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使用人工智能预测基因序列,以研究代码子使用模式
Tomer Sidi1, Shir Bahiri-Elitzur2, Tamir Tuller2,3
1Department of Computer Science, University of Haifa, Haifa 3303221, Israel.
概括
人工智能模型学习了细菌和真核生物中复杂的编码子使用模式,显著超过了基本方法. 这些发现推动了我们对进化选择的理解,并为优化蛋白质表达提供了工具.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 选择性压力塑造了密码子的使用,优化了尚未完全理解的生物信号.
- 的使用模式受到进化过程和基因表达水平的影响.
研究的目的:
- 训练人工智能模型,根据各种生物体的氨基酸序列来预测密码子的使用情况.
- 调查自然发生的编码子模式可以在多大程度上被学习并用于改进预测.
- 探索预测准确性,基因表达,蛋白质长度和进化因素之间的关系.
主要方法:
- 训练有素的人工智能 (AI) 模型对*Saccharomyces cerevisiae*,*Schizosaccharomyces pombe*,*Escherichia coli*和*Bacillus subtilis*的蛋白质序列进行了训练.
- 对不同长度和表达水平的蛋白质单独数据集的评估模型预测.
- 将人工智能模型的性能与原始频率基预测方法进行比较.
主要成果:
- 人工智能模型显著优于基于频率的方法,表明进化选择的编码子使用中可以学习的依赖性.
- 高表达基因的预测准确性更高,在细菌中比真核生物更高,支持选择压力和有效种群规模之间的联系.
- 模型显示,S. cerevisiae和细菌中较长的蛋白质的准确性有所提高,这表明与共翻译折叠有联系;基因功能和保存也影响了性能.
结论:
- 当代的人工智能方法可以有效地学习复杂的,进化选择的密码体使用模式.
- 开发的基于深度学习的预测工具提供了对内源和异源蛋白质表达的编码子优化的洞察.
- 这些发现支持了将子使用复杂性与选择性压力和有效种群规模联系起来的假设.
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