18O2-Labeling in Quantitative Proteomic Strategies - American

Apr 1, 2009 - Continued advances in software will facilitate widespread use of enzyme-catalyzed 18O2-labeling to determine changes in protein abundanc...
1 downloads 9 Views 319KB Size
18

O2-Labeling in Quantitative Proteomic Strategies: A Status Report Catherine Fenselau*,† and Xudong Yao‡ Department of Chemistry & Biochemistry, University of Maryland, College Park, Maryland 20742, and Department of Chemistry, University of Connecticut, Storrs, Connecticut 06269 Received November 13, 2008

Enzyme-catalyzed 18O2-labeling offers a universal strategy for uniform labeling of all peptides from any kind of proteins, including post-translationally modified proteins. It is applicable to clinical samples with unrivaled sensitivity. This review discusses strengths and limitations, and advocates the separation of proteolysis from the labeling step. Continued advances in software will facilitate widespread use of enzyme-catalyzed 18O2-labeling to determine changes in protein abundances. Keywords: quantitative proteomics • isotope labeling • clinical proteomics • 18O-labeling • absolute quantitation • tissue proteomics • plasma proteome • biofilms • mitochondria • plasma membrane • formalin-fixed paraffin • laser capture micro-dissection • affinity fractionation • protein cross-linking • glycoproteins

Introduction This brief overview will assess the advantages and disadvantages that have been found by many investigators to be associated with the use of 18O2-labeling in quantitative comparisons on a proteome scale. Efforts to mitigate the disadvantages will be presented. Some applications of this flexible technique will be summarized as well.

What Is the Labeling Reaction? 18 O2-labeling is unique because the introduction of the heavy isotopes is catalyzed by serine proteases, rather than being the result of chemical alkylation. This enzyme catalysis is itself the source of both advantages and disadvantages. In the reaction, two atoms of heavy oxygen are introduced into the carboxylic terminus of each peptide. On a proteome scale, this is a global strategy, which labels all the proteolytic products of digested protein mixtures. (C-terminal peptides from proteins and protein truncates are usually exceptions.) Each heavy peptide weighs 4 Da more than its 16O2-labeled light analogue. In execution, the mixtures of heavy and light peptides are mixed, and isotope ratios of peptide pairs are determined by LC-MS. Ideally, this is done in conjunction with MS/MS measurements that provide peptide identification. A spectrum of such a pair of molecular ions is shown in Figure 1. As is the case in Figure 1, a small amount of 18O1 is often observed, and calculations of the ratio often accommodate and correct this.1-5 In many cases, a good approximation is provided by the ratio of the heights of the 16O2- (Io) and 18O2-labeled (I4) peaks. The introduction of one heavy atom of oxygen can be achieved by cleavage of amide bonds in 18O-water. The incorporation of a second atom is catalyzed when the peptide products are reversibly bound as covalent intermediates with the enzyme, and these intermediates are dissociated by 18Owater. Repetitive binding by the enzyme active site provides

* Towhomcorrespondenceshouldbeaddressed.E-mail:[email protected]. † University of Maryland. ‡ University of Connecticut.

2140 Journal of Proteome Research 2009, 8, 2140–2143 Published on Web 04/01/2009

Figure 1. An example of the isotope envelopes in the mass spectrum of a mixture of peptides labeled with 16O2 and 18O2.

multiple exchange cycles and maximal labeling. This mechanism for peptide labeling is illustrated in Figure 2. The occurrence of peptide binding by the protease offers the advantage that cleavage of the protein can be carried out separately from labeling of the peptide.6 Each reaction can be carried out at its optimal conditions, which differ slightly. Multiplexed kinetic measurements have demonstrated that different peptides are labeled with different rates. In general, peptides terminated by arginine are bound more strongly than peptides terminated by lysine, for example, KM ) 1300 ( 300 µM for YGGFMR and KM ) 4400 ( 700 µM for YGGFMK, while kcat values are more similar, 3500 ( 500 and 2800 ( 300 (min-1), respectively.6 It is important that researchers extend the exchange reaction sufficiently long enough to fully label all the peptides in the mixture. Most work has been done using trypsin to provide labeled peptides that end in arginine or lysine; however, the catalytic capabilities of Glu-C,7 chymotrypsin6 and Lys-C8 have also been demonstrated. These enzymes have different amino acid specificities, different optimal pH values, 10.1021/pr8009879 CCC: $40.75

 2009 American Chemical Society

reviews

18

O2-Labeling in Quantitative Proteomic Strategies

Figure 2. Illustration for binding and labeling of a peptide by a serine protease, based on the mechanism of endo-serine protease catalysis.49

and different tolerances to urea and buffers, all of which may be advantageous with different samples.

