Quantitative Cross-linking/Mass Spectrometry Using Isotope-labeled Cross-linkers and MaxQuant

The function of proteins is often linked to conformational rearrangements. Quantitative cross-linking/mass spectrometry (QCLMS)1 using isotope-labeled cross-linkers (1–4) is emerging as a new strategy to study such conformation changes of proteins (5). 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 QCLMS approach has great potential for detecting protein conformational changes in macro protein assemblies and possibly also complex protein mixtures such as large protein networks. However, great challenges result from the size and complexity of data sets generated when studying such large and complex protein systems. Manually interrogating QCLMS data (6, 10) by experts can be superior to the performance of automated algorithms, however it is also time consuming, subject to human handling errors and invites the omission of important controls. Consequently, a benchmark study (7) relied on a semiautomated quantitation setup for cross-linking data by exploring the functionality of a quantitative proteomics software Pinpoint (Thermo Fisher Scientific, San Jose, CA). However, still, manually inspecting and correcting quantitation results from Pinpoint was tedious, required expertise and will become increasingly impractical as data size increases. Recently, Kukacka et al. presented a workflow using mMass at the example of calmodulin (17 kDa) in presence and absence of Ca2+ (11). However, the scalability of this approach remains to be shown. As a prove-of-principle, we established a computational workflow to quantify the signals of cross-linked peptides in an automated manner (5). We developed an elementary computational tool, XiQ (5), which allowed us to accurately quantify our model data set. Yet, XiQ has three major drawbacks: (1) it is not optimized for chromatographic feature detection; (2) XiQ is a command line based application and lacks an easy user interface; (3) XiQ does not visualize its output and hence does not facilitate manual inspection and validation. To overcome these disadvantages, we exploited the well-established chromatographic feature detection function and user friendly interface of one of the most commonly used quantitative proteomics software tools, MaxQuant (12). Although 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. Finally, we suggest an integrated workflow of MaxQuant and semi-automated processing. Pinpoint provides a platform for validating and correcting fully automated quantitation results, improving both data recall rate and quantitation accuracy. Fig. 1. Automated quantitation for cross-linked peptides using MaxQuant. A, The workflow of automated quantitation for cross-linked peptides using MaxQuant. B, Example mass spectrometric signals of C3-unique (left), C3b-unique (right) and C3-C3b common (middle) ...