Algorithm for optimized mRNA design improves stability and immunogenicity

Abstract

Messenger RNA (mRNA) vaccines are being used to combat the spread of COVID-19 (refs. 1–3 ), but they still exhibit critical limitations caused by mRNA instability and degradation, which are major obstacles for the storage, distribution and efficacy of the vaccine products 4 . Increasing secondary structure lengthens mRNA half-life, which, together with optimal codons, improves protein expression 5 . Therefore, a principled mRNA design algorithm must optimize both structural stability and codon usage. However, owing to synonymous codons, the mRNA design space is prohibitively large—for example, there are around 2.4 × 10 632 candidate mRNA sequences for the SARS-CoV-2 spike protein. This poses insurmountable computational challenges. Here we provide a simple and unexpected solution using the classical concept of lattice parsing in computational linguistics, where finding the optimal mRNA sequence is analogous to identifying the most likely sentence among similar-sounding alternatives 6 . Our algorithm LinearDesign finds an optimal mRNA design for the spike protein in just 11 minutes, and can concurrently optimize stability and codon usage. LinearDesign substantially improves mRNA half-life and protein expression, and profoundly increases antibody titre by up to 128 times in mice compared to the codon-optimization benchmark on mRNA vaccines for COVID-19 and varicella-zoster virus. This result reveals the great potential of principled mRNA design and enables the exploration of previously unreachable but highly stable and efficient designs. Our work is a timely tool for vaccines and other mRNA-based medicines encoding therapeutic proteins such as monoclonal antibodies and anti-cancer drugs 7,8 .

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References 58

1. 1.

   Baden LR, et al. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N. Engl. J. Med. 2021;384:403-416.

   Not in our library10.1056/nejmoa2035389

2. 2.

   Polack FP, et al. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. N. Engl. J. Med. 2020;383:2603-2615.

   Not in our library10.1056/nejmoa2034577

3. 3.

   Gebre MS, et al. Optimization of non-coding regions for a non-modified mRNA COVID-19 vaccine. Nature. 2022;601:410-414.

   Not in our library10.1038/s41586-021-04231-6

4. 4.
   Addressing the Cold Reality of mRNA Vaccine Stability · 2021

   No notice
5. 5.
   mRNA structure regulates protein expression through changes in functional half-life · 2019

   No notice
6. 6.

   Hall, K. B. Best-First Word-Lattice Parsing: Techniques for Integrated Syntactic Language Modeling. PhD thesis, Brown Univ. (2005).

   Not in our library

7. 7.

   Schlake T, et al. mRNA: a novel avenue to antibody therapy? Mol. Ther. 2019;27:773-784.

   Not in our library10.1016/j.ymthe.2019.03.002

8. 8.

   Reinhard K, et al. An RNA vaccine drives expansion and efficacy of claudin-CAR-T cells against solid tumors. Science. 2020;367:446-453.

   Not in our library10.1126/science.aay5967

9. 9.

   Wolff JA, et al. Direct gene transfer into mouse muscle in vivo. Science. 1990;247:1465-1468.

   Not in our library10.1126/science.1690918

10. 10.

    Pardi N, Hogan MJ, Porter FW, Weissman D. mRNA vaccines—a new era in vaccinology. Nat. Rev. Drug Discov. 2018;17:261-279.

    Not in our library10.1038/nrd.2017.243

11. 11.

    Mauro VP, Chappell SA. A critical analysis of codon optimization in human therapeutics. Trends Mol. Med. 2014;20:604-13.

    Not in our library10.1016/j.molmed.2014.09.003

12. 12.

    Gustafsson C, Govindarajan S, Minshull J. Codon bias and heterologous protein expression. Trends Biotechnol. 2004;22:346-353.

    Not in our library10.1016/j.tibtech.2004.04.006

13. 13.

    Nabiyouni M, Prakash A, Fedorov A. Vertebrate codon bias indicates a highly GC-rich ancestral genome. Gene. 2013;519:113-119.

    Not in our library10.1016/j.gene.2013.01.033

14. 14.

    Sahin U, Karikó K, Türeci Ö. mRNA-based therapeutics—developing a new class of drugs. Nat. Rev. Drug Discov. 2014;13:759-780.

    Not in our library10.1038/nrd4278

15. 15.

    Mathews DH, Sabina J, Zuker M, Turner DH. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 1999;288:911-940.

    Not in our library10.1006/jmbi.1999.2700

16. 16.

    Mathews DH, et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. USA. 2004;101:7287-7292.

    Not in our library10.1073/pnas.0401799101

17. 17.

    Sharp PM, Li WH. The codon adaptation index–a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 1987;15:1281-1295.

    Not in our library10.1093/nar/15.3.1281

18. 18.

    Durbin, R., Eddy, S. R., Krogh, A. & Mitchison G. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids (Cambridge Univ. Press, 1998).

