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Computational Studies of Protein Str...

Caballero Orduna, Diego.

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Computational Studies of Protein Structure.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational Studies of Protein Structure./
作者:	Caballero Orduna, Diego.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
面頁冊數:	130 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
Contained By:	Dissertation Abstracts International77-12B(E).
標題:	Physics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10150084
ISBN:	9781369051254

Computational Studies of Protein Structure.
Caballero Orduna, Diego.

Computational Studies of Protein Structure. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 130 p.

Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.

Thesis (Ph.D.)--Yale University, 2016.

Despite the abundance of crystallographic and structural data and many recent advances in computational methods for protein design, we still lack a quantitative and predictive understanding of the driving forces that control protein folding and stability. For example, we do not know the relative magnitudes of the side-chain entropy, van der Waals contact interactions, and other enthalpic contributions to the free energy of folded proteins. The inadequacy of current computational approaches to the analysis and design of protein structures has hampered the development of many novel therapeutic and diagnostic agents, and is arguably one of the biggest open challenges in biophysics and biochemistry.

ISBN: 9781369051254Subjects--Topical Terms:

516296
Physics.

Computational Studies of Protein Structure.
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245 1 0 $a Computational Studies of Protein Structure.
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300 $a 130 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
500 $a Adviser: Corey S. O'Hern.
502 $a Thesis (Ph.D.)--Yale University, 2016.
520 $a Despite the abundance of crystallographic and structural data and many recent advances in computational methods for protein design, we still lack a quantitative and predictive understanding of the driving forces that control protein folding and stability. For example, we do not know the relative magnitudes of the side-chain entropy, van der Waals contact interactions, and other enthalpic contributions to the free energy of folded proteins. The inadequacy of current computational approaches to the analysis and design of protein structures has hampered the development of many novel therapeutic and diagnostic agents, and is arguably one of the biggest open challenges in biophysics and biochemistry.
520 $a My work has built on the pioneering work of Ponder and Richards in the 1980s, who while at Yale demonstrated that steric constraints and packing criteria in protein interiors were the most stringent criteria in determining protein conformations. I present my work developing and using a sterically centered molecular dynamics force field to study amino acid conformations. I specifically use this force field to model hydrophobic amino acids and study the fundamental driving forces that determine amino acid side-chain conformations.
520 $a In my research, I have employed a hard-sphere plus stereo-chemical constraint molecular dynamics model. I have complemented this approach with other numerical and computational techniques such as approximate Markov state models to predict and rank amino acid conformations in different environments.
520 $a This dissertation presents three separate but related computational studies. In the first one, I present an analysis of the equilibrium backbone conformations that the amino acid Alanine can take. I also study the inter-conversion mechanisms between these equilibrium states and compare my predictions with crystallographic data.
520 $a I then generalize the method to study side-chain dihedral angle equilibrium conformations and the different transition mechanisms between them in the amino acids Leucine and Isoleucine. I analyze bond and dihedral angle correlations and predict a novel transition mechanism between different side-chain conformations which is consistent with crystallographic data.
520 $a I then employ my hard-sphere plus stereo-chemical constraint model to comprehensively study and predict the side-chain dihedral angle distributions of eight different hydrophobic residues in the context of high resolution protein crystal structures. I determine how well the model can predict amino acid side-chain conformations when only local intra-residue interactions are included and when both intra- and inter-residue interactions are included.
520 $a This thesis work will not only lead to a more fundamental understanding of the underlying physical forces behind amino acid conformations. Future progress built on this work will also enable the reliable design of specific protein-ligand motifs, the development of efficient computational methods to rationally re-design protein cores and interfaces with tunable stabilities, specificities and affinities, and numerous applications in biomedicine.
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