Literature DB >> 16787998

Assessing site-interdependent phylogenetic models of sequence evolution.

Nicolas Rodrigue1, Hervé Philippe, Nicolas Lartillot.   

Abstract

In recent works, methods have been proposed for applying phylogenetic models that allow for a general interdependence between the amino acid positions of a protein. As of yet, such models have focused on site interdependencies resulting from sequence-structure compatibility constraints, using simplified structural representations in combination with a set of statistical potentials. This structural compatibility criterion is meant as a proxy for sequence fitness, and the methods developed thus far can incorporate different site-interdependent fitness proxies based on other measurements. However, no methods have been proposed for comparing and evaluating the adequacy of alternative fitness proxies in this context, or for more general comparisons with canonical models of protein evolution. In the present work, we apply Bayesian methods of model selection-based on numerical calculations of marginal likelihoods and posterior predictive checks-to evaluate models encompassing the site-interdependent framework. Our application of these methods indicates that considering site-interdependencies, as done here, leads to an improved model fit for all data sets studied. Yet, we find that the use of pairwise contact potentials alone does not suitably account for across-site rate heterogeneity or amino acid exchange propensities; for such complexities, site-independent treatments are still called for. The most favored models combine the use of statistical potentials with a suitably rich site-independent model. Altogether, the methodology employed here should allow for a more rigorous and systematic exploration of different ways of modeling explicit structural constraints, or any other site-interdependent criterion, while best exploiting the richness of previously proposed models.

Mesh:

Substances:

Year:  2006        PMID: 16787998     DOI: 10.1093/molbev/msl041

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


  31 in total

Review 1.  The interface of protein structure, protein biophysics, and molecular evolution.

Authors:  David A Liberles; Sarah A Teichmann; Ivet Bahar; Ugo Bastolla; Jesse Bloom; Erich Bornberg-Bauer; Lucy J Colwell; A P Jason de Koning; Nikolay V Dokholyan; Julian Echave; Arne Elofsson; Dietlind L Gerloff; Richard A Goldstein; Johan A Grahnen; Mark T Holder; Clemens Lakner; Nicholas Lartillot; Simon C Lovell; Gavin Naylor; Tina Perica; David D Pollock; Tal Pupko; Lynne Regan; Andrew Roger; Nimrod Rubinstein; Eugene Shakhnovich; Kimmen Sjölander; Shamil Sunyaev; Ashley I Teufel; Jeffrey L Thorne; Joseph W Thornton; Daniel M Weinreich; Simon Whelan
Journal:  Protein Sci       Date:  2012-04-23       Impact factor: 6.725

2.  Amino acid coevolution induces an evolutionary Stokes shift.

Authors:  David D Pollock; Grant Thiltgen; Richard A Goldstein
Journal:  Proc Natl Acad Sci U S A       Date:  2012-04-30       Impact factor: 11.205

3.  Using non-reversible context-dependent evolutionary models to study substitution patterns in primate non-coding sequences.

Authors:  Guy Baele; Yves Van de Peer; Stijn Vansteelandt
Journal:  J Mol Evol       Date:  2010-07-11       Impact factor: 2.395

4.  Genealogical Working Distributions for Bayesian Model Testing with Phylogenetic Uncertainty.

Authors:  Guy Baele; Philippe Lemey; Marc A Suchard
Journal:  Syst Biol       Date:  2015-11-01       Impact factor: 15.683

Review 5.  Models of coding sequence evolution.

Authors:  Wayne Delport; Konrad Scheffler; Cathal Seoighe
Journal:  Brief Bioinform       Date:  2008-10-29       Impact factor: 11.622

6.  Mutational effects on stability are largely conserved during protein evolution.

Authors:  Orr Ashenberg; L Ian Gong; Jesse D Bloom
Journal:  Proc Natl Acad Sci U S A       Date:  2013-12-09       Impact factor: 11.205

7.  Bayesian comparisons of codon substitution models.

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

8.  Rapid likelihood analysis on large phylogenies using partial sampling of substitution histories.

Authors:  A P Jason de Koning; Wanjun Gu; David D Pollock
Journal:  Mol Biol Evol       Date:  2009-09-25       Impact factor: 16.240

9.  History can matter: non-Markovian behavior of ancestral lineages.

Authors:  Reed A Cartwright; Nicolas Lartillot; Jeffrey L Thorne
Journal:  Syst Biol       Date:  2011-03-11       Impact factor: 15.683

10.  Efficient context-dependent model building based on clustering posterior distributions for non-coding sequences.

Authors:  Guy Baele; Yves Van de Peer; Stijn Vansteelandt
Journal:  BMC Evol Biol       Date:  2009-04-30       Impact factor: 3.260

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.