Application of de Novo Sequencing to Large-Scale Complex Proteomics Data Sets

peer-reviewed · Journal of Proteome Research · 2016

peer-reviewed · Journal of Proteome Research · 2016. Arun Devabhaktuni et al. Dependent on concise, predefined protein sequence databases, traditional search algorithms perform poorly…
Date 2016-03-04
Type peer-reviewed
Venue Journal of Proteome Research
Publisher American Chemical Society (ACS)
Contribution downstream-application
DOI 10.1021/acs.jproteome.5b00861
Citations (OpenAlex) 50
Venue 2-year citedness 3.48

Abstract

Dependent on concise, predefined protein sequence databases, traditional search algorithms perform poorly when analyzing mass spectra derived from wholly uncharacterized protein products. Conversely, de novo peptide sequencing algorithms can interpret mass spectra without relying on reference databases. However, such algorithms have been difficult to apply to complex protein mixtures, in part due to a lack of methods for automatically validating de novo sequencing results. Here, we present novel metrics for benchmarking de novo sequencing algorithm performance on large-scale proteomics data sets and present a method for accurately calibrating false discovery rates on de novo results. We also present a novel algorithm (LADS) that leverages experimentally disambiguated fragmentation spectra to boost sequencing accuracy and sensitivity. LADS improves sequencing accuracy on longer peptides relative to that of other algorithms and improves discriminability of correct and incorrect sequences. Using these advancements, we demonstrate accurate de novo identification of peptide sequences not identifiable using database search-based approaches.

Authors

  1. Arun Devabhaktuni · Stanford University
  2. Joshua E. Elias · Stanford University

Methods and tools

Cites (12)

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