Quantitative Analysis of Protein Covalent Labeling Mass Spectrometry Data in the Mass Spec Studio
| Autor: | Daniel S. Ziemianowicz, Vladimir Sarpe, David C. Schriemer |
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| Rok vydání: | 2019 |
| Předmět: |
Models
Molecular Normalization (statistics) Resolution (mass spectrometry) Protein Conformation Chemistry 010401 analytical chemistry Kinesins Spec# 010402 general chemistry Missing data Mass spectrometry 01 natural sciences 0104 chemical sciences Analytical Chemistry Protein structure Tandem Mass Spectrometry Isotope Labeling Humans Database search engine Imputation (statistics) Biological system computer Software computer.programming_language |
| Zdroj: | Analytical Chemistry. 91:8492-8499 |
| ISSN: | 1520-6882 0003-2700 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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