Publikationsdatenbank

A minimal model of peptide binding predicts ensemble properties of serum antibodies (2012)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Greiff, Victor

Redestig, Henning

Lück, Juliane

Bruni, Nicole

Valai, Atijeh

Hartmann, Susanne

Rausch, Sebastian

Schuchhardt, Johannes

Or-Guil, Michal

Quelle

BMC genomics

Bandzählung: 13

Heftzählung: 1

Seiten: 79 – 93

ISSN: 1471-2164

Sprache

Englisch

Verweise

URL (Volltext): http://edocs.fu-berlin.de/docs/receive/FUDOCS_document_000000019812

DOI: 10.1186/1471-2164-13-79

Pubmed: 22353141

Kontakt

Institut für Immunologie

Robert-von-Ostertag-Str. 7-13
14163 Berlin
+49 30 838 51834
immunologie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

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.

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.

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.