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A General Empirical Model of Protein Evolution Derived from Multiple Protein Families Using a Maximum-Likelihood Approach
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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.
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“We showed that for both datasets, models that have previously been used to study the animal phylogeny fell short, to various degrees, of adequately describing among-site amino acid preferences”
From Improved Modeling of Compositional Heterogeneity Supports Sponges as Sister to All Other Animals (2017) · cites this paper as [21]
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Metadata
- Authors
- Whelan, S., & Goldman, N.
- Affiliations
- University of Cambridge (GB)
- ORCID
- Goldman, Nick
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-05
- Volume
- 18
- Issue
- 5
- Pages
- 691-699
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- OpenAlex
- W2062018285
- Cited by
- 2,849
- References
- 18
- Citations by year
- 2026: 66 · 2025: 95 · 2024: 114 · 2023: 113 · 2022: 138 · 2021: 184 · 2020: 153 · 2019: 143 · 2018: 123 · 2017: 131 · 2016: 111 · 2015: 145 · 2014: 175 · 2013: 142 · 2012: 150
- FWCI
- 11.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Molecular Biology and Evolution (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Funders
- Wellcome Trust
- Biotechnology and Biological Sciences Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- protein evolution, amino acid substitution models, maximum likelihood estimation, globular proteins, protein families, phylogenetic inference, sequence alignment
- MeSH terms
- Algorithms, Amino Acid Sequence, Amino Acids, Computer Simulation, Models, Biological, Phylogeny, Probability, Proteins, Likelihood Functions, Sequence Alignment, Evolution, Molecular
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