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Parallel computing methods or probin...

Stanford University.

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Parallel computing methods or probing biomolecular kinetics and thermodynamics.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Parallel computing methods or probing biomolecular kinetics and thermodynamics./
作者:	Jayachandran, Guha.
面頁冊數:	120 p.
附註:	Adviser: Vijay S. Pande.
Contained By:	Dissertation Abstracts International68-06B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3267538
ISBN:	9780549060802

Parallel computing methods or probing biomolecular kinetics and thermodynamics.
Jayachandran, Guha.

Parallel computing methods or probing biomolecular kinetics and thermodynamics. - 120 p.

Adviser: Vijay S. Pande.

Thesis (Ph.D.)--Stanford University, 2007.

Regarding distributed computing, recent client and server developments to the Folding@Home project are described. This project now has over 250,000 active clients participating worldwide and performs at over 800 teraflops.

ISBN: 9780549060802Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Parallel computing methods or probing biomolecular kinetics and thermodynamics.
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245 1 0 $a Parallel computing methods or probing biomolecular kinetics and thermodynamics.
300 $a 120 p.
500 $a Adviser: Vijay S. Pande.
500 $a Source: Dissertation Abstracts International, Volume: 68-06, Section: B, page: 3897.
502 $a Thesis (Ph.D.)--Stanford University, 2007.
520 $a Regarding distributed computing, recent client and server developments to the Folding@Home project are described. This project now has over 250,000 active clients participating worldwide and performs at over 800 teraflops.
520 $a Probing biomolecular kinetics and thermodynamics with computation has long been a goal of research. Two great aims in particular have been to elucidate protein folding dynamics and to accurately compute protein-ligand binding free energies. The computational expense of atomistic molecular dynamics simulation has been a key obstacle to achieving these objectives.
520 $a In this dissertation, the combination of massively parallel computing and new methods is applied toward the challenges. Methods, results, and biophysical insights gained are all described. The dissertation specifically covers three main areas: global distributed computing, novel methods for probing protein kinetics, and a new highly parallelized method for computing protein-ligand binding free energies.
520 $a Analyses of dynamics based on massive parallel simulation (tens of thousands of trajectories) of the 36 residue villin headpiece and a 46 residue domain of protein A are presented. The kinetics analysis methods presented include a new technique for probing the sensitivity of a system to given perturbations. The work also demonstrates the construction of a Markov model for the dynamics of villin. It demonstrates the power of Markov models for describing very long-timescale protein phenomena.
520 $a An analysis of the two helical proteins' folding mechanisms addresses whether the proteins' helices can form independently and what order they form in, with the findings agreeing with prior experiments. On the methodological side, the work introduces ways in which large datasets, of the type first collected in distributed computing studies, are amenable to the application of machine learning techniques.
520 $a The dissertation also presents a rigorous derivation of an easily parallelized method for computation of absolute binding free energies. It demonstrates this method, which utilizes docking and molecular dynamics, to compute the binding free energies for FKBP12 and a set of its ligands. Results were obtained orders of magnitude faster than older techniques and agreed reasonably with known experimental values.
590 $a School code: 0212.
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