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Sequence-Based Nanobody-Antigen Binding Prediction

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Abstract

Nanobodies (Nb) are monomeric heavy-chain fragments derived from heavy-chain only antibodies naturally found in Camelids and Sharks. Their considerably small size (\(\sim \)3–4 nm; 13 kDa) and favorable biophysical properties make them attractive targets for recombinant production. Furthermore, their unique ability to bind selectively to specific antigens, such as toxins, chemicals, bacteria, and viruses, makes them powerful tools in cell biology, structural biology, medical diagnostics, and future therapeutic agents in treating cancer and other serious illnesses. However, a critical challenge in nanobodies production is the unavailability of nanobodies for a majority of antigens. Although some computational methods have been proposed to screen potential nanobodies for given target antigens, their practical application is highly restricted due to their reliance on 3D structures. Moreover, predicting nanobody-antigen interactions (binding) is a time-consuming and labor-intensive task. This study aims to develop a machine-learning method to predict Nanobody-Antigen binding solely based on the sequence data. We curated a comprehensive dataset of Nanobody-Antigen binding and non-binding data and devised an embedding method based on gappedk-mers to predict binding based only on sequences of nanobody and antigen. Our approach achieves up to\(90\%\) accuracy in binding prediction and is significantly more efficient compared to the widely-used computational docking technique.

U. Sardar and S. Ali—Equal Contribution.

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References

  1. Ali, S., Bello, B., Chourasia, P., Punathil, R.T., Zhou, Y., Patterson, M.: PWM2Vec: an efficient embedding approach for viral host specification from coronavirus spike sequences. Biology11(3), 418 (2022)

    Article CAS PubMed PubMed Central  Google Scholar 

  2. Ali, S., Patterson, M.: Spike2vec: an efficient and scalable embedding approach for covid-19 spike sequences. In: IEEE International Conference on Big Data (Big Data), pp. 1533–1540 (2021)

    Google Scholar 

  3. Berman, H.M., et al.: The protein data bank. Nucleic Acids Res.28(1), 235–242 (2000)

    Article CAS PubMed PubMed Central  Google Scholar 

  4. Burley, S.K., et al.: Rcsb protein data bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res.47(D1), D464–D474 (2019)

    Article CAS PubMed  Google Scholar 

  5. Cohen, T., Halfon, M., Schneidman-Duhovny, D.: Nanonet: rapid and accurate end-to-end nanobody modeling by deep learning. Front. Immunol.13, 958584 (2022)

    Article CAS PubMed PubMed Central  Google Scholar 

  6. Cortez-Retamozo, V., et al.: Efficient cancer therapy with a nanobody-based conjugate. Can. Res.64(8), 2853–2857 (2004)

    Article CAS  Google Scholar 

  7. Deffar, K., Shi, H., Li, L., Wang, X., Zhu, X.: Nanobodies-the new concept in antibody engineering. Afr. J. Biotechnol.8(12), 2645–2652 (2009)

    CAS  Google Scholar 

  8. Farhan, M., Tariq, J., Zaman, A., Shabbir, M., Khan, I.: Efficient approximation algorithms for strings kernel based sequence classification. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 6935–6945 (2017)

    Google Scholar 

  9. Guruprasad, K., Reddy, B.B., Pandit, M.W.: Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng. Des. Sel.4(2), 155–161 (1990)

    Article CAS  Google Scholar 

  10. Haimov, B., Srebnik, S.: A closer look into the\(\alpha \)-helix basin. Sci. Rep.6(1), 38341 (2016)

    Article CAS PubMed PubMed Central  Google Scholar 

  11. Hou, Q., et al.: Serendip-ce: sequence-based interface prediction for conformational epitopes. Bioinformatics37(20), 3421–3427 (2021)

    Article CAS PubMed PubMed Central  Google Scholar 

  12. Hutchinson, E.G., Thornton, J.M.: A revised set of potentials for\(\beta \)-turn formation in proteins. Protein Sci.3(12), 2207–2216 (1994)

    Article CAS PubMed PubMed Central  Google Scholar 

  13. Kim, C.A., Berg, J.M.: Thermodynamic\(\beta \)-sheet propensities measured using a zinc-finger host peptide. Nature362(6417), 267–270 (1993)

    Article CAS PubMed  Google Scholar 

  14. Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol.157(1), 105–132 (1982)

    Article CAS PubMed  Google Scholar 

  15. Van der M., L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. (JMLR)9(11), 2579–2605 (2008)

    Google Scholar 

  16. Miller, N.L., Clark, T., Raman, R., Sasisekharan, R.: Learned features of antibody-antigen binding affinity. Front. Mol. Biosci.10, 1112738 (2023)

    Article CAS PubMed PubMed Central  Google Scholar 

  17. Mitchell, L.S., Colwell, L.J.: Analysis of nanobody paratopes reveals greater diversity than classical antibodies. Protein Eng. Des. Sel.31(7–8), 267–275 (2018)

