Proteomic Identification of Monoclonal Antibodies from Serum
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- 作者：Daniel R. Boutz ; Andrew P. Horton ; Yariv Wine ; Jason J. Lavinder ; George Georgiou ; Edward M. Marcotte
- 刊名：Analytical Chemistry
- 出版年：2014
- 出版时间：May 20, 2014
- 年：2014
- 卷：86
- 期：10
- 页码：4758-4766
- 全文大小：562K
- 年卷期：v.86,no.10(May 20, 2014)
- ISSN：1520-6882
文摘
Characterizing the in vivo dynamics of the polyclonal antibody repertoire in serum, such as that which might arise in response to stimulation with an antigen, is difficult due to the presence of many highly similar immunoglobulin proteins, each specified by distinct B lymphocytes. These challenges have precluded the use of conventional mass spectrometry for antibody identification based on peptide mass spectral matches to a genomic reference database. Recently, progress has been made using bottom-up analysis of serum antibodies by nanoflow liquid chromatography/high-resolution tandem mass spectrometry combined with a sample-specific antibody sequence database generated by high-throughput sequencing of individual B cell immunoglobulin variable domains (V genes). Here, we describe how intrinsic features of antibody primary structure, most notably the interspersed segments of variable and conserved amino acid sequences, generate recurring patterns in the corresponding peptide mass spectra of V gene peptides, greatly complicating the assignment of correct sequences to mass spectral data. We show that the standard method of decoy-based error modeling fails to account for the error introduced by these highly similar sequences, leading to a significant underestimation of the false discovery rate. Because of these effects, antibody-derived peptide mass spectra require increased stringency in their interpretation. The use of filters based on the mean precursor ion mass accuracy of peptide-spectrum matches is shown to be particularly effective in distinguishing between 鈥渢rue鈥?and 鈥渇alse鈥?identifications. These findings highlight important caveats associated with the use of standard database search and error-modeling methods with nonstandard data sets and custom sequence databases.