A Mutation-Selection Model of Protein Evolution under Persistent Positive Selection.

Area of Science:

• Evolutionary biology
• Population genetics
• Molecular evolution

Background:

• Persistent positive selection (PPS) drives protein evolution during environmental changes, adaptive radiations, and host-pathogen interactions.
• Traditional methods using the ratio of substitution rates (ω > 1) may be conservative due to deleterious mutations at selected sites.

Purpose of the Study:

• To model protein evolution under persistent positive selection (PPS) using first principles of population genetics.
• To develop a more accurate method for detecting molecular adaptation by directly estimating selection coefficients.

Main Methods:

• Developed a mutation-selection model for protein evolution under PPS, treating it as an irreversible Markov process.
• Implemented a penalized-likelihood approach to estimate selection coefficients directly at protein sites.
• Applied the model to detect PPS in plant RuBisCO and influenza HA proteins.

Main Results:

• Protein evolution under PPS exhibits a strongly asymmetrical distribution of selection coefficients.
• The model demonstrates that the ω > 1 criterion is conservative and arbitrary.
• Successfully detected PPS in plant RuBisCO and influenza HA proteins using the new method.

Conclusions:

• Direct estimation of selection coefficients provides a more robust approach to detecting molecular adaptation than using ω.
• The new model enhances the ability to identify evolutionary adaptation in proteins under persistent positive selection.