DeepTESR: A Deep Learning Framework to Predict the Degree of Translational Elongation Short Ramp for Gene Expression Control

Controlling
translational elongation is essential for efficient
protein synthesis. Ribosome profiling has revealed that the speed
of ribosome movement is correlated with translational efficiency in
the translational elongation ramp. In this work, we present a new
deep learning model, called DeepTESR, to predict the degree of translational
elongation short ramp (TESR) from mRNA sequence. The proposed deep
learning model exhibited superior performance in predicting the TESR
scores for 226 981 TESR sequences, resulting in the mean absolute
error (MAE) of 0.285 and a coefficient of determination R2 of 0.627, superior to the conventional machine learning
models (e.g., MAE of 0.335 and R2 of 0.571
for LightGBM). We experimentally validated that heterologous fluorescence
expression of proteins with randomly selected TESR was moderately
correlated with the predictions. Furthermore, a genome-wide analysis
of TESR prediction in the 4305 coding sequences of Escherichia
coli showed conserved TESRs over the clusters of orthologous
groups. In this sense, DeepTESR can be used to predict the degree
of TESR for gene expression control and to decipher the mechanism
of translational control with ribosome profiling. DeepTESR is available
at https://github.com/fmblab/DeepTESR.

调控翻译延伸对于高效蛋白质合成至关重要。通过核糖体分析揭示了核糖体移动速度与翻译延伸阶段的翻译效率之间存在相关性。在本研究中，我们提出了一种名为DeepTESR的新深度学习模型，用于从mRNA序列预测翻译延伸短斜坡（TESR）的程度。所提出的深度学习模型在预测226,981个TESR序列的TESR评分方面表现出卓越的性能，其均方误差（MAE）为0.285，决定系数R²为0.627，优于传统的机器学习模型（例如，LightGBM的MAE为0.335，R²为0.571）。我们通过实验验证了，随机选择的TESR蛋白的非同源荧光表达与预测值存在中等程度的关联。此外，对大肠杆菌4305个编码序列中的TESR预测进行的全基因组分析表明，同源基因簇中存在保守的TESR。在此意义上，DeepTESR可用于预测基因表达调控中的TESR程度，并借助核糖体分析揭示翻译调控机制。DeepTESR可在https://github.com/fmblab/DeepTESR获取。