A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin
Year: 2016
Type: article
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.
Source: BMC Bioinformatics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
Awards: Fast k-mer Counting to Quantify Gene Expression and Improve Genome Assembly, BioChancePLUS-2: Verbundprojekt: Individualisierte Medizin: Tool zu Therapieentscheidung - Apherese / Immunadsorptions-Chip, Teilprojekt 1: Klinische Charakterisierung von RA-Patienten, CAREER: Model-based Reconstruction of Ancient Biological Networks, BioChancePLUS-2: Verbundprojekt: Individualisierte Medizin: Tool zu Therapieentscheidung - Apherese / Immunadsorptions-Chip, Teilprojekt 2: Erstellung einer projektbezogenen Datenbank zur Erfassung der projektrelevanten Daten, Better Network Modules: New Tools for Protein Network Analysis, Algorithms for Managing Uncertainty in Chromosome Conformation Capture Data, Accurate Computational Detection of Influenza Reassortments, MedSys: Verbundprojekt: Therapeutische Systemimmunologie, Teilprojekt G, MedSys: Verbundprojekt: Therapeutische Systemimmunologie, Teilprojekt D, CAREER: Model-based Reconstruction of Ancient Biological Networks, III-CXT-Small: Graphs to Diversity: extracting genomic variation from sequence graphs
OpenAlex ID: W2322460582