Literature DB >> 17572082

Protein evolution constraints and model-based techniques to study them.

Jeffrey L Thorne1.   

Abstract

There have been substantial improvements in statistical tools for assessing the evolutionary roles of mutation and natural selection from interspecific sequence data. The importance of having the rate at which a point mutation occurs depend on the DNA sequence at sites surrounding the mutation is now better appreciated and can be accommodated in probabilistic models of protein evolution. To quantify the evolutionary impact of some aspect of phenotype, one promising strategy is to develop a system for predicting phenotype from the DNA sequence and to then infer how the evolutionary rates of sequence change are affected by the predicted phenotypic consequences of the changes. Although statistical tools for characterizing protein evolution are improving, the list of candidate phenomena that can affect rates of protein evolution is long and the relative contributions of these phenomena are only beginning to be disentangled.

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Year:  2007        PMID: 17572082     DOI: 10.1016/j.sbi.2007.05.006

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  9 in total

1.  Bayesian comparisons of codon substitution models.

Authors:  Nicolas Rodrigue; Nicolas Lartillot; Hervé Philippe
Journal:  Genetics       Date:  2008-09-14       Impact factor: 4.562

2.  Accelerated simulation of evolutionary trajectories in origin-fixation models.

Authors:  Ashley I Teufel; Claus O Wilke
Journal:  J R Soc Interface       Date:  2017-02       Impact factor: 4.118

3.  Relationship between protein thermodynamic constraints and variation of evolutionary rates among sites.

Authors:  Julian Echave; Eleisha L Jackson; Claus O Wilke
Journal:  Phys Biol       Date:  2015-03-19       Impact factor: 2.583

4.  Beyond Thermodynamic Constraints: Evolutionary Sampling Generates Realistic Protein Sequence Variation.

Authors:  Qian Jiang; Ashley I Teufel; Eleisha L Jackson; Claus O Wilke
Journal:  Genetics       Date:  2018-01-30       Impact factor: 4.562

5.  Enabling Inference for Context-Dependent Models of Mutation by Bounding the Propagation of Dependency.

Authors:  Frederick A Matsen; Peter L Ralph
Journal:  J Comput Biol       Date:  2022-07-01       Impact factor: 1.549

6.  PALM: a paralleled and integrated framework for phylogenetic inference with automatic likelihood model selectors.

Authors:  Shu-Hwa Chen; Sheng-Yao Su; Chen-Zen Lo; Kuei-Hsien Chen; Teng-Jay Huang; Bo-Han Kuo; Chung-Yen Lin
Journal:  PLoS One       Date:  2009-12-07       Impact factor: 3.240

7.  Local packing density is the main structural determinant of the rate of protein sequence evolution at site level.

Authors:  So-Wei Yeh; Tsun-Tsao Huang; Jen-Wei Liu; Sung-Huan Yu; Chien-Hua Shih; Jenn-Kang Hwang; Julian Echave
Journal:  Biomed Res Int       Date:  2014-07-09       Impact factor: 3.411

8.  How structural and physicochemical determinants shape sequence constraints in a functional enzyme.

Authors:  Luciano A Abriata; Timothy Palzkill; Matteo Dal Peraro
Journal:  PLoS One       Date:  2015-02-23       Impact factor: 3.240

9.  A mechanistic stress model of protein evolution accounts for site-specific evolutionary rates and their relationship with packing density and flexibility.

Authors:  Tsun-Tsao Huang; María Laura del Valle Marcos; Jenn-Kang Hwang; Julian Echave
Journal:  BMC Evol Biol       Date:  2014-04-09       Impact factor: 3.260

  9 in total

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