ProInfer: An interpretable protein inference tool leveraging on biological networks

PLoS Comput Biol. 2023 Mar 17;19(3):e1010961. doi: 10.1371/journal.pcbi.1010961. eCollection 2023 Mar.

Abstract

In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Databases, Protein
  • Mass Spectrometry
  • Peptides* / chemistry
  • Proteome / analysis
  • Proteomics / methods
  • Software

Substances

  • Peptides
  • Proteome

Grants and funding

This work was supported by the Ministry of Education Singapore via an AcRF Tier 2 award (MOE2019-T2-1-042 to WWBG and LW) and a AcRF Tier 1 award RT11/21 to WWBG). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.