pp 1-10 | Cite as

Proteochemometrics for the Prediction of Peptide Binding to Multiple HLA Class II Proteins

  • Ivan Dimitrov
  • Ventsislav Yordanov
  • Darren R. Flower
  • Irini Doytchinova
Part of the Methods in Pharmacology and Toxicology book series


Proteochemometrics (PCM) is a method for deriving quantitative structure–activity relationships (QSAR). It models the bioactivity of multiple ligands against multiple target proteins. Thus, PCM is a key method used to develop multi-target drug molecules. In the present protocol, the PCM method is applied to a set of peptides binding to seven polymorphic HLA class II proteins from locus DP. The peptides bind to an open-ended binding site on DP proteins which accepts a nonameric binding core. As the peptides in the studied set are 15-mers, the initial set is processed by an iterative self-consistent algorithm to select the most probable binding nonamer for each 15-mer peptide. The final set containing the most probable binding nonamers is used to derive the PCM model. The model is validated by external set of proteins and enters the server EpiTOP which is freely accessible at


Proteochemometrics HLA class II binding prediction Partial least squares EpiTOP 


  1. 1.
    Janeway CA (2001) Immunobiology: the immune system in health and disease. Churchill Livingstone, New York, NYGoogle Scholar
  2. 2.
    Robinson J, Halliwell JA, Hayhurst JH, Flicek P, Parham P, Marsh SGE (2015) The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Res 43:D423–D431Google Scholar
  3. 3.
    Lapins M, Wikberg J (2010) Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques. BMC Bioinformatics 11:339Google Scholar
  4. 4.
    van Westen GJP, Wegner JK, IJzerman AP, van Vlijmen HWT, Bender A (2011) Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets. Med Chem Commun 2:16–30Google Scholar
  5. 5.
    Cortés-Ciriano I, Ain QU, Subramanian V, Lenselink EB, Méndez-Lucio O, IJzerman AP, Wohlfahrt G, Prusis P, Malliavin TE, van Westen GJP, Bender A (2015) Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects. Med Chem Commun 6:24–50Google Scholar
  6. 6.
    Lapinsh M, Prusis P, Gutcaits A, Lundstedt T, Wikberg JE (2001) Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions. Biochim Biophys Acta 1525:180–190Google Scholar
  7. 7.
    Prusis P, Uhlén S, Petrovska R, Lapinsh M, Wikberg JE (2006) Prediction of indirect interactions in proteins. BMC Bioinformatics 7:167Google Scholar
  8. 8.
    Lapinsh M, Prusis P, Lundstedt T, Wikberg JE (2002) Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands. Mol Pharmacol 61:1465–1475Google Scholar
  9. 9.
    Sidney J, Steen A, Moore C, Ngo S, Chung J, Peters B, Sette A (2011) Five HLA-DP molecules frequently expressed in the worldwide human population share a common HLA supertypic binding specificity. J Immunol 184:2492–2503Google Scholar
  10. 10.
    Dai S, Murphy GA, Crawford F, Mack DG, Falta MT, Marrack P, Kappler JW, Fontenot AP (2010) Crystal structure of HLA-DP2 and implications for chronic beryllium disease. Proc Natl Acad Sci U S A 107:7425–7430Google Scholar
  11. 11.
    Patronov A, Dimitrov I, Flower DR, Doytchinova I (2011) Peptide binding prediction for the human class II MHC allele HLADP2: a molecular docking approach. BMC Struct Biol 11:32Google Scholar
  12. 12.
    Patronov A, Dimitrov I, Flower DR, Doytchinova I (2012) Peptide binding to HLA-DP proteins at pH 5.0 and pH 7.0: a quantitative molecular docking study. BMC Struct Biol 12:20Google Scholar
  13. 13.
    Hellberg S, Sjostrom M, Skagerberg B, Wold S (1987) Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem 30:1126−1135Google Scholar
  14. 14.
    Eriksson L, Jonsson J, Sjostrom M, Wold S (1989) Multivariate parametrization of coded and non-coded amino acids by thin layer chromatography. Prog Clin Biol Res 291:131–134Google Scholar
  15. 15.
    Wold S, Ruhe A, Wold H, Dunn WJ (1984) The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 5:735–743Google Scholar
  16. 16.
    Dimitrov I, Garnev P, Flower DR, Doytchinova I (2010) Peptide binding to the HLA-DRB1 sypertype: a proteochemometric analysis. Eur J Med Chem 45:236–243Google Scholar
  17. 17.
    Dimitrov I, Garnev P, Flower DR, Doytchinova I (2010) Epi TOP – a proteochemometric tool for MHC class II binding prediction. Bioinformatics 26:2066–2068Google Scholar
  18. 18.
    Dimitrov I, Doytchinova I (2015) Peptide binding prediction to five most frequent HLA-DQ proteins – a proteochemometric approach. Mol Inf 34:467–476Google Scholar
  19. 19.
    Yordanov V, Dimitrov I, Doytchinova I (2018) Proteochemometrics-based prediction of peptide binding to HLA-DP proteins. J Chem Inf Model 58:297–304. Scholar

Copyright information

© Springer Science+Business Media New York 2018

Authors and Affiliations

  • Ivan Dimitrov
    • 1
  • Ventsislav Yordanov
    • 1
  • Darren R. Flower
    • 2
  • Irini Doytchinova
    • 1
  1. 1.Faculty of PharmacyMedical University of SofiaSofiaBulgaria
  2. 2.School of Life and Health SciencesAston UniversityBirminghamUK

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