In the postgenome era, biologists have sought to measure the complete complement of proteins, termed proteomics. Currently, the most effective method to measure the proteome is with shotgun, or bottom-up, proteomics, in which the proteome is digested into peptides that are identified followed by protein inference. Despite continuous improvements to all steps of the shotgun proteomics workflow, observed proteome coverage is often low; some proteins are identified by a single peptide sequence. Complete proteome sequence coverage would allow comprehensive characterization of RNA splicing variants and all posttranslational modifications, which would drastically improve the accuracy of biological models. There are many reasons for the sequence coverage deficit, but ultimately peptide length determines sequence observability. Peptides that are too short are lost because they match many protein sequences and their true origin is ambiguous. The maximum observable peptide length is determined by several analytical challenges. This paper explores computationally how peptide lengths produced from several common proteome digestion methods limit observable proteome coverage. Iterative proteome cleavage strategies are also explored. These simulations reveal that maximized proteome coverage can be achieved by use of an iterative digestion protocol involving multiple proteases and chemical cleavages that theoretically allow 92.9% proteome coverage. 1. Introduction In the postgenome era, biologists have sought system-wide measurements of RNA, proteins, and, metabolites, termed transcriptomics, proteomics, and metabolomics, respectively. Shotgun, or bottom-up, proteomics has become the most comprehensive method for proteome identification and quantification [1]. However, observed protein sequence coverage is often low. The ability to cover 100% of protein sequences in a biological system was likened to surrealism in a recent review by Meyer et al. [2]. Multiple steps in the traditional shotgun proteomics workflow contribute to the deficit in observed sequence coverage, including proteome isolation, proteome digestion, peptide separation, peptide MS/MS, and identification by peptide-spectrum matching. Proteome isolation has been extensively evaluated [3, 4]. Several types of peptide separation have been explored [5–7]. Mass spectrometers are becoming more sensitive and versatile [8–10]. Peptide-spectrum matching algorithms are adapting to new data types [11] and becoming more sensitive [12, 13]. Proteome fragmentation into sequenceable peptides is one step with
References
[1]
Y. Zhang, B. R. Fonslow, B. Shan, M.-C. Baek, and J. R. Yates, “Protein analysis by shotgun/bottom-up proteomics,” Chemical Reviews, vol. 113, no. 4, pp. 2343–2394, 2013.
[2]
B. Meyer, D. G. Papasotiriou, and M. Karas, “100% protein sequence coverage: a modern form of surrealism in proteomics,” Amino Acids, vol. 41, no. 2, pp. 291–310, 2011.
[3]
J. M. Gilmore and M. P. Washburn, “Advances in shotgun proteomics and the analysis of membrane proteomes,” Journal of Proteomics, vol. 73, no. 11, pp. 2078–2091, 2010.
[4]
M. Rey, H. Mrázek, P. Pompach et al., “Effective removal of nonionic detergents in protein mass spectrometry, hydrogen/deuterium exchange, and proteomics,” Analytical Chemistry, vol. 82, no. 12, pp. 5107–5116, 2010.
[5]
A. Motoyama and J. R. Yates III, “Multidimensional LC separations in shotgun proteomics,” Analytical Chemistry, vol. 80, no. 19, pp. 7187–7193, 2008.
[6]
Y. Wang, F. Yang, M. A. Gritsenko et al., “Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells,” Proteomics, vol. 11, no. 10, pp. 2019–2026, 2011.
[7]
L. H. Betancourt, P.-J. de Bock, A. Staes et al., “SCX charge state selective separation of tryptic peptides combined with 2D-RP-HPLC allows for detailed proteome mapping,” Journal of Proteomics, vol. 91, pp. 164–171, 2013.
[8]
A. Michalski, E. Damoc, J.-P. Hauschild et al., “Mass spectrometry-based proteomics using Q exactive, a high-performance benchtop quadrupole orbitrap mass spectrometer,” Molecular & Cellular Proteomics, vol. 10, no. 9, 2011.
[9]
J. V. Olsen, J. C. Schwartz, J. Griep-Raming et al., “A dual pressure linear ion trap orbitrap instrument with very high sequencing speed,” Molecular & Cellular Proteomics, vol. 8, no. 12, pp. 2759–2769, 2009.
[10]
C. K. Frese, A. F. M. Altelaar, M. L. Hennrich et al., “Improved peptide identification by targeted fragmentation using CID, HCD and ETD on an LTQ-Orbitrap velos,” Journal of Proteome Research, vol. 10, no. 5, pp. 2377–2388, 2011.
[11]
R. J. Chalkley, P. R. Baker, K. F. Medzihardszky, A. J. Lynn, and A. L. Burlingame, “In-depth analysis of tandem mass spectrometry data from disparate instrument types,” Molecular & Cellular Proteomics, vol. 7, no. 12, pp. 2386–2398, 2008.
[12]
Y. Shen, N. Toli？, S. O. Purvine, and R. D. Smith, “Improving collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD) fourier transform MS/MS degradome-peptidome identifications using high accuracy mass information,” Journal of Proteome Research, vol. 11, no. 2, pp. 668–677, 2012.
