OPTIMIZER

OPTIMIZER optimizes coding sequences by adjusting codon usage to enhance gene expression for heterologous expression in prokaryotic hosts.

Key Features:

• One Amino Acid–One Codon Method: Assigns a single codon to each amino acid to produce uniform codon usage and potentially improve translational efficiency.
• Guided Random Method (Monte Carlo Algorithm): Applies a Monte Carlo algorithm to introduce stochastic, guided adjustments to codon selection.
• Minimized Change Optimization: Maximizes codon adaptation while minimizing the number of alterations to the original DNA sequence.
• Pre-computed Codon Usage Tables: Uses codon usage tables derived from highly expressed genes across more than 150 prokaryotic species.
• Iterative Algorithm for Table Generation: Generates codon usage tables via an iterative algorithm that predicts groups of genes under strong translational selection.
• Mean Codon Usage Table (Ribosomal Proteins): Offers an option to use a mean codon usage table based on ribosomal protein genes.
• tRNA Gene-Copy Number Incorporation: Provides the option to incorporate tRNA gene-copy numbers into the optimization strategy.

Scientific Applications:

• Heterologous Gene Expression Optimization: Enhances expression levels of recombinant genes in heterologous prokaryotic systems.
• Host-Specific Codon Adaptation: Adapts coding sequences to host-specific codon usage patterns derived from highly expressed genes.
• Genetic Engineering and Synthetic Biology: Supports design of codon-optimized constructs for genetic engineering and synthetic biology in prokaryotes.

Methodology:

Provides three optimization methods (one amino acid–one codon, guided random via a Monte Carlo algorithm, and minimized change optimization) and uses pre-computed codon usage tables generated by an iterative algorithm identifying genes under strong translational selection, with options to use a mean ribosomal protein codon table and incorporate tRNA gene-copy numbers.