Abstract
The conceptually simple step from cross-linking/mass spectrometry (CLMS) to quantitative cross-linking/mass spectrometry (QCLMS) is compounded by technical challenges. Currently, quantitative proteomics software is tightly integrated with the protein identification workflow. This prevents automatically quantifying other m/z features in a targeted manner including those associated with cross-linked peptides. Here we present a new release of MaxQuant that permits starting the quantification process from an m/z feature list. Comparing the automated quantification to a carefully manually curated test set of cross-linked peptides obtained by cross-linking C3 and C3b with BS3 and isotope-labeled BS3-d4 revealed a number of observations: (1) Fully automated process using MaxQuant can quantify cross-links in our reference data set with 68% recall rate and 88% accuracy. (2) Hidden quantification errors can be converted into exposed failures by label-swap replica, which makes label-swap replica an essential part of QCLMS. (3) Cross-links that failed during automated quantification can be recovered by semi-automated re-quantification. The integrated workflow of MaxQuant and semi-automated assessment provides the maximum of quantified cross-links. In contrast, work on larger data sets or by less experienced users will benefit from full automation in MaxQuant.
Highlights
From the ‡Wellcome Trust Centre for Cell Biology, Institute of Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK; §Computational Systems Biochemistry, Max-Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany; ¶Chair of Bioanalytics, Institute of Biotechnology, Technische Universitat Berlin, 13355 Berlin, Germany
Applications include the trans-membrane protein complex F-type ATPases (6), the multidomain protein C3 converting into C3b (7), modeling the structure of iC3 (8) and the maturation of the proteasome lid complex (9). These show that the quantitative cross-linking/mass spectrometry (QCLMS) approach has great potential for detecting protein conformational changes in macro protein assemblies and possibly complex protein mixtures such as large protein networks
Automated Quantitation for Cross-linked Peptides Using MaxQuant—As one of the most commonly used quantitation software tools for proteomics studies, MaxQuant has a wellestablished algorithm for chromatographic feature detection
Summary
Developed originally for the analysis of SILAC data (13) MaxQuant has undergone recent expansion of workflows, including label-free quantitation (14) and widening its vendor support (15). Based on our initial assessment of MaxQuant’s weaknesses in the context of QCLMS (5), we developed here a new version of MaxQuant for carrying out automated quantitation in cross-link experiments (Fig. 1). We generated a reference data set, based on our benchmark QCLMS analysis of C3 and C3b (7), to test the performance of this and future new tools. The results showed that experiments with replicated analysis and label-swap provided effective quality control for fully automated quantitation. Pinpoint provides a platform for validating and correcting fully automated quantitation results, improving both data recall rate and quantitation accuracy
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