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DNA sequence design model for multi-scene fusion

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Abstract

Due to its unique properties and excellent sequence design methods, DNA finds wide applications in computing, information storage, molecular circuits, and biological diagnosis. Previous efforts to enhance the efficiency and precision of DNA sequence design have led to the proposal of various universal DNA sequence design methods. These methods optimize the arrangement of the four bases to reduce sequence similarity and meet specific criteria. However, prior investigations have predominantly focused on sequence design within single-scene frameworks, overlooking the complexities associated with designing for multi-scene fusion, such as ion-bridge mismatch, tri-base sequence design, and others. To address this gap, we fused four common scenes and introduced two novel constraint models to facilitate DNA sequence design for multi-scene fusion. Additionally, we developed a dynamic virus spread algorithm as the core for optimizing DNA sequences and evaluated it using 23 well-known benchmark functions. Furthermore, our algorithm outperformed eight popular swarm evolutionary algorithms in eight dominant results. Finally, we simulated the optimization of four distinct scenes, demonstrating that our sequences met expected performance levels in their respective areas. Thus, our work provides a practical tool for designing DNA sequences tailored to various specific applications.

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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Adleman LM (1994) Molecular computation of solutions to combinatorial problems. Science 266(5187):1021–1024. https://doi.org/10.1126/science.7973651

    Article  MATH  Google Scholar 

  2. Yang S, Boegels BWA, Wang F, Xu C, Dou HJ, Mann S, Fan CH, de Greef TFA (2024) DNA as a universal chemical substrate for computing and data storage. Nat Rev Chem 8(3):179–194. https://doi.org/10.1038/s41570-024-00576-4

    Article  Google Scholar 

  3. Chen J, Fu SN, Zhang CY, Liu HY, Su X (2022) DNA logic circuits for cancer theranostics. Small 18(20):2108008. https://doi.org/10.1002/smll.202108008

    Article  Google Scholar 

  4. Raza MH, Desai S, Aravamudhan S, Zadegan R (2023) An outlook on the current challenges and opportunities in DNA data storage. Biotechnol Adv 66:108155. https://doi.org/10.1016/j.biotechadv.2023.108155

    Article  Google Scholar 

  5. Yao Y, Liu Y, Liu X, Zhang X, Shi PJ, Zhang XK, Zhang Q, Wei XP (2024) Bubble DNA tweezer: a triple-conformation sensor responsive to concentration-ratios. iScience 27(3):109074. https://doi.org/10.1016/j.isci.2024.109074

    Article  MATH  Google Scholar 

  6. Xiong XW, Zhu T, Zhu Y, Cao MY, Xiao J, Li L, Wang F, Fan CH, Pei H (2022) Molecular convolutional neural networks with DNA regulatory circuits. Nat Mach Intell 4(7):625–635. https://doi.org/10.1038/s42256-022-00502-7

    Article  MATH  Google Scholar 

  7. Liu SL, Jiang Q, Zhao X, Zhao RF, Wang YN, Wang YM, Liu JB, Shang YX, Zhao S, Wu TT, Zhang YL, Nie GJ, Ding BQ (2021) A DNA nanodevice-based vaccine for cancer immunotherapy. Nat Mater 20(3):421–430. https://doi.org/10.1038/s41563-020-0793-6

    Article  MATH  Google Scholar 

  8. Zhou F, Ni H, Zhu GL, Bershadsky L, Sha RJ, Seeman NC, Chaikin PM (2023) Toward three-dimensional DNA industrial nanorobots. Sci Robot 8(85):eadf1274. https://doi.org/10.1126/scirobotics.adf1274

    Article  Google Scholar 

  9. Todisco M, Ding D, Szostak JW (2024) Transient states during the annealing of mismatched and bulged oligonucleotides. Nucleic Acids Res 52(5):2174–2187. https://doi.org/10.1093/nar/gkae091

