Novel methods to optimize gene and statistic test for evaluation - an application for Escherichia coli

Area of Science:

• Biotechnology
• Molecular Biology
• Bioinformatics

Background:

• Recombinant protein technology is crucial in life sciences, with significant market value.
• Native gene expression in hosts faces challenges like codon usage bias and sequence incompatibility, reducing yields.
• Existing gene optimization methods require enhancement to overcome these limitations.

Purpose of the Study:

• To develop novel gene optimization methods addressing host expression system incompatibilities.
• To improve the efficiency and yield of recombinant protein production.
• To provide a computational tool for gene sequence optimization.

Main Methods:

• Utilizing neural networks, Bayesian theory, and Euclidean distance for gene sequence analysis.
• Developing predictive models for codon usage bias, GC content, and regulatory sequences.
• Implementing a demonstrative program in Matlab R2014a for practical application.

Main Results:

• Achieved high correlation coefficients (0.86, 0.73, 0.90) in neural network training, validation, and testing.
• Optimized genes demonstrated association with high expression levels and suitable codon adaptation index values.
• The proposed methods showed strong alignment with existing experimental data.

Conclusions:

• The novel gene optimization methods show significant potential for enhancing recombinant protein expression.
• The developed Matlab program offers a practical tool for researchers in gene expression studies.
• Further research in gene optimization and expression is warranted based on these findings.