Computational approaches to protein inference in shotgun proteomics

Abstract

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.

Common errors in mass spectrometry‐based analysis of post‐translational modifications

“…leucine and isoleucine) makes protein inference more complicated . Although there have been efforts to computationally solve the problems of protein inference, this issue is inherent to MS‐based proteomics and there will always be a certain level of concern regarding ‘which protein products from which genes were truly identified.’ Thus, there is still an urgent need for newer/orthogonal methods to complement the long‐pursued protein inference issue that is most acute in bottom‐up proteomics experiments. In the same fashion, PTM site(s) identified on peptides cannot be localized onto a protein when the peptide sequence itself is not unique to one protein.…”

Metaproteome Analysis of Endodontic Infections in Association with Different Clinical Conditions

“…Bottom-up or shotgun proteomics is a high-throughput technology that can characterize a very large number of proteins at the same time. In this approach, proteins present in a sample are first digested into peptides by proteolytic enzymes, usually trypsin, which cleaves the protein specifically at the carboxy-terminal region of arginine and lysine residues [13]. Next, peptides are ionized by matrix-assisted laser desorption/ionization (MALDI) or electrospray ionization (ESI), which is coupled to LC, and then analyzed by a mass spectrometer.…”

Genomics and Proteomics for Clinical Discovery and Development

“…To attain a correct list of identified proteins from a set of peptide sequences with their identification scores does seem not to be straightforward, and remains quite challenging due to the following major difficulties: (1) loss of precise connectivity between peptides obtained by protein digestion and original proteins raises the protein inference problem, (Nesvizhskii and Aebersold 2005;Nesvizhskii 2010;Li and Radivojac 2012;Nesvizhskii et al 2003) which is the very intrinsic problem for shotgun proteomics. In other words, many peptide sequences can be originated from more than one protein in a database (e.g.…”

“…Furthermore, it has been suggested that a better quantitative estimation of peptide/protein might also help protein inference by improving the quantity adjustment of peptide detectability (Li and Radivojac 2012;Li et al 2009), and provide additional input information for protein inference. Protein inference can be viewed as a special case of protein label-free quantification.…”