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Solvation Thermodynamic Mapping in C...

Ramsey, Steven.

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Solvation Thermodynamic Mapping in Computer Aided Drug Design.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Solvation Thermodynamic Mapping in Computer Aided Drug Design./
Author:	Ramsey, Steven.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	98 p.
Notes:	Source: Dissertation Abstracts International, Volume: 80-04(E), Section: B.
Contained By:	Dissertation Abstracts International80-04B(E).
Subject:	Biophysics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10979800
ISBN:	9780438732483

Solvation Thermodynamic Mapping in Computer Aided Drug Design.
Ramsey, Steven.

Solvation Thermodynamic Mapping in Computer Aided Drug Design. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 98 p.

Source: Dissertation Abstracts International, Volume: 80-04(E), Section: B.

Thesis (Ph.D.)--City University of New York, 2019.

The displacement of water from surfaces upon biomolecular recognition and association makes a significant contribution to the free energy changes of these processes. We therefore posit that accurate characterization of local structural and thermodynamic molecular water properties can improve computational model accuracy and predictivity of recognition and association processes. In this thesis, we discuss Solvation Thermodynamic Mapping (STM) methods that we have developed using inhomogeneous fluid solvation theory (IST) to better characterize active site water structural and thermodynamic properties on protein surfaces and the open source tools that we have developed, GIST-CPPTRAJ and SSTMap, which implement these methods which we have distributed to both the academic and industrial scientific community. These methods include a nearest neighbor approximation for water entropies, a significant improvement over previous entropy formulations. We then discuss our application of these tools to the rational modification of (--)-stepholidine, a lead compound for human dopamine receptor 3 (D3R), which led to a handful of promising analogues. Finally, we describe a new method of creating pharmacophores from solvation thermodynamic maps applied retrospectively to 7 protein targets. The results documented here demonstrate promising applications of STM methods for prospective drug design. In our conclusions, we discuss potential improvements to the molecular modeling work with the goal of improving accuracy of predictions in prospective drug design projects.

ISBN: 9780438732483Subjects--Topical Terms:

518360
Biophysics.

Solvation Thermodynamic Mapping in Computer Aided Drug Design.
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500 $a Adviser: Thomas P. Kurtzman.
502 $a Thesis (Ph.D.)--City University of New York, 2019.
520 $a The displacement of water from surfaces upon biomolecular recognition and association makes a significant contribution to the free energy changes of these processes. We therefore posit that accurate characterization of local structural and thermodynamic molecular water properties can improve computational model accuracy and predictivity of recognition and association processes. In this thesis, we discuss Solvation Thermodynamic Mapping (STM) methods that we have developed using inhomogeneous fluid solvation theory (IST) to better characterize active site water structural and thermodynamic properties on protein surfaces and the open source tools that we have developed, GIST-CPPTRAJ and SSTMap, which implement these methods which we have distributed to both the academic and industrial scientific community. These methods include a nearest neighbor approximation for water entropies, a significant improvement over previous entropy formulations. We then discuss our application of these tools to the rational modification of (--)-stepholidine, a lead compound for human dopamine receptor 3 (D3R), which led to a handful of promising analogues. Finally, we describe a new method of creating pharmacophores from solvation thermodynamic maps applied retrospectively to 7 protein targets. The results documented here demonstrate promising applications of STM methods for prospective drug design. In our conclusions, we discuss potential improvements to the molecular modeling work with the goal of improving accuracy of predictions in prospective drug design projects.
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