Optimizing the dynamics of protein expression.

Area of Science:

• Molecular Biology
• Biotechnology
• Bioinformatics

Background:

• Heterologous gene expression requires codon adaptation for efficient protein synthesis.
• Standard codon optimization methods often yield suboptimal results.
• Understanding ribosome dynamics is crucial for predicting protein expression levels.

Purpose of the Study:

• To introduce the codon-specific elongation model (COSEM) as an alternative to standard codon optimization.
• To develop a predictive model for protein synthesis rates based on ribosome dynamics.
• To implement COSEM in a software tool (OCTOPOS) for optimizing heterologous gene expression.

Main Methods:

• Simulating ribosome dynamics during mRNA translation using COSEM.
• Integrating simulated protein synthesis rates with other factors (e.g., translation accuracy) into a protein expression score.
• Applying the scoring algorithm for codon optimization and deoptimization.
• Validating the model's predictions using proteomic data from diverse expression systems.
• Experimentally optimizing manA and ova gene expression in Salmonella enterica serovar Typhimurium.

Main Results:

• COSEM provides organism- and context-dependent predictions of protein synthesis rates.
• The protein expression score accurately predicts proteomic data across prokaryotic, eukaryotic, and human systems.
• Optimized expression of manA and ova genes in Salmonella enterica serovar Typhimurium.
• Achieved a threefold increase in protein yield compared to wild-type and commercially optimized sequences.

Conclusions:

• COSEM offers a more accurate approach to codon optimization by considering ribosome dynamics.
• The OCTOPOS software facilitates fine-tuning of protein expression.
• COSEM-based optimization significantly enhances heterologous protein production, outperforming standard methods.