Using data independent acquisition (DIA) to model high-responding peptides for targeted proteomics experiments

Molecular and Cellular Proteomics

Volumn 14, Issue 9, 2015, Pages 2331-2340

  Searle, Brian C ^a,b   Egertson, Jarrett D ^a   Bollinger, James G ^a   Stergachis, Andrew B ^a   MacCoss, Michael J ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

^b PROTEOME SOFTWARE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PEPTIDE DERIVATIVE; PEPTIDE;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER MODEL; COMPUTER PROGRAM; INFORMATION PROCESSING; MASS SPECTROMETRY; PEPTIDE ANALYSIS; PRIORITY JOURNAL; PROTEOMICS; CHEMISTRY; HUMAN; METABOLISM; PROCEDURES; SOFTWARE;

ALGORITHMS; HUMANS; MASS SPECTROMETRY; NEURAL NETWORKS (COMPUTER); PEPTIDES; PROTEOMICS; SOFTWARE;

EID: 84940547175     PISSN: 15359476     EISSN: 15359484     Source Type: Journal    
DOI: 10.1074/mcp.M115.051300     Document Type: Article

References (36)

1. Marx, V. (2013) Targeted proteomics. Nat. Methods 10, 19-22
2. Liebler, D. C., and Zimmerman, L. J. (2013) Targeted quantitation of proteins by mass spectrometry. Biochemistry 52, 3797-3806
3. Picotti, P., and Aebersold, R. (2012) Selected reaction monitoring-based proteomics: workflows, potential, pitfalls, and future directions. Nat. Methods 9, 555-566
4. Stergachis, A. B., MacLean, B., Lee, K., Stamatoyannopoulos, J. A., and MacCoss, M. J. (2011) Rapid empirical discovery of optimal peptides for targeted proteomics. Nat. Methods 8, 1041-1043
5. Bereman, M. S., MacLean, B., Tomazela, D. M., Liebler, D. C., and Mac-Coss, M.J. (2012) The development of selected reaction monitoring methods for targeted proteomics via empirical refinement. Proteomics 12, 1134-1141
6. Mallick, P., Schirle, M., Chen, S. S., Flory, M. R., Lee, H., Martin, D., Ranish, J., Raught, B., Schmitt, R., Werner, T., Kuster, B., and Aebersold, R. (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25, 125-131
7. Fusaro, V. A., Mani, D. R., Mesirov, J. P., and Carr, S. A. (2009) Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat. Biotechnol. 27, 190-198
8. Eyers, C. E., Lawless, C., Wedge, D. C., Lau, K. W., Gaskell, S. J., and Hubbard, S. J. (2011) CONSeQuence: Prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches. Mol. Cell. Proteomics 10, M110.003384
9. Muntel, J., Boswell, S. A., Tang, S., Ahmed, S., Wapinski, I., Foley, G., Steen, H., and Springer, M. (2015) Abundance-based classifier for the prediction of mass spectrometric peptide detectability upon enrichment. Mol. Cell. Proteomics 14, 430-440
10. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986) Learning representations by back-propagating errors. Nature 323, 533-536
11. Prakash, A., Tomazela, D. M., Frewen, B., Maclean, B., Merrihew, G., Peterman, S., and Maccoss, M. J. (2009) Expediting the development of targeted SRM assays: using data from shotgun proteomics to automate method development. J. Proteome Res. 8, 2733-2739
12. Mead, J. A., Bianco, L., Ottone, V., Barton, C., Kay, R. G., Lilley, K. S., Bond, N. J., and Bessant, C. (2009) MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions. Mol. Cell. Proteomics 8, 696-705
13. Egertson, J. D., MacLean, B., Johnson, R., Xuan, Y., and MacCoss, M. J. (2015) Multiplexed peptide analysis using data independent acquisition and skyline. Nat. Protoc. 10, 887-903
14. Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., and Selbach, M. (2011) Global quantification of mammalian gene expression control. Nature 473, 337-342
15. Hoopmann, M. R., Finney, G. L., and MacCoss, M. J. (2007) High speed data reduction, feature selection, and MS/MS spectrum quality assessment of shotgun proteomics datasets using high-resolution mass spectrometry. Anal. Chem. 79, 5630-5632