• Manual interpretation of MSMS spectra is facilitated by the C-terminal labels.

Benefits of Decoupling Digestion and Labeling

Disadvantages of Proteolytic Labeling

Separation of these two reactions adds greatly to the versatility of the method. For example, proteins can be digested in gels in 16O-water, and the peptides can be recovered and labeled. Bantscheff and colleagues point out that one can digest and identify the proteins, and subsequently decide what peptides to label for comparative quantitation.9 At the technical level, decoupling proteolysis and peptide labeling eliminates the need to dry proteins for redissolution in 18O-water. The peptide products are more readily lyophilized and redissolved. Decoupling also allows the use of an excess of immobilized protease for labeling, which increases the overall rate and extent of labeling. It is possible to digest proteins with Lys-C, which is active at a higher concentration of urea, and then cleave again and label all products with trypsin.1 Blonder and co-workers recommend carrying out tryptic digestion of proteins in 60% methanol and labeling of peptides in 20% methanol.10

Disadvantages are summarized here, and the list is followed by discussions of progress that has been made in alleviating some of these issues. • The method is limited to binary comparisons or series thereof. • The label is introduced at the peptide level. • The 4 Da mass difference can overlap natural isotopes. • Incomplete labeling is reported. • Back exchange is reported. • Like other stable isotope labels, the in-spectrum dynamic range is limited.

Labeling for Clinical and Animal Samples Metabolic labeling is an outstanding strategy for quantitative comparisons of protein pools from cells grown in culture. However, this is not a viable approach to label protein samples from humans or most animals. Among the many reactions proposed to label proteins or peptides in clinical biopsies and animal tissue, only 18O2-labeling introduces no chemical contamination. The only byproduct is water. The immobilized enzyme catalyst is physically removed. The method also works with excellent sensitivity. Zang and colleagues11 report sensitivity adequate to label less than 5 µg of protein obtained from ∼10 000 cells captured by laser microdissection from mastectomy specimens, and comment that this sensitivity was not achievable with isotope coded affinity tagging (ICAT), designed for at least 100 µg. A research group from Cellzome AG has reported 50 fmol sensitivity9 for the 18O2-labeling method applied to proteins that have been fractionated on SDS gels.

Advantages of Proteolytic Labeling The literature and the discussion above provide the basis for the following summary of the advantages of 18O2 labeling. • The method constitutes a universal strategy, which provides labeled peptides from all kinds of proteins, carrying any kind of post-translational modification. • The method provides uniform labeling of all the peptides in each protein (except the C-terminal peptide). • This is a residue-specific catalytic method. • It is applicable to samples at the low femtomole level. • Reagents are cost-effective and readily available. • It is compatible with all peptide-level fractionation methods, including affinity fractionations. • The light and heavy peptide pairs coelute in high pressure liquid chromatography.

Labels Are Introduced at the Peptide Level In 18O2-labeling, as in iTRAQ, protein pools are combined only after the proteins have been converted to peptides. This dictates that sample processing should be minimized at the protein level, to avoid differential loss, and that the requisite fractionation should be carried out at the peptide level. Advanced technologies that have been used for fractionation of 18O-labeled peptides include ion exchange chromatography and reverse phase HPLC,12 affinity chromatography and HPLC,13,14 solution isoelectric focusing of peptides followed by HPLC,15,16 gel isoelectric focusing of peptides combined with HPLC,17 and capillary isoelectric focusing combined with HPLC.18

