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Open Access Research article

A minimal model of peptide binding predicts ensemble properties of serum antibodies

Victor Greiff16, Henning Redestig1256, Juliane Lück1, Nicole Bruni13, Atijeh Valai1, Susanne Hartmann4, Sebastian Rausch4, Johannes Schuchhardt5 and Michal Or-Guil1*

Author Affiliations

1 Systems Immunology Lab, Department of Biology, Humboldt University Berlin, and Research Center ImmunoSciences, Charité University Medicine Berlin, Berlin, Germany

2 Bayer CropScience N.V., Technologiepark, 38, 9052 Zwijnaarde, Gent, Belgium

3 Studienmethodik und Statistik, Universitätsspital Basel, Basel, Switzerland

4 Department of Molecular Parasitology, Humboldt University Berlin, Berlin, Germany

5 MicroDiscovery GmbH, Berlin, Germany

6 Contributed equally to this study

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BMC Genomics 2012, 13:79  doi:10.1186/1471-2164-13-79

Published: 21 February 2012

Abstract

Background

The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings.

Results

Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones.

Conclusions

Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.