A nested mixture model for protein identification using mass spectrometry

TL;DR

This paper introduces a nested mixture model for mass spectrometry data that improves protein and peptide identification accuracy by integrating the identification process into a single statistical framework, outperforming existing methods.

Contribution

The paper proposes a novel one-stage nested mixture model that jointly infers proteins and peptides, enhancing accuracy over traditional two-stage approaches.

Findings

Single-stage approach yields more accurate peptide identification.

Comparable protein identification accuracy with existing methods, with some scenarios favoring the new model.

Model performs well on simulated and real yeast data.

Abstract

Mass spectrometry provides a high-throughput way to identify proteins in biological samples. In a typical experiment, proteins in a sample are first broken into their constituent peptides. The resulting mixture of peptides is then subjected to mass spectrometry, which generates thousands of spectra, each characteristic of its generating peptide. Here we consider the problem of inferring, from these spectra, which proteins and peptides are present in the sample. We develop a statistical approach to the problem, based on a nested mixture model. In contrast to commonly used two-stage approaches, this model provides a one-stage solution that simultaneously identifies which proteins are present, and which peptides are correctly identified. In this way our model incorporates the evidence feedback between proteins and their constituent peptides. Using simulated data and a yeast data set, we compare and contrast our method with existing widely used approaches (PeptideProphet/ProteinProphet) and with a recently published new approach, HSM. For peptide identification, our single-stage approach yields consistently more accurate results. For protein identification the methods have similar accuracy in most settings, although we exhibit some scenarios in which the existing methods perform poorly.