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PandoraGAN: Generating Antiviral Peptides Using Generative Adversarial Network

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Abstract

The continuous increase in pathogenic viruses and the intensive laboratory research emphasizes the need for cost- and time-efficient drug development. This accelerates research for alternate drug candidates like antiviral peptides (AVP) that have therapeutic and prophylactic potential and gaining attention in recent times. However, diversity in their sequences, limited and non-uniform characterization often limit their applications. Isolating newer peptide backbones with required characteristics is a cumbersome process with many design–test–build cycles. Advanced deep learning approaches such as generative adversarial networks (GAN) can be helpful to expedite the initial stage of developing novel peptide drugs. In this study, we developed PandoraGAN that uses a manually curated training dataset of 130 highly active peptides that include peptides from known databases (such as AVPdb) and literature to generate novel antiviral peptides. The underlying architecture in PandoraGAN is able to learn a good representation of the implicit properties of antiviral peptides. The generated sequences from PandoraGAN are validated based on physico-chemical properties. They are also compared with the training dataset statistically using Pearson’s correlation and Mann–Whitney U-test. We, therefore, confirm that PandoraGAN is capable of generating a novel antiviral peptide backbone showing similar properties to that of the known highly active antiviral peptides. This approach exhibits a potential to discover novel patterns of AVP which may have not been seen earlier with traditional methods. To our knowledge, this is the first ever use of GAN models for antiviral peptides across the viral spectrum.

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Code and Data Availability

Antiviral sequences generated by PandoraGAN are available on the PandoraGAN server—https://pandora-gan.herokuapp.com/. The code and data is available at https://github.com/thoughtworks/antiviral-peptide-predictions-using-gan/.

Notes

  1. http://weizhong-lab.ucsd.edu/cdhit-web-server/cgi-bin/index.cgi.

  2. https://www.ddbj.nig.ac.jp/ddbj/code-e.html.

  3. https://github.com/geek-ai/Texygen.

References

  1. Vilas Boas LCP, Campos ML, Berlanda RLA, de Carvalho Neves N, Franco OL. Antiviral peptides as promising therapeutic drugs. Cell Mol Life Sci. 2019;76(18):3525–42.

    Article  Google Scholar 

  2. Mahendran ASK, Lim YS, Fang C-M, Loh H-S, Le CF. The potential of antiviral peptides as covid-19 therapeutics. Front Pharmacol. 2020;11:1475. https://doi.org/10.3389/fphar.2020.575444.

    Article  Google Scholar 

  3. Agarwal G, Gabrani R. Antiviral peptides: identification and validation. Int J Pept Res Ther. 2020;27:149–68.

    Article  Google Scholar 

  4. Qureshi A, Thakur N, Tandon H, Kumar M. AVPdb: a database of experimentally validated antiviral peptides targeting medically important viruses. Nucleic Acids Res (Database issue). 2014;42:1147–53.

    Article  Google Scholar 

  5. Wang G, Li X, Wang Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res. 2016;44(D1):1087–93.

    Article  Google Scholar 

  6. Di Natale C, La Manna S, De Benedictis I, Brandi P, Marasco D. Perspectives in peptide-based vaccination strategies for syndrome coronavirus 2 pandemic. Front Pharmacol. 2020;11:1779. https://doi.org/10.3389/fphar.2020.578382.

    Article  Google Scholar 

  7. Lin E, Lin C-H, Lane H-Y. Relevant applications of generative adversarial networks in drug design and discovery: molecular de novo design, dimensionality reduction, and de novo peptide and protein design. Molecules. 2020. https://doi.org/10.3390/molecules25143250.

    Article  Google Scholar 

  8. Killoran N, Lee LJ, Delong A, Duvenaud D, Frey BJ. Generating and designing DNA with deep generative models. CoRR. 2017. arXiv:1712.06148.

  9. Gupta A, Zou J. Feedback gan (fbgan) for dna: a novel feedback-loop architecture for optimizing protein functions. 2018. arXiv:1804.01694.

  10. Yelmen B, Decelle A, Ongaro L, Marnetto D, Tallec C, Montinaro F, Furtlehner C, Pagani L, Jay F. Creating artificial human genomes using generative models. BioRxiv. 2019. https://doi.org/10.1101/769091.

    Article  Google Scholar 

  11. Strokach A, Kim PM. Deep generative modeling for protein design. Curr Opin Struct Biol. 2022;72:226–36. https://doi.org/10.1016/j.sbi.2021.11.008.

    Article  Google Scholar 

  12. Anand N, Huang P-S. Generative modeling for protein structures. In: Proceedings of the 32nd international conference on neural information processing systems. NIPS’18. Red Hook: Curran Associates Inc.; 2018. p. 7505–16.

