15N metabolic labeling quantification workflow in Arabidopsis using Protein Prospector

Metabolic labeling using stable isotopes is widely used for the relative quantification of proteins in proteomic studies. In plants, metabolic labeling using 15N has great potential, but the associated complexity of data analysis has limited its usage. Here, we present the 15N stable-isotope labeled protein quantification workflow utilizing open-access web-based software Protein Prospector (PP). Further, we discuss several important features of 15N labeling required to make reliable and precise protein quantification. These features include ratio adjustment based on labeling efficiency, median and interquartile range for protein ratios, isotope cluster pattern matching to flag incorrect monoisotopic peak assignment, and caching of quantification results for fast retrieval.

Accurate and automated high-coverage identification of chemically cross-linked peptides with MaxLynx

Cross-linking combined with mass spectrometry (XL-MS) provides a wealth of information about the 3D structure of proteins and their interactions. We introduce MaxLynx, a novel computational proteomics workflow for XL-MS integrated into the MaxQuant environment. It is applicable to non-cleavable and MS-cleavable cross linkers. For both we have generalized the Andromeda peptide database search engine to efficiently identify cross-linked peptides. For non-cleavable peptides, we implemented a novel di-peptide Andromeda score, which is the basis for a computationally efficient N-squared search engine. Additionally, partial scores summarize the evidence for the two constituents of the di-peptide individually. A posterior error probability based on total and partial scores is used to control false discovery rates. For MS-cleavable cross linkers a scoring of signature peaks is combined with the conventional Andromeda score on the cleavage products. The MaxQuant 3D-peak detection was improved to ensure more accurate determination of the monoisotopic peak of isotope patterns for heavy molecules, which cross-linked peptides typically are. A wide selection of filtering parameters can replace manual filtering of identifications, which is often necessary when using other pipelines. On benchmark datasets of synthetic peptides, MaxLynx outperforms all other tested software on data for both types of cross linkers as well as on a proteome-wide dataset of cross-linked D. melanogaster cell lysate. The workflow also supports ion-mobility enhanced MS data. MaxLynx runs on Windows and Linux, contains an interactive viewer for displaying annotated cross-linked spectra and is freely available at https://www.maxquant.org/.

Improved Monoisotopic Mass Estimation for Deeper Proteome Coverage

Accurate assignment of monoisotopic peaks is essential for the identification of peptides in bottom-up proteomics. Misassignment or inaccurate attribution of peptidic ions leads to lower sensitivity and fewer total peptide identifications. In the present work we present a performant, open-source, cross-platform algorithm, Monocle, for the rapid reassignment of instrument assigned precursor peaks to monoisotopic peptide assignments. We demonstrate that the present algorithm can be integrated into many common proteomics pipelines and provides rapid conversion from multiple data source types. Finally, we show that our monoisotopic peak assignment results in up to a two-fold increase in total peptide identifications compared to analyses lacking monoisotopic correction and a 44% improvement over previous monoisotopic peak correction algorithms.

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides

ABSTRACTCross-linking/mass spectrometry (CLMS) has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A seldom-used search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by ‘in-search assignment of the monoisotopic peak’ in Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available datasets. Xi-MPA always delivered the highest number of identifications with ~2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.