NeuralCodOpt: Codon optimization for the development of DNA vaccines

IF 2.6 4区生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2025-02-12 DOI:10.1016/j.compbiolchem.2025.108377

Tapan Chowdhury , Aishwarya Saha , Ananya Saha , Arnab Chakraborty , Nibir Das

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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.

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来源期刊

Computational Biology and Chemistry 生物-计算机：跨学科应用

CiteScore

6.10

自引率

3.20%

发文量

142

审稿时长

24 days

期刊介绍： Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.