PreprintCited by 199RNA and protein synthesis mechanismsRNA Research and SplicingRNA Interference and Gene Delivery

Theoretical basis for stabilizing messenger RNA through secondary structure design

Open access
Wayment-Steele, Hannah K., Kim, Do Soon, Choe, Christian A., Nicol, John J., Wellington-Oguri, Roger, Watkins, Andrew M., Sperberg, R. Andres Parra, Huang, Po-Ssu +2 more·2020·Nucleic Acids Research

Preprint — published as 10.1093/nar/gkab764 · Paper page

Abstract

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery, and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. These computational tests were carried out on both model mRNAs and COVID-19 mRNA vaccine candidates. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term ‘superfolder’ mRNAs. These designs exhibit wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity, and their folding is robust to temperature, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1, and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable. Significance statement: Messenger RNA (mRNA) medicines that encode and promote translation of a target protein have shown promising use as vaccines in the current SARS-CoV-2 pandemic as well as infectious diseases due to their speed of design and manufacturing. However, these molecules are intrinsically prone to hydrolysis, leading to poor stability in aqueous buffer and major challenges in distribution. Here, we present a principled biophysical model for predicting RNA degradation, and demonstrate that the stability of any mRNA can be increased at least two-fold over conventional design techniques. Furthermore, the predicted stabilization is robust to post-design modifications. This conceptual framework and accompanying algorithm can be immediately deployed to guide re-design of mRNA vaccines and therapeutics to increase in vitro stability.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2020.08.22.262931

