ArticleJournal of molecular evolution2020
Site-Specific Amino Acid Distributions Follow a Universal Shape.
Article in Journal of molecular evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Accurate prediction of substitution rates at protein sites with a mutation-selection model.Scientific reports · 2025Article
- A fitness distribution law for amino-acid replacements.bioRxiv : the preprint server for biology · 2024Article
- Substitution Models of Protein Evolution with Selection on Enzymatic Activity.Molecular biology and evolution · 2024Article
- Multiple mechanisms explain loss of anthocyanins from betalain-pigmented Caryophyllales, including repeated wholesale loss of a key anthocyanidin synthesis enzyme.The New phytologist · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
In many applications of evolutionary inference, a model of protein evolution needs to be fitted to the amino acid variation at individual sites in a multiple sequence alignment. Most existing models fall into one of two extremes: Either they provide a coarse-grained description that lacks biophysical realism (e.g., dN/dS models), or they require a large number of parameters to be fitted (e.g., mutation-selection models). Here, we ask whether a middle ground is possible: Can we obtain a realistic description of site-specific amino acid frequencies while severely restricting the number of free parameters in the model? We show that a distribution with a single free parameter can accurately capture the variation in amino acid frequency at most sites in an alignment, as long as we are willing to restrict our analysis to predicting amino acid frequencies by rank rather than by amino acid identity. This result holds equally well both in alignments of empirical protein sequences and of sequences evolved under a biophysically realistic all-atom force field. Our analysis reveals a near universal shape of the frequency distributions of amino acids. This insight has the potential to lead to new models of evolution that have both increased realism and a limited number of free parameters.
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