A Combinatorial Perspective of the Protein Inference Problem

TL;DR

This paper introduces a combinatorial approach to protein inference in shotgun proteomics, providing a closed-form model that improves efficiency and offers insights into peptide contributions, with competitive results against existing methods.

Contribution

The paper presents a novel combinatorial model for protein inference that yields closed-form solutions and enhances computational efficiency over previous methods like ProteinProphet.

Findings

The model provides bounds and empirical estimates for protein probabilities.

It demonstrates competitive accuracy with ProteinProphet.

The approach efficiently handles unique and degenerate peptides.

Abstract

In a shotgun proteomics experiment, proteins are the most biologically meaningful output. The success of proteomics studies depends on the ability to accurately and efficiently identify proteins. Many methods have been proposed to facilitate the identification of proteins from the results of peptide identification. However, the relationship between protein identification and peptide identification has not been thoroughly explained before. In this paper, we are devoted to a combinatorial perspective of the protein inference problem. We employ combinatorial mathematics to calculate the conditional protein probabilities (Protein probability means the probability that a protein is correctly identified) under three assumptions, which lead to a lower bound, an upper bound and an empirical estimation of protein probabilities, respectively. The combinatorial perspective enables us to obtain a closed-form formulation for protein inference. Based on our model, we study the impact of unique peptides and degenerate peptides on protein probabilities. Here, degenerate peptides are peptides shared by at least two proteins. Meanwhile, we also study the relationship of our model with other methods such as ProteinProphet. A probability confidence interval can be calculated and used together with probability to filter the protein identification result. Our method achieves competitive results with ProteinProphet in a more efficient manner in the experiment based on two datasets of standard protein mixtures and two datasets of real samples. We name our program ProteinInfer. Its Java source code is available at http://bioinformatics.ust.hk/proteininfer