  13. Xie X, Kim PM. HelixGAN: a bidirectional generative adversarial network with search in latent space for generation under constraints. In: MLSB. 2021.

  14. Li G, Iyer B, Prasath S, Ni Y, Salomonis N. Deepimmuno: deep learning-empowered prediction and generation of immunogenic peptides for t-cell immunity. Brief Bioinform. 2021. https://doi.org/10.1093/bib/bbab160.

    Article  Google Scholar 

  15. Repecka D, Jauniskis V, Karpus L, Rembeza E, Zrimec J, Poviloniene S, Rokaitis I, Laurynenas A, Abuajwa W, Savolainen O, Meskys R, Engqvist MKM, Zelezniak A. Expanding functional protein sequence space using generative adversarial networks. bioRxiv. 2019. https://doi.org/10.1101/789719.

  16. Wu Z, Johnston KE, Arnold FH, Yang KK. Protein sequence design with deep generative models. Curr Opin Chem Biol. 2021;65:18–27. https://doi.org/10.1016/j.cbpa.2021.04.004.

    Article  Google Scholar 

  17. Rossetto AM, Zhou W. Gandalf: A prototype of a gan-based peptide design method. In: Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics. BCB ’19. New York: Association for Computing Machinery; 2019. p. 61–6. https://doi.org/10.1145/3307339.3342183.

  18. Li J, Topaloglu RO, Ghosh S. Quantum generative models for small molecule drug discovery. IEEE Trans Quantum Eng. 2021;2:1–8. https://doi.org/10.1109/TQE.2021.3104804.

    Article  Google Scholar 

  19. Blanchard AE, Stanley C, Bhowmik D. Using gans with adaptive training data to search for new molecules. J Cheminform. 2021. https://doi.org/10.1186/s13321-021-00494-3.

    Article  Google Scholar 

  20. Tucs A, Tran DP, Yumoto A, Ito Y, Uzawa T, Tsuda K. Generating ampicillin-level antimicrobial peptides with activity-aware generative adversarial networks. ChemRxiv. 2020. https://doi.org/10.26434/chemrxiv.12116136.v1.

  21. Chang KY, Yang J-R. Analysis and prediction of highly effective antiviral peptides based on random forests. PLoS One. 2013;8(8):70166.

    Article  Google Scholar 

  22. Beltrán Lissabet JF, Belén LH, Farias JG. AntiVPP 1.0: a portable tool for prediction of antiviral peptides. Comput Biol Med. 2019;107:127–30.

    Article  Google Scholar 

  23. Thakur N, Qureshi A, Kumar M. AVPpred: collection and prediction of highly effective antiviral peptides. Nucleic Acids Res (Web Server issue). 2012;40:199–204.

    Article  Google Scholar 

  24. Qureshi A, Tandon H, Kumar M. AVP-IC50 Pred: multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50). Biopolymers. 2015;104(6):753–63.

    Article  Google Scholar 

  25. Liang X, Zhang X, Lian K, Tian X, Zhang M, Wang S, Chen C, Nie C, Pan Y, Han F, Wei Z, Zhang W. Antiviral effects of Bovine antimicrobial peptide against TGEV in vivo and in vitro. J Vet Sci. 2020;21(5):80.

    Article  Google Scholar 

  26. Shi S, Nguyen PK, Cabral HJ, Diez-Barroso R, Derry PJ, Kanahara SM, Kumar VA. Development of peptide inhibitors of hiv transmission. Bioact Mater. 2016;1(2):109–21.

    Article  Google Scholar 

  27. Chupradit K, Moonmuang S, Nangola S, Kitidee K, Yasamut U, Mougel M, Tayapiwatana C. Current peptide and protein candidates challenging hiv therapy beyond the vaccine era. Viruses. 2017. https://doi.org/10.3390/v9100281.

    Article  Google Scholar 

  28. Coffey MJ, Woffendin C, Phare SM, Strieter RM, Markovitz DM. RANTES inhibits HIV-1 replication in human peripheral blood monocytes and alveolar macrophages. Am J Physiol. 1997;272(5 Pt 1):1025–9.

    Google Scholar 

  29. Xia S, Liu M, Wang C, Xu W, Lan Q, Feng S, Qi F, Bao L, Du L, Liu S, et al. Inhibition of sars-cov-2 (previously 2019-ncov) infection by a highly potent pan-coronavirus fusion inhibitor targeting its spike protein that harbors a high capacity to mediate membrane fusion. Cell Res. 2020;30(4):343–55.

