Generating high quality libraries for DIA MS with empirically corrected peptide predictions

Brian C. Searle (), Kristian E. Swearingen, Christopher A. Barnes, Tobias Schmidt, Siegfried Gessulat, Bernhard Küster and Mathias Wilhelm
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Brian C. Searle: Institute for Systems Biology
Kristian E. Swearingen: Institute for Systems Biology
Christopher A. Barnes: Novo Nordisk Research Center Seattle, Inc.
Tobias Schmidt: Technical University of Munich
Siegfried Gessulat: Technical University of Munich
Bernhard Küster: Technical University of Munich
Mathias Wilhelm: Technical University of Munich

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract Data-independent acquisition approaches typically rely on experiment-specific spectrum libraries, requiring offline fractionation and tens to hundreds of injections. We demonstrate a library generation workflow that leverages fragmentation and retention time prediction to build libraries containing every peptide in a proteome, and then refines those libraries with empirical data. Our method specifically enables rapid, experiment-specific library generation for non-model organisms, which we demonstrate using the malaria parasite Plasmodium falciparum, and non-canonical databases, which we show by detecting missense variants in HeLa.

Date: 2020
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DOI: 10.1038/s41467-020-15346-1

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