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Accurate design of translational output by a neural network model of ribosome distribution

Nature Structural & Molecular Biology volume 25, pages 577–582 (2018)Cite this article

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Abstract

Synonymous codon choice can have dramatic effects on ribosome speed and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs. Here, we have modeled this variation in translation elongation by using a feed-forward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. Our approach revealed sequence features affecting translation elongation and characterized large technical biases in ribosome profiling. We applied our model to design synonymous variants of a fluorescent protein spanning the range of translation speeds predicted with our model. Levels of the fluorescent protein in budding yeast closely tracked the predicted translation speeds across their full range. We therefore demonstrate that our model captures information determining translation dynamics in vivo; that this information can be harnessed to design coding sequences; and that control of translation elongation alone is sufficient to produce large quantitative differences in protein output.

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Fig. 1: Design and performance of a neural network model of translation elongation.
Fig. 2: Performance comparisons on low-coverage genes and with competing models.
Fig. 3: Interpretation of models of translation-elongation rates.
Fig. 4: Design of synonymous sequences showing that elongation rate affects translation output.

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  • 09 July 2018

    In the version of this article initially published, Supplementary Notes 1—3 and Supplementary Tables 1 and 2 were omitted from the Supplementary Text and Figures file. The error has been corrected.

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Acknowledgements

We are grateful to N. Ingolia and S. McCurdy for discussion. This work was supported by the National Cancer Institute of the National Institutes of Health, under award R21CA202960 to L.F.L., and by the National Institute of General Medical Sciences of the National Institutes of Health, under award P50GM102706 to the Berkeley Center for RNA Systems Biology. R.T. was supported by the Department of Defense through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. This work made use of the Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley, supported by National Institutes of Health S10 Instrumentation grant OD018174, and the UC Berkeley flow cytometry core facilities.

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Author notes

  1. These authors contributed equally: Robert Tunney, Nicholas J. McGlincy.

Authors and Affiliations

  1. Graduate Group in Computational Biology, University of California, Berkeley, Berkeley, CA, USA

    Robert Tunney

  2. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

    Nicholas J. McGlincy & Lior Pachter

  3. California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, USA

    Nicholas J. McGlincy, Monica E. Graham, Nicki Naddaf & Liana F. Lareau

  4. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Lior Pachter

  5. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Lior Pachter

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Contributions

L.F.L., R.T., and N.J.M. designed the study, with input from L.P. R.T. developed the software and performed modeling, and R.T., L.P., and L.F.L. analyzed and interpreted the computational results. N.J.M. designed and created the yeast strains and performed expression experiments, with assistance from M.E.G. and N.N. M.E.G. performed yeast ribosome profiling. N.J.M. and L.F.L. analyzed and interpreted the experimental data. R.T. and L.F.L. wrote the manuscript, with input from all authors.

Corresponding author

Correspondence to Liana F. Lareau.

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Integrated supplementary information

Supplementary Figure 1 Neural network model structure.

(A) Counts are predicted at the A site codon. In the model shown, a sequence neighborhood spanning from 5 codons upstream of the A site (codon -5) to 4 codons downstream of the A site (codon + 4) is used as the predictive region. This neighborhood is divided into codons and encoded via one-hot encoding (purple) for input into a regression model. We also encode the same region as nucleotide features (green) and include these features in the model. Finally, we compute RNA structure scores on three 30 nt sliding structure windows that span the width of a typical 28 nt footprint. These windows start 17, 16, and 15 nucleotides before the start of the A site. (B) These features are concatenated in a vector, which is used as the input to a fully connected feedforward neural network model. Each model in this paper contains one hidden layer with 200 hidden units, and a tanh activation function on the hidden units. The output layer contains one unit with a ReLU activation function to enforce nonnegativity of predicted scaled counts.

Supplementary Figure 2 Change in MSE upon including mRNA structure.

