A method for detecting positive selection at single amino acid sites

Area of Science:

• Evolutionary Biology
• Molecular Evolution
• Bioinformatics

Background:

• Identifying selective pressures at the amino acid level is crucial for understanding protein evolution and function.
• Existing methods may face challenges in accurately detecting selection across diverse protein families and evolutionary distances.

Purpose of the Study:

• To develop and validate a novel method for detecting positive selection at single amino acid sites.
• To assess the method's accuracy and reliability using computer simulations and real biological data.

Main Methods:

• Reconstruction of phylogenetic trees based on synonymous substitution rates.
• Testing codon site neutrality using synonymous and nonsynonymous substitution counts across the tree.
• Application of the method to human leukocyte antigen (HLA) genes, HIV envelope proteins, and influenza hemagglutinin.

Main Results:

• The method accurately estimates synonymous and nonsynonymous substitutions, with low false-positive rates for detecting selection.
• True-positive detection rates increase with stronger selective forces and greater total branch lengths in phylogenetic trees.
• Positive selection was predominantly detected at antigen recognition sites (ARSs) in HLA genes, with new sites identified in non-ARSs.

Conclusions:

• The developed method reliably detects positive selection at amino acid sites, confirmed by its application to HLA, HIV, and influenza proteins.
• The findings suggest potential new roles for identified positively selected sites in antigen recognition and protein function.
• This method offers a valuable tool for functional site prediction in the era of massive sequence data accumulation.