    Article  MATH  Google Scholar 

  10. Hartemink AJ, Gifford DK, Khodor J (1999) Automated constraint based nucleotide sequence selection for DNA computation. Biosystems 52(1–3):227–235. https://doi.org/10.1016/S0303-2647(99)00050-7

    Article  MATH  Google Scholar 

  11. Yang GJ, Wang B, Zheng XD, Zhou CJ, Zhang Q (2017) IWO algorithm based on niche crowding for DNA sequence design. Interdiscip Sci 9(3):341–349. https://doi.org/10.1007/s12539-016-0160-0

    Article  MATH  Google Scholar 

  12. Li X, Wang B, Lv H, Yin Q, Zhang Q, Wei XP (2020) Constraining DNA sequences with a triplet-bases unpaired. IEEE T Nanobiosci 19(2):299–307. https://doi.org/10.1109/TNB.2020.2971644

    Article  MATH  Google Scholar 

  13. Zhang Q, Xia X (2013) The quality optimization of DNA sequences with improved genetic algorithm. J Comput Theor Nanos 10(5):1192–1195. https://doi.org/10.1166/jctn.2013.2827

    Article  MATH  Google Scholar 

  14. Li X, Wei ZQ, Wang B, Song T (2021) Stable DNA sequence over close-ending and pairing sequences constraint. Front Genet 12:644484. https://doi.org/10.3389/fgene.2021.644484

    Article  Google Scholar 

  15. Breslauer KJ, Frank R, Blocker HL, Marky A (1986) Predicting DNA duplex stability from the base sequence. P Natl Acad Sci USA 83(11):3746–3750. https://doi.org/10.1073/pnas.83.11.3746

    Article  MATH  Google Scholar 

  16. Zeraati M, Langley DB, Schofield P, Moye AL, Rouet R, Hughes WE, Bryan TM, Dinger ME, Christ D (2018) I-motif DNA structures are formed in the nuclei of human cells. Nat Chem 10:631–637. https://doi.org/10.1038/s41557-018-0046-3

    Article  Google Scholar 

  17. Zhu DL, Huang ZW, Liao SK, Zhou CJ, Yan SQ, Chen G (2023) Improved bare bones particle swarm optimization for DNA sequence design. IEEE T Nanobiosci 22(3):603–613. https://doi.org/10.1109/TNB.2022.3220795

    Article  Google Scholar 

  18. Liu KQ, Wang B, Lv H, Wei XP, Zhang Q (2019) A BPSON algorithm applied to DNA codes design. IEEE Access 7:88811–88821. https://doi.org/10.1109/ACCESS.2019.2924708

    Article  MATH  Google Scholar 

  19. Yao Y, Ren JK, Bi R, Liu Q (2021) Bacterial foraging algorithm based on activity of bacteria for DNA computing sequence design. IEEE Access 9:2110–2124. https://doi.org/10.1109/ACCESS.2020.3047469

    Article  MATH  Google Scholar 

  20. Yang XW, Zhou CJ (2024) DNA sequences under multiple guanine-cytosine (GC) base pairs constraint. IEEE T Nanobiosci 23(2):252–261. https://doi.org/10.1109/TNB.2023.3316431

    Article  Google Scholar 

  21. Cheng YH, Kuo CN, Lai CM (2016) Effective natural PCR-RFLP primer design for SNP genotyping using teaching–learning-based optimization with elite strategy. IEEE T Nanobiosci 15(7):657–665. https://doi.org/10.1109/TNB.2016.2597867

    Article  Google Scholar 

  22. Zhang X, Xiao WX, Xiao WJ (2020) DeepHE: accurately predicting human essential genes based on deep learning. Plos Comput Biol 16(9):e1008229. https://doi.org/10.1371/journal.pcbi.1008229

    Article  MATH  Google Scholar 

  23. Jiang Z, Shen YY, Liu R (2023) Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches. Plos Comput Biol 19(9):e1011428. https://doi.org/10.1371/journal.pcbi.1011428

