Informatics Methods to Enhance Bacterial Peptide Detection
- 1University of Victoria
Abstract
Detecting low-abundance bacterial peptides in samples dominated by human proteins using liquid chromatography tandem mass spectrometry (LC MS/MS) is challenging. Bacterial peptides are typically far less abundant than host-derived peptides, and sequence similarity between bacterial and human peptides can complicate confident attribution to a bacterial origin. To address this problem, we developed and benchmarked an in silico spectral library protein identification workflow for detecting Staphylococcus aureus USA300 peptides against a background of A375 cells. Peptide and protein identifications obtained at a 1% peptide-level false discovery rate were compared between the in silico spectral library search and a conventional sequence database search across a defined bacterial dilution series containing 0, 1%, 2%, 10%, 25%, 50%, and 100% bacterial protein. A human sequence similarity filter was applied to identifications from both search strategies to reduce the likelihood of retaining human derived or human like peptide sequences. Both search strategies showed concentration dependent bacterial detection and shared a practical detection limit of approximately 1 to 2% bacterial protein but exhibited distinct performance profiles. The in silico spectral library search produced fewer bacterial-like background identifications in the human-only control, whereas MS2Rescore substantially increased spectral library identifications at higher bacterial concentrations, exceeding database search at 100% bacterial protein. The partially overlapping peptide sets identified by the two strategies indicate that they provide complementary coverage of the bacterial proteome.
Related
- Sudan Dye Adulteration in Foods: A Comprehensive Review of Health Risks, Detection Methods, Regulatory Responses, and Preventive Strategies.
- A review on foundation models for polymer informatics
- AdaptDelivery: a polymer informatics platform for predicting structural properties of polymers via machine learning
- Scalable Thermoset Informatics Integrated with Autonomous Experimentation for On-Demand Discovery
- Polymer Informatics in the Era of Foundation Models: From Representation to Autonomous Design
BibTeX
@misc{ren2026informatics,
title = {{Informatics Methods to Enhance Bacterial Peptide Detection}},
author = {B. Ren and K. Weke and J. Alfaro and D. R. Goodlett},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.06.757124},
url = {https://www.biorxiv.org/content/10.64898/2026.10.06.757124v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Informatics Methods to Enhance Bacterial Peptide Detection
AU - B. Ren
AU - K. Weke
AU - J. Alfaro
AU - D. R. Goodlett
PY - 2026
DA - 2026/10/06
PB - bioRxiv
DO - 10.64898/2026.10.06.757124
UR - https://www.biorxiv.org/content/10.64898/2026.10.06.757124v1
AB - Detecting low-abundance bacterial peptides in samples dominated by human proteins using liquid chromatography tandem mass spectrometry (LC MS/MS) is challenging. Bacterial peptides are typically far less abundant than host-derived peptides, and sequence similarity between bacterial and human peptides can complicate confident attribution to a bacterial origin. To address this problem, we developed and benchmarked an in silico spectral library protein identification workflow for detecting Staphylococcus aureus USA300 peptides against a background of A375 cells. Peptide and protein identifications obtained at a 1% peptide-level false discovery rate were compared between the in silico spectral library search and a conventional sequence database search across a defined bacterial dilution series containing 0, 1%, 2%, 10%, 25%, 50%, and 100% bacterial protein. A human sequence similarity filter was applied to identifications from both search strategies to reduce the likelihood of retaining human derived or human like peptide sequences. Both search strategies showed concentration dependent bacterial detection and shared a practical detection limit of approximately 1 to 2% bacterial protein but exhibited distinct performance profiles. The in silico spectral library search produced fewer bacterial-like background identifications in the human-only control, whereas MS2Rescore substantially increased spectral library identifications at higher bacterial concentrations, exceeding database search at 100% bacterial protein. The partially overlapping peptide sets identified by the two strategies indicate that they provide complementary coverage of the bacterial proteome.
ER -