# CoMPaseD: advanced planning of proteomic experiments aiming to identify small proteins

## TL;DR

This paper introduces CoMPaseD, a tool that helps scientists choose the best proteases for experiments to better identify small proteins in proteome studies.

## Contribution

The novel contribution is a protease score and the CoMPaseD tool, which uses Monte-Carlo simulations to predict optimal protease combinations for proteomic experiments.

## Key findings

- CoMPaseD's predicted scores correlate well with experimentally derived scores for small proteomes of Bacillus subtilis and Methanosarcina mazei.
- The tool can guide protease selection based on factors like protein size, localization, or isoelectric point.
- The Python-based tool is freely available and demonstrates broad applicability for proteomic studies.

## Abstract

In proteome studies, the application of alternative proteases, exclusively or in addition to trypsin, often increases protein sequence or proteome coverage. It has recently been shown that, in particular, the analysis of small proteins benefits from such multi-protease approaches. However, selecting the most optimal combination of proteases either requires laboursome experiments or the decision of an experienced user, which might be biased. In this manuscript, we present a protease score that enables the objective comparison of multiple-protease digestions and a Python-based tool named CoMPaseD (Comparison of Multiple Protease  Digestions), which utilizes Monte-Carlo simulations to predict this score for a user-defined set of proteases and any combination of these. By analysis of the small proteomes of the two model organisms Bacillus subtilis and Methanosarcina mazei with five proteases and different experimental setups, we demonstrate a good correlation between experimentally derived and predicted scores. This highlights the broad applicability of CoMPaseD, which can effectively guide the selection of proteases to enhance the characterization of specific subsets of the proteome, e.g. based on factors such as protein size, localization or isoelectric point. CoMPaseD is freely available at https://github.com/MicrobialProteomics/CoMPaseD.