title: Improving evolutionary models for mitochondrial protein data with site-class specific amino acid exchangeability matrices.
abstract: Adequate modeling of mitochondrial sequence evolution is an essential component of mitochondrial phylogenomics (comparative mitogenomics). There is wide recognition within the field that lineage-specific aspects of mitochondrial evolution should be accommodated through lineage-specific amino-acid exchangeability matrices (e.g., mtMam for mammalian data). However, such a matrix must be applied to all sites and this implies that all sites are subject to the same, or largely similar, evolutionary constraints. This assumption is unjustified. Indeed, substantial differences are expected to arise from three-dimensional structures that impose different physiochemical environments on individual amino acid residues. The objectives of this paper are (1) to investigate the extent to which amino acid evolution varies among sites of mitochondrial proteins, and (2) to assess the potential benefits of explicitly modeling such variability. To achieve this, we developed a novel method for partitioning sites based on amino acid physiochemical properties. We apply this method to two datasets derived from complete mitochondrial genomes of mammals and fish, and use maximum likelihood to estimate amino acid exchangeabilities for the different groups of sites. Using this approach we identified large groups of sites evolving under unique physiochemical constraints. Estimates of amino acid exchangeabilities differed significantly among such groups. Moreover, we found that joint estimates of amino acid exchangeabilities do not adequately represent the natural variability in evolutionary processes among sites of mitochondrial proteins. Significant improvements in likelihood are obtained when the new matrices are employed. We also find that maximum likelihood estimates of branch lengths can be strongly impacted. We provide sets of matrices suitable for groups of sites subject to similar physiochemical constraints, and discuss how they might be used to analyze real data. We also discuss how the general approach might be employed to improve a variety of mitogenomic-based research activities.
authors: Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada. kathy.dunn@dal.ca
authors: Katherine A
authors: KA
authors: Dunn
authors: Wenyi
authors: W
authors: Jiang
authors: Christopher
authors: C
authors: Field
authors: Joseph P
authors: JP
authors: Bielawski
mesh_terms: D000465:Algorithms; D019943:Amino Acid Substitution; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D019143:Evolution, Molecular; D005399:Fishes; D006801:Humans; D016013:Likelihood Functions; D008322:Mammals; D024101:Mitochondrial Proteins / Q000737:chemistry / Q000235:genetics*; D008957:Models, Genetic; D015203:Reproducibility of Results
chemical_list: D024101:Mitochondrial Proteins
references: Mol Biol Evol. 2012 Oct;29(10):2921-36
references: 22491036
references: Mol Phylogenet Evol. 2013 Nov;69(2):404-16
references: 22705825
references: Mol Biol Evol. 1998 Dec;15(12):1600-11
references: 9866196
references: Genetics. 1998 May;149(1):445-58
references: 9584116
references: Bioinformatics. 2008 Oct 15;24(20):2317-23
references: 18718941
references: Mol Biol Evol. 1993 Nov;10(6):1396-401
references: 8277861
references: Nucleic Acids Res. 2008 Jan;36(Database issue):D202-5
references: 17998252
references: Bioinformation. 2005 Mar 12;1(1):2-4
references: 17597840
references: Proteins. 1998 Aug 15;32(3):289-95
references: 9715905
references: Mol Phylogenet Evol. 2009 Jul;52(1):268-72
references: 19475713
references: Proc Biol Sci. 2002 Jul 7;269(1498):1313-6
references: 12079652
references: Mol Biol Evol. 2008 Sep;25(9):1943-53
references: 18583353
references: Protein Eng. 1995 Jul;8(7):641-5
references: 8577693
references: PLoS One. 2010 Dec 23;5(12):e14419
references: 21203450
references: Proc Biol Sci. 2007 Feb 22;274(1609):489-98
references: 17476768
references: J Mol Biol. 2000 Sep 8;302(1):205-17
references: 10964570
references: Mol Biol Evol. 2007 Jan;24(1):1-5
references: 17043087
references: Mol Biol Evol. 2008 Jul;25(7):1307-20
references: 18367465
references: Mol Biol Evol. 2003 Oct;20(10):1692-704
references: 12885968
references: BMC Evol Biol. 2008 Dec 16;8:331
references: 19087270
references: Mol Biol Evol. 2004 Jun;21(6):1095-109
references: 15014145
references: PLoS One. 2011;6(9):e24127
references: 21969854
references: J Mol Evol. 1994 Sep;39(3):306-14
references: 7932792
references: Bioinformatics. 2011 Oct 1;27(19):2655-63
references: 21840876
references: Syst Biol. 2003 Oct;52(5):705-16
references: 14530137
references: Mol Biol Evol. 2001 May;18(5):691-9
references: 11319253
references: Mol Biol Evol. 2007 Feb;24(2):388-97
references: 17110464
references: Mol Phylogenet Evol. 2012 Jan;62(1):329-45
references: 22040765
references: BMC Evol Biol. 2007 Aug 16;7 Suppl 2:S8
references: 17767736
references: Philos Trans R Soc Lond B Biol Sci. 2008 Dec 27;363(1512):3965-76
references: 18852096
references: Syst Biol. 2011 Jan;60(1):3-15
references: 20952756
references: PLoS One. 2011;6(8):e22783
references: 21850235
references: PLoS One. 2011;6(11):e27138
references: 22073275
references: Bioinformatics. 2006 Nov 1;22(21):2688-90
references: 16928733
references: BMC Evol Biol. 2009 Jan 25;9:20
references: 19166626
references: J Mol Evol. 2013 Apr;76(4):205-15
references: 23443835
references: BMC Evol Biol. 2011 Mar 10;11:65
references: 21392378
references: Mol Biol Evol. 2012 Jun;29(6):1695-701
references: 22319168