    Article  Google Scholar 

  30. Zhao H, Zhou J, Zhang K, Chu H, Liu D, Poon VK, Chan CC, Leung HC, Fai N, Lin YP, Zhang AJ, Jin DY, Yuen KY, Zheng BJ. A novel peptide with potent and broad-spectrum antiviral activities against multiple respiratory viruses. Sci Rep. 2016;6:22008.

    Article  Google Scholar 

  31. Ding X, Zhang X, Chong H, Zhu Y, Wei H, Wu X, He J, Wang X, He Y. Enfuvirtide (T20)-based lipopeptide is a potent HIV-1 cell fusion inhibitor—implications for viral entry and inhibition. J Virol. 2017;91(18):10–128.

    Article  Google Scholar 

  32. Lempp FA, Qu B, Wang Y-X, Urban S. Hepatitis b virus infection of a mouse hepatic cell line reconstituted with human sodium taurocholate cotransporting polypeptide. J Virol. 2016;90(9):4827–31.

    Article  Google Scholar 

  33. T.U., Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2018;47(D1):506–15. https://doi.org/10.1093/nar/gky1049. https://academic.oup.com/nar/article-pdf/47/D1/D506/27437297/gky1049.pdf

  34. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of the 27th international conference on neural information processing systems—volume 2. NIPS’14. Cambridge: MIT Press; 2014. p. 2672–80.

  35. Che T, Li Y, Zhang R, Hjelm RD, Li W, Song Y, Bengio Y. Maximum-likelihood augmented discrete generative adversarial networks. CoRR. 2017. arXiv:1702.07983.

  36. Yu L, Zhang W, Wang J, Yu Y. Seqgan: Sequence generative adversarial nets with policy gradient. CoRR. 2017. arXiv:1609.05473 [cs.LG]

  37. Guo J, Lu S, Cai H, Zhang W, Yu Y, Wang J. Long text generation via adversarial training with leaked information. CoRR. 2017. arXiv:1709.08624.

  38. Lin K, Li D, He X, Zhang Z, Sun M-t. Adversarial ranking for language generation. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, editors. Advances in neural information processing systems, vol 30. Curran Associates, Inc.; 2017. p. 3155–65. https://proceedings.neurips.cc/paper/2017/file/bf201d5407a6509fa536afc4b380577e-Paper.pdf.

  39. Zhang Y, Gan Z, Fan K, Chen Z, Henao R, Shen D, Carin L. Adversarial feature matching for text generation. In: International conference on machine learning, PMLR. 2017. p. 4006–15.

  40. Kusner MJ, Hernández-Lobato JM. Gans for sequences of discrete elements with the gumbel-softmax distribution. 2016. arXiv:1611.04051.

  41. Srivastava RK, Greff K, Schmidhuber J. Highway networks. CoRR. 2015. arXiv:1505.00387 [cs.LG].

  42. Kang SJ, Kim DH, Mishig-Ochir T, Lee BJ. Antimicrobial peptides: their physicochemical properties and therapeutic application. Arch Pharm Res. 2012;35(3):409–13.

    Article  Google Scholar 

  43. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJL. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25(11):1422–3. https://doi.org/10.1093/bioinformatics/btp163.

    Article  Google Scholar 

  44. Pande A, Patiyal S, Lathwal A, Arora C, Kaur D, Dhall A, Mishra G, Kaur H, Sharma N, Jain S, et al. Computing wide range of protein/peptide features from their sequence and structure. bioRxiv. 2019;599126.

  45. Schaduangrat N, Nantasenamat C, Prachayasittikul V, Shoombuatong W. Meta-iAVP: a sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation. Int J Mol Sci. 2019;20(22):5743.

    Article  Google Scholar 

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Acknowledgements

We would like to acknowledge Govt. of India for launching the Drug Discovery Hacakthon in July 2020 during the COVID-19 to develop computational methods for COVID-19 drug discovery and extrapolate these algorithms to other generic drug discovery challenges.

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The authors declare no funds, grants, or other support was received.

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SS lead the development of the GAN architecture along with Divye S. PA conceptualized the problem and analyzed the data. DS provided domain guidance as well as manual dataset curation and JV provided overall guidance.

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Correspondence to Pooja Arora or Jayaraman Valadi.

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This study was conducted as part of the Drug discovery hackathon (DDH) 2020 organized by Govt. of India. The copyright and commercial aspects are to be followed as mentioned in DDH policies.

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Surana, S., Arora, P., Singh, D. et al. PandoraGAN: Generating Antiviral Peptides Using Generative Adversarial Network. SN COMPUT. SCI. 4, 607 (2023). https://doi.org/10.1007/s42979-023-02203-3

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