16. Hsieh, E. J., Hoopmann, M.R., MacLean, B., and MacCoss, M. J. (2010) Comparison of database search strategies for high precursor mass accuracy MS/MS data. Proteome Res. 9, 1138-1143
17. Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., and MacCoss, M. J. (2007) Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat. Methods 4, 923-925
18. Frewen, B. E., Merrihew, G. E., Wu, C. C., Noble, W. S., and MacCoss, M. J. (2006) Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries. Anal Chem. 78, 5678-5684
19. MacLean, B., Tomazela, D. M., Shulman, N., Chambers, M., Finney, G. L., Frewen, B., Kern, R., Tabb, D. L., Liebler, D. C., and MacCoss, M. J. (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966-968
20. The pANT7-cGST Clone Collection, https://dnasu.org/DNASU/Home.do
21. de Graaf, E. L., Altelaar, A. F., van Breukelen, B., Mohammed, S., and Heck, AJ. (2011) Improving SRM assay development: a global comparison between triple quadrupole, ion trap, and higher energy CID peptide fragmentation spectra. J. Proteome Res. 10, 4334-4341
22. Kawashima, S., and Kanehisa, M. (2000) AAindex: amino acid index database. Nucleic Acids Res. 28, 374
23. Ding, C., and Peng, H. J. (2005) Minimum redundancy feature selection from microarray gene expression data. Bioinform. Comput. Biol. 3, 185-205
24. Peng, H., Long, F., and Ding, C. (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226-1238
25. Bengio, Y., and LeCun, L. (2007) Scaling Learning Algorithms towards AI in Bottou L, Chapelle O, DeCoste D, Weston J (Eds.) Large-scale kernel machines (pp 321-360) Cambridge, MA, MIT Press
26. Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922
27. Zhang, Z. (2005) Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. Anal. Chem. 77, 6364-6373
28. Richardson, J. S., and Richardson, D. C. (1988) Amino acid preferences for specific locations at the ends of alpha helices. Science 240, 1648-1652
29. Yutani, K., Ogasahara, K., Tsujita, T., and Sugino, Y. (1987) Dependence of conformational stability on hydrophobicity of the amino acid residue in a series of variant proteins substituted at a unique position of tryptophan synthase alpha subunit. Proc. Natl. Acad. Sci. U.S.A. 84, 4441-4444
30. Wilce, M. C., Aguilar, M. I., and Hearn, M. T. (1995) Physicochemical basis of amino acid hydrophobicity scales: evaluation of four new scales of amino acid hydrophobicity coefficients derived from RP-HPLC of peptides. Anal. Chem. 67, 1210-1219
31. Maxfield, F. R., and Scheraga, H. A. (1976) Status of empirical methods for the prediction of protein backbone topography. Biochemistry 15, 5138-5153
32. George, R. A., and Heringa, J. (2002) An analysis of protein domain linkers: their classification and role in protein folding. Protein Eng. 15, 871-879
33. Nakashima, H., and Nishikawa, K. (1992) The amino acid composition is different between the cytoplasmic and extracellular sides in membrane proteins. FEBS Lett. 303, 141-146
34. Palau, J., Argos, P., and Puigdomenech, P. (1981) Protein secondary structure: Studies on the limits of prediction accuracy. Int. J. Peptide Protein Res. 19, 394-401
35. Vasquez, M., Nemethy, G., and Scheraga, H. A. (1983) Computed conformational states of the 20 naturally occurring amino acid residues and of the prototype residue α-aminobutyric acid. Macromolecules 16, 1043-1049
36. Fukuchi, S., and Nishikawa, K. (2001) Protein surface amino acid compositions distinctively differ between thermophilic and mesophilic bacteria. J. Mol. Biol. 309, 835-843