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

Abstract

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.