    Article  MATH  Google Scholar 

  24. Zhang C, Ma XY, Zheng XD, Ke YG, Chen KT, Liu DS, Lu ZH, Yang J, Yan H (2022) Programmable allosteric DNA regulations for molecular networks and nanomachines. Sci Adv 8(5):eabl4589. https://doi.org/10.1126/sciadv.abl4589

    Article  Google Scholar 

  25. Wei Y, Li BM, Wang X, Duan YX (2014) Magnified fluorescence detection of silver(I) ion in aqueous solutions by using nano-graphite-DNA hybrid and DNase I. Biosens Bioelectron 58:276–281. https://doi.org/10.1016/j.bios.2014.02.075

    Article  MATH  Google Scholar 

  26. Pan LQ, Wang ZY, Li YF, Xu F, Zhang Q, Zhang C (2017) Nicking enzyme-controlled toehold regulation for DNA logic circuits. Nanoscale 9(46):18223–18228. https://doi.org/10.1039/c7nr06484e

    Article  MATH  Google Scholar 

  27. Guo YJ, Yao DB, Zheng B, Sun XB, Zhou X, Wei B, Xiao SY, He M, Li CX, Liang HJ (2020) pH-controlled detachable DNA circuitry and its application in resettable self-assembly of spherical nucleic acids. ACS Nano 14(7):8317–8327. https://doi.org/10.1021/acsnano.0c02329

    Article  Google Scholar 

  28. Chaves-González JM, Vega-Rodríguez MA (2014) DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm. Biosystems 116:49–64. https://doi.org/10.1016/j.biosystems.2013.12.005

    Article  MATH  Google Scholar 

  29. SantaLucia J, Allawi HT, Seneviratne A (1996) Improved nearest-neighbor parameters for predicting DNA duplex stability. Biochemistry 35(11):3555–3562. https://doi.org/10.1021/bi951907q

    Article  MATH  Google Scholar 

  30. Shin SY, Lee IH, Kim DM, Zhang BT (2005) Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing. IEEE T Evolut Comput 9(2):143–158. https://doi.org/10.1109/TEVC.2005.844166

    Article  MATH  Google Scholar 

  31. Liu F, Ha HD, Han DJ, Seo TS (2013) Photoluminescent graphene oxide microarray for multiplex heavy metal ion analysis. Small 9(20):3410–3414. https://doi.org/10.1002/smll.201300499

    Article  MATH  Google Scholar 

  32. Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: virus colony search. Adv Eng Softw 92:65–88. https://doi.org/10.1016/j.advengsoft.2015.11.004

    Article  MATH  Google Scholar 

  33. Al-Betar MA, Alyasseri ZAA, Awadallah MA, Abu Doush I (2020) Coronavirus herd immunity optimizer (CHIO). Neural Comput Appl 33(10):5011–5042. https://doi.org/10.1007/s00521-020-05296-6

    Article  Google Scholar 

  34. Yao Y, Zhang X, Liu X, Liu Y, Zhang X, Zhang Q (2023) Static virus spread algorithm for DNA sequence design. arXiv preprint arXiv:2311.02120. https://doi.org/10.48550/arXiv.2311.02120

  35. Tellier R, Li YG, Cowling BJ, Tang JW (2019) Recognition of aerosol transmission of infectious agents: a commentary. BMC Infect Dis 19:101. https://doi.org/10.1186/s12879-019-3707-y

    Article  MATH  Google Scholar 

  36. Fine P, Eames K, Heymann DL (2011) "Herd immunity’’: a rough guide. Clin Infect Dis 52(7):911–916. https://doi.org/10.1093/cid/cir007

    Article  MATH  Google Scholar 

  37. Uraki R, Kiso M, Iida S et al (2022) Characterization and antiviral susceptibility of SARS-CoV-2 Omicron BA.2. Nature 607(7917):119–127. https://doi.org/10.1038/s41586-022-04856-1

