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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Selection among site-dependent structurally constrained substitution models of protein evolution by approximate
David Ferreiro1,2, Catarina Branco1,2, Miguel Arenas1,2
1CINBIO, Universidade de Vigo, 36310 Vigo, Spain.
Bioinformatics (Oxford, England)
|February 19, 2024
Summary
We developed ProteinModelerABC for selecting protein evolution models. This tool accurately selects among structurally constrained substitution (SCS) and empirical models, improving phylogenetic inferences.
Area of Science:
- Computational Biology
- Molecular Evolution
- Bioinformatics
Background:
- Accurate phylogenetic inference relies on appropriate molecular evolution models.
- Traditional empirical substitution models for proteins have limitations and unrealistic assumptions.
- Structurally constrained substitution (SCS) models offer greater realism, particularly site-dependent evolution, but are complex to implement.
Purpose of the Study:
- To present a novel computational framework, ProteinModelerABC, for selecting among protein substitution models.
- To implement approximate Bayesian computation (ABC) for model selection, incorporating both empirical and site-dependent SCS models.
- To evaluate the accuracy of the ABC approach for model selection using simulated and real protein family data.
Main Methods:
- Developed ProteinModelerABC, a computational framework implementing approximate Bayesian computation (ABC).
- Integrated diverse empirical and site-dependent structurally constrained substitution (SCS) models within the framework.
- Employed ABC with and without regression adjustments for model selection, validated using extensive simulated data.
Main Results:
- The ProteinModelerABC framework demonstrates acceptable accuracy in selecting among SCS and empirical protein evolution models.
- Analysis of diverse protein families revealed that SCS models provide a better fit compared to the best-fitting empirical models.
- The method successfully handles the complexity of site-dependent evolution inherent in SCS models.
Conclusions:
- ProteinModelerABC offers a robust and accurate method for selecting appropriate protein substitution models.
- Structurally constrained substitution models, particularly site-dependent ones, are superior to empirical models for protein evolution analysis.
- The developed framework facilitates more reliable phylogenetic inferences in molecular evolution studies.
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