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[ subject:"Bioinformatics." ]
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Statistical Inference via Structural...
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Wang, Kuangyu.
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Statistical Inference via Structurally and Functionally Informed Models of Protein Evolution.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Statistical Inference via Structurally and Functionally Informed Models of Protein Evolution./
作者:
Wang, Kuangyu.
面頁冊數:
137 p.
附註:
Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
Contained By:
Dissertation Abstracts International76-07B(E).
標題:
Bioinformatics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3690388
ISBN:
9781321584424
Statistical Inference via Structurally and Functionally Informed Models of Protein Evolution.
Wang, Kuangyu.
Statistical Inference via Structurally and Functionally Informed Models of Protein Evolution.
- 137 p.
Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
Thesis (Ph.D.)--North Carolina State University, 2015.
Models of protein evolution tend to pay little attention to functional constraints, although structural constraints are often incorporated. Structure-informed models of protein-coding sequence evolution tend to ignore functional constraints and the possibility that a protein could have more than one native conformations. This dissertation details the derivation of a structurally and functionally informed model that allows protein-coding DNA evolution to be influenced by multiple tertiary structures.
ISBN: 9781321584424Subjects--Topical Terms:
553671
Bioinformatics.
Statistical Inference via Structurally and Functionally Informed Models of Protein Evolution.
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Source: Dissertation Abstracts International, Volume: 76-07(E), Section: B.
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Adviser: Jeffrey L. Thorne.
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Thesis (Ph.D.)--North Carolina State University, 2015.
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Models of protein evolution tend to pay little attention to functional constraints, although structural constraints are often incorporated. Structure-informed models of protein-coding sequence evolution tend to ignore functional constraints and the possibility that a protein could have more than one native conformations. This dissertation details the derivation of a structurally and functionally informed model that allows protein-coding DNA evolution to be influenced by multiple tertiary structures.
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The joint effects of gene expression (a functional constraint) and relative solvent accessibility (RSA), a structural constraint, in modeling protein sequence evolution are investigated in Chapter 2. Hypothesis tests are introduced to explore the relationship between RSA and codon usage at the genomic scale as well as at the individual gene scale. The genome-level results show that synonymous codons exhibit significant RSA preference bias in human. In mouse, the result is not statistically significant, possibly because of insufficient data and possibly because the relationship between RSA and synonymous codon usage is highly species-specific.
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The effects of gene expression on synonymous codon usage are studied using probabilistic modeling. The combined effects of RSA and gene expression in influencing amino acid usage are modeled using the same probabilistic approach. Although RSA has greater influence on amino acid usage, the effect of gene expression is nevertheless significant at the synonymous codon level and should not be ignored.
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The assumption that each protein has a known and fixed tertiary structure is relaxed in Chapter 3. A model of protein evolution is developed that incorporates the RSA of protein positions according to each of multiple tertiary structures. The approach is illustrated with five applications to ligand-binding proteins. Statistical comparison shows that incorporating both protein conformations yields a better evolutionary model than one-structure models and than a structure-unaware model. Our approach allows quantification of the relative evolutionary impact of each structure on each protein site. These relative impacts can then be visualized on the protein tertiary structure.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3690388
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