    Article CAS PubMed PubMed Central  Google Scholar 

  18. Mitchell, L.S., Colwell, L.J.: Comparative analysis of nanobody sequence and structure data. Proteins Struct. Funct. Bioinf.86(7), 697–706 (2018)

    Article CAS  Google Scholar 

  19. Muyldermans, S.: Nanobodies: natural single-domain antibodies. Ann. Rev. Biochem.82, 775–797 (2013)

    Article CAS PubMed  Google Scholar 

  20. Myung, Y., Pires, D.E., Ascher, D.B.: Csm-ab: graph-based antibody-antigen binding affinity prediction and docking scoring function. Bioinformatics38(4), 1141–1143 (2022)

    Article CAS PubMed  Google Scholar 

  21. Peng, H.P., Lee, K.H., Jian, J.W., Yang, A.S.: Origins of specificity and affinity in antibody-protein interactions. Proc. Natl. Acad. Sci.111(26), E2656–E2665 (2014)

    Article CAS PubMed PubMed Central  Google Scholar 

  22. Ramon, A., Saturnino, A., Didi, K., Greenig, M., Sormanni, P.: Abnativ: vq-vae-based assessment of antibody and nanobody nativeness for engineering, selection, and computational design. In: bioRxiv, p. 2023-04 (2023)

    Google Scholar 

  23. Revets, H., De Baetselier, P., Muyldermans, S.: Nanobodies as novel agents for cancer therapy. Expert Opin. Biol. Ther.5(1), 111–124 (2005)

    Article CAS PubMed  Google Scholar 

  24. Roberts, M., Hayes, W., Hunt, B.R., Mount, S.M., Yorke, J.A.: Reducing storage requirements for biological sequence comparison. Bioinformatics20(18), 3363–3369 (2004)

    Article CAS PubMed  Google Scholar 

  25. Rossant, C.J., et al.: Phage display and hybridoma generation of antibodies to human cxcr2 yields antibodies with distinct mechanisms and epitopes. MAbs6(6), 1425–1438 (2014)

    Article PubMed PubMed Central  Google Scholar 

  26. Schwede, T.: Protein modeling: what happened to the “protein structure gap’’? Structure21(9), 1531–1540 (2013)

    Article CAS PubMed  Google Scholar 

  27. Sormanni, P., Aprile, F.A., Vendruscolo, M.: Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins. Proc. Natl. Acad. Sci.112(32), 9902–9907 (2015)

    Article CAS PubMed PubMed Central  Google Scholar 

  28. Tam, C., Kumar, A., Zhang, K.Y.: Nbx: machine learning-guided re-ranking of nanobody-antigen binding poses. Pharmaceuticals14(10), 968 (2021)

    Article CAS PubMed PubMed Central  Google Scholar 

  29. Valdés-Tresanco, M.S., Valdés-Tresanco, M.E., Jiménez-Gutiérrez, D.E., Moreno, E.: Structural modeling of nanobodies: a benchmark of state-of-the-art artificial intelligence programs. Molecules28(10), 3991 (2023)

    Article PubMed PubMed Central  Google Scholar 

  30. Yang, Y.X., Huang, J.Y., Wang, P., Zhu, B.T.: Area-affinity: a web server for machine learning-based prediction of protein-protein and antibody-protein antigen binding affinities. J. Chem. Inf. Model.63, 3230–3237 (2023)

    Article CAS PubMed PubMed Central  Google Scholar 

  31. Ye, C., Hu, W., Gaeta, B.: Prediction of antibody-antigen binding via machine learning: development of data sets and evaluation of methods. JMIR Bioinf. Biotechnol.3(1), e29404 (2022)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Lahore University of Management Sciences, Lahore, Pakistan

    Usama Sardar, Muhammad Sohaib Ayub, Muhammad Shoaib, Khurram Bashir & Imdad Ullah Khan

  2. Georgia State University, Atlanta, GA, USA

    Sarwan Ali & Murray Patterson

Authors
  1. Usama Sardar

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  2. Sarwan Ali

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  3. Muhammad Sohaib Ayub

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  4. Muhammad Shoaib

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  5. Khurram Bashir

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  6. Imdad Ullah Khan

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  7. Murray Patterson

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Corresponding author

Correspondence toMurray Patterson.

Editor information

Editors and Affiliations

  1. University of North Texas, Denton, TX, USA

    Xuan Guo

  2. University of Southern California, Los Angeles, CA, USA

    Serghei Mangul

  3. Georgia State University, Atlanta, GA, USA

    Murray Patterson

  4. Georgia State University, Atlanta, GA, USA

    Alexander Zelikovsky

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sardar, U.et al. (2023). Sequence-Based Nanobody-Antigen Binding Prediction. In: Guo, X., Mangul, S., Patterson, M., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2023. Lecture Notes in Computer Science(), vol 14248. Springer, Singapore. https://doi.org/10.1007/978-981-99-7074-2_18

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