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Mol. Biol. Evol. 18(5):691699. 2001 2001 by the Society for Molecular Biology and Evolution. ISSN: 0737-4038
 

Summary: 691
Mol. Biol. Evol. 18(5):691­699. 2001
2001 by the Society for Molecular Biology and Evolution. ISSN: 0737-4038
A General Empirical Model of Protein Evolution Derived from Multiple
Protein Families Using a Maximum-Likelihood Approach
Simon Whelan and Nick Goldman
Department of Zoology, University of Cambridge, Cambridge, England
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

  

Source: Anisimova, Maria - Institute of Scientific Computing, Eidgenössische Technische Hochschule Zürich (ETHZ)

 

Collections: Biology and Medicine