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How synonymous mutations alter enzyme structure and function over long timescales

Nature Chemistry volume 15, pages 308–318 (2023)Cite this article

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Abstract

The specific activity of enzymes can be altered over long timescales in cells by synonymous mutations that alter a messenger RNA molecule’s sequence but not the encoded protein’s primary structure. How this happens at the molecular level is unknown. Here, we use multiscale modelling of three Escherichia coli enzymes (type III chloramphenicol acetyltransferase, d-alanine–d-alanine ligase B and dihydrofolate reductase) to understand experimentally measured changes in specific activity due to synonymous mutations. The modelling involves coarse-grained simulations of protein synthesis and post-translational behaviour, all-atom simulations to test robustness and quantum mechanics/molecular mechanics calculations to characterize enzymatic function. We show that changes in codon translation rates induced by synonymous mutations cause shifts in co-translational and post-translational folding pathways that kinetically partition molecules into subpopulations that very slowly interconvert to the native, functional state. Structurally, these states resemble the native state, with localized misfolding near the active sites of the enzymes. These long-lived states exhibit reduced catalytic activity, as shown by their increased activation energies for the reactions they catalyse.

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Fig. 1: A multiscale approach for understanding the influence of synonymous codons on the structure and function of enzymes.
Fig. 2: Fast translation partitions more CAT-III into post-translational kinetically trapped entangled states.
Fig. 3: Slow translation partitions more DDLB into post-translational kinetically trapped entangled states.
Fig. 4: No kinetically trapped states arise in synonymous variants of DHFR.
Fig. 5: Illustration of the G metric and non-covalent lasso topology.
Fig. 6: Co- and post-translational folding pathways of CAT-III, DDLB and DHFR.

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Data availability

Data supporting the main findings of this study are available within the Article and its Supplementary Information and source data files. We cannot feasibly provide all ~5.3 TB of molecular dynamics trajectory data, but we provide the input data that were used to perform the simulations in this study in the repository subdirectory https://github.com/obrien-lab/cg_simtk_protein_folding/blob/master/example/input_data.tar.xz. All of the data that support the findings of this study, as well as the biological materials that were used to test the enzymatic activity of the DDLB and DHFR variants and for the LiP-MS experiments, are available from the corresponding author upon reasonable request. The raw mass spectrometry data for DDLB and DHFR have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD031425. A website (https://obrien-lab.github.io/visualize_entanglements/) was created to provide interactive visualization of the key misfolded, entangled structures predicted in this study. Source data are provided with this paper.

Code availability

All of the computer code developed in this work is available in the GitHub repositories https://github.com/obrien-lab/cg_simtk_protein_folding and https://github.com/obrien-lab/Activation-Energy-Estimation-Workflow under the MIT License. Detailed instructions on code usage, basic theory and examples of the input/output are available in the wiki pages of the above repositories.

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Acknowledgements

S.D.F. acknowledges support from the National Institutes of Health (NIH) Director’s New Innovator Award (DP2GM140926) and National Science Foundation (MCB-2045844). S.J.B. acknowledges support from the NIH (GM-122595), Eberly Family Distinguished Chair in Science and Howard Hughes Medical Institute. E.P.O. acknowledges support from the National Science Foundation (MCB-1553291) and NIH (R35-GM124818). Computations in this work were carried out on the Extreme Science and Engineering Discovery Environment supercomputer32 (which is supported by MCB-160069) and the Pennsylvania State University’s Institute for Computational and Data Sciences’ Roar supercomputer. The CLS Behring Fermentation Facility and Huck Institutes of the Life Sciences at the Pennsylvania State University provided equipment and training to grow and purify DHFR biological replicates. We thank T. Berek and P. Kashyap for help with growing and purifying the DHFR biological replicates.

