Palmitoylated proteins: purification and identification

PROTOCOL Palmitoylated proteins: purification and identification Junmei Wan1, Amy F Roth1, Aaron O Bailey2,3 & Nicholas G Davis1 1Department of Pharmacology, Wayne State University School of Medicine, Detroit, Michigan 48201, USA. 2Department of Cell Biology, The Scripps Research Institute, La Jolla, California 92037, USA. 3Present address: Program in Cell and Developmental Biology, University of Virginia School of Medicine, Charlottesville, Virginia 22908, USA. Correspondence should be addressed to N.G.D. ([email protected]). This proteomic protocol purifies and identifies palmitoylated proteins (i.e., S-acylated proteins) from complex protein extracts. The method relies on an acyl-biotinyl exchange chemistry in which biotin moieties are substituted for the thioester-linked protein acyl-modifications through a sequence of three in vitro chemical steps: (i) blockade of free thiols with N-ethylmaleimide; (ii) cleavage of the Cys-palmitoyl thioester linkages with hydroxylamine; and (iii) labeling of thiols, newly exposed by the hydroxylamine, with biotin–HPDP (Biotin-HPDP-N-[6-(Biotinamido)hexyl]-3¢-(2¢-pyridyldithio)propionamide. The biotinylated proteins are then affinity-purified using streptavidin–agarose and identified by multi-dimensional protein identification technology (MuDPIT), a high-throughput, tandem mass spectrometry (MS/MS)–based proteomic technology. MuDPIT also affords a semi-quantitative analysis that may be used to assess the gross changes induced to the global palmitoylation profile by mutation or drugs. Typically, 2–3 weeks are required for this analysis. SH NEM INTRODUCTION Protein palmitoylation (or, more correctly, protein S-acylation) is the acyl-biotinyl exchange (ABE) chemistry of Drisdel and Green, the thioesterification of fatty acyl moieties, typically the 16-carbon an in vitro method that substitutes biotinyl moieties for the palmitoyl moiety, to selected protein cysteines. Similar to prenyla- thioester-linked palmitoyl modifications6. The ABE-generated biotion and myristoylation, and often in combination with these two tinylated proteins can then be affinity-purified using streptavidin– lipidations, palmitoylation may serve to tether proteins to mem- agarose and identified by proteomic mass spectrometry (MS). ABE brane cytosolic surfaces (for reviews, see refs. 1–3). Many signaling comprises a sequence of three chemical steps: (i) an exhaustive proteins, including key players in cancer and synaptic signaling, are blockade of free thiols with N-ethylmaleimide (NEM); (ii) hydropalmitoylated. Notably, many G proteins rely on palmitoylation for xylamine treatment to release thioester-linked palmitoyl moieties, proper membrane-localized function, including H- and N-Ras, restoring the modified cysteine to thiols, (iii) which are then some Rho proteins, as well as the a subunits of most heterotrimeric biotinylated using a thiol-reactive biotinylation reagent (Fig. 1). G proteins. In distinction to prenylation and myristoylation, As the biotinylation reagent, we have opted to use biotin–HPDP, palmitoylation frequently also is found as a transmembrane which has several advantages over the biotin–BMCC originally used (TM) protein modification. For the TM proteins, embedded in by Drisdel and Green6. Biotin–HPDP, which disulfide bonds to the the membrane by its hydrophobic, bilayer-spanning TM domains, thiols, is more thiol-specific; furthermore, it facilitates an easy the addition of palmitoyl tethers seems superfluous. For these TM palmitoylproteins (PPs), a palmitoylation role in tarBind to Stre geting to raft-like membrane domains often pta streptavidin– vidi n agarose is invoked; the saturated palmitoyl moiety S S Biot in SH NE M should increase affinity for the cholesterolSH NEM and sphingolipid-rich, liquid-ordered memO brane domain. Another unique feature of Agarose S S S protein palmitoylation that excites interest is bead Bio tin NEM its reversibility. The regulated addition and (thiol blockade) (elution) removal of palmitoyl tethers provides an HPDP–biotin β-ME (biotinylation of free thiols) attractive mechanism for controlling membrane and/or raft association. Such control of targeting by reverse palmitoylation, however, has yet been demonstrated only for a Stre pta vidi NE M n small handful of PPs. NE HS Biot M in NEM The protocol detailed below, first pubNEM NH2OH lished as part of a global analysis of palmiO O S HO (thioester cleavage) toylation in the yeast Saccharomyces SH Agarose cerevisiae4,5, purifies and identifies the subbead set of proteins that are palmitoylated from highly complex protein extracts. This PP purification is a proteomic extrapolation of Figure 1 | Schematic of the proteomic acyl-biotinyl exchange methodology. S S S SH S NEM NEM NEM NEM SH S NEM S S S NEM NEM NEM SH © 2007 Nature Publishing Group http://www.nature.com/natureprotocols Published online 21 June 2007; doi:10.1038/nprot.2007.225 NATURE PROTOCOLS | VOL.2 NO.7 | 2007 | 1573 release of proteins bound to the streptavidin–agarose affinity matrix, through b-mercaptoethanol-mediated cleavage of the biotin– Cys linkage. An important control, included in all our analyses, is the processing of an equal portion of the initial protein extract through a parallel ABE protocol that omits