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Protein dynamics and its prediction ...

Trott, Oleg.

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Protein dynamics and its prediction using machine learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Protein dynamics and its prediction using machine learning./
作者:	Trott, Oleg.
面頁冊數:	64 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 1735.
Contained By:	Dissertation Abstracts International65-04B.
標題:	Biophysics, General. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3129038
ISBN:	0496762987

Protein dynamics and its prediction using machine learning.
Trott, Oleg.

Protein dynamics and its prediction using machine learning. - 64 p.

Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 1735.

Thesis (Ph.D.)--Columbia University, 2004.

In the first part of this dissertation, chemical exchange and the use of R1rho rotating frame relaxation experiment for its study are discussed. Beginning with the Bloch-McConnell equations, new expressions are derived for the spin relaxation rate constant in the rotating frame, R1rho for chemical exchange between two or more sites that have distinct magnetic environments and Larmor frequencies. The 2-site results are accurate provided that the spin relaxation decay is dominated by a single exponential damping constant and are applicable to a wider range of conditions than existing theoretical descriptions. The n-site results are accurate when the population of one of the sites is much greater than that of others.

ISBN: 0496762987Subjects--Topical Terms:

1019105
Biophysics, General.

Protein dynamics and its prediction using machine learning.
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100 1 $a Trott, Oleg. $3 1928674
245 1 0 $a Protein dynamics and its prediction using machine learning.
300 $a 64 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 1735.
500 $a Adviser: Arthur G. Palmer.
502 $a Thesis (Ph.D.)--Columbia University, 2004.
520 $a In the first part of this dissertation, chemical exchange and the use of R1rho rotating frame relaxation experiment for its study are discussed. Beginning with the Bloch-McConnell equations, new expressions are derived for the spin relaxation rate constant in the rotating frame, R1rho for chemical exchange between two or more sites that have distinct magnetic environments and Larmor frequencies. The 2-site results are accurate provided that the spin relaxation decay is dominated by a single exponential damping constant and are applicable to a wider range of conditions than existing theoretical descriptions. The n-site results are accurate when the population of one of the sites is much greater than that of others.
520 $a The second part of this dissertation discusses the prediction of protein backbone flexibility using machine learning. Using a data set of 16 proteins, a neural network has been trained to predict backbone 15N generalized order parameters from the three-dimensional structures of proteins. The average prediction accuracy, as measured by the Pearson correlation coefficient between experimental and predicted values of the square of the generalized order parameter is 71.4%. The network parameterization contains six input features. Predicted order parameters for non-terminal amino acid residues depends most strongly on local packing density and the probability that the residue is located in regular secondary structure.
590 $a School code: 0054.
650 4 $a Biophysics, General. $3 1019105
650 4 $a Artificial Intelligence. $3 769149
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710 2 0 $a Columbia University. $3 571054
773 0 $t Dissertation Abstracts International $g 65-04B.
790 1 0 $a Palmer, Arthur G., $e advisor
790 $a 0054
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3129038