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Calculating precise and accurate fre...

Shirts, Michael R.

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Calculating precise and accurate free energies in biomolecular systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Calculating precise and accurate free energies in biomolecular systems./
作者:	Shirts, Michael R.
面頁冊數:	197 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-11, Section: B, page: 5742.
Contained By:	Dissertation Abstracts International65-11B.
標題:	Chemistry, Pharmaceutical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3153076
ISBN:	0496135228

Calculating precise and accurate free energies in biomolecular systems.
Shirts, Michael R.

Calculating precise and accurate free energies in biomolecular systems. - 197 p.

Source: Dissertation Abstracts International, Volume: 65-11, Section: B, page: 5742.

Thesis (Ph.D.)--Stanford University, 2005.

Extensive work over the last 20 years has gone into developing the theoretical and computational apparatus to predict condensed phase free energies, but the current state of accuracy greatly limits its utility. For example, the threshold accuracy for pharmaceutically useful predictions of drug binding affinity is 0.5--1.0 kcal/mol. Obtaining such precision in a repeatable and consistent manner is an unsolved problem. In this dissertation, I bring together new and neglected methodologies and the sampling power available through distributed computing to demonstrate that absolute free energies can be calculated for atomistic models of biologically relevant systems with a useful level of precision and accuracy.

ISBN: 0496135228Subjects--Topical Terms:

550957
Chemistry, Pharmaceutical.

Calculating precise and accurate free energies in biomolecular systems.
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502 $a Thesis (Ph.D.)--Stanford University, 2005.
520 $a Extensive work over the last 20 years has gone into developing the theoretical and computational apparatus to predict condensed phase free energies, but the current state of accuracy greatly limits its utility. For example, the threshold accuracy for pharmaceutically useful predictions of drug binding affinity is 0.5--1.0 kcal/mol. Obtaining such precision in a repeatable and consistent manner is an unsolved problem. In this dissertation, I bring together new and neglected methodologies and the sampling power available through distributed computing to demonstrate that absolute free energies can be calculated for atomistic models of biologically relevant systems with a useful level of precision and accuracy.
520 $a I first present a rederivation of the neglected Bennett acceptance ratio method for computing free energies between two states, demonstrating using statistical methods that it is the most efficient method possible using certain sets of information from the two states. I also present data from various systems demonstrating that this method is in practice several times more efficient than other commonly used methods.
520 $a I evaluate the free energies of solvation of amino acid side chain analogs, testing three common biomolecular force fields and six published water models, the first real tests of the free energy behavior of these important biological models. I find that the computed solvation free energies systematically underestimate the affinity of these molecules in for all force fields and water models. I also demonstrate the under-optimized nature of the current force fields by reparameterizing a water model to yield zero average error and reduced RMS error over this set of molecules.
520 $a Finally, I present data of the binding of a set of eight ligands to the FKBP12 protein. Using the power of the Folding Home distributed computing network, several orders of magnitude more computational power than previously possible is applied to atomistic ligand binding prediction. Additionally, common uncontrolled approximations such as geometrical constraints on the protein or ligand are avoided and absolute instead of relative binding affinities are calculated. RMS error is 2.4 kcal/mol, with a RMS to a linear fit of 1.2 kcal/mol. These computations represent an important advance in rigorously physical drug binding prediction.
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