We compared the performance of a model using a sequence neighborhood from codons -7 to + 5 against models that each also include the folding energy of a single 30 nt window. Each point shows the change in MSE upon including a particular window. The greatest improvement in MSE is achieved by including a window starting at nucleotide position -17 and ending at position 12. This is roughly coterminal with a typical 28 nt ribosome footprint (nucleotide positions -15 to + 12).

Supplementary Figure 3 Relative contributions of A-site codons and context.

(A) Pearson correlation of observed vs. predicted scaled counts per codon, for a model using codons -3 to + 2 and associated nucleotides (x-axis) and a model using the same region but without the A site (y-axis). Codons whose inclusion in the model leads to significantly better prediction (higher correlation between the observed and predicted scaled counts), per a t-test of the Fisher transformation of correlations with an FDR of 5%, are shown in red. (B) We extracted the sequence context (codons -3 to + 2) for all positions where the squared error was higher for an A-site-only model than for a model with codons -3 to + 2 but no A site. The proportion of codons at each position in this set was compared to the overall distribution of codons in the test set with a two-sided proportion test, using an FDR of 5%, and codons with adjusted p < 0.05 are shown in the plot.

Supplementary Figure 4 Predictive values of codon positions.

Predictive value of codon positions in (A) a human ribosome profiling data set using Circligase II (Iwasaki, S. et al., Nature. 534(7608), 558-61, 2016), and (B) a yeast ribosome profiling dataset using Circligase I (Schuller, A. et al., Mol. Cell. 66, 194–205.e5, 2017). As in Fig. 3a, we trained a reference model on codons -7 to + 5 (with nucleotide features over the same neighborhood), and then a series of leave-one-out models each excluding exactly one codon in the sequence neighborhood, along with the corresponding nucleotides. For each model, we compute Pearson correlations between the true and predicted scaled counts over all codons in the test set. Shown is the difference in Pearson correlations between the reference model and the leave-one-out models. Higher Δr indicates increased importance of that codon position to model predictions.

Supplementary Figure 5 Circligase I and II preferences.

(A) Mean contributions to scaled counts at the 5´ end of a ribosome footprint, for yeast data sets generated with Circligase II (our data) and Circligase I (Schuller, A. et al., Mol. Cell. 66, 194–205.e5, 2017). Scores are from the -5 codon position. To generate these scores, we trained models only on 28 nt footprints with their 5´ end aligning with the beginning of the -5 codon. (B) Ligation efficiency of CircLigase I enzyme, as in Fig. 3g.

Supplementary Figure 6 Flow cytometry gating strategy.

Scatter plot of forward scatter area (FSC, arbitrary units) against side scatter area (SSC, arbitrary units) for each of the 50000 events collected for a representative flow cytometry sample of diploid yeast expressing mCherry and a differentially optimized eCitrine. Events are colored by their density on the plot, low density points being colored blue moving to high density events being colored dark red. Events outside the plotted area are denoted by grey lines at the edge of the plot. Annotated by red regions are seven areas of high local density defined by the curv2filter method, each with the number of events they contain. For each sample, events within the most populous region were taken forward for further analysis; in this representative sample, these would be the events within Area 3.

Supplementary Figure 7 yECitrine mRNA level and fluorescence.

(A) eCitrine:mCherry fluorescence ratio as in Fig. 3c, including this ratio for the yECitrine sequence (magenta). (B) eCitrine:mCherry mRNA ratio measured by qPCR in biological replicates of four strains (colors as in Fig. 4). Each data point represents the ratio of medians of three technical replicates, normalized to the median ratio of the highest expression strain.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Notes 1–3 and Supplementary Tables 1 and 2

Reporting Summary

Supplementary Table 3

Codon contributions. Weights learned as described in the methods for each codon in positions -5 to +4, used to create figure 3B

Supplementary Dataset 1

Annotated sequences of plasmids for eCitrine strain creation, in genbank format

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Tunney, R., McGlincy, N.J., Graham, M.E. et al. Accurate design of translational output by a neural network model of ribosome distribution. Nat Struct Mol Biol 25, 577–582 (2018). https://doi.org/10.1038/s41594-018-0080-2

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