    Not in our library

19. 19.

    Bar-Hillel Y, Perles M, Shamir E. On formal properties of simple phrase structure grammars. Zeitschrift für Phonetik, Sprachwissenschaft und Kommunikationsforschung. 1961;14:143-172.

    Not in our library

20. 20.

    Nederhof, M. J. & Satta, G. Probabilistic parsing as intersection. In Proc. 8th International Conference on Parsing Technologies 137–148 (2003).

    Not in our library

21. 21.

    Huang L, et al. LinearFold: linear-time approximate RNA folding by 5′-to-3′ dynamic programming and beam search. Bioinformatics. 2019;35:i295-i304.

    Not in our library10.1093/bioinformatics/btz375

22. 22.

    Cohen B, Skiena S. Natural selection and algorithmic design of mRNA. J. Comput. Biol. 2003;10:419-432.

    Not in our library10.1089/10665270360688101

23. 23.

    Terai G, Kamegai S, Asai K. CDSfold: an algorithm for designing a protein-coding sequence with the most stable secondary structure. Bioinformatics. 2016;32:828-834.

    Not in our library10.1093/bioinformatics/btv678

24. 24.
    UniProt: a hub for protein information · 2014

    No notice
25. 25.

    Huang, L. & Sagae, K. Dynamic programming for linear-time incremental parsing. In Proc. 48th Annual Meeting of the Association for Computational Linguistics 1077–1086 (Association for Computational Linguistics, 2010).

    Not in our library

26. 26.

    Yang R, et al. A core-shell structured COVID-19 mRNA vaccine with favorable biodistribution pattern and promising immunity. Signal Transduct. Target. Ther. 2021;6:213.

    Not in our library10.1038/s41392-021-00634-z

27. 27.

    Liu L, et al. Structural basis of Toll-like receptor 3 signaling with double-stranded RNA. Science. 2008;320:379-381.

    Not in our library10.1126/science.1155406

28. 28.

    Mignone F, Gissi C, Liuni S, Pesole G. Untranslated regions of mRNAs. Genome Biol. 2002;3:reviews0004.1.

    Not in our library10.1186/gb-2002-3-3-reviews0004

29. 29.
    Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics · 2022

    No notice
30. 30.

    Rana MM. Polymer-based nano-therapies to combat COVID-19 related respiratory injury: progress, prospects, and challenges. J. Biomater. Sci. Polym. Ed. 2021;32:1219-1249.

    Not in our library10.1080/09205063.2021.1909412

31. 31.

    Wrapp D, et al. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science. 2020;367:1260-1263.

    Not in our library10.1126/science.abb2507

32. 32.

    Cunningham AL, et al. Efficacy of the herpes zoster subunit vaccine in adults 70 years of age or older. N. Engl. J. Med. 2016;375:1019-1032.

    Not in our library10.1056/nejmoa1603800

33. 33.

    Raab D, Graf M, Notka F, Schödl T, Wagner R. The GeneOptimizer algorithm: using a sliding window approach to cope with the vast sequence space in multiparameter DNA sequence optimization. Syst. Synth. Biol. 2010;4:215-225.

    Not in our library10.1007/s11693-010-9062-3

34. 34.
    Theoretical basis for stabilizing messenger RNA through secondary structure design · 2021

    No notice
35. 35.

    Hie B, Zhong ED, Berger B, Bryson B. Learning the language of viral evolution and escape. Science. 2021;371:284-288.

    Not in our library10.1126/science.abd7331

36. 36.

    Madani A., et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol.10.1038/s41587-022-01618-2 (2023).

    Not in our library10.1038/s41587-022-01618-2

37. 37.

    Karikó K, Buckstein M, Ni H, Weissman D. Suppression of RNA recognition by Toll-like receptors: the impact of nucleoside modification and the evolutionary origin of RNA. Immunity. 2005;23:165-175.

    Not in our library10.1016/j.immuni.2005.06.008

38. 38.

    Karikó K, et al. Incorporation of pseudouridine into mRNA yields superior nonimmunogenic vector with increased translational capacity and biological stability. Mol. Ther. 2008;16:1833-1840.

    Not in our library10.1038/mt.2008.200

39. 39.

    Rivas E. The four ingredients of single-sequence RNA secondary structure prediction. a unifying perspective. RNA Biol. 2013;10:1185-1196.

    Not in our library10.4161/rna.24971

40. 40.

    Nussinov R, Jacobson AB. Fast algorithm for predicting the secondary structure of single-stranded RNA. Proc. Natl Acad. Sci. USA. 1980;77:6309-6313.

    Not in our library10.1073/pnas.77.11.6309

Cited by papers we hold 1

• Therapeutic cancer vaccines: advancements, challenges and prospects · 2023
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