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Updated: Jan 21, 2026

Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics
Published on: October 13, 2020
Improved Protein Inference from Multiple Protease Bottom-Up Mass Spectrometry Data
Rachel M Miller1, Robert J Millikin1, Connor V Hoffmann1
1Department of Chemistry , University of Wisconsin , 1101 University Avenue , Madison , Wisconsin 53706 , United States.
Abstract:
Peptides detected by tandem mass spectrometry (MS/MS) in bottom-up proteomics serve as proxies for the proteins expressed in the sample. Protein inference is a process routinely applied to these peptides to generate a plausible list of candidate protein identifications. The use of multiple proteases for parallel protein digestions expands sequence coverage, provides additional peptide identifications, and increases the probability of identifying peptides that are unique to a single protein, which are all valuable for protein inference. We have developed and implemented a multi-protease protein inference algorithm in MetaMorpheus, a bottom-up search software program, which incorporates the calculation of protease-specific q-values and preserves the association of peptide sequences and their protease of origin. This integrated multi-protease protein inference algorithm provides more accurate results than either the aggregation of results from the separate analysis of the peptide identifications produced by each protease (separate approach) in MetaMorpheus, or results that are obtained using Fido, ProteinProphet, or DTASelect2. MetaMorpheus' integrated multi-protease data analysis decreases the ambiguity of the protein group list, reduces the frequency of erroneous identifications, and increases the number of post-translational modifications identified, while combining multi-protease search and protein inference into a single software program.
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