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Development of structure-based compu...

Pierce, Brian Gregory.

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Development of structure-based computational methods for prediction and design of protein-protein interactions.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Development of structure-based computational methods for prediction and design of protein-protein interactions./
作者:	Pierce, Brian Gregory.
面頁冊數:	163 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/twdaoapp/servlet/advanced?query=3298669
ISBN:	9780549433613

Development of structure-based computational methods for prediction and design of protein-protein interactions.
Pierce, Brian Gregory.

Development of structure-based computational methods for prediction and design of protein-protein interactions. - 163 p.

Adviser: Zhiping Weng.

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

Protein-protein interactions play a key role in the functioning of cells and pathways, and understanding these interactions on a physical and structural level can help greatly in developing therapeutics for diseases. The large amount of protein structures available presents an immense opportunity to model and predict protein interactions using computational techniques. Here we describe the development of algorithms to predict protein complex structures (referred to as protein docking) and to design proteins to improve their interaction affinities. We also present experimental results validating our protein design approach.

ISBN: 9780549433613Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Development of structure-based computational methods for prediction and design of protein-protein interactions.
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245 1 0 $a Development of structure-based computational methods for prediction and design of protein-protein interactions.
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500 $a Adviser: Zhiping Weng.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0035.
502 $a Thesis (Ph.D.)--Boston University, 2008.
520 $a Protein-protein interactions play a key role in the functioning of cells and pathways, and understanding these interactions on a physical and structural level can help greatly in developing therapeutics for diseases. The large amount of protein structures available presents an immense opportunity to model and predict protein interactions using computational techniques. Here we describe the development of algorithms to predict protein complex structures (referred to as protein docking) and to design proteins to improve their interaction affinities. We also present experimental results validating our protein design approach.
520 $a The protein docking work we present includes the symmetric multimer docking program M-ZDOCK as well as ZRANK which rescores docking predictions using a weighted potential. Both programs have been successful when applied to docking benchmarks and in the CAPRI experiment. In addition, we have used the M-ZDOCK program to produce a tetrameric model for a disease-associated protein, the latent nuclear antigen of the Kaposi's sarcoma-associated herpesvirus.
520 $a We have also developed a protein design algorithm to improve the binding between two proteins, given their complex structure This was applied to a T cell receptor (TCR) to enhance its binding to the Major Histocompatibility Complex and peptide. Several of the point mutations predicted by our algorithm were verified experimentally to bind several times stronger than wild type; we then combined these mutations to produce a TCR with approximately 100-fold affinity improvement. Further testing of combinations of TCR point mutations has led to striking results regarding the kinetics and cooperativity of the mutations. Finally, we have used our protein design algorithm to predict designability of protein complexes from the Protein Data Bank, and identified the complex between CD4 and HIV gp120 as a target for future structure-based design efforts. Preliminary results for this project are given.
590 $a School code: 0017.
650 4 $a Biology, Biostatistics. $3 1018416
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710 2 $a Boston University. $3 1017454
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791 $a Ph.D.
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