title: A minimal model of peptide binding predicts ensemble properties of serum antibodies.
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.
authors.affiliation: Systems Immunology Lab, Department of Biology, Humboldt University Berlin, and Research Center ImmunoSciences, Charité University Medicine Berlin, Berlin, Germany.
authors.forename: Victor
authors.initials: V
authors.lastname: Greiff
authors.forename: Henning
authors.initials: H
authors.lastname: Redestig
authors.forename: Juliane
authors.initials: J
authors.lastname: Lück
authors.forename: Nicole
authors.initials: N
authors.lastname: Bruni
authors.forename: Atijeh
authors.initials: A
authors.lastname: Valai
authors.forename: Susanne
authors.initials: S
authors.lastname: Hartmann
authors.forename: Sebastian
authors.initials: S
authors.lastname: Rausch
authors.forename: Johannes
authors.initials: J
authors.lastname: Schuchhardt
authors.forename: Michal
authors.initials: M
authors.lastname: Or-Guil
mesh_terms: D000465:Algorithms; D000818:Animals; D000906:Antibodies / Q000276:immunology*; D000907:Antibodies, Bacterial / Q000097:blood / Q000276:immunology*; D000911:Antibodies, Monoclonal / Q000276:immunology; D003198:Computer Simulation; D018604:Epitope Mapping; D007074:Immunoglobulin G / Q000097:blood / Q000276:immunology; D051379:Mice; D008807:Mice, Inbred BALB C; D018448:Models, Immunological; D009351:Nematospiroides dubius / Q000276:immunology; D010455:Peptides / Q000737:chemistry / Q000276:immunology*; D011485:Protein Binding / Q000276:immunology; D012044:Regression Analysis; D012680:Sensitivity and Specificity
chemical_list: D000906:Antibodies; D000907:Antibodies, Bacterial; D000911:Antibodies, Monoclonal; D007074:Immunoglobulin G; D010455:Peptides