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 50

  1. [1]Wu F., Zhao S., Yu B., Chen Y. M., Wang W., Song Z. G., Hu Y., Tao Z. W., Tian J. H., Pei Y. Y., Yuan M. L., Zhang Y. L., Dai F. H., Liu Y., Wang Q. M., Zheng J. J., Xu L., Holmes E. C., and Zhang Y. Z.. 2020. A New Coronavirus Associated With Human Respiratory Disease in China. Nature 579(7798): 265– 269. 32015508 PMC7094943
  2. [2]Chauhan G., Madou M. J., Kalra S., Chopra V., Ghosh D., and Martinez-Chapa S. O.. Nanotechnology for COVID-19: Therapeutics and Vaccine Research. ACS Nano. 32571007
  3. [3]McKay P. F., Hu K., Blakney A. K., Samnuan K., Brown J. C., Penn R., Zhou J., Bouton C. R., Rogers P., Polra K., Lin P. J. C., Barbosa C., Tam Y. K., Barclay W. S., and Shattock R. J.. 2020. Self-amplifying RNA SARS-CoV-2 lipid nanoparticle vaccine candidate induces high neutralizing antibody titers in mice. Nature Communications 11(1): 3523. PMC7347890 32647131
  4. [4]Kaczmarek J. C., Kowalski P. S., and Anderson D. G.. 2017. Advances in the delivery of RNA therapeutics: from concept to clinical reality. Genome Medicine 9(1): 60. 28655327 PMC5485616
  5. [5]Erasmus J. H., and Fuller D. H.. 2020. Preparing for Pandemics: RNA Vaccines at the Forefront. Molecular Therapy 28(7): 1559– 1560. 32579882 PMC7309706
  6. [7]Zhao P., Hou X., Yan J., Du S., Xue Y., Li W., Xiang G., and Dong Y.. 2020. Long-term storage of lipid-like nanoparticles for mRNA delivery. Bioactive Materials 5(2): 358– 363. 32206737 PMC7078456
  7. [9]Zhang N.-N., Li X.-F., Deng Y.-Q., Zhao H., Huang Y.-J., Yang G., Huang W.-J., Gao P., Zhou C., Zhang R.-R., Guo Y., Sun S.-H., Fan H., Zu S.-L., Chen Q., He Q., Cao T.-S., Huang X.-Y., Qiu H.-Y., Nie J.-H., Jiang Y., Yan H.-Y., Ye Q., Zhong X., Xue X.-L., Zha Z.-Y., Zhou D., Yang X., Wang Y.-C., Ying B., and Qin C.-F.. 2020. A Thermostable mRNA Vaccine against COVID-19. Cell. PMC7377714 32795413
  8. [10]Markham R., and Smith J. D.. 1952. The structure of ribonucleic acids. 1. Cyclic nucleotides produced by ribonuclease and by alkaline hydrolysis. Biochem J 52(4): 552– 557. 13018277 PMC1198056
  9. [11]Oivanen M., Kuusela S., and Lönnberg H.. 1998. Kinetics and Mechanisms for the Cleavage and Isomerization of the Phosphodiester Bonds of RNA by Brønsted Acids and Bases. Chemical Reviews 98(3): 961– 990. 11848921
  10. [12]Cataldo F. 2006. Ozone Degradation of Biological Macromolecules: Proteins, Hemoglobin, RNA, and DNA. Ozone: Science & Engineering 28(5): 317– 328. Journal Article.
  11. [13]Baldridge K. C., Zavala J., Surratt J., Sexton K. G., and Contreras L. M.. 2015. Cellular RNA is chemically modified by exposure to air pollution mixtures. Inhalation Toxicology 27(1): 74– 82. Journal Article. 25600141
  12. [14]Li Y., and Breaker R. R.. 1999. Kinetics of RNA Degradation by Specific Base Catalysis of Transesterification Involving the 2’-Hydroxyl Group. J. Amer. Chem. Soc. 121(23): 5364– 5372. Journal Article.
  13. [15]Martick M., and Scott W. G.. 2006. Tertiary Contacts Distant From the Active Site Prime a Ribozyme for Catalysis. Cell 126(2): 309– 320. 16859740 PMC4447102
  14. [16]Kaukinen U., Lyytikäinen S., Mikkola S., and Lönnberg H.. 2002. The Reactivity of Phosphodiester Bonds Within Linear Single-Stranded Oligoribonucleotides Is Strongly Dependent on the Base Sequence. Nucleic Acids Res. 30(2). PMC99829 11788709
  15. [17]Mchedlov-Petrossyan N. O., Vodolazkaya N. A., Yakubovskaya A. G., Grigorovich A. V., Alekseeva V. I., and Savvina L. P.. 2007. A novel probe for determination of electrical surface potential of surfactant micelles: N,N’-di-n-octadecylrhodamine. Journal of Physical Organic Chemistry 20(5): 332– 344.
  16. [18]Clear K. J., Virga K., Gray L., and Smith B. D.. 2016. Using Membrane Composition to Fine-Tune the P Ka of an Optical Liposome pH Sensor. J. Mater. Chem. C. Mater. 4(14). PMC4830428 27087967
  17. [19]Geall A. J., Verma A., Otten G. R., Shaw C. A., Hekele A., Banerjee K., Cu Y., Beard C. W., Brito L. A., Krucker T., O’Hagan D. T., Singh M., Mason P. W., Valiante N. M., Dormitzer P. R., Barnett S. W., Rappuoli R., Ulmer J. B., and Mandl C. W.. 2012. Nonviral delivery of self-amplifying RNA vaccines. Proc. Natl. Acad. Sci. 109(36). PMC3437863 22908294
  18. [20]Brunelle J. L., and Green R.. 2013. Chapter Five - In Vitro Transcription from Plasmid or PCR-amplified DNA. Methods in Enzymology. Lorsch J., editor. Academic Press, pp. 101– 114. 24034317
  19. [21]Kisters K., Niedner W., Fafera I., and Zidek W.. 1990. Plasma and intracellular Mg2+ concentrations in pre-eclampsia. Journal of Hypertension 8(4). 2160486
  20. [22]Mikkola S., Kaukinen U., and Lönnberg H.. 2001. The Effect of Secondary Structure on Cleavage of the Phosphodiester Bonds of RNA. Cell Biochem. Biophys. 34(1). 11394443
  21. [23]Regulski E. E., and Breaker R. R.. 2008. In-line Probing Analysis of Riboswitches. Methods Mol. Biol. 419. 18369975
  22. [24]Lorenz R., Bernhart S. H., Honer Zu Siederdissen C., Tafer H., Flamm C., Stadler P. F., and Hofacker I. L.. 2011. ViennaRNA Package 2.0. Algorithms Mol Biol 6: 26. 22115189 PMC3319429
  23. [25]Zadeh J. N., Steenberg C. D., Bois J. S., Wolfe B. R., Pierce M. B., Khan A. R., Dirks R. M., and Pierce N. A.. 2011. NUPACK: Analysis and design of nucleic acid systems. J Comput Chem 32(1): 170– 173. 20645303
  24. [26]Reuter J. S., and Mathews D. H.. 2010. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics 11: 129. 20230624 PMC2984261
  25. [27]Do C. B., Woods D. A., and Batzoglou S.. 2006. CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics 22(14): e90– 98. 16873527