Pushing for Complete Introduction of Two Atoms of 18O Some of the guidelines for achieving complete labeling are obvious. Use water enriched >95% in 18O. Use a high concentration of catalyst, made possible by the use of immobilized enzyme. Extend incubation times and, if necessary, refresh the enzyme catalyst after several hours. Other experimental suggestions are more subtle. Peptides with neutral C-terminal carboxylate groups are more strongly bound in the active sites of trypsin and Lys-C endoprotease, which have high local negative charge than peptides with ionized C-terminal carboxylate groups. Thus, it is expected that slightly acidic pH conditions will increase the concentration of peptides with the neutral C-terminal carboxylate, and increase the rate of the exchange reaction. Indeed, several laboratories have pointed out that the optimal pH for reversible peptide binding by trypsin is slightly acidic. Staes et al.19 recommend pH 4.5; Zang et al.11 use pH 6.75; and Hajkova et al.20 suggest that pH 6.0 is optimal. Other workers report that mixed aqueous/organic solvents improve the incorporation of isotopes catalyzed by serine proteases. Brown and Fenselau21 used 30% acetonitrile with Glu-C protease. Blonder et al.10 report 20% methanol with trypsin. Nelson et al.22 recommend 50% methanol with trypsin.

Minimizing Back Exchange Residual protease activity in labeled peptides is responsible for almost all of the back exchange of 18O observed by many Journal of Proteome Research • Vol. 8, No. 5, 2009 2141

reviews

Fenselau and Yao 16

researchers. If O-water is added to the labeled peptides while the catalytic protease is still present, enzyme-catalyzed back exchange can occur. This is most readily avoided by the use of immobilized protease, and its physical removal from the reaction.21 Extending this idea further, Sevinsky et al.17 have recommended the use of immobilized trypsin in both protein digestion and peptide labeling. Others have suggested lowering the pH to inactivate the enzyme,2,23,24 reducing and alkylating the enzyme,19 heating18 and cooling25,26 the digestion. LopezFerrer et al.5 report that trypsin is inactivated by ultrasound irradiation. However, none of these latter efforts to deactivate trypsin appear to terminate back exchange as completely as removing the enzyme immobilized on beads. Slow chemical exchange of carboxylate oxygens has been documented at low pH and elevated temperatures,27,28 which could contribute back exchange of labeled carboxyl termini if the pH is not controlled. Equilibrium with the solvent is reported to be achieved for peptides after about 11 days in 1% trifluoroacetic acid at room temperature.29 This suggests that side chain carboxylate groups could nonspecifically incorporate 18 O only if a peptide is stored in acidic 18O-water for a long period of time. Blounder and colleagues report that they have detected no nonspecific labeling in four global surveys carried out using trypsin-catalyzed 18O-labeling.10

Software for Automated Identification and Quantitation of Peptides The use of 18O2-labeling in high throughput workflows requires software that will automatically recognize light and heavy peptide pairs (∆M ) 4 Da), compute ratios from integration of paired cluster areas, and collect or connect to corresponding MS/MS spectra for peptide identification. Until recently, no software was available to meet these needs, and this has restricted the adaption of the method. Recently, freeware has been described from several sources, which includes some or all of these capabilities. These include ZoomQuant,30 msInspect,31 and Census.32 At least one new commercial offering holds promise for this application, the Mascot Distiller Quantitation Toolbox (Matrix Science). We agree with the anonymous reviewer who said “This technology has gained less acceptance than expected due to the data analysis being more challenging. If this bottleneck could be resolved and commercially acceptable solutions were provided, this technology would propel itself to the forefront for proteome systems where metabolic labeling cannot be employed.”

A Flexible Technique with Many Applications The flexibility of 18O2-labeling has allowed successful comparative quantitation across a range of samples. Selected examples include: • Laser capture microdissection11 • Human liver biopsies26,33 • Formalin-fixed, paraffin embedded tissue34 • Plasma membrane proteins22,35 • Secreted adipose proteins36 • Nuclear proteins15 • Mitochondrial proteins16,37 • Low molecular weight serum proteome38 • Human plasma proteome39,40 • Bacterial biofilms41 • Spatial mapping of protein abundances in brain tissue42 The precision and accuracy reported for ratios depends on the difference in the quantities of the two peptides. In one of 2142

Journal of Proteome Research • Vol. 8, No. 5, 2009

Figure 3. Partial mass spectrum of a peptide mixture produced by incubation with Glu-C endoprotease and N-glycopeptidase F (PNGase) in which peptide pairs can be seen that differ by 4 Da and by 6 Da (modified from ref 7).

