Rational Design of DNA Sequences with Non-Orthogonal Binding Interactions

Author Joseph Don Berleant



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Joseph Don Berleant
  • Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

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Joseph Don Berleant. Rational Design of DNA Sequences with Non-Orthogonal Binding Interactions. In 29th International Conference on DNA Computing and Molecular Programming (DNA 29). Leibniz International Proceedings in Informatics (LIPIcs), Volume 276, pp. 4:1-4:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.DNA.29.4

Abstract

Molecular computation involving promiscuous, or non-orthogonal, binding interactions between system components is found commonly in natural biological systems, as well as some proposed human-made molecular computers. Such systems are characterized by the fact that each computational unit, such as a domain within a DNA strand, may bind to several different partners with distinct, prescribed binding strengths. Unfortunately, implementing systems of molecular computation that incorporate non-orthogonal binding is difficult, because researchers lack a robust, general-purpose method for designing molecules with this type of behavior. In this work, we describe and demonstrate a process for the rational design of DNA sequences with prescribed non-orthogonal binding behavior. This process makes use of a model that represents large sets of non-orthogonal DNA sequences using fixed-length binary strings, and estimates the differential binding affinity between pairs of sequences through the Hamming distance between their corresponding binary strings. The real-world applicability of this model is supported by simulations and some experimental data. We then select two previously described systems of molecular computation involving non-orthogonal interactions, and apply our sequence design process to implement them using DNA strand displacement. Our simulated results on these two systems demonstrate both digital and analog computation. We hope that this work motivates the development and implementation of new computational paradigms based on non-orthogonal binding.

Subject Classification

ACM Subject Classification
  • Hardware → Emerging architectures
  • Hardware → Biology-related information processing
Keywords
  • DNA sequence design
  • binding networks
  • promiscuous binding
  • non-orthogonal binding
  • isometric graph embeddings

