential of the target and non-target proteins computed on the common active site surface. Electrostatic differences are used to identify residues that are not conserved between the target and non-target proteins. Based upon these "3D-mutations", functional groups and site points---subgoals in the de novo design process---that differentiate the target and non-target residues are placed in the active site to be incorporated into the ligands. Finally, the de novo designed ligands are flexibly docked in the target and non-target proteins and their predicted binding affinity computed. Selectivity is determined by comparing the predicted binding affinity across the target and non-target proteins.
520 $a These methodologies were applied to two selective design challenges. In chapter 2, "De Novo Design for Steric Differences", the goal was to design selective cyclooxygenase-2 inhibitors. We show that our selective design methodology automatically perceived the differences between cyclooxygenase-1 (non-target) and cyclooxygenase-2 (target).
520 $a In Chapter 3, "De Novo Design for Electrostatic Differences", the methodologies that identify electrostatic differences are applied to the design of selective Pneumocystis carinii Dihydrofolate reductase (DHFR) inhibitors. The active sites of P. carinii and Homo sapiens DHFR were compared and six "3D-mutations" identified. INVENTON automatically placed functional groups and site-points---used by the de novo design algorithm---to make favorable interactions with the target protein and at best unfavorable, and at worst neutral, interactions with the non-target protein. Candidates were invented and analyzed by flexible docking in P. carinii and H. sapiens DHFR. About 36% of the invented candidates were classified as selective P. carinii DHFR inhibitors and about 56% were classified as selective H. sapiens DHFR inhibitors. (Abstract shortened by UMI.)
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773 0 $t Dissertation Abstracts International $g 64-11B.
790 1 0 $a Benjamin, Ilan, $e advisor
790 $a 0036
791 $a Ph.D.
792 $a 2003
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