Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation

Motivation The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being su...

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Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 40; H. 3
Hauptverfasser: Ferreiro, David, Branco, Catarina, Arenas, Miguel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 04.03.2024
Oxford Publishing Limited (England)
Schlagworte:
Availability
Bayes Theorem
Bayesian analysis
Computation
Constraints
Evolution
Evolution, Molecular
Graphical user interface
Likelihood Functions
Mathematical models
Models, Genetic
Molecular evolution
Molecular modelling
Original Paper
Phylogeny
Protein families
Proteins
Proteins - genetics
Substitutes
ISSN:1367-4803, 1367-4811, 1367-4811
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Zusammenfassung:Motivation The selection among substitution models of molecular evolution is fundamental for obtaining accurate phylogenetic inferences. At the protein level, evolutionary analyses are traditionally based on empirical substitution models but these models make unrealistic assumptions and are being surpassed by structurally constrained substitution (SCS) models. The SCS models often consider site-dependent evolution, a process that provides realism but complicates their implementation into likelihood functions that are commonly used for substitution model selection. Results We present a method to perform selection among site-dependent SCS models, also among empirical and site-dependent SCS models, based on the approximate Bayesian computation (ABC) approach and its implementation into the computational framework ProteinModelerABC. The framework implements ABC with and without regression adjustments and includes diverse empirical and site-dependent SCS models of protein evolution. Using extensive simulated data, we found that it provides selection among SCS and empirical models with acceptable accuracy. As illustrative examples, we applied the framework to analyze a variety of protein families observing that SCS models fit them better than the corresponding best-fitting empirical substitution models. Availability and implementation ProteinModelerABC is freely available from https://github.com/DavidFerreiro/ProteinModelerABC, can run in parallel and includes a graphical user interface. The framework is distributed with detailed documentation and ready-to-use examples.
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btae096