Link Between Individual Codon Frequencies and Protein Expression: Going Beyond Codon Adaptation Index.

Area of Science:

• Molecular Biology
• Biotechnology
• Bioinformatics

Background:

• Synonymous codon composition influences gene expression levels.
• Existing algorithms optimize codon usage for recombinant gene expression.
• The precise mechanism linking codon composition to protein abundance remains largely unexplained.

Purpose of the Study:

• To propose novel statistical and empirical methods for predicting protein expression levels from nucleotide sequences.
• To elucidate the role of individual codons in determining overall protein abundance.
• To provide tools for designing proteins with optimized expression in specific organisms.

Main Methods:

• Development of the Codon Expression Index Score (CEIS) method.
• Development of the Codon Productivity Score (CPS) method.
• Validation against quantitative proteome data from Escherichia coli.

Main Results:

• CEIS and CPS predict protein expression levels based on codon influence and isoacceptor frequencies.
• Predictions achieved a correlation of up to r = 0.7 with experimental proteome data.
• The proposed methods demonstrate superior performance compared to existing approaches.

Conclusions:

• Codon composition significantly impacts protein abundance.
• CEIS and CPS offer a more accurate understanding of codon-mediated gene expression.
• These methods facilitate the rational design of highly expressed proteins for biotechnological applications.