Abstract
Background The design of nucleotide sequences with defined properties is long-standing problem in bioengineering. An important application is protein expression, be it in the context of research or the production of mRNA vaccines. The rate of protein synthesis depends on the 5’ untranslated region (5’UTR) of the mRNAs, and recently, deep learning models were proposed to predict the translation output of mRNAs from the 5’UTR sequence. At the same time, large data sets of endogenous and reporter mRNA translation have become available.
Results In this study we use complementary data obtained in two different cell types to assess the accuracy and generality of currently available models of translation. We find that while performing well on the data sets on which they were trained, deep learning models do not generalize well to other data sets, in particular of endogenous mRNAs, which differ in many properties from reporter constructs.
Conclusions These differences limit the ability of deep learning models to uncover mechanisms of translation control and to predict the impact of genetic variation. We suggest directions that combine high-throughput measurements and machine learning to unravel mechanisms of translation control and improve construct design.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Contributing authors: niels.schlusser{at}unibas.ch; a.sevine{at}unibas.ch; muskan.pandey{at}unibas.ch;