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

BMC Genomics

Research Article

Susanne Hartmann¹  Sebastian Rausch¹  Johannes Schuchhardt²  Juliane Lück³  Michal Or-Guil³  Victor Greiff³  Atijeh Valai³  Henning Redestig⁴  Nicole Bruni⁵ 

[1] Department of Molecular Parasitology, Humboldt University Berlin, Berlin, Germany;MicroDiscovery GmbH, Berlin, Germany;Systems Immunology Lab, Department of Biology, Humboldt University Berlin, and Research Center ImmunoSciences, Charité University Medicine Berlin, Berlin, Germany;Systems Immunology Lab, Department of Biology, Humboldt University Berlin, and Research Center ImmunoSciences, Charité University Medicine Berlin, Berlin, Germany;Bayer CropScience N.V., Technologiepark, 38, 9052, Zwijnaarde, Gent, Belgium;MicroDiscovery GmbH, Berlin, Germany;Systems Immunology Lab, Department of Biology, Humboldt University Berlin, and Research Center ImmunoSciences, Charité University Medicine Berlin, Berlin, Germany;Studienmethodik und Statistik, Universitätsspital Basel, Basel, Switzerland;

关键词: Predictive Performance;    Peptide Library;    Epitope Prediction;    Antibody Diversity;    Antibody Mixture;   

DOI  :  10.1186/1471-2164-13-79

 received in 2011-06-11, accepted in 2012-02-21,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsMultivariate 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.ConclusionsOur 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.

【 授权许可】

Unknown   
© Greiff et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.