[13]
S. Kim, N. Mischerikow, N. Bandeira et al., “The generating function of CID, ETD, and CID/ETD pairs of tandem mass spectra: applications to database search,” Molecular & Cellular Proteomics, vol. 9, no. 12, pp. 2840–2852, 2010.
[14]
S. Linnarsson, “Recent advances in DNA sequencing methods—general principles of sample preparation,” Experimental Cell Research, vol. 316, no. 8, pp. 1339–1343, 2010.
[15]
B. Rietschel, T. N. Arrey, B. Meyer et al., “Elastase digests: new ammunition for shotgun membrane proteomics,” Molecular & Cellular Proteomics, vol. 8, no. 5, pp. 1029–1043, 2009.
[16]
G. Choudhary, S.-L. Wu, P. Shieh, and W. S. Hancock, “Multiple enzymatic digestion for enhanced sequence coverage of proteins in complex proteomic mixtures using capillary LC with ion trap MS/MS,” Journal of Proteome Research, vol. 2, no. 1, pp. 59–67, 2003.
[17]
H. Moura, R. R. Terilli, A. R. Woolfitt et al., “Proteomic analysis and label-free quantification of the large Clostridium difficile toxins,” International Journal of Proteomics, vol. 2013, Article ID 293782, 10 pages, 2013.
[18]
B. Q. Tran, C. Hernandez, P. Waridel et al., “Addressing trypsin bias in large scale (Phospho)proteome analysis by size exclusion chromatography and secondary digestion of large post-trypsin peptides,” Journal of Proteome Research, vol. 10, no. 2, pp. 800–811, 2011.
[19]
N. Neuhauser, N. Nagaraj, P. McHardy et al., “High performance computational analysis of large-scale proteome data sets to assess incremental contribution to coverage of the human genome,” Journal of Proteome Research, vol. 12, no. 6, pp. 2858–2868, 2013.
[20]
D. L. Swaney, C. D. Wenger, and J. J. Coon, “Value of using multiple proteases for large-scale mass spectrometry-based proteomics,” Journal of Proteome Research, vol. 9, no. 3, pp. 1323–1329, 2010.
[21]
J. R. Wi？niewski and M. Mann, “Consecutive proteolytic digestion in an enzyme reactor increases depth of proteomic and phosphoproteomic analysis,” Analytical Chemistry, vol. 84, no. 6, pp. 2631–2637, 2012.
[22]
J. R. Wi？niewski, A. Zougman, N. Nagaraj, and M. Mann, “Universal sample preparation method for proteome analysis,” Nature Methods, vol. 6, no. 5, pp. 359–362, 2009.
[23]
R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2008.
[24]
D. L. Crimmins, S. M. Mische, and N. D. Denslow, “Chemical cleavage of proteins in solution,” in Current Protocols in Protein Science, John Wiley & Sons, New York, NY, USA, 2001.
[25]
R. Kaiser and L. Metzka, “Enhancement of cyanogen bromide cleavage yields for methionyl-serine and methionyl-threonine peptide bonds,” Analytical Biochemistry, vol. 266, no. 1, pp. 1–8, 1999.
[26]
Y. A. Andreev, S. A. Kozlov, A. A. Vassilevski, and E. V. Grishin, “Cyanogen bromide cleavage of proteins in salt and buffer solutions,” Analytical Biochemistry, vol. 407, no. 1, pp. 144–146, 2010.
[27]
M. M. Vestling, M. A. Kelly, and C. Fenselau, “Optimization by mass spectrometry of a tryptophan-specific protein cleavage reaction,” Rapid Communications in Mass Spectrometry, vol. 8, no. 9, pp. 786–790, 1994.
[28]
G. R. Jacobson, M. H. Schaffer, G. R. Stark, and T. C. Vanaman, “Specific chemical cleavage in high yield at the amino peptide bonds of cysteine and cystine residues,” The Journal of Biological Chemistry, vol. 248, no. 19, pp. 6583–6591, 1973.
[29]
M. Iwasaki, T. Masuda, M. Tomita, and Y. Ishihama, “Chemical cleavage-assisted tryptic digestion for membrane proteome analysis,” Journal of Proteome Research, vol. 8, no. 6, pp. 3169–3175, 2009.
[30]
J. G. Meyer, S. Kim, D. Maltby, M. Ghassemian, N. Bandeira, and E. A. Komives, “Expanding proteome coverage with orthogonal-specificity alpha-lytic proteases,” Molecular & Cellular Proteomics, vol. 13, no. 3, pp. 823–835, 2014.
[31]
J. R. Wi？niewski, D. F. Zielinska, and M. Mann, “Comparison of ultrafiltration units for proteomic and N-glycoproteomic analysis by the filter-aided sample preparation method,” Analytical Biochemistry, vol. 410, no. 2, pp. 307–309, 2011.
[32]
J. E. P. Syka, J. J. Coon, M. J. Schroeder, J. Shabanowitz, and D. F. Hunt, “Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 26, pp. 9528–9533, 2004.
