Peptide Antibodies pp 205-214

Part of the Methods in Molecular Biology book series (MIMB, volume 1348)

Structural Characterization of Peptide Antibodies

Abstract

The role of proteins as very effective immunogens for the generation of antibodies is indisputable. Nevertheless, cases in which protein usage for antibody production is not feasible or convenient compelled the creation of a powerful alternative consisting of synthetic peptides. Synthetic peptides can be modified to obtain desired properties or conformation, tagged for purification, isotopically labeled for protein quantitation or conjugated to immunogens for antibody production. The antibodies that bind to these peptides represent an invaluable tool for biological research and discovery. To better understand the underlying mechanisms of antibody–antigen interaction here we present a pipeline developed by us to structurally classify immunoglobulin antigen binding sites and to infer key sequence residues and other variables that have a prominent role in each structural class.

Keywords

Peptide antibody Structure Clustering Linear epitope 

References

  1. 1.
    Singh H, Ansari HR, Raghava GPS (2013) Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PLoS One 8(5). ARTN e62216. doi:10.1371/journal.pone.0062216Google Scholar
  2. 2.
    Wu TT, Kabat EA (2008) An analysis of the sequences of the variable regions of Bence Jones proteins and myeloma light chains and their implications for antibody complementarity (Reprinted from J Exp Med, vol 132, pg 211–250, 1970). J Immunol 180(11):7057–7096PubMedGoogle Scholar
  3. 3.
    Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B (2010) The immune epitope database 2.0. Nucleic Acids Res 38(Database Issue):D854–D862. doi:10.1093/nar/gkp1004 PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Dutta S, Burkhardt K, Young J, Swaminathan GJ, Matsuura T, Henrick K, Nakamura H, Berman HM (2009) Data deposition and annotation at the worldwide protein data bank. Mol Biotechnol 42(1):1–13. doi:10.1007/S12033-008-9127-7 CrossRefPubMedGoogle Scholar
  5. 5.
    Al-Lazikani B, Lesk AM, Chothia C (1997) Standard conformations for the canonical structures of immunoglobulins. J Mol Biol 273(4):927–948. doi:10.1006/jmbi.1997.1354 CrossRefPubMedGoogle Scholar
  6. 6.
    Kabat EA, Wu TT (1991) Identical V-region amino-acid-sequences and segments of sequences in antibodies of different specificities - relative contributions of Vh and Vl genes, minigenes, and complementarity-determining regions to binding of antibody-combining sites. J Immunol 147(5):1709–1719PubMedGoogle Scholar
  7. 7.
    Lefranc MP, Pommie C, Ruiz M, Giudicelli V, Foulquier E, Truong L, Thouvenin-Contet V, Lefranc G (2003) IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains. Dev Comp Immunol 27(1):55–77CrossRefPubMedGoogle Scholar
  8. 8.
    Honegger A, Pluckthun A (2001) Yet another numbering scheme for immunoglobulin variable domains: an automatic modeling and analysis tool. J Mol Biol 309(3):657–670. doi:10.1006/Jmbi.2001.4662 CrossRefPubMedGoogle Scholar
  9. 9.
    Abhinandan KR, Martin AC (2008) Analysis and improvements to Kabat and structurally correct numbering of antibody variable domains. Mol Immunol 45(14):3832–3839. doi:10.1016/j.molimm.2008.05.022 CrossRefPubMedGoogle Scholar
  10. 10.
    Chothia C, Lesk AM (1987) Canonical structures for the hypervariable regions of immunoglobulins. J Mol Biol 196(4):901–917CrossRefPubMedGoogle Scholar
  11. 11.
    Kunik V, Peters B, Ofran Y (2012) Structural consensus among antibodies defines the antigen binding site. PLoS Comput Biol 8(2). Artn E1002388. doi:10.1371/Journal.Pcbi.1002388.Google Scholar
  12. 12.
    Zemla A, Venclovas C, Moult J, Fidelis K (1999) Processing and analysis of CASP3 protein structure predictions. Proteins 3:22–29CrossRefPubMedGoogle Scholar
  13. 13.
    Zhang Y, Skolnick J (2004) Scoring function for automated assessment of protein structure template quality. Proteins 57(4):702–710. doi:10.1002/prot.20264 CrossRefPubMedGoogle Scholar
  14. 14.
    Rousseeuw PJ (1987) Silhouettes - a graphical aid to the interpretation and validation of cluster-analysis. J Comput Appl Math 20:53–65. doi:10.1016/0377-0427(87)90125-7 CrossRefGoogle Scholar
  15. 15.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32. doi:10.1023/A:1010933404324 CrossRefGoogle Scholar
  16. 16.
    Archer KJ, Kirnes RV (2008) Empirical characterization of random forest variable importance measures. Comput Stat Data Anal 52(4):2249–2260. doi:10.1016/J.Csda.2007.08.015 CrossRefGoogle Scholar
  17. 17.
    Zemla A (2003) LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res 31(13):3370–3374PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Biochemistry and Molecular BiologyUniversity of Southern DenmarkOdenseDenmark
  2. 2.Center for Biological Sequence Analysis, Department of Systems BiologyTechnical University of DenmarkLyngbyDenmark

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