Current limitations in predicting mRNA translation with deep learning models
Schlusser, Niels (University of Basel)
González, Asier (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Pandey, Muskan (ETH Zurich)
Zavolan, Mihaela (University of Basel)
Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular

Fecha:	2024
Resumen:	The design of nucleotide sequences with defined properties is a 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. In this study, we use complementary data obtained in two different cell types to assess the accuracy and generality of currently available models for predicting translational output. 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. 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. The online version contains supplementary material available at 10. 1186/s13059-024-03369-6.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Translation control ; Deep learning ; Explainable AI ; Systems biology
Publicado en:	Genome biology, Vol. 25 (August 2024) , art. 227, ISSN 1474-760X

DOI: 10.1186/s13059-024-03369-6
PMID: 39164757

25 p, 3.0 MB

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