the most careful studies, Bantscheff and co-workers9 found that, for ratios between 1:1 and 1:3, accuracy is within 10%. They attribute much of this variance to in-gel digestion and sample handling. They report errors as high as 40% for 1:10 ratios, and suggest that this also reflects characteristics of ionization and detection in mass spectrometry. Other experience indicates that baseline noise is the biggest problem.12 In the analysis of proteins in biopsy samples, Zang et al.11 report relative standard deviations of 2-46% for protein ratios in the range of 1.0-6.4. Each value for deviation is based on the variation among all the peptides assigned to each protein. These authors suggest that higher coverage, isotope ratio measurements of more peptides from each protein, will improve the accuracy of the ratio determined for the protein pair. Overall, values reported for peptide pairs carrying 18O2labels and 16O2-labels are in good agreement with those reported for other methods involving stable isotope labeling. The precision of quantitative comparisons of protein mixtures can be improved by reversed labeling,16,41,43,44 a principle adapted from earlier biochemical studies using stable and radioactive isotopes. Most importantly, however, the use of forward and reverse labeling allows identification of on/off proteins, proteins that are not detectable in one of the samples being compared. Forward and reverse labeling in a study of P450 isoforms in mouse liver contributed to successful measurements of fold changes ranging between 0.02 and 32.44 Although analyses of these isotope ratios have been carried out on all commercial analyzers, higher accuracy measurements favor accuracy and precision, and also facilitate recognition of pairs. High-resolution Fourier transform ion cyclotron mass spectrometry has been used to extend an accurate mass and time tag strategy to quantitation of isotope pairs in protein mixtures from cultured breast cancer cells25 and human liver biopsies.33 In addition to comparative quantitation, enzyme-catalyzed 18 O2-labeling has also been used to provide labeled peptides

reviews

18

O2-Labeling in Quantitative Proteomic Strategies 23,45

for absolute quantitation, introduced to distinguish artifacts in affinity pull-down experiments,46 and combined with IMAC and phosphatase to identify changes in phosphorylation.13 Peptides joined by chemical or disulfide cross-linking can be distinguished because the presence of two carboxyl termini generates an 8 Da mass increment.7,47,48 Incubation of peptide mixtures with PNGase in 18O-water introduces one atom of 18O into aspartate side chains to mark sites originally glycosylated. This reaction can be combined with trypsin-catalyzed carboxyl terminal labeling to distinguish glycopeptides by a 6 Da increment.7 In Figure 3, a glycopeptide pair separated by 6 Da is distinguished from nonglycosylated peptides, which incorporate only 2 atoms of 18O.

Summary 18 O2-labeling can be considered the Linux of isotope labeling methods. It does not come in a proprietary kit, but rather each laboratory can adapt the method to its own applications. It offers a universal strategy for uniform labeling of all peptides from any kind of proteins, including post-translationally modified proteins. It is applicable to clinical samples with unrivaled sensitivity. Fundamentally, it is limited to binary comparisons or series thereof, and operationally, its use requires a workflow with minimal manipulation of proteins, since the light and heavy samples are combined at the peptide level. With continued advances in software and instrumentation, the 18O2labeling method promises increased applications for determining protein changes, both in the abundances and the post/ co-translational modifications of real-world proteome samples.

