Predictable tuning of protein expression in bacteria.

Area of Science:

• Molecular Biology
• Systems Biology
• Synthetic Biology

Background:

• The Shine-Dalgarno sequence is crucial for initiating translation in prokaryotes.
• Precise control over protein expression levels is essential for synthetic biology applications.
• Existing methods for modulating protein expression often require complex genetic modifications.

Purpose of the Study:

• To quantitatively assess the impact of Shine-Dalgarno sequence variations on protein expression in Escherichia coli.
• To develop a predictive model and a practical tool for fine-tuning gene expression.
• To enable targeted modulation of protein production levels through minimal sequence changes.

Main Methods:

• Systematic design and synthesis of DNA sequences with varied Shine-Dalgarno elements.
• High-throughput measurement of protein expression levels for designed sequences.
• Development of an empirical model (EMOPEC) based on experimental data to predict expression outcomes.
• Validation of the EMOPEC tool for its ability to guide sequence design for desired expression levels.

Main Results:

• Established a comprehensive dataset correlating Shine-Dalgarno sequence features with protein expression levels.
• Developed EMOPEC, a freely accessible computational tool for predicting and optimizing protein expression.
• Demonstrated that modifying only a few bases in the Shine-Dalgarno region can effectively modulate protein output.
• Achieved high accuracy, with 91% of designed sequences yielding protein levels within a twofold range of the target.

Conclusions:

• The Shine-Dalgarno sequence is a key determinant of protein expression levels in E. coli.
• EMOPEC provides a powerful and accessible method for precise control of gene expression.
• This approach facilitates the engineering of E. coli for diverse synthetic biology and biotechnology applications.