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Computational methods for protein-pr...

Boston University.

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Computational methods for protein-protein complex structure prediction and mass spectrometry-based protein identification.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Computational methods for protein-protein complex structure prediction and mass spectrometry-based protein identification./
作者:	Tong, Weiwei.
面頁冊數:	141 p.
附註:	Adviser: Zhiping Weng.
Contained By:	Dissertation Abstracts International69-01B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3298682
ISBN:	9780549440567

Computational methods for protein-protein complex structure prediction and mass spectrometry-based protein identification.
Tong, Weiwei.

Computational methods for protein-protein complex structure prediction and mass spectrometry-based protein identification. - 141 p.

Adviser: Zhiping Weng.

Thesis (Ph.D.)--Boston University, 2008.

Nearly all major processes in living cells are carried out by complex apparatus consisting of protein molecules. This thesis describes computational tools developed to help investigate two fundamental questions about proteins that underlie cell functions: how they interact with each other and form complex structures; and how they are expressed and modified in different cell states.

ISBN: 9780549440567Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Computational methods for protein-protein complex structure prediction and mass spectrometry-based protein identification.
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100 1 $a Tong, Weiwei. $3 1023462
245 1 0 $a Computational methods for protein-protein complex structure prediction and mass spectrometry-based protein identification.
300 $a 141 p.
500 $a Adviser: Zhiping Weng.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0475.
502 $a Thesis (Ph.D.)--Boston University, 2008.
520 $a Nearly all major processes in living cells are carried out by complex apparatus consisting of protein molecules. This thesis describes computational tools developed to help investigate two fundamental questions about proteins that underlie cell functions: how they interact with each other and form complex structures; and how they are expressed and modified in different cell states.
520 $a In order to address the first question, several methods are developed to predict protein-protein complex structures. Protein interactions are energy driven processes. The prediction of protein complex structures is the search for the global minimum on the binding free-energy landscape. An approach is described that uses Van der Wools energy, desolvation energy and shape complementarity as the scoring functions and a five-dimensional fast Fourier transform algorithm to expedite the search.
520 $a Two methods to screen and optimize the predicted protein complex structures are also introduced. They incorporate additional energy terms and clustering algorithms to provide more precise estimations of the binding free-energy. The same methods can also be used to predict hot spots, the mutations of which significantly alter the binding kinetics.
520 $a To study the protein expression profiles, a two-step approach for protein identification using peptide mass fingerprinting data is developed. Peptide mass fingerprinting uses peptide masses determined by mass spectrometry to identify the peptides and subsequently, the proteins in the sample Peaks in the mass spectrum are associated with known peptide sequences in the database based on log-likelihood ratio test. A statistical algorithm is then used to identify proteins by comparing the probability of each protein's presence in the sample, given the peak assignments with the background probability. This method also discovers post-translational modifications in the identified proteins.
520 $a The protein binding prediction program successfully predicts protein complex structures that closely resemble their native forms, as observed by x-ray crystallography or NMR. The refinements and hot spot predictions also give accurate and consistent results. The database search program that interprets mass spectrometry data is evaluated with artificial and experimental data. The program identifies proteins in the sample with high sensitivity and specificity. The results presented in this thesis demonstrate that computational methods help to better understand the structure and the composition of the protein machineries. All of the methods described herein have been implemented and made available for the research community over the Internet.
590 $a School code: 0017.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Engineering, Biomedical. $3 1017684
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710 2 $a Boston University. $3 1017454
773 0 $t Dissertation Abstracts International $g 69-01B.
790 $a 0017
790 1 0 $a Weng, Zhiping, $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoeng/servlet/advanced?query=3298682