References (1) Yao, X.; Freas, A.; Ramirez, J.; Demirev, P. A.; Fenselau, C. Anal. Chem. 2001, 73, 2836-42. Erratum in Anal. Chem. 2004, 76, 2675. (2) Johnson, K. L.; Muddiman, D. C. J. Am. Soc. Mass Spectrom. 2004, 15, 437–45. (3) Halligan, B. D.; Slyper, R. Y.; Twigger, S. N.; Hicks, W.; Olivier, M.; Greene, A. S. J. Am. Soc. Mass Spectrom. 2005, 16, 302–6. (4) Mason, C. J.; Therneau, T. M.; Eckel-Passow, J. E.; Johnson, K. L.; Oberg, A. L.; Olson, J. E.; Nair, K. S.; Muddiman, D. C.; Bergen, H. R., III. Mol. Cell. Proteomics 2007, 6, 305–18. (5) Lopez-Ferrer, D.; Heibeck, T. H.; Petritis, K.; Hixson, K. K.; Qian, W.; Monroe, M. E.; Mayampurath, A.; Moore, R. J.; Belov, M. E.; Camp, D. G., II; Smith, R. D. J. Proteome Res. 2008, 7, 3860–7. (6) Yao, X.; Afonso, C.; Fenselau, C. J. Proteome Res. 2003, 2, 147–52. (7) Reynolds, K. J.; Yao, X.; Fenselau, C. J. Proteome Res. 2002, 1, 27– 33. (8) Miyagi, M.; Rao, K. C. Mass Spectrom. Rev. 2007, 26, 121–36. (9) Bantscheff, M.; Dumpelfeld, B.; Kuster, B. Rapid Commun. Mass Spectrom. 2004, 18, 869–76. (10) Blonder, J.; Hale, M. L.; Chan, K. C.; Yu, L. R.; Lucas, D. A.; Conrads, T. P.; Zhou, M.; Popoff, M. R.; Issaq, H. J.; Stiles, B. G.; Veenstra, T. D. J. Proteome Res. 2005, 4, 523–31. (11) Zang, L.; Palmer Toy, D.; Hancock, W. S.; Sgroi, D. C.; Karger, B. L. J. Proteome Res. 2004, 3, 604–12. (12) Heller, M.; Mattou, H.; Menzel, C.; Yao, X. J. Am. Soc. Mass Spectrom. 2003, 14, 704–18. (13) Bonenfant, D.; Schmelzle, T.; Jacinto, E.; Crespo, J. L.; Mini, T.; Hall, M. N.; Jenoe, P. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 880–5. (14) Liu, T.; Qian, W. J.; Strittmatter, E. F.; Camp, D. G., 2nd; Anderson, G. A.; Thrall, B. D.; Smith, R. D. Anal. Chem. 2004, 76, 5345–53. (15) An, Y.; Fu, Z.; Gutierrez, P.; Fenselau, C. J. Proteome Res. 2005, 4, 2126–32. (16) Wang, J.; Gutierrez, P.; Edwards, N.; Fenselau, C. J. Proteome Res. 2007, 6, 4601–7. (17) Sevinsky, J. R.; Brown, K. J.; Cargile, B. J.; Bundy, J. L.; Stephenson, J. L., Jr. Anal. Chem. 2007, 79, 2158–62.

