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International Conference on Cellular Automata

ACRI 2018: Cellular Automata pp 3–15Cite as

Cellular Automata Model for Proteomics and Its Application in Cancer Immunotherapy

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11115)

Abstract

This paper presents our first version of Protein modeling Cellular Automata Machine (PCAM). The peptide chain of amino acid backbone of a protein having n number of amino acids is designed with an 8n cell uniform CA employing one of the 64 three neighborhood CA (3NCA) rules. Each amino acid of a protein chain is modeled by a group of eight CA cells. Variation of the interaction pattern of a protein backbone under different physical conditions is modeled with different sixty-four 3NCA rules. Another set of twenty 8-bit patterns are next designed to encode the molecular structure of side chains of twenty amino acids. The eight CA cells representing an amino acid in the chain is initialized with the 8 bit pattern of its side-chain. A set of features extracted from evolution of PCAM are mapped to real life experimental results. The PCAM model is validated from cancer immunotherapy experimental results for MAb-PD-L1 interaction on multiple MAbs (Monoclonal Antibodies) with the protein PD-L1 associated in human immunity.

Keywords

  • Cellular automata
  • Proteomics
  • Cancer immunotherapy

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References

  1. Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Brief. Bioinform. 18(5), 851–869 (2017)

    Google Scholar 

  2. Libbrecht, M.W., Noble, W.S.: Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16(6), 321 (2015)

    CrossRef  Google Scholar 

  3. Moreira, I.S., et al.: SpotOn: high accuracy identification of protein-protein interface hot-spots. Sci. Rep. 7(1), 8007 (2017)

    CrossRef  Google Scholar 

  4. Burks, C., Farmer, D.: Towards modeling DNA sequences as automata. Physica 10D 10(1–2), 157–167 (1984)

    MathSciNet  MATH  Google Scholar 

  5. Sirakoulis, G., Karafyllidis, I., Mizas, C., Mardiris, V., Thanailakis, A., Tsalides, P.: A cellular automaton model for the study of DNA sequence evolution. Comput. Biol. Med. 33(5), 439–453 (2003)

    CrossRef  Google Scholar 

  6. Xiao, X., Shao, S., Ding, Y., Chen, X.: Digital coding for amino acid based on cellular automata. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 5, pp. 4593–4598, October 2004

    Google Scholar 

  7. Xiao, X., Wang, P., Chou, K.-C.: Cellular automata and its applications in protein bioinformatics. Curr. Protein Pept. Sci. 12(6), 508–519 (2011)

    CrossRef  Google Scholar 

  8. Cristea, P.: Independent component analysis for genetic signals. In: SPIE Conference BIOS 2001-International Biomedical Optics Symposium, San Jose, pp. 20–26, January 2001

    Google Scholar 

  9. Pan, Y.-X., et al.: Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. J. Protein Chem. 22(4), 395–402 (2003)

    CrossRef  Google Scholar 

  10. Ghosh, S., et al.: On invertible three neighborhood null-boundary uniform cellular automata. Complex Syst. 20(1), 47 (2011)

    MathSciNet  MATH  Google Scholar 

  11. Haralick, R.M., Shanmugam, K.: Textural features for image classification. IEEE Trans. Syst. Man, Cybern. 3(6), 610–621 (1973)

    CrossRef  Google Scholar 

  12. De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The mahalanobis distance. Chemom. Intell. Lab. Syst. 50(1), 1–18 (2000)

    CrossRef  Google Scholar 

  13. Tan, S., Zhang, C.W.H., Gao, G.F.: Seeing is believing: anti-PD-1/PD-L1 monoclonal antibodies in action for checkpoint blockade tumor immunotherapy. Sig. Transduct. Target. Ther. 1, 16029 (2016)

    CrossRef  Google Scholar 

  14. Zhang, F., et al.: Structural basis of the therapeutic anti-PD-L1 antibody atezolizumab. Oncotarget 8(52), 90215–90224 (2017). PMC.Web. 12 March 2018

    Google Scholar 

  15. Tan, S., et al.: Distinct PD-L1 binding characteristics of therapeutic monoclonal antibody durvalumab. Protein Cell 9(1), 135–139 (2018)

    CrossRef  Google Scholar 

  16. Wishart, D.S., et al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46(D1), D1074–D1082 (2017)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. CARLBio Pvt. Ltd., Kolkata, India

    Soumyabrata Ghosh & Parimal Pal Chaudhuri

Authors
  1. Soumyabrata Ghosh
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  2. Parimal Pal Chaudhuri
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Corresponding author

Correspondence to Soumyabrata Ghosh .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Giancarlo Mauri

  2. University of Perpignan, Perpignan, France

    Samira El Yacoubi

  3. University of Milano-Bicocca, Milan, Italy

    Alberto Dennunzio

  4. University of Tokyo, Tokyo, Japan

    Katsuhiro Nishinari

  5. University of Milano-Bicocca, Milan, Italy

    Luca Manzoni

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Ghosh, S., Chaudhuri, P.P. (2018). Cellular Automata Model for Proteomics and Its Application in Cancer Immunotherapy. In: Mauri, G., El Yacoubi, S., Dennunzio, A., Nishinari, K., Manzoni, L. (eds) Cellular Automata. ACRI 2018. Lecture Notes in Computer Science(), vol 11115. Springer, Cham. https://doi.org/10.1007/978-3-319-99813-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-99813-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99812-1

  • Online ISBN: 978-3-319-99813-8

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