ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data.

Area of Science:

• Biochemistry
• Bioinformatics
• Proteomics

Background:

• The Proteome Informatics Research Group (iPRG) conducted a study in 2016 to address challenges in proteoform inference and false discovery rate (FDR) estimation.
• Bottom-up proteomics data analysis presents complexities in accurately identifying and quantifying protein isoforms.

Purpose of the Study:

• To evaluate methods for proteoform inference and FDR estimation using bottom-up proteomics data.
• To assess participant performance in identifying proteins and estimating proteoform-level FDR.
• To test a new submission system for handling proteomics data analysis methods.

Main Methods:

• Generated triplicate Q Exactive Orbitrap liquid chromatography-tandem mass spectrometry datasets from four *Escherichia coli* samples.
• Spiked samples with equimolar mixtures of recombinant proteins to mimic homologous pairs.
• Provided participants with raw data and sequence files for proteoform identification and FDR estimation.

Main Results:

• The proteoform inference task was challenging, with only eight unique submissions received.
• No single method consistently outperformed others across all samples.
• Participants' submissions lacked complete executable R Markdown or IPython Notebooks, despite provided examples.

Conclusions:

• The study generated a unique "ground-truth" dataset for proteoform identification, now available to researchers.
• The developed virtual private server (VPS) and submission validator system are scalable for future studies.
• Enhanced promotion and participation are needed for future iPRG studies to maximize engagement and data generation.