Quantifying Phylogenetic Conservation in Protein Molecular Evolution

molecular evolution;p53 tumor suppressor;rate heterogeneity

Fernandes, Andrew Dellano ; Steffen Heber, Committee Member,William R. Atchley, Committee Chair,Eric A. Stone, Committee Member,Charles E. Smith, Committee Member,Fernandes, Andrew Dellano ; Steffen Heber ; Committee Member ; William R. Atchley ; Committee Chair ; Eric A. Stone ; Committee Member ; Charles E. Smith ; Committee Member

University:North Carolina State University

关键词: molecular evolution;    p53 tumor suppressor;    rate heterogeneity;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5828/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

This dissertation examines the problem of quantifying amino acid conservation in proteins molecular evolution. Ideally, this conservation is quantified by inferring the rate of evolution at each amino acid site of a multiple-alignment. However, current rate-inference methods have three problematic assumptions. The methods assume that (a) the rates of all sites are independent, (b) the rates are drawn from a known prior distribution, and (c) the mean rate across sites is approximately one. The problems are two-fold. First, the assumptions of site-rate independence and known mean rate are contradictory. To see the contradiction, consider a two-site alignment with known rate of ~0.5 at site one. The rate at site two is unknown under the independent-sites assumption, but is ~1.5 by the assumption of known mean rate. Second, if the rates are drawn from a known prior distribution, the assumption of known distribution implies the question "which distribution?". Previous work has focused only on selecting better families of rate distributions, often at the expense of additionally parameterizing the evolutionary model. Herein, I develop a method of inferring rates requiring only the assumption of known mean rate, and not requiring additional parameterization. Thus a model of evolution based on our method is a more general framework for inferring rates than previous work. Since a known mean rate is required to distinguish evolutionary rate from time, our method is arguably the most general possible that allows rate and time to be fully and independently identified. The method is assessed by investigating conservation in the Myc, Max, and p53 transcription-factor families.