  26. [28]Terai G., Kamegai S., and Asai K.. 2016. CDSfold: An Algorithm for Designing a Protein-Coding Sequence With the Most Stable Secondary Structure. Bioinformatics 32(6). 26589279
  27. [29]Zhang H., Zhang L., Li Z., Liu K., Liu B., Mathews D. H., and Huang L.. 2020. LinearDesign: Efficient Algorithms for Optimized mRNA Sequence Design. Arxiv.
  28. [30]Cohen B., and Skiena S.. 2003. Natural Selection and Algorithmic Design of mRNA. Journal of Computational Biology 10(3–4). 12935336
  29. [31]Chen S.-J. 2008. RNA folding: conformational statistics, folding kinetics, and ion electrostatics. Annual review of biophysics 37: 197– 214. PMC2473866 18573079
  30. [32]Zhao X., Li G., and Liang S.. 2013. Several Affinity Tags Commonly Used in Chromatographic Purification. J Anal Methods Chem 2013. PMC3893739 24490106
  31. [33]Sharp P. M., and Li W. H.. 1987. The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15(3): 1281– 1295. 3547335 PMC340524
  32. [34]Wayment-Steele H. K., Kladwang W., Participants E., and Das R.. 2020. RNA secondary structure packages ranked and improved by high-throughput experiments. bioRxiv. PMC9839360 36192461
  33. [35]Mauger D. M., Cabral B. J., Presnyak V., Su S. V., Reid D. W., Goodman B., Link K., Khatwani N., Reynders J., Moore M. J., and McFadyen I. J.. 2019. mRNA structure regulates protein expression through changes in functional half-life. Proc Natl Acad Sci U S A 116(48): 24075– 24083. 31712433 PMC6883848
  34. [36]Lee J., Kladwang W., Lee M., Cantu D., Azizyan M., Kim H., Limpaecher A., Yoon S., Treuille A., and Das R.. 2014. RNA design rules from a massive open laboratory. Proc Natl Acad Sci U S A 111(6): 2122– 2127. 24469816 PMC3926058
  35. [37]Kozak M. 1990. Downstream secondary structure facilitates recognition of initiator codons by eukaryotic ribosomes. Proceedings of the National Academy of Sciences 87(21): 8301. PMC54943 2236042
  36. [38]Mustoe A. M., Corley M., Laederach A., and Weeks K. M.. 2018. Messenger RNA Structure Regulates Translation Initiation: A Mechanism Exploited from Bacteria to Humans. Biochemistry 57(26): 3537– 3539. 29894169 PMC6398326
  37. [39]Hur S. 2019. Double-Stranded RNA Sensors and Modulators in Innate Immunity. Annu Rev Immunol 37: 349– 375. 30673536 PMC7136661
  38. [40]Yoffe A. M., Prinsen P., Gopal A., Knobler C. M., Gelbart W. M., and Ben-Shaul A.. 2008. Predicting the sizes of large RNA molecules. Proc Natl Acad Sci U S A 105(42): 16153– 16158. 18845685 PMC2570976
  39. [41]Gopal A., Egecioglu D. E., Yoffe A. M., Ben-Shaul A., Rao A. L. N., Knobler C. M., and Gelbart W. M.. 2014. Viral RNAs Are Unusually Compact. PLoS One 9(9): e105875. 25188030 PMC4154850
  40. [42]Karikó K., Ni H., Capodici J., Lamphier M., and Weissman D.. 2004. mRNA Is an Endogenous Ligand for Toll-like Receptor 3. J. Biol. Chem. 279(13): 12542– 12550. 14729660
  41. [43]Wrapp D., Wang N., Corbett K. S., Goldsmith J. A., Hsieh C.-L., Abiona O., Graham B. S., and McLellan J. S.. 2020. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 367(6483): 1260. 32075877 PMC7164637
  42. [44]Wu K., Werner A. P., Moliva J. I., Koch M., Choi A., Stewart-Jones G. B. E., Bennett H., Boyoglu-Barnum S., Shi W., Graham B. S., Carfi A., Corbett K. S., Seder R. A., and Edwards D. K.. 2021. mRNA-1273 vaccine induces neutralizing antibodies against spike mutants from global SARS-CoV-2 variants. bioRxiv:2021.2001.2025.427948.
  43. [47]Tegally H., Wilkinson E., Giovanetti M., Iranzadeh A., Fonseca V., Giandhari J., Doolabh D., Pillay S., San E. J., Msomi N., Mlisana K., von Gottberg A., Walaza S., Allam M., Ismail A., Mohale T., Glass A. J., Engelbrecht S., Van Zyl G., Preiser W., Petruccione F., Sigal A., Hardie D., Marais G., Hsiao M., Korsman S., Davies M.-A., Tyers L., Mudau I., York D., Maslo C., Goedhals D., Abrahams S., Laguda-Akingba O., Alisoltani-Dehkordi A., Godzik A., Wibmer C. K., Sewell B. T., Lourenço J., Alcantara L. C. J., Pond S. L. K., Weaver S., Martin D., Lessells R. J., Bhiman J. N., Williamson C., and de Oliveira T.. 2020. Emergence and rapid spread of a new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) lineage with multiple spike mutations in South Africa. medRxiv:2020.2012.2021.20248640.
  44. [48]Leppek K., Fujii K., Quade N., Susanto T. T., Boehringer D., Lenarčič T., Xue S., Genuth N. R., Ban N., and Barna M.. 2020. Gene- and Species-Specific Hox mRNA Translation by Ribosome Expansion Segments. Molecular Cell 80(6): 980– 995.e913. 33202249 PMC7769145
  45. [49]Ahmed S. F., Quadeer A. A., and McKay M. R.. 2020. Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies. Viruses 12(3). PMC7150947 32106567
  46. [50]Qamar M. T. u., Rehman A., Ashfaq U. A., Awan M. Q., Fatima I., Shahid F., and Chen L.-L.. 2020. Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches. bioRxiv. PMC7755200 33351863
  47. [51]Prachar M., Justesen S., Steen-Jensen D. B., Thorgrimsen S., Jurgons E., Winther O., and Bagger F. O.. 2020. COVID-19 Vaccine Candidates: Prediction and Validation of 174 SARS-CoV-2 Epitopes. bioRxiv. PMC7686376 33235258
  48. [52]Wrapp D., Wang N., Corbett K. S., Goldsmith J. A., Hsieh C.-L., Abiona O., Graham B. S., and McLellan J. S.. 2020. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 367(6483): 1260– 1263. 32075877 PMC7164637
  49. [53]Liu H., Wu R., Yuan L., Tian G., Huang X., Wen Y., Ma X., Huang Y., Yan Q., Zhao Q., Cao S., and Wen X.. 2017. Introducing a Cleavable Signal Peptide Enhances the Packaging Efficiency of Lentiviral Vectors Pseudotyped With Japanese Encephalitis Virus Envelope Proteins. Virus research 229: 9– 16. 27993624
  50. [54]Kocsis L., and Szepesvári C.. 2006. Bandit Based Monte-Carlo Planning. Springer Berlin Heidelberg, Berlin, Heidelberg. 282– 293.

