A General Empirical Model of Protein Evolution Derived from Multiple Protein Families Using a Maximum-Likelihood Approach

Abstract

Phylogenetic inference from amino acid sequence data uses mainly empirical models of amino acid replacement and is therefore dependent on those models. Two of the more widely used models, the Dayhoff and JTT models, are estimated using similar methods that can utilize large numbers of sequences from many unrelated protein families but are somewhat unsatisfactory because they rely on assumptions that may lead to systematic error and discard a large amount of the information within the sequences. The alternative method of maximum-likelihood estimation may utilize the information in the sequence data more efficiently and suffers from no systematic error, but it has previously been applicable to relatively few sequences related by a single phylogenetic tree. Here, we combine the best attributes of these two methods using an approximate maximum-likelihood method. We implemented this approach to estimate a new model of amino acid replacement from a database of globular protein sequences comprising 3,905 amino acid sequences split into 182 protein families. While the new model has an overall structure similar to those of other commonly used models, there are significant differences. The new model outperforms the Dayhoff and JTT models with respect to maximum-likelihood values for a large majority of the protein families in our database. This suggests that it provides a better overall fit to the evolutionary process in globular proteins and may lead to more accurate phylogenetic tree estimates. Potentially, this matrix, and the methods used to generate it, may also be useful in other areas of research, such as biological sequence database searching, sequence alignment, and protein structure prediction, for which an accurate description of amino acid replacement is required.

Informational Gene Phylogenies Do Not Support a Fourth Domain of Life for Nucleocytoplasmic Large DNA Viruses

“…In each case, the LG matrix [44] was chosen over the JTT [36] and WAG [37] matrices used in the original analysis by both the AIC and BIC criteria (see Table S1). We nonetheless performed our analyses with all three matrices in order to compare our results to those of Boyer et al [18].…”

“…The analyses of Boyer et al [18] used traditional evolutionary models (JTT [36] and WAG [37]) of the type that can be sensitive to LBA and/or compositional heterogeneity [22], [32]. Moreover, previous analyses of deep phylogeny have revealed that substantial differences in evolutionary rates and composition affect molecular sequences sampled from the three cellular domains [15].…”

Multiple Gene Duplication and Rapid Evolution in the groEL Gene: Functional Implications

“…PROTTEST tests for 72 amino acid substitution models. The best substitution matrix suggested by PROTTEST, the WAG matrix (Whelan and Goldman 2001), was used for ML analyses, using PHYML (Guindon and Gascuel 2003). The ML analysis was used to correct the consensus tree considering a 0.016 fraction of the total sites as invariant and used a gamma distribution with a = 0.908 as obtained from PROTTEST.…”

Wider than Thought Phylogenetic Occurrence of Apicortin, A Characteristic Protein of Apicomplexan Parasites

“…Bayesian analysis was performed using MrBayes v3.1.2 (Ronquist and Huelsenbeck 2003). Default priors and the WAG model (Whelan and Goldman 2001) were used assuming equal rates across sites. Two independent analyses were run with three heated and one cold chains (temperature parameter 0.2) for 3 × 10 6 generations, with a sampling frequency of 0.01 and the first 25 % of the generations were discarded as burn-in.…”