Abstract

The relationships between codon usage and protein expression levels have been extensively studied in various organisms. Highly expressed genes have often been shown to have stronger compositional codon bias. However, previous studies often failed to separate the effects of regulatory control from codon usage patterns. These studies have also been deficient with respect to both gene and protein coverage. In this study, we investigated the role of codon usage patterns in Saccharomyces cerevisiae using the Movable Open Reading Frame (MORF) collection library. The new collection is based on the recent annotation of yeast genome and is made with high– efficiency and high–fidelity cloning procedures, providing the most complete collection of ORFs available for any organism. It is also the first collection of proteins where all the proteins are under common regulatory control. Thus, it provides the best opportunity to investigate the specific role of codon usage in determining levels of protein expression. A simple measure of synonymous codon bias, the Codon Adaptation Index (CAI), managed to predict expression levels with an accuracy of 76%. CAI however, carries a strong bias to predict highly expressed genes. As an alternative, we also investigated codon usage patterns using a new measure, developed as the log likelihood ratio of codon frequencies from known high and low expressing proteins. Our methods can predict expression levels with an accuracy of 82%. Other properties of the sequences that can be used to predict expression were also explored. Strong codon composition dependence, rare codons or clusters of rare codons, and strong nucleotide dependencies have been found throughout the MORF sequences. However, we concluded that these features are not sufficiently strong to independently affect expression and a more complex set of interactions are likely at work in determining protein expression levels in cells.

Library of Congress Subject Headings

Saccharomyces cerevisiae--Genetics; Proteins--Analysis; Genetic code--Analysis

Publication Date

5-1-2007

Document Type

Thesis

Department, Program, or Center

Thomas H. Gosnell School of Life Sciences (COS)

Advisor

Gopal, Shuba

Advisor/Committee Member

Skuse, Gary

Advisor/Committee Member

Halavin, James

Comments

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: QH470.S23 B88 2007

Campus

RIT – Main Campus

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