Unbiased Quantitative Models of Protein Translation Derived from Ribosome Profiling Data.
Translation of RNA to protein is a core process for any living organism. While for some steps of this process the effect on protein production is understood, a holistic understanding of translation still remains elusive. In silico modelling is a promising approach for elucidating the process of protein synthesis. Although a number of computational models of the process have been proposed, their application is limited by the assumptions they make. Ribosome profiling (RP), a relatively new sequencing-based technique capable of recording snapshots of the locations of actively translating ribosomes, is a promising source of information for deriving unbiased data-driven translation models. However, quantitative analysis of RP data is challenging due to high measurement variance and the inability to discriminate between the number of ribosomes measured on a gene and their speed of translation. We propose a solution in the form of a novel multi-scale interpretation of RP data that allows for deriving models with translation dynamics extracted from the snapshots. We demonstrate the usefulness of this approach by simultaneously determining for the first time per-codon translation elongation and per-gene translation initiation rates of Saccharomyces cerevisiae from RP data for two versions of the Totally Asymmetric Exclusion Process (TASEP) model of translation. We do this in an unbiased fashion, by fitting the models using only RP data with a novel optimization scheme based on Monte Carlo simulation to keep the problem tractable. The fitted models match the data significantly better than existing models and their predictions show better agreement with several independent protein abundance datasets than existing models. Results additionally indicate that the tRNA pool adaptation hypothesis is incomplete, with evidence suggesting that tRNA post-transcriptional modifications and codon context may play a role in determining codon elongation rates.
The Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands; Platform Green Synthetic Biology, Delft, The Netherlands; Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands.
Alexey A
AA
Gritsenko
The Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands.
Marc
M
Hulsman
The Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands; Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands.
Marcel J T
MJ
Reinders
The Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands; Platform Green Synthetic Biology, Delft, The Netherlands; Kluyver Centre for Genomics of Industrial Fermentation, Delft, The Netherlands; Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
Dick
D
de Ridder
D019295:Computational Biology / Q000379:methods*; D008957:Models, Genetic; D014176:Protein Biosynthesis / Q000235:genetics*; D012331:RNA, Fungal / Q000235:genetics / Q000378:metabolism; D012333:RNA, Messenger / Q000235:genetics* / Q000378:metabolism*; D012270:Ribosomes / Q000235:genetics / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism
D012331:RNA, Fungal; D012333:RNA, Messenger