Building a tRNA thermometer to estimate microbial adaptation to temperature
Building a tRNA thermometer to estimate microbial adaptation to temperature
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DOI：
10.1093/nar/gkaa1030
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发表时间：
2020-12-02
影响因子：
14.9
通讯作者：
Buckler, Edward S.
中科院分区：
文献类型：
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作者：
Cimen, Emre;Jensen, Sarah E.;Buckler, Edward S.
由于环境温度影响生物化学反应，生活在极端温度条件下的生物体通过调整蛋白质组成和结构来维持生物化学功能。虽然不可能通过实验确定每个已知微生物物种的最佳生长温度（OGT），但适应不同温度的生物体在DNA、RNA和蛋白质组成方面存在可测量的差异，这使得仅从基因组序列就可以预测OGT。在本研究中，我们利用tRNA序列构建了一个“tRNA温度计”模型来预测OGT。我们使用来自100种古细菌和683种细菌的序列作为输入来训练两个卷积神经网络模型。第一对来自不同物种的个体tRNA序列可以预测哪个来自更嗜热的生物，准确度在0.538到0.992之间。第二种方法使用物种中trna的完整集合来预测最佳生长温度，最大r(2)为0.86；尽管输入数据量显著减少，但与文献中其他预测精度相当。该模型改进了以前的OGT预测模型，提供了一个输入数据需求最小的模型，消除了费力的特征提取和数据预处理步骤，扩大了有效下游分析的范围。
Because ambient temperature affects biochemical reactions, organisms living in extreme temperature conditions adapt protein composition and structure to maintain biochemical functions. While it is not feasible to experimentally determine optimal growth temperature (OGT) for every known microbial species, organisms adapted to different temperatures have measurable differences in DNA, RNA and protein composition that allow OGT prediction from genome sequence alone. In this study, we built a 'tRNA thermometer' model using tRNA sequence to predict OGT. We used sequences from 100 archaea and 683 bacteria species as input to train two Convolutional Neural Network models. The first pairs individual tRNA sequences from different species to predict which comes from a more thermophilic organism, with accuracy ranging from 0.538 to 0.992. The second uses the complete set of tRNAs in a species to predict optimal growth temperature, achieving a maximum r(2) of 0.86; comparable with other prediction accuracies in the literature despite a significant reduction in the quantity of input data. This model improves on previous OGT prediction models by providing a model with minimum input data requirements, removing laborious feature extraction and data preprocessing steps and widening the scope of valid downstream analyses.