Literature DB >> 11070062

Exploring a phylogenetic approach for the detection of correlated substitutions in proteins.

P Tuff1, P Darlu.   

Abstract

The remarkable conservation of protein structure, compared with that of sequences, suggests that in the course of evolution, residue substitutions which tend to destabilize a particular structure must be compensated by other substitutions that confer greater stability on that structure. Several approaches have been designed to detect correlated changes in a set of homologous sequences. However, most of them do not take into account the phylogeny of the sequences, and it has been shown that their detection power is weak. It remains unclear whether coevolution could be a general process at the level of amino acids of proteins. In the present study, we analyze the phylogenetic reconstruction of 15 sets of homologous proteins to assess, under different conditions, whether a significant amount of coevolving sites can be detected. Two criteria are used to detect significantly cosubstituting sites. One criterion corresponds to that of Shindyalov, Kolchanov, and Sander. The second one is based on intensive simulations of evolution of protein sequences along a phylogeny to estimate the significance of the number of observed cosubstitutions for pairs of sites. Our results show an important sensitivity of the detection of cosubstituting sites to the model used for the phylogenetic reconstruction. Not considering the uncertainty associated with the reconstructed data might lead to detecting numerous false-positive pairs of sites. Finally, significant amounts of coevolving pairs could be found only when substitutions affecting the physicochemical properties of the amino acids were considered. Such results suggest evidence of a cosubstitution mechanism in protein evolution. However, the identification of nonambiguous coevolving sites is still unresolved.

Mesh:

Substances:

Year:  2000        PMID: 11070062     DOI: 10.1093/oxfordjournals.molbev.a026273

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  11 in total

1.  Context dependence and coevolution among amino acid residues in proteins.

Authors:  Zhengyuan O Wang; David D Pollock
Journal:  Methods Enzymol       Date:  2005       Impact factor: 1.600

2.  Coevolutionary patterns in cytochrome c oxidase subunit I depend on structural and functional context.

Authors:  Zhengyuan O Wang; David D Pollock
Journal:  J Mol Evol       Date:  2007-11       Impact factor: 2.395

3.  Evaluation of Ancestral Sequence Reconstruction Methods to Infer Nonstationary Patterns of Nucleotide Substitution.

Authors:  Tomotaka Matsumoto; Hiroshi Akashi; Ziheng Yang
Journal:  Genetics       Date:  2015-05-06       Impact factor: 4.562

4.  Coevolution in defining the functional specificity.

Authors:  Saikat Chakrabarti; Anna R Panchenko
Journal:  Proteins       Date:  2009-04

5.  Coevolution in RNA molecules driven by selective constraints: evidence from 5S rRNA.

Authors:  Nan Cheng; Yuanhui Mao; Youyi Shi; Shiheng Tao
Journal:  PLoS One       Date:  2012-09-04       Impact factor: 3.240

6.  Structural constraints identified with covariation analysis in ribosomal RNA.

Authors:  Lei Shang; Weijia Xu; Stuart Ozer; Robin R Gutell
Journal:  PLoS One       Date:  2012-06-19       Impact factor: 3.240

7.  Detecting coevolution without phylogenetic trees? Tree-ignorant metrics of coevolution perform as well as tree-aware metrics.

Authors:  J Gregory Caporaso; Sandra Smit; Brett C Easton; Lawrence Hunter; Gavin A Huttley; Rob Knight
Journal:  BMC Evol Biol       Date:  2008-12-03       Impact factor: 3.260

8.  Prediction of contact residue pairs based on co-substitution between sites in protein structures.

Authors:  Sanzo Miyazawa
Journal:  PLoS One       Date:  2013-01-16       Impact factor: 3.240

9.  Detecting groups of coevolving positions in a molecule: a clustering approach.

Authors:  Julien Dutheil; Nicolas Galtier
Journal:  BMC Evol Biol       Date:  2007-11-30       Impact factor: 3.260

10.  Reducing the false positive rate in the non-parametric analysis of molecular coevolution.

Authors:  Francisco M Codoñer; Shirley O'Dea; Mario A Fares
Journal:  BMC Evol Biol       Date:  2008-04-10       Impact factor: 3.260

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.