FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets

In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry of protein modifications is not readily available. To overcome this problem, we developed multiFLEX-LF, a computational tool that builds upon FLEXIQuant, which detects modified peptide precursors and quantifies their modification extent by monitoring the differences between observed and expected intensities of the unmodified precursors. multiFLEX-LF relies on robust linear regression to calculate the modification extent of a given precursor relative to a within-study reference. multiFLEX-LF can analyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyze modification dynamics and coregulated modifications, we hierarchically clustered the precursors of all proteins based on their computed relative modification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time points during mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modification events. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and the quantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF can drive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies. multiFLEX-LF is available at https://gitlab.com/SteenOmicsLab/multiflex-lf.

The potential for using shell proteins in gastropod systematics, assessed in patellogastropod limpets

This investigation of the application of shell protein information to gastropod systematics initially utilized available Lottia gigantea sequences and a transcriptome of Patelloida mimula developed here. Levels of differentiation between predicted sequences of reciprocal best-hit potential homologues in P. mimula and L. gigantea suggested that they could be useful within families, and possibly in higher taxa using some shell-associated proteins, particularly the peroxidases. Subsequently, proteomic analyses of the acid-soluble fraction of extractions from 17 shells and five tissue samples were conducted by combined liquid chromatography/mass spectrometry with nano-electrospray ionization. All proteins with abundance more than 1.2% in the L. gigantea shell proteome were identified with 100% confidence in most extractions by SearchGui/PeptideShaker analyses. In total, 259 of 379 peptides predicted from in silico digestion of L. gigantea shell proteins were represented by validated peptide spectrum matches in one or more specimens. Systematics applications were investigated by analysing metrics such as protein coverage by peptides and phylogenetic analyses of peptide presence/absence. The investigation suggested that diagnostic profiles based on fixed presence/absence differences can be used to separate species pairs. However, further development of analytical techniques and accumulation of reference databases is required for realising fully the systematics potential of the shell proteome.

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics

Recent studies have revealed diverse amino acid, post-translational, and noncanonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications using high-resolution mass spectrometry proteomics. Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate its high specificity and sensitivity for the discovery of substoichiometric protein modifications in complex cellular extracts. SAMPEI mapping of mouse macrophage differentiation revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation. SAMPEI's robust parametrization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).