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In vivo functional phenotypes from a computational epistatic model of evolution
Sophia Alvarez1, Charisse M Nartey1, Nicholas Mercado1
1Department of Biological Sciences, University of Texas at Dallas, Richardson, TX 75080.
Summary
Computational models can now evolve proteins with enhanced functionality. Our new algorithm, SEEC, uses natural protein family data to create more active variants, advancing evolutionary biology and biomedical applications.
Area of Science:
- Evolutionary biology
- Computational biology
- Biochemistry
Background:
- Computational models of evolution are crucial for understanding sequence variation, phylogeny, and evolutionary pathways.
- Validating the in vivo functionality of model outputs is essential for accurate evolutionary algorithms.
- Epistasis, interactions between mutations, plays a key role in protein evolution.
Purpose of the Study:
- To demonstrate the power of epistasis inferred from natural protein families to evolve functional protein variants.
- To introduce and validate a novel algorithm, Sequence Evolution with Epistatic Contributions (SEEC).
- To assess the in vivo functionality and activity of evolved protein variants.
Main Methods:
- Developed the SEEC algorithm incorporating epistasis inferred from natural protein families.
- Used the Hamiltonian of joint sequence probabilities as a fitness metric.
- Experimentally tested in vivo beta-lactamase activity of evolved Escherichia coli TEM-1 variants.
Main Results:
- SEEC evolved protein variants with dozens of mutations while preserving essential catalytic and interaction sites.
- Evolved variants retained family-like functionality and exhibited higher activity than wild-type.
- Different epistasis inference methods simulated diverse selection strengths, with weaker selection recapitulating neutral evolution.
Conclusions:
- SEEC effectively evolves functional protein variants with enhanced activity using natural epistasis.
- The algorithm can simulate different evolutionary dynamics, including neutral evolution.
- SEEC holds potential for applications in neofunctionalization, viral fitness landscape characterization, and vaccine development.
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