    Article  MATH  Google Scholar 

  38. Sanjuán R, Domingo-Calap P (2016) Mechanisms of viral mutation. Cell Mol Life Sci 73(23):4433–4448. https://doi.org/10.1007/s00018-016-2299-6

    Article  MATH  Google Scholar 

  39. Chu DK, Akl EA, Duda S, Solo K, Yaacoub S, Schunemann HJ (2020) Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet 395(10242):1973–1987. https://doi.org/10.1016/S0140-6736(20)31142-9

    Article  Google Scholar 

  40. Seyyedabbasi A, Kiani F (2023) Sand cat swarm optimization: a nature-inspired algorithm to solve global optimization problems. Eng Comput-Germany 39(4):2627–2651. https://doi.org/10.1007/s00366-022-01604-x

    Article  MATH  Google Scholar 

  41. Zhao WG, Zhang ZX, Wang LY (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intel 87:103300. https://doi.org/10.1016/j.engappai.2019.103300

    Article  Google Scholar 

  42. Zhao WG, Wang LY, Zhang ZX (2019) Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl-Based Syst 163:283–304. https://doi.org/10.1016/j.knosys.2018.08.030

    Article  MATH  Google Scholar 

  43. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  MATH  Google Scholar 

  44. Xue JK, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(1):22–34. https://doi.org/10.1080/21642583.2019.1708830

    Article  MATH  Google Scholar 

  45. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  MATH  Google Scholar 

  46. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  MATH  Google Scholar 

  47. Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. IEEE T Evolut Comput 3(2):82–102. https://doi.org/10.1109/4235.771163

    Article  MATH  Google Scholar 

  48. Sun AL, Jia FC, Zhang YF, Wang XN (2015) Hybridization-induced Ag(I) dissociation from an immobilization-free and label-free hairpin DNA: toward a novel electronic monitoring platform. Analyst 140(8):2634–2637. https://doi.org/10.1039/c5an00046g

    Article  MATH  Google Scholar 

  49. Wang RY, Zhou XH, Shi HC, Luo Y (2016) T-T mismatch-driven biosensor using triple functional DNA-protein conjugates for facile detection of Hg2+. Biosens Bioelectron 78:418–422. https://doi.org/10.1016/j.bios.2015.11.082

    Article  Google Scholar 

  50. Huang NH, Li RT, Fan C, Wu KY, Zhang Z, Chen JX (2019) Rapid sequential detection of Hg2+ and biothiols by a probe DNA-MOF hybrid sensory system. J Inorg Biochem 197:110690. https://doi.org/10.1016/j.jinorgbio.2019.04.004

    Article  Google Scholar 

  51. Zhang XK, Zhang Q, Liu Y, Wang B, Zhou SH (2020) A molecular device: a DNA molecular lock driven by the nicking enzymes. Comput Struct Biotec 18:2107–2116. https://doi.org/10.1016/j.csbj.2020.08.004

    Article  MATH  Google Scholar 

  52. Qi HJ, Yue SZ, Bi S, Ding CF, Song WL (2018) DNA logic assembly powered by a triplex-helix molecular switch for extracellular pH imaging. Chem Commun 54(61):8498–8501. https://doi.org/10.1039/c8cc04615h

    Article  Google Scholar 

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Acknowledgements

This work is supported by 111 Project (No. D23006), the National Natural Science Foundation of China (No. 62272079), Natural Science Foundation of Liaoning Province (No. 2022-KF-12-14), the Postgraduate Education Reform Project of Liaoning Province (No. LNYJG2022493), the Dalian Outstanding Young Science and Technology Talent Support Program (No. 2022RJ08).

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  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China

    Yao Yao, Yanfen Zheng, Shuang Cui, Yaqing Hou, Qiang Zhang & Xiaopeng Wei

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  1. Yao Yao
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Yao, Y., Zheng, Y., Cui, S. et al. DNA sequence design model for multi-scene fusion. Neural Comput & Applic 37, 5499–5520 (2025). https://doi.org/10.1007/s00521-024-10905-9

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