Document Open Access Logo

Balancing Minimum Free Energy and Codon Adaptation Index for Pareto Optimal RNA Design

Authors Xinyu Gu, Yuanyuan Qi, Mohammed El-Kebir

PDF
Thumbnail PDF

File

LIPIcs.WABI.2023.21.pdf
  • Filesize: 5.7 MB
  • 20 pages

Document Identifiers

Author Details

Xinyu Gu
  • Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
Yuanyuan Qi
  • Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
Mohammed El-Kebir
  • Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA

Funding

  • El-Kebir, Mohammed: National Science Foundation award number CCF 2046488

Cite As Get BibTex

Xinyu Gu, Yuanyuan Qi, and Mohammed El-Kebir. Balancing Minimum Free Energy and Codon Adaptation Index for Pareto Optimal RNA Design. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 21:1-21:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.WABI.2023.21

Abstract

The problem of designing an RNA sequence v that encodes for a given target protein w plays an important role in messenger RNA (mRNA) vaccine design. Due to codon degeneracy, there exist exponentially many RNA sequences for a single target protein. These candidate RNA sequences may adopt different secondary structure conformations with varying minimum free energy (MFE), affecting their thermodynamic stability and consequently mRNA half-life. In addition, species-specific codon usage bias, as measured by the codon adaptation index (CAI), also plays an essential role in translation efficiency. While previous works have focused on optimizing either MFE or CAI, more recent works have shown the merits of optimizing both objectives. Importantly, there is a trade-off between MFE and CAI, i.e. optimizing one objective is at the expense of the other. Here, we formulate the Pareto Optimal RNA Design problem, seeking the set of Pareto optimal solutions for which no other solution exists that is better in terms of both MFE and CAI. We introduce DERNA (DEsign RNA), which uses the weighted sum method to enumerate the Pareto front by optimizing convex combinations of both objectives. DERNA uses dynamic programming to solve each convex combination in O(|w|³) time and O(|w|²) space. Compared to a previous approach that only optimizes MFE, we show on a benchmark dataset that DERNA obtains solutions with identical MFE but superior CAI. Additionally, we show that DERNA matches the performance in terms of solution quality of LinearDesign, a recent approach that similarly seeks to balance MFE and CAI. Finally, we demonstrate our method’s potential for mRNA vaccine design using SARS-CoV-2 spike as the target protein.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
Keywords
  • Multi-objective optimization
  • dynamic programming
  • RNA sequence design
  • reverse translation
  • mRNA vaccine design