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Authors and Affiliations

  1. Department of Chemistry, Pennsylvania State University, University Park, PA, USA

    Yang Jiang, Syam Sundar Neti, Ian Sitarik, Priya Pradhan, Squire J. Booker & Edward P. O’Brien

  2. Department of Chemistry, Johns Hopkins University, Baltimore, MD, USA

    Philip To, Yingzi Xia & Stephen D. Fried

  3. Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD, USA

    Stephen D. Fried

  4. Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA, USA

    Squire J. Booker

  5. Howard Hughes Medical Institute, Pennsylvania State University, University Park, PA, USA

    Squire J. Booker

  6. Bioinformatics and Genomics Graduate Program, The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA

    Edward P. O’Brien

  7. Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA

    Edward P. O’Brien

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Contributions

E.P.O. designed the research. Y.J. developed the computational methods with contributions from E.P.O. Y.J. wrote the computer code and carried out the simulations and computations. S.S.N. and S.J.B. designed the experimental validation for the DDLB variants. S.S.N., I.S., E.P.O. and S.J.B. designed the experimental validation for the DHFR variants. S.S.N., I.S. and P.P. performed the specific activity experiments. S.D.F. designed the LiP-MS experiments for DDLB and DHFR. P.T. and Y.X. performed the LiP-MS experiments. All of the authors analysed the data and wrote the manuscript.

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Correspondence to Edward P. O’Brien.

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Nature Chemistry thanks Kevin Pagel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Results (virtual screening and disentangling timescale), Supplementary Discussions, Supplementary Tables 1–15 and Supplementary Figs. 1–15.

Reporting Summary

Supplementary Video 1

Co- and post-translational folding trajectory of a misfolded CAT-III with a non-native entanglement formed after synthesis. The conformation gets trapped in state P13 at the end. The nascent chain is shown in cyan, where the closed loop formed by the native contact is highlighted in red and the segment threading the red loop is highlighted in blue. At each nascent chain length, the conformation is obtained from the last frame of the simulation trajectory. The time of ejection, dissociation and post-translation processes are presented using the experimental timescale on the top left of the scene.

Supplementary Video 2

Co- and post-translational folding trajectory of a correctly folded CAT-III without any non-native entanglements formed. The nascent chain is coloured based on secondary structure elements, where α helices are magenta and β sheets are yellow. At each nascent chain length, the conformation is obtained from the last frame of the simulation trajectory. The time of ejection, dissociation and post-translation processes are presented using the experimental timescale on the top left of the scene.

Supplementary Video 3

Co- and post-translational folding trajectory of a misfolded DDLB with a non-native entanglement formed during synthesis that persisted in the post-translational dynamics. The conformation gets trapped in state P4 at the end. The nascent chain is shown in cyan, where the closed loop formed by the native contact is highlighted in red and the segment threading the red loop is highlighted in blue. At each nascent chain length, the conformation is obtained from the last frame of the simulation trajectory. The time of ejection, dissociation and post-translation processes are presented using the experimental timescale on the top left of the scene.

Supplementary Video 4

Co- and post-translational folding trajectory of a correctly folded DDLB without any non-native entanglements formed. The nascent chain is coloured based on secondary structure elements, where α helices are magenta and β sheets are yellow. At each nascent chain length, the conformation is obtained from the last frame of the simulation trajectory. The time of ejection, dissociation and post-translation processes are presented using the experimental timescale on the top left of the scene.

Source data

Source Data Fig. 1

Data presented in the figure and raw data points for statistics.

Source Data Fig. 2

Data presented in the figure and raw data points for statistics.

Source Data Fig. 3

Data presented in the figure and raw data points for statistics.

Source Data Fig. 4

Data presented in the figure and raw data points for statistics.

Source Data Fig. 5

Data presented in the figure.

Source Data Fig. 6

Raw pathway probabilities.

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Jiang, Y., Neti, S.S., Sitarik, I. et al. How synonymous mutations alter enzyme structure and function over long timescales. Nat. Chem. 15, 308–318 (2023). https://doi.org/10.1038/s41557-022-01091-z

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