Datasets to train machine learning algorithms to predict the translational dynamics and predict the correlation between a given mRNA level and proteins produced from them have relied on data generated on libraries of synthetic mRNAs. These synthetic mRNAs have different properties than endogenous ones, likely limiting predictions obtained from algorithms trained on synthetic mRNAs. To improve this situation, the Zavolan and Becskei labs have compiled a multi-omic assessment of the translation dynamics of endogenous mRNAs in liver cancer cells. These data should help the development of predictive algorithms of translational dynamics and these in turn help the design of therapeutic mRNAs e.g.vaccines.
Abstract
The limited correlation between mRNA and protein levels within cells highlighted the need to study mechanisms of translational control. To decipher the factors that determine the rates of individual steps in mRNA translation, machine learning approaches are currently applied to large libraries of synthetic constructs, whose properties are generally different from those of endogenous mRNAs. To fill this gap and thus enable the discovery of elements driving the translation of individual endogenous mRNAs, we here report steady-state and dynamic multi-omics data from human liver cancer cell lines, specifically (i) ribosome profiling data from unperturbed cells as well as following the block of translation initiation (ribosome run-off, to trace translation elongation), (ii) protein synthesis rates estimated by pulsed stable isotope labeled amino acids in cell culture (pSILAC), and (iii) mean ribosome load on individual mRNAs determined by mRNA sequencing of polysome fractions (polysome profiling). These data will enable improved predictions of mRNA sequence-dependent protein output, which is crucial for engineering protein expression and for the design of mRNA vaccines.
Read the Publication in Scientific Data (Open Access)
Abstract and figure from González, Pandey et al (2025) Sci Data published under a CC BY 4.0 license.