Cited by

Sorted by citations

Authors
Wayment-Steele, H. K., Kim, D. S., Choe, C. A., Nicol, J. J., Wellington-Oguri, R., Watkins, A. M., Sperberg, R. A. P., Huang, P.-S., Participants, E., & Das, R.
Affiliations
  • Eterna Massive Open Laboratory (US)
  • Northwestern University (US)
  • Stanford University (US)
Corresponding author
Das, Rhiju
Venue
Nucleic Acids Research
Publisher
openRxiv
Published
2020-08-24
Volume
49
Issue
18
Pages
10604-10617
ISSN
0305-1048 (ISSN-L)
OpenAlex
W3200792349
Cited by
199
References
0
Citations by year
2026: 37 · 2025: 52 · 2024: 34 · 2023: 31 · 2022: 27 · 2021: 16 · 2020: 2
FWCI
8.47
Type
journal article
Language
en
Open access
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • National Science Foundation
  • Northwestern University
  • Oregon State University
  • National Institute of Advanced Industrial Science and Technology
  • Baidu
  • National Institutes of Health
  • National Institute of General Medical Sciences (R35GM122579)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
Keywords
mRNA stabilization, RNA hydrolysis, mRNA vaccines, mRNA therapeutics, SARS-CoV-2 spike protein, mRNA half-life

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review