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Supplementary Materials

References

  1. Barry Cohen and Steven Skiena. Natural selection and algorithmic design of mRNA. Journal of Computational Biology, 10(3-4):419-432, 2003. Google Scholar
  2. Jared L Cohon. Multiobjective programming and planning, volume 140. Courier Corporation, 2004. Google Scholar
  3. The UniProt Consortium. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Research, 51(D1):D523-D531, November 2022. URL: https://doi.org/10.1093/nar/gkac1052.
  4. Daan JA Crommelin, Thomas J Anchordoquy, David B Volkin, Wim Jiskoot, and Enrico Mastrobattista. Addressing the cold reality of mRNA vaccine stability. Journal of Pharmaceutical Sciences, 110(3):997-1001, 2021. URL: https://doi.org/10.1016/j.xphs.2020.12.006.
  5. I. Das and J. E. Dennis. A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Structural Optimization, 14(1):63-69, August 1997. URL: https://doi.org/10.1007/BF01197559.
  6. Yiliang Ding, Yin Tang, Chun Kit Kwok, Yu Zhang, Philip C Bevilacqua, and Sarah M Assmann. In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature, 505(7485):696-700, 2014. Google Scholar
  7. Susan M Freier, Ryszard Kierzek, John A Jaeger, Naoki Sugimoto, Marvin H Caruthers, Thomas Neilson, and Douglas H Turner. Improved free-energy parameters for predictions of RNA duplex stability. Proceedings of the National Academy of Sciences, 83(24):9373-9377, 1986. URL: https://doi.org/10.1073/pnas.83.24.9373.
  8. Claes Gustafsson, Sridhar Govindarajan, and Jeremy Minshull. Codon bias and heterologous protein expression. Trends in Biotechnology, 22(7):346-353, 2004. URL: https://doi.org/10.1016/j.tibtech.2004.04.006.
  9. Ivo L Hofacker, Walter Fontana, Peter F Stadler, L Sebastian Bonhoeffer, Manfred Tacker, Peter Schuster, et al. Fast folding and comparison of RNA secondary structures. Monatshefte fur chemie, 125:167-167, 1994. Google Scholar
  10. Yuan Huang, Chan Yang, Xin-feng Xu, Wei Xu, and Shu-wen Liu. Structural and functional properties of sars-cov-2 spike protein: potential antivirus drug development for covid-19. Acta Pharmacologica Sinica, 41(9):1141-1149, 2020. Google Scholar
  11. Robert Kleinkauf, Martin Mann, and Rolf Backofen. antaRNA: ant colony-based RNA sequence design. Bioinformatics, 31(19):3114-3121, 2015. Google Scholar
  12. Rune B Lyngso, Michael Zuker, and CN Pedersen. Fast evaluation of internal loops in RNA secondary structure prediction. Bioinformatics (Oxford, England), 15(6):440-445, 1999. URL: https://doi.org/10.1093/bioinformatics/15.6.440.
  13. Elisabeth Mahase. Covid-19: Moderna vaccine is nearly 95% effective, trial involving high risk and elderly people shows. BMJ: British Medical Journal (Online), 371, 2020. Google Scholar
  14. David H Mathews, Matthew D Disney, Jessica L Childs, Susan J Schroeder, Michael Zuker, and Douglas H Turner. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proceedings of the National Academy of Sciences, 101(19):7287-7292, 2004. URL: https://doi.org/10.1073/pnas.0401799101.
  15. David H Mathews, Jeffrey Sabina, Michael Zuker, and Douglas H Turner. Expanded sequence dependence of thermodynamic parameters improves prediction of rna secondary structure. Journal of molecular biology, 288(5):911-940, 1999. URL: https://doi.org/10.1006/jmbi.1999.2700.
  16. David M Mauger, B Joseph Cabral, Vladimir Presnyak, Stephen V Su, David W Reid, Brooke Goodman, Kristian Link, Nikhil Khatwani, John Reynders, Melissa J Moore, et al. mRNA structure regulates protein expression through changes in functional half-life. Proceedings of the National Academy of Sciences, 116(48):24075-24083, 2019. URL: https://doi.org/10.1073/pnas.1908052116.
  17. SA Meo, IA Bukhari, J Akram, AS Meo, and D COVID Klonoff. COVID-19 vaccines: comparison of biological, pharmacological characteristics and adverse effects of pfizer/biontech and moderna vaccines. Eur Rev Med Pharmacol Sci, pages 1663-1669, 2021. Google Scholar
  18. Yasukazu Nakamura, Takashi Gojobori, and Toshimichi Ikemura. Codon usage tabulated from international dna sequence databases: status for the year 2000. Nucleic acids research, 28(1):292-292, 2000. URL: https://doi.org/10.1093/nar/28.1.292.
  19. Vladimir Presnyak, Najwa Alhusaini, Ying-Hsin Chen, Sophie Martin, Nathan Morris, Nicholas Kline, Sara Olson, David Weinberg, Kristian E. Baker, Brenton R. Graveley, and Jeff Coller. Codon optimality is a major determinant of mRNA stability. Cell, 160(6):1111-1124, 2015. URL: https://doi.org/10.1016/j.cell.2015.02.029.
  20. Giovanni Salvatori, Laura Luberto, Mariano Maffei, Luigi Aurisicchio, Giuseppe Roscilli, Fabio Palombo, and Emanuele Marra. Sars-cov-2 spike protein: an optimal immunological target for vaccines. Journal of translational medicine, 18(1):222, 2020. URL: https://doi.org/10.1186/s12967-020-02392-y.
  21. Cédric Saule and Robert Giegerich. Pareto optimization in algebraic dynamic programming. Algorithms for Molecular Biology, 10(1):1-20, 2015. Google Scholar
  22. Paul M Sharp and Wen-Hsiung Li. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic acids research, 15(3):1281-1295, 1987. URL: https://doi.org/10.1093/nar/15.3.1281.
  23. Goro Terai, Satoshi Kamegai, and Kiyoshi Asai. CDSfold: an algorithm for designing a protein-coding sequence with the most stable secondary structure. Bioinformatics, 32(6):828-834, 2016. URL: https://doi.org/10.1093/bioinformatics/btv678.
  24. Tamir Tuller, Yedael Y. Waldman, Martin Kupiec, and Eytan Ruppin. Translation efficiency is determined by both codon bias and folding energy. Proceedings of the National Academy of Sciences, 107(8):3645-3650, 2010. URL: https://doi.org/10.1073/pnas.0909910107.
  25. Tamir Tuller and Hadas Zur. Multiple roles of the coding sequence 5' end in gene expression regulation. Nucleic acids research, 43(1):13-28, 2015. Google Scholar
  26. Douglas H Turner, Naoki Sugimoto, and Susan M Freier. RNA structure prediction. Annual review of biophysics and biophysical chemistry, 17(1):167-192, 1988. Google Scholar
  27. Yue Wan, Kun Qu, Qiangfeng Cliff Zhang, Ryan A Flynn, Ohad Manor, Zhengqing Ouyang, Jiajing Zhang, Robert C Spitale, Michael P Snyder, Eran Segal, et al. Landscape and variation of RNA secondary structure across the human transcriptome. Nature, 505(7485):706-709, 2014. Google Scholar
  28. Hannah K Wayment-Steele, Do Soon Kim, Christian A Choe, John J Nicol, Roger Wellington-Oguri, Andrew M Watkins, R Andres Parra Sperberg, Po-Ssu Huang, Eterna Participants, and Rhiju Das. Theoretical basis for stabilizing messenger RNA through secondary structure design. Nucleic Acids Research, 49(18):10604-10617, September 2021. URL: https://doi.org/10.1093/nar/gkab764.
  29. David E. Weinberg, Premal Shah, Stephen W. Eichhorn, Jeffrey A. Hussmann, Joshua B. Plotkin, and David P. Bartel. Improved ribosome-footprint and mrna measurements provide insights into dynamics and regulation of yeast translation. Cell Reports, 14(7):1787-1799, 2016. URL: https://doi.org/10.1016/j.celrep.2016.01.043.
  30. L. Zadeh. Optimality and non-scalar-valued performance criteria. IEEE Transactions on Automatic Control, 8(1):59-60, 1963. URL: https://doi.org/10.1109/TAC.1963.1105511.
  31. He Zhang, Liang Zhang, Ang Lin, Congcong Xu, Ziyu Li, Kaibo Liu, Boxiang Liu, Xiaopin Ma, Fanfan Zhao, Huiling Jiang, et al. Algorithm for optimized mRNA design improves stability and immunogenicity. Nature, pages 1-3, 2023. URL: https://doi.org/10.1038/s41586-023-06127-z.
  32. Michael Zuker and Patrick Stiegler. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic acids research, 9(1):133-148, 1981. URL: https://doi.org/10.1093/nar/9.1.133.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact