Artificial Intelligence in Vaccine and Drug Design

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Vaccine Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2410))

Abstract

Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in recent years. There has also been tremendous growth in the fields of data science, informatics, and artificial intelligence needed to handle this immense data flow. At the intersection of wet lab and data science is the field of bioinformatics, which seeks to apply computational tools to better understanding of the biological sciences. Like so many other areas of biology, bioinformatics has transformed immunology research leading to the discipline of immunoinformatics. Within this field, many new databases and computational tools have been created that increasingly drive immunology research, in many cases drawing upon artificial intelligence and machine learning to predict complex immune system behaviors, for example, prediction of B cell and T cell epitopes. In this book chapter, we provide an overview of computational tools and artificial intelligence being used for protein modeling, drug screening, vaccine design, and highlight how these tools are being used to transform approaches to pandemic countermeasure development, by reference to the current COVID-19 pandemic.

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References

  1. Frankenfield J (2021) Artificial intelligence. Retrieved from: https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp#:~:text=Artificial%20intelligence%20(AI)%20refers%20to,as%20learning%20and%20problem%2Dsolving

  2. McCarthy J (2004) What is Artificial Intelligence? Retrieved from: http://www-formal.stanford.edu/jmc/whatisai.pdf

  3. Panesar A (2020) What is artificial intelligence? In: Machine learning and AI for healthcare. pp 1–18

    Google Scholar 

  4. Bishop CM (2013) Model-based machine learning. Philos Trans A Math Phys Eng Sci 371:20120222

    PubMed  PubMed Central  Google Scholar 

  5. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D (2020) Improved protein structure prediction using potentials from deep learning. Nature 577:706–710

    Article  CAS  PubMed  Google Scholar 

  6. Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S, Baker D (2020) Improved protein structure prediction using predicted interresidue orientations. Proc Natl Acad Sci U S A 117:1496–1503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hessler G, Baringhaus KH (2018) Artificial intelligence in drug design. Molecules 23(10):2520

    Article  PubMed Central  Google Scholar 

  8. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297

    Article  Google Scholar 

  9. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  10. Duda RO, Hart PE, Stork GE (2001) Pattern classification, 2nd edn. Wiley, New York, NY, pp 20–83

    Google Scholar 

  11. Zhang L, Tan J, Han D, Zhu H (2017) From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today 22:1680–1685

    Article  PubMed  Google Scholar 

  12. Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V (2015) Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model 55:263–274

    Article  CAS  PubMed  Google Scholar 

  13. Unterthiner T, Mayr A, Klambauer G, Steijaert M, Ceulemans H, Wegner J, Hochreiter S (2014) Deep learning as an opportunity in virtual screening. Proceedings of the NIPS workshop on deep learning and representation learning, Montreal, QC, Canada. 8–13 December 2014. Accessed 15 Sept 2018, pp 1058–1066

    Google Scholar 

  14. Mayr A, Klambauer G, Unterthiner T, Hochreither S (2016) Deep Tox: toxicity prediction using deep learning. Front Environ Sci 2016:3

    Google Scholar 

  15. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK (2021) Artificial intelligence in drug discovery and development. Drug Discov Today 26:80–93

    Article  CAS  PubMed  Google Scholar 

  16. Huang PS, Boyken SE, Baker D (2016) The coming of age of de novo protein design. Nature 537:320–327

    Article  CAS  PubMed  Google Scholar 

  17. Hartenfeller M, Schneider G (2011) Enabling future drug discovery by de novo design. WIREs Comput Mol Sci 1:742–759

    Article  CAS  Google Scholar 

  18. Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ertl P, Schuffenhauer AJ (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. ChemInform 1(1):8

    Article  Google Scholar 

  20. Segler MHS, Kogej T, Tyrchan C, Waller MP (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4:120–131

    Article  CAS  PubMed  Google Scholar 

  21. Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G (2018) Generative recurrent networks for de novo drug design. Mol Inf 37:1700111

    Article  Google Scholar 

  22. Muller AT, Hiss JA, Schneider G (2018) Recurrent neural network model for constructive peptide design. J Chem Inf Model 58:472–479

    Article  CAS  PubMed  Google Scholar 

  23. Jabbari P, Rezaei R (2019) Artificial intelligence and immunotherapy. Expert Rev Clin Immunol 15:689–691

    Article  CAS  PubMed  Google Scholar 

  24. Hepler NL, Scheffler K, Weaver S et al (2014) IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform. PLoS Comput Biol 10(9):e1003842

    Article  PubMed  PubMed Central  Google Scholar 

  25. Pavillon N, Hobro AJ, Akira S et al (2018) Noninvasive detection of macrophage activation with single-cell resolution through machine learning. Proc Nat Acad Sci 115:E2676–E2685

    Article  PubMed  PubMed Central  Google Scholar 

  26. Sun R, Limkin EJ, Vakalopoulou M et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180–1191

    Article  CAS  PubMed  Google Scholar 

  27. Moghram BA, Nabil E, Badr A (2018) Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design. Comput Methods Prog Biomed 153:161–170

    Article  Google Scholar 

  28. Nagpal G, Chaudhary K, Agrawal P et al (2018) Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants. J Transl Med 16(1):181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Nagpal G, Gupta S, Chaudhary K et al (2015) VaccineDA: prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. Sci Rep 5:12478

    Article  PubMed  PubMed Central  Google Scholar 

  30. Dash R, Das R, Junaid M et al (2017) In silico-based vaccine design against Ebola virus glycoprotein. Adv Appl Bioinf Chem 10:11–28

    Google Scholar 

  31. Heinson AI, Gunawardana Y, Moesker B et al (2017) Enhancing the biological relevance of machine learning classifiers for reverse vaccinology. Int J Mol Sci 18(2):312

    Article  PubMed Central  Google Scholar 

  32. Daubenberger CA (2007) TLR9 agonists as adjuvants for prophylactic and therapeutic vaccines. Curr Opin Mol Ther 9:45–52

    CAS  PubMed  Google Scholar 

  33. Ahuja AS, Reddy VP, Marques O (2020) Artificial intelligence and COVID-19: a multidisciplinary approach. Integr Med Res 9(3):100434

    Article  PubMed  PubMed Central  Google Scholar 

  34. Lee EK, Nakaya HI, Yuan F, Querec TD, Burel G, Pietz FH, Benecke BA, Pulendran B (2016) Machine learning for predicting vaccine immunogenicity. INFORMS J Appl Anal 46:368–390

    Article  Google Scholar 

  35. Liu T, Shi K, Li W (2020) Deep learning methods improve linear B-cell epitope prediction. BioData Min 13:1

    Article  PubMed  PubMed Central  Google Scholar 

  36. Chen B, Khodadoust MS, Olsson N et al (2019) Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol 37:1332–1343

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. McGowan E, Rosenthal R, Fiore-Gartland A, Macharia G, Balinda S, Kapaata A, Umviligihozo G, Muok E, Dalel J, Streatfield CL, Coutinho H, Dilernia D, Monaco DC, Morrison D, Yue L, Hunter E, Nielsen M, Gilmour J, Hare J (2021) Utilizing computational machine learning tools to understand immunogenic breadth in the context of a CD8 T-cell mediated HIV response. Front Immunol 12:609884

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Dimitrov I, Zaharieva N, Doytchinova I (2020) Bacterial immunogenicity prediction by machine learning methods. Vaccine 8(4):709

    Article  Google Scholar 

  39. Thomas S (2020) The structure of the membrane protein of SARS-CoV-2 resembles the sugar transporter semiSWEET. Pathog Immun 5(1):342–363

    Article  PubMed  PubMed Central  Google Scholar 

  40. Thomas S (2021) Map** the non-structural transmembrane proteins of SARS-CoV-2. J Comp Biol 28:909–921

    Google Scholar 

  41. Lu Wang L, Lo K, Chandrasekhar Y, Reas R, Yang J, Eide D, Funk K, Kinney R, Liu Z, Merrill W, Mooney P, Murdick D, Rishi D, Sheehan J, Shen Z, Stilson B, Wade AD, Wang K, Wilhelm C, **e B, Raymond D, Weld DS, Etzioni O, Kohlmeier S (2020) CORD-19: the Covid-19 open research dataset. Ar**v [preprint]. 2020 Apr 22:ar**v:2004.10706v2

    Google Scholar 

  42. Fast E, Chen B (2020) Potential T-cell and B-cell epitopes of 2019-nCoV. bioRxiv [preprint]

    Google Scholar 

  43. Malone B, Simovski B, Moliné C, Cheng J, Gheorghe M, Fontenelle H, Vardaxis I, Tennøe S, Malmberg JA, Stratford R, Clancy T (2020) Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs. Sci Rep 10(1):22375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kabra R, Singh S (1867) Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics. Biochim Biophys Acta Mol basis Dis 2021(1):165978

    Google Scholar 

  45. Dai W, Zhang B, Jiang XM, Su H, Li J, Zhao Y et al (2020) Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease. Science 368:1331–1335

    Article  CAS  PubMed  Google Scholar 

  46. Mohapatra S, Nath P, Chatterjee M, Das N, Kalita D, Roy P, Satapathi S (2020) Repurposing therapeutics for COVID-19: rapid prediction of commercially available drugs through machine learning and docking. PLoS One 15(11):e0241543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kumari M, Subbarao N (2021) Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases. Comput Biol Med 132:104317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zhang Y, Tang LV (2021) Overview of targets and potential drugs of SARS-CoV-2 according to the viral replication. J Proteome Res 20(1):49–59

    Article  CAS  PubMed  Google Scholar 

  49. Esmail S, Danter W (2021) Viral pandemic preparedness: a pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing. Stem Cells Transl Med 10(2):239–250

    Article  CAS  PubMed  Google Scholar 

  50. Wang S, Zha Y, Li W, Wu Q, Li X, Niu M, Wang M, Qiu X, Li H, Yu H, Gong W, Bai Y, Li L, Zhu Y, Wang L, Tian J (2020) A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 56(2):2000775

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Poongodi M, Hamdi M, Malviya M, Sharma A, Dhiman G, Vimal S (2021) Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods. Pers Ubiquitous Comput 26:1–11

    Google Scholar 

  52. Liang W, Yao J, Chen A, Lv Q, Zanin M, Liu J, Wong S, Li Y, Lu J, Liang H, Chen G, Guo H, Guo J, Zhou R, Ou L et al (2020) Early triage of critically ill COVID-19 patients using deep learning. Nat Commun 11(1):3543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D (2021) Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng 14:4–15

    Article  PubMed  Google Scholar 

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Thomas, S., Abraham, A., Baldwin, J., Piplani, S., Petrovsky, N. (2022). Artificial Intelligence in Vaccine and Drug Design. In: Thomas, S. (eds) Vaccine Design. Methods in Molecular Biology, vol 2410. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1884-4_6

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  • DOI: https://doi.org/10.1007/978-1-0716-1884-4_6

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1883-7

  • Online ISBN: 978-1-0716-1884-4

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