Anwar Ali Mostafa, Khodary Saif M, Ahmed Eman Ali, Osama Aya, Ezzeldin Shahd, Tanios Anthony, Mahgoub Sebaey, Magdeldin Sameh
Proteomics and Metabolomics Research Program, Basic Research Department, Children's Cancer Hospital 57357 (CCHE-57357), Cairo, Egypt.
Department of Genetics, Faculty of Agriculture, Cairo University, Giza, Egypt.
Front Mol Biosci. 2023 Jul 4;10:1218518. doi: 10.3389/fmolb.2023.1218518. eCollection 2023.
The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values ( values) for codon-tRNA wobble interaction efficiencies. The initial implementation of the tAI had significant flaws. For instance, generated weights were optimized based on gene expression in , 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 weights, which is not ideal for obtaining the best set of 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 (https://github.com/AliYoussef96/gtAI), which employs a genetic algorithm to obtain the best set of 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.
tRNA适配指数（tAI）是一种翻译效率指标，它考虑了密码子与tRNA摆动相互作用效率的加权值（ 值）。tAI的最初实现存在重大缺陷。例如，生成的 权重是基于 中的基因表达进行优化的，而不同物种之间的基因表达预计会有所不同。因此，开发了一种物种特异性方法（stAI）来克服这些限制。然而，stAI方法采用爬山算法来优化 权重，这对于获得最佳的 权重集并不理想，因为即使使用不同的起始位置，在复杂的搜索空间中它也可能难以找到全局最大值。此外，与原始实现相比，它在计算真菌基因组的tAI时表现不佳。我们开发了一种名为遗传tAI（gtAI）的新方法，它作为一个Python包（https://github.com/AliYoussef96/gtAI）实现，该方法采用遗传算法来获得最佳的 权重集，并遵循一种基于密码子使用的新工作流程，能更好地计算来自生命三个域的基因组的tAI。与stAI相比，gtAI显著提高了与密码子适配指数（CAI）的相关性以及对蛋白质丰度（经验数据）的预测。
