IgM

IgM software tool uses rationally designed peptide arrays to efficiently probe antibody repertoire reactivity (igome) profiles, often neglected as system-level biomarkers. The tool generates a peptide mimotope library that reflects the common IgM repertoire of 10,000 healthy donors and designs an appropriately sized subset of this library as a potential diagnostic tool.

The process involves panning a 7-mer random peptide phage display library on pooled human IgM, followed by next-generation sequencing of the selected phage. The resulting 224,087 sequences are clustered into 790 sequence clusters, and a set of 594 representative mimotopes is used to symmetrically probe the space of IgM reactivities in patients' sera.

The tool allows for scaling the mimotope set while preserving the symmetric sampling of the mimotope sequence and reactivity spaces. BLAST searches of the mimotope sequences against the non-redundant protein database reveal significant idiotypic connectivity of the targeted igome. The mimotope profiles serve as proof-of-principle predictors for random diagnoses, with the library estimated to have the potential to extract more than ten unique reactivity profiles.

Topic

Protein interactions;Small molecules;Immunology;Probes and primers;Biomarkers

Detail

  • Operation: Database search;Sequence clustering;Expression analysis

  • Software interface: Command-line interface

  • Language: R

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: Norwegian, Bulgarian Fund for Scientific Research Grant, and EEA/Norway.

  • Input: -

  • Output: -

  • Contact: Anastas Pashov a_pashov@microbio.bas.bg

  • Collection: -

  • Maturity: -

Publications

  • Diagnostic Profiling of the Human Public IgM Repertoire With Scalable Mimotope Libraries.
  • Pashov A, et al. Diagnostic Profiling of the Human Public IgM Repertoire With Scalable Mimotope Libraries. Diagnostic Profiling of the Human Public IgM Repertoire With Scalable Mimotope Libraries. 2019; 10:2796. doi: 10.3389/fimmu.2019.02796
  • https://doi.org/10.3389/FIMMU.2019.02796
  • PMID: 31849974
  • PMC: PMC6901697

Download and documentation


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