A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin

Rob Patro, Raquel Norel, Robert J. Prill, Julio Saez-Rodriguez, Peter Lorenz, Felix Steinbeck, Bjoern Ziems, Mitja Luštrek, Nicola Barbarini, Alessandra Tiengo, Riccardo Bellazzi, Hans Jürgen Thiesen, Gustavo Stolovitzky, Carl Kingsford

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg). Results: We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg. To show this, we experimentally constructed and tested the computationally constructed designs. We further show experimentally that these designed peptides are more accurate that those produced by a recent method for the same task. Pythia-design is based on combining random walks with an ensemble of probabilistic support vector machines (SVM) classifiers, and we show that it produces a diverse set of designed peptides, an important property to develop robust sets of candidates for construction. We show that by combining Pythia-design and the method of (PloS ONE 6(8):23616, 2011), we are able to produce an even more accurate collection of designed peptides. Analysis of the experimental validation of Pythia-design peptides indicates that binding of IVIg is favored by epitopes that contain trypthophan and cysteine. Conclusions: Our method, Pythia-design, is able to generate a diverse set of binding and non-binding peptides, and its designs have been experimentally shown to be accurate.

Original languageEnglish
Article number155
JournalBMC Bioinformatics
Volume17
Issue number1
DOIs
Publication statusPublished - Apr 8 2016
Externally publishedYes

Keywords

  • Antibodies
  • Machine learning
  • Protein binding
  • Protein design

ASJC Scopus subject areas

  • Applied Mathematics
  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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