Direct maximization of protein identifications from tandem mass spectra

Marina Spivak, Jason Weston, Michael J. MacCoss and William Stafford Noble

Submitted for publication.

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Abstract

The goal of many shotgun proteomics experiments is to identify with high confidence as many proteins as possible from a complex biological mixture. Existing solutions to this problem typically subdivide the task into two stages, first identifying a collection of peptides with a low false discovery rate, and then inferring from the peptides a corresponding set of proteins. In contrast, we formulate the protein identification problem as a single optimization problem, which we solve using machine learning methods. The resulting algorithm directly controls the relevant error rate, can incorporate a wide variety of evidence and, for complex samples, provides 24--74% more protein identifications than the current state of the art.

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