# Less missing values—evaluation of proteomics workflows for the quantification of (small) proteins

## TL;DR

This study evaluates methods to improve the detection of small proteins in proteomics, resulting in a comprehensive dataset for Clostridioides difficile.

## Contribution

The study provides a systematic evaluation of workflows for quantifying small proteins and generates the most comprehensive C. difficile protein dataset to date.

## Key findings

- Small protein enrichment improves identification and quantification of low-abundance small proteins.
- Spectral libraries enhance the number of robustly quantified proteins and lower detection limits.
- The dataset covers 84.7% of the predicted C. difficile proteome and 61.4% of predicted small proteins.

## Abstract

Quantitative information on protein abundance is crucial to understand biological processes and is therefore frequently gathered in proteomic studies. However, the quality of a quantitative proteomic dataset is greatly affected by the number of missing values, which need to be minimized to produce robust and meaningful data. In this context, small proteins (≤100 amino acids) pose specific analytical challenges, which hinder their efficient identification and quantitative characterization in complex proteomes. In this study, methods for sample preparation and MS-data processing are systematically evaluated for their contribution to identification and quantification of small proteins of Clostridioides difficile 630 Δerm. Results show that small protein enrichment can enhance the number of identified and quantified proteins also for low abundant small proteins. Through application of spectral libraries for identification of MS spectra the number of robustly quantified proteins is increased and a lower limit of their detection is reached. Additionally, the dataset presented here is currently the most comprehensive protein repository for C. difficile covering 84.7% of the predicted proteome and 61.4% of all predicted small proteins of this important pathogen.

This study systematically evaluates methods for improving the identification and quantification of small proteins using bottom-up mass spectrometry, thereby providing the most comprehensive dataset of proteins from the important pathogen Clostridioides difficile.