Protein Identification from Tandem Mass Spectra with Probabilistic Language Modeling

Abstract

Abstract. This paper presents an interdisciplinary investigation of statistical information retrieval (IR) techniques for protein identification from tandem mass spectra, a challenging problem in proteomic data analysis. We formulate the task as an IR problem, by constructing a "query vector" whose elements are system-predicted peptides with confidence scores based on spectrum analysis of the input sample, and by defining the vector space of "documents" with protein profiles, each of which is constructed based on the theoretical spectrum of a protein. This formulation establishes a new connection from the protein identification problem to a probabilistic language modeling approach as well as the vector space models in IR, and enables us to compare fundamental differences in the IR models and common approaches in protein identification. Our experiments on benchmark spectrometry query sets and large protein databases demonstrate that the IR models significantly outperform wellestablished methods in protein identification, by enhancing precision in highrecall regions in particular, where the conventional approaches are weak.

Computational approaches to protein inference in shotgun proteomics

“…Some probabilistic algorithms do not address degenerate peptides [63,65,68,70], while some such as ProteinProphet [21] combine probabilistic inference with the parsimony principle (for degenerate peptides) and protein grouping (for indistinguishable proteins). In the following subsections, we provide an in-depth discussion of the three major probabilistic methods: ProteinProphet [21], MSBayesPro [61], and Fido [71], and briefly mention several other methods.…”

“…Several classes of probabilistic algorithms have been proposed so far [ 21 , 24 , 60 , 61 , 63 - 71 ], with different strategies and levels of rigor in addressing protein groups and different run-time performance. Some probabilistic algorithms do not address degenerate peptides [ 63 , 65 , 68 , 70 ], while some such as ProteinProphet [ 21 ] combine probabilistic inference with the parsimony principle (for degenerate peptides) and protein grouping (for indistinguishable proteins). In the following subsections, we provide an in-depth discussion of the three major probabilistic methods: ProteinProphet [ 21 ], MSBayesPro [ 61 ], and Fido [ 71 ], and briefly mention several other methods.…”

“…Yang et al recently investigated protein inference from an information retrieval (IR) point of view [ 68 ]. This work is interesting because it leverages methods in the IR field to the protein identification problem in proteomics.…”