Quantitative Analysis of Protein Covalent Labeling Mass Spectrometry Data in the Mass Spec Studio

Abstract

Covalent labeling with mass spectrometry (CL-MS) provides a direct measure of the chemical and structural features of proteins with the potential for resolution at the amino-acid level. Unfortunately, most applications of CL-MS are limited to narrowly defined differential analyses, where small numbers of residues are compared between two or more protein states. Extending the utility of high-resolution CL-MS for structure-based applications requires more robust computational routines and the development of methodology capable of reporting of labeling yield accurately. Here, we provide a substantial improvement in the analysis of CL-MS data with the development of an extended plug-in built within the Mass Spec Studio development framework (MSS-CLEAN). All elements of data analysisfrom database search to site-resolved and normalized labeling outputare accommodated, as illustrated through the nonselective labeling of the human kinesin Eg5 with photoconverted 3,3′-azibutan-1-ol. In developing the new features within the CL-MS plug-in, we identified additional complexities associated with the application of CL reagents, arising primarily from digestion-induced bias in yield measurements and ambiguities in site localization. A strategy is presented involving the use of redundant site labeling data from overlapping peptides, the imputation of missing data, and a normalization routine to determine relative protection factors. These elements together provide for a robust structural interpretation of CL-MS/MS data while minimizing the over-reporting of labeling site resolution. Finally, to minimize bias, we recommend that digestion strategies for the generation of useful overlapping peptides involve the application of complementary enzymes that drive digestion to completion.

Correlation between Labeling Yield and Surface Accessibility in Covalent Labeling Mass Spectrometry

“…Some structural information may be lost as a result and thus influence the validity of our claims. While our data analysis routine has been shown to generate reliable estimates of labeling, 57 to test our conclusions we generated a very high-quality data subset via PRM and a hybrid fragmentation mode (EThcD) on a subset of PA-labeled peptides, which showed reasonable yields in both the holo and proteolyzed control states. Indeed, in certain cases some peptides showed remarkable resemblance to the SASA model (Figure 6 and Figures S12 and S13), whereas others did not.…”

“…All data were analyzed with the CLEAN module in Mass Spec Studio v2.3.0 . A database search was performed using MS-GF+ within CLEAN, using default parameters except for the following: minPepLength = 5, maxPepLength = 40, minPepCharge = 2, maxPepCharge = 5, instrumentID = Q Exactive, massTolerance = 10 ppm, numModsPerPeptide = 2, usePercolator = False, Cut-offQValue = 0.05.…”

“…where L(r i ) represents the labeling yield for residue i in the structured (s) and unstructured (u) states. 57 Molecular Dynamics and Calculation of Structural Properties. The Protein Data Bank (PDB) structure of ADPbound Eg5 (PDB: 1II6) was used for MD simulations.…”

Utilization of Hydrophobic Microenvironment Sensitivity in Diethylpyrocarbonate Labeling for Protein Structure Prediction

“…Additionally, as more exposed residues are more likely to be covalently labeled, our score term rewarded models with labeled histidine and lysine residues that exhibited higher relative SASA values. While covalent labeling data can be difficult to accurately quantify, , we have implemented a score term that relies only upon the residue DEPC-label status for structure prediction improvement. This is the first implementation of DEPC labeling data-guided structure prediction into the Rosetta software suite.…”

Membrane Protein Structures and Interactions From Covalent Labeling Coupled With Mass Spectrometry

“…This labeling reagent can irreversibly insert into any X─H bond (X stands for C, O, N, or S) and generate a variety of products, which can lead to some difficulties in getting residue-level information (Manzi et al, 2017;Limpikirati et al, 2018). The challenge of pinpointing carbene labeling sites requires a different approach to data analysis than is typically used in HRF or FPOP (Jumper et al, 2012;Ziemianowicz, Sarpe, & Schriemer, 2019). Most recently, labeling methods based on trifluoromethyl radicals (•CF 3 ) have been reported.…”