Detecting Human Contaminant Genetically Variant Peptides in Nonhuman Samples.

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Proteomics data analysis relies on searching experimental spectra against curated protein databases.
• Standard databases often include organism proteomes and common contaminants like trypsin and keratins.
• However, other contaminants may be present but are not routinely included in search databases.

Purpose of the Study:

• To identify and characterize a novel set of protein contaminants in mass spectrometry-based proteomics data.
• To provide evidence for the detection of human genetically variant peptides (GVPs) in nonhuman samples.
• To assess the prevalence and origin of these GVPs in proteomics datasets.

Main Methods:

• Reanalysis of previously collected nonhuman data-dependent acquisition (DDA) and data-independent acquisition (DIA) mass spectrometry datasets.
• Identification of peptides containing single amino acid polymorphisms (human GVPs).
• Statistical analysis to determine the likelihood of nonhuman origin for detected GVPs.

Main Results:

• Between 0 and 135 human GVPs were detected per analyzed nonhuman dataset.
• Evidence suggests that detected GVPs are unlikely to originate from nonhuman biological sources.
• A common subset of eight GVPs was frequently detected across multiple datasets.

Conclusions:

• Human GVPs represent a previously unrecognized class of contaminants in proteomics.
• The routine detection of GVPs in nonhuman samples necessitates their inclusion in standard contaminant databases.
• Updating search databases with GVPs will improve the accuracy and reliability of proteomics data interpretation.