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References

  1. Yaron E Antebi, James M Linton, Heidi Klumpe, Bogdan Bintu, Mengsha Gong, Christina Su, Reed McCardell, and Michael B Elowitz. Combinatorial signal perception in the BMP pathway. Cell, 170(6):1184-1196, 2017. Google Scholar
  2. Eric B Baum. Building an associative memory vastly larger than the brain. Science, 268(5210):583-585, 1995. Google Scholar
  3. Callista Bee, Yuan-Jyue Chen, Melissa Queen, David Ward, Xiaomeng Liu, Lee Organick, Georg Seelig, Karin Strauss, and Luis Ceze. Molecular-level similarity search brings computing to DNA data storage. Nature communications, 12(1):1-9, 2021. Google Scholar
  4. Joseph Berleant. DNA sequence design of non-orthogonal binding networks, and application to DNA data storage. PhD thesis, Massachusetts Institute of Technology, 2023. Google Scholar
  5. Joseph Berleant, Kristin Sheridan, Anne Condon, Virginia Vassilevska Williams, and Mark Bathe. Isometric Hamming embeddings of weighted graphs. Discrete Applied Mathematics, 332:119-128, 2023. Google Scholar
  6. Kevin M Cherry and Lulu Qian. Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature, 559(7714):370-376, 2018. Google Scholar
  7. Vašek Chvátal. Recognizing intersection patterns. Annals of Discrete Mathematics, 8:249-252, 1980. URL: https://doi.org/10.1002/net.3230210602.
  8. Dragomir Ž Djoković. Distance-preserving subgraphs of hypercubes. Journal of Combinatorial Theory, Series B, 14(3):263-267, 1973. Google Scholar
  9. David Eppstein. Recognizing partial cubes in quadratic time. Journal of Graph Algorithms and Applications, 15(2):269-293, 2011. Google Scholar
  10. Constantine Glen Evans, Jackson O'Brien, Erik Winfree, and Arvind Murugan. Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly. arXiv preprint arXiv:2207.06399, 2022. Google Scholar
  11. Ronald L Graham and Peter M Winkler. On isometric embeddings of graphs. Transactions of the American Mathematical Society, 288(2):527-536, 1985. Google Scholar
  12. Haukur Gudnason, Martin Dufva, Dang Duong Bang, and Anders Wolff. Comparison of multiple DNA dyes for real-time pcr: effects of dye concentration and sequence composition on DNA amplification and melting temperature. Nucleic acids research, 35(19):e127, 2007. Google Scholar
  13. Simon Hood, Daniel Recoskie, Joe Sawada, and Dennis Wong. Snakes, coils, and single-track circuit codes with spread k. Journal of Combinatorial Optimization, 30:42-62, 2015. Google Scholar
  14. Matthew R Lakin, Simon Youssef, Filippo Polo, Stephen Emmott, and Andrew Phillips. Visual dsd: a design and analysis tool for dna strand displacement systems. Bioinformatics, 27(22):3211-3213, 2011. Google Scholar
  15. Tomas Malinauskas and E Yvonne Jones. Extracellular modulators of Wnt signalling. Current opinion in structural biology, 29:77-84, 2014. Google Scholar
  16. Arvind Murugan, Zorana Zeravcic, Michael P Brenner, and Stanislas Leibler. Multifarious assembly mixtures: Systems allowing retrieval of diverse stored structures. Proceedings of the National Academy of Sciences, 112(1):54-59, 2015. Google Scholar
  17. Andrew Neel and Max Garzon. Semantic retrieval in DNA-based memories with Gibbs energy models. Biotechnology progress, 22(1):86-90, 2006. Google Scholar
  18. Maxim P Nikitin. Non-complementary strand commutation as a fundamental alternative for information processing by DNA and gene regulation. Nature Chemistry, pages 1-13, 2023. Google Scholar
  19. Nicolas Peyret, P Ananda Seneviratne, Hatim T Allawi, and John SantaLucia. Nearest-neighbor thermodynamics and NMR of DNA sequences with internal A⊙A, C⊙C, G⊙G, and T⊙T mismatches. Biochemistry, 38(12):3468-3477, 1999. Google Scholar
  20. Lulu Qian and Erik Winfree. Scaling up digital circuit computation with DNA strand displacement cascades. Science, 332(6034):1196-1201, 2011. Google Scholar
  21. Lulu Qian, Erik Winfree, and Jehoshua Bruck. Neural network computation with DNA strand displacement cascades. Nature, 475:368-72, July 2011. URL: https://doi.org/10.1038/nature10262.
  22. John SantaLucia Jr. and Donald Hicks. The thermodynamics of DNA structural motifs. Annual Review of Biophysics and Biomolecular Structure, 33(1):415-440, 2004. URL: https://doi.org/10.1146/annurev.biophys.32.110601.141800.
  23. Georg Seelig, David Soloveichik, David Yu Zhang, and Erik Winfree. Enzyme-free nucleic acid logic circuits. science, 314(5805):1585-1588, 2006. Google Scholar
  24. Kristin Sheridan, Joseph Berleant, Mark Bathe, Anne Condon, and Virginia Vassilevska Williams. Factorization and pseudofactorization of weighted graphs. Discrete Applied Mathematics, 337:81-105, 2023. URL: https://doi.org/10.1016/j.dam.2023.04.019.
  25. Sergey V. Shpectorov. On scale embeddings of graphs into hypercubes. Eur. J. Comb., 14(2):117-130, March 1993. URL: https://doi.org/10.1006/eujc.1993.1016.
  26. Richard C Singleton. Generalized snake-in-the-box codes. IEEE Transactions on Electronic Computers, pages 596-602, 1966. Google Scholar
  27. Kyle J Tomek, Kevin Volkel, Elaine W Indermaur, James M Tuck, and Albert J Keung. Promiscuous molecules for smarter file operations in DNA-based data storage. Nature Communications, 12(1):3518, 2021. Google Scholar
  28. Peter M Winkler. Isometric embedding in products of complete graphs. Discrete Applied Mathematics, 7(2):221-225, 1984. Google Scholar
  29. Qikai Xu, Michael R Schlabach, Gregory J Hannon, and Stephen J Elledge. Design of 240,000 orthogonal 25mer DNA barcode probes. Proceedings of the National Academy of Sciences, 106(7):2289-2294, 2009. Google Scholar
  30. Weishun Zhong, David J Schwab, and Arvind Murugan. Associative pattern recognition through macro-molecular self-assembly. Journal of Statistical Physics, 167:806-826, 2017. Google Scholar
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