(18) Storms, H. F.; van der Heijden, R.; Tjaden, U. R.; van der Greef, J. Rapid Commun. Mass Spectrom. 2006, 20, 3491–7. (19) Staes, A.; Demol, H.; Van Damme, J.; Martens, L.; Vandekerckhove, J.; Gevaert, K. J. Proteome Res. 2004, 3, 786–91. (20) Hajkova, D.; Rao, K. C.; Miyagi, M. J. Proteome Res. 2006, 5, 1667– 73. (21) Brown, K. J.; Fenselau, C. J. Proteome Res. 2004, 3, 455–62. (22) Nelson, C. J.; Hegeman, A. D.; Harms, A. C.; Sussman, M. R. Mol. Cell. Proteomics 2006, 5, 1382–95. (23) Stewart, I. I.; Thomson, T.; Figeys, D. Rapid Commun. Mass Spectrom. 2001, 15, 2456–65. (24) Angel, P. M.; Orlando, R. Anal. Biochem. 2006, 359, 26–34. (25) Patwardhan, A. J.; Strittmatter, E. F.; Camp, D. G., II; Smith, R. D.; Pallavicini, M. G. Proteomics 2006, 6, 2903–15. (26) Kristiansen, T. Z.; Harsha, H. C.; Gronborg, M.; Maitra, A.; Pandey, A. J. Proteome Res. 2008, 7, 4670–7. (27) Murphy, R. C.; Clay, K. L. Biomed. Mass Spectrom. 1979, 6, 309– 14. (28) Murphy, R. C.; Clay, K. L. Methods Enzymol. 1982, 86, 547–51. (29) Niles, R.; Witkowska, H.; Allen, S.; Hall, S.; Fisher, S.; Hardt, M. Anal. Chem. 2009, 81; DOI: 10.1021/ac802484d. (30) Hicks, W. A.; Halligan, B. D.; Slyper, R. Y.; Twigger, S. N.; Greene, A. S.; Olivier, M. J. Am. Soc. Mass Spectrom. 2005, 16, 916–25. (31) Bellew, M.; Coram, M.; Fitzgibbon, M.; Igra, M.; Randolph, T.; Wang, P.; May, D.; Eng, J.; Fang, R.; Lin, C.; Chen, J.; Goodlett, D.; Whiteaker, J.; Paulovich, A.; McIntosh, M. Bioinformatics 2006, 22, 1902–9. (32) Park, S. K.; Venable, J. D.; Xu, T.; Yates, J. R., III. Nat. Methods 2008, 5, 319–22. (33) Diamond, D. L.; Jacobs, J. M.; Paeper, B.; Proll, S. C.; Gritsenko, M. A.; Carithers, R. L., Jr.; Larson, A. M.; Yeh, M. M.; Camp, D. G., III; Smith, R. D.; Katze, M. G. Hepatology 2007, 46, 649–57. (34) Nazarian, J.; Santi, M.; Hathout, Y.; MacDonald, T. J. Proteomics: Clin. Appl. 2008, 2, 915–24. (35) Stockwin, L. H.; Blonder, J.; Bumke, M. A.; Lucas, D. A.; Chan, K. C.; Conrads, T. P.; Issaq, H. J.; Veenstra, T. D.; Newton, D. L.; Rybak, S. M. J. Proteome Res. 2006, 5, 2996–3007. (36) Chen, X.; Cushman, S. W.; Pannell, L. K.; Hess, S. J. Proteome Res. 2005, 4, 570–7. (37) Smith, J. R.; Matus, I. R.; Beard, D. A.; Greene, A. S. Proteomics 2008, 8, 446–62. (38) Hood, B. L.; Lucas, D. A.; Kim, G.; Chan, K. C.; Blonder, J.; Issaq, H. J.; Veenstra, T. D.; Conrads, T. P.; Pollet, I.; Karsan, A. J. Am. Soc. Mass Spectrom. 2005, 16, 1221–30. (39) Qian, W. J.; Monroe, M. E.; Liu, T.; Jacobs, J. M.; Anderson, G. A.; Shen, Y.; Moore, R. J.; Anderson, D. J.; Zhang, R.; Calvano, S. E.; Lowry, S. F.; Xiao, W.; Moldawer, L. L.; Davis, R. W.; Tompkins, R. G.; Camp, D. G., II; Smith, R. D. Mol. Cell. Proteomics 2005, 4, 700–9. (40) Qian, W. J.; Liu, T.; Petyuk, V. A.; Gritsenko, M. A.; Petritis, B. O.; Polpitiya, A. D.; Kaushal, A.; Xiao, W.; Finnerty, C. C.; Jeschke, M. G.; Jaitly, N.; Monroe, M. E.; Moore, R. J.; Moldawer, L. L.; Davis, R. W.; Tompkins, R. G.; Herndon, D. N.; Camp, D. G.; Smith, R. D. J. Proteome Res. 2009, 8, 290–9. (41) Ang, C. S.; Veith, P. D.; Dashper, S. G.; Reynolds, E. C. Proteomics 2008, 8, 1645–60. (42) Petyuk, V. A.; Qian, W. J.; Chin, M. H.; Wang, H.; Livesay, E. A.; Monroe, M. E.; Adkins, J. N.; Jaitly, N.; Anderson, D. J.; Camp, D. G., II; Smith, D. J.; Smith, R. D. Genome Res. 2007, 17, 328–36. (43) Wang, Y. K.; Ma, Z.; Quinn, D. F.; Fu, E. W. Anal. Chem. 2001, 73, 3742–50. (44) Lane, C. S.; Wang, Y.; Betts, R.; Griffiths, W. J.; Patterson, L. H. Mol. Cell. Proteomics 2007, 6, 953–62. (45) Desiderio, D. M.; Kai, M. Biomed. Mass Spectrom. 1983, 10, 471–9. (46) Mirgorodskaya, E.; Wanker, E.; Otto, A.; Lehrach, H.; Gobom, J. J. Proteome Res. 2005, 4, 2109–16. (47) Back, J. W.; Notenboom, V.; de Koning, L. J.; Muijsers, A. O.; Sixma, T. K.; de Koster, C. G.; de Jong, L. Anal. Chem. 2002, 74, 4417–22. (48) Gao, Q.; Xue, S.; Doneanu, C. E.; Shaffer, S. A.; Goodlett, D. R.; Nelson, S. D. Anal. Chem. 2006, 78, 2145–9. (49) Walsh, C. T. Posttranslational Modification of Proteins; Roberts and Company Publishers: Englewood, CO, 2006; p 209.

PR8009879

Journal of Proteome Research • Vol. 8, No. 5, 2009 2143