AUTHOR=Anwar Ali Mostafa , Khodary Saif M. , Ahmed Eman Ali , Osama Aya , Ezzeldin Shahd , Tanios Anthony , Mahgoub Sebaey , Magdeldin Sameh 

TITLE=gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=10

YEAR=2023

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2023.1218518

DOI=10.3389/fmolb.2023.1218518

ISSN=2296-889X

ABSTRACT=<p>The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values (<italic>S</italic><sub><italic>ij</italic></sub> values) for codon–tRNA wobble interaction efficiencies. The initial implementation of the tAI had significant flaws. For instance, generated <italic>S</italic><sub><italic>ij</italic></sub> weights were optimized based on gene expression in <italic>Saccharomyces cerevisiae</italic>, which is expected to vary among different species. Consequently, a species-specific approach (stAI) was developed to overcome those limitations. However, the stAI method employed a hill climbing algorithm to optimize the <italic>S</italic><sub><italic>ij</italic></sub> weights, which is not ideal for obtaining the best set of <italic>S</italic><sub><italic>ij</italic></sub> weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. In addition, it did not perform well in computing the tAI of fungal genomes in comparison with the original implementation. We developed a novel approach named genetic tAI (gtAI) implemented as a Python package (<ext-link ext-link-type="uri" xlink:href="https://github.com/AliYoussef96/gtAI" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/AliYoussef96/gtAI</ext-link>), which employs a genetic algorithm to obtain the best set of <italic>S</italic><sub><italic>ij</italic></sub> weights and follows a new codon usage-based workflow that better computes the tAI of genomes from the three domains of life. The gtAI has significantly improved the correlation with the codon adaptation index (CAI) and the prediction of protein abundance (empirical data) compared to the stAI.</p>