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Prediction of protein binding and fo...

Ouyang, Zheng.

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Prediction of protein binding and folding from molecular geometry and empirical scoring functions.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Prediction of protein binding and folding from molecular geometry and empirical scoring functions./
Author:	Ouyang, Zheng.
Description:	153 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-12, Section: A, page: 4229.
Contained By:	Dissertation Abstracts International71-12A.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3431262
ISBN:	9781124305257

Prediction of protein binding and folding from molecular geometry and empirical scoring functions.
Ouyang, Zheng.

Prediction of protein binding and folding from molecular geometry and empirical scoring functions. - 153 p.

Source: Dissertation Abstracts International, Volume: 71-12, Section: A, page: 4229.

Thesis (Ph.D.)--University of Illinois at Chicago, 2010.

Two fundamental processes of living cell, protein binding and protein folding, were studied through building computational models on experimental data. A protein-protein docking program and a protein folding rate prediction tool were developed.

ISBN: 9781124305257Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Prediction of protein binding and folding from molecular geometry and empirical scoring functions.
LDR:02798nam 2200301 4500 001 1401614
005 20111017084406.5
008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124305257
035 $a (UMI)AAI3431262
035 $a AAI3431262
040 $a UMI $c UMI
100 1 $a Ouyang, Zheng. $3 1680760
245 1 0 $a Prediction of protein binding and folding from molecular geometry and empirical scoring functions.
300 $a 153 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-12, Section: A, page: 4229.
500 $a Adviser: Jie Liang.
502 $a Thesis (Ph.D.)--University of Illinois at Chicago, 2010.
520 $a Two fundamental processes of living cell, protein binding and protein folding, were studied through building computational models on experimental data. A protein-protein docking program and a protein folding rate prediction tool were developed.
520 $a We presented a novel geometry-based docking algorithm with effective conformation-search and improved scoring functions for predicting the structure of protein-protein complex. Ligand proteins docking only need undergo transformation and rotation along surface normal vectors. The computational time of our algorithm is approximately proportional to O(mxn). The hierarchical scoring scheme improved the scoring performance by clustering protein binding complexes according to the energy score distribution of sampled interfaces. A total of 330 protein complexes were collected to build and evaluate our method. To predict protein folding rate from amino acid sequence, we introduced two new quantity, Nalpha, which is a count of the number of well-packed nonlocal contacts in the native structure of a protein, and NNP (nearest neighbor pair) which is a pair of residues which are nearest neighbor of each other in the primary sequence.
520 $a Compared to existing interface prediction methods, our method shows higher success rates: 71.4% for enzyme-inhibitors and 44.0% for other interfaces. Compared with other docking methods, we achieved similar level of performance: 60% and 40% success rate within top 40 predictions respectively. For protein folding rate prediction, our method shows an excellent correlation with experimental observations; the correlation coefficients are 0.98 for all tested proteins. These accuracy levels are superior to any other methods in the literature. And our method is the first that solely uses the amino acid sequence information alone for predicting the folding rates of proteins.
590 $a School code: 0799.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Information Science. $3 1017528
690 $a 0715
690 $a 0723
710 2 $a University of Illinois at Chicago. $3 1020478
773 0 $t Dissertation Abstracts International $g 71-12A.
790 1 0 $a Liang, Jie, $e advisor
790 $a 0799
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3431262