NeuralCodOpt: Codon optimization for the development of DNA vaccines

Abstract

Inefficient gene translation, driven by organisms’ codon preferences, is an emerging research area since this results in sluggish processes and diminished protein yields. Our research culminates in deriving efficient, optimized codon sequences by considering organism-specific Relative Codon Adaptiveness (RCA) ranges. In this research work, we have developed a novel algorithm, Neural Codon Optimization (NeuralCodOpt), to automate the process of codon optimization tailored to a specific organism and input sequence. Our algorithm has two main parts: the target Codon Adaptation Index generation using K-Means and the automation of sequence optimization using reinforcement learning. This algorithm has been tested across a set of 130 species, yielding highly optimal results that are quite significant compared to the previous works. NeuralCodOpt has shown a high accuracy of 86.7%, which would substantially contribute to Deoxyribonucleic Acid (DNA) vaccines by improving the efficiency of DNA expression vectors. These vectors are crucial in DNA vaccination and gene therapy as they enhance protein expression levels. By further incorporating it into plasmid construction, the translational efficiency of DNA vaccines will be significantly improved.