the hydroxylamine thioester-cleavage step. In the absence of hydroxylamine, palmitoyl modifications should not be removed and PPs should not be biotinylated or purified. However, owing either to inappropriate biotinylation or non-specific streptavidin–agarose binding, some proteins do end up purified into the minus-hydroxylamine ( HA) control sample (Fig. 2). These HA sample proteins also are nonspecifically purified into the experimental plus-hydroxylamine (+HA) sample and thus represent a source of false-positive identifications. In addition to contaminant proteins, there also is a clear set of +HA sample proteins that show the HA-dependent purification indicative of likely PP status (Fig. 2). The challenge is to distinguish true PPs from the non-specifically purified contaminant proteins; in the protocol below, this is accomplished using a quantitative MS analysis that compares each protein’s representation in parallel +HA and HA samples. Multi-dimensional protein identification technology analysis HA samples (+ and ) are analyzed by multi-dimensional protein identification technology (MuDPIT), a high-throughput, tandem MS–based proteomic technology developed for the analysis of highly complex protein samples7,8. MuDPIT is distinguished by its orthogonal 2D peptide separation, which occurs before, but in-line with, the tandem MS. Following exhaustive proteolysis of the protein samples, the peptides are fractionated from one another both by charge and by hydrophobicity, continuously eluting into the tandem MS for sequencing over an 8–12 h timeframe. A single typical MuDPIT run of a complex sample may sequence tens of thousands of peptides that link to hundreds or thousands of database proteins. As MuDPIT is a highly complex technology that utilizes expensive MS instrumentation, it is assumed that the researcher embarking on the protocol below will first establish a collaboration with an experienced scientist with ample experience in running MuDPIT. –HA sample (averaged spectral counts/protein ID) © 2007 Nature Publishing Group http://www.nature.com/natureprotocols PROTOCOL 300 50 40 200 30 nded 100 20 Expa 10 100 200 300 10 20 30 40 50 +HA sample (averaged spectral counts/protein ID) Figure 3 | An x, y-scatter plot depiction of yeast proteins identified by multi-dimensional protein identification technology (MuDPIT) of plus- and minus-hydroxylamine (HA) samples. Each of the 1,558 proteins identified by MuDPIT analysis in our yeast proteomic analysis is plotted as averaged, normalized +HA sample spectral counts (x-coordinate) against averaged, normalized HA sample spectral counts (y-coordinate)4. The palmitoyl proteins (PPs), including both the 15 PPs that were known to be palmitoylated at the outset of this analysis and the 35 PPs newly identified and confirmed by this analysis, are shown in red. The right-hand panel shows 1574 | VOL.2 NO.7 | 2007 | NATURE PROTOCOLS NH2OH: – + Figure 2 | SDS–polyacrylamide gel electrophoresis (SDS-PAGE) of purified parallel plus- and minus-hydroxylamine (HA) yeast samples just before proteomic multi-dimensional protein identification technology (MuDPIT) analysis. A protein extract derived from total yeast membranes was split in half and purified through the +HA and HA acyl-biotinyl exchange (ABE) protocols in parallel. After the final streptavidin–agarose purification, a comparison was made of 1% of each of the two samples by SDS-PAGE and silver-staining. Presumptive contaminant proteins, showing HA-independent purification, are marked to the right by hash marks, while the candidate PPs, which show HA-dependent purification, are indicated with arrows. Reprinted with permission from ref. 4. MuDPIT, in addition to providing comprehensive sample coverage, also affords a crude but facile quantitative analysis. Unlike other quantitative proteomic approaches, which typically require differential labeling of samples with heavy and light isotopes and complex downstream data analysis9, MuDPIT allows a crude quantitation which is based upon a parameter that is part of the standard MuDPIT dataset: the spectral count number. The spectral count number is the number of sequenced peptides that link to each identified protein. Within a single MuDPIT run, abundant sample proteins often are independently re-identified multiple times, both through identification of the protein’s different component peptides and through iterative re-identification of the same peptide eluting in multiple fractions owing to peak broadening within the initial chromatographic separations. The spectral count number, which includes these redundant peptide identifications, has been demonstrated to be a useful metric for comparing a protein’s relative abundance among samples10. Quantitation based on spectral count has proven critical both for identifying PPs and for analyzing the effects of treatments that perturb palmitoylation4. Distinguishing PPs from the contaminant protein background is based on comparing spectral counts for each protein from parallel +HA and HA samples. Non-specifically purified contaminant proteins show roughly equivalent abundances in +HA and HA samples, whereas the PPs show exclusive, or substantially higher, +HA sample abundances. Figure 3, a spectral count analysis of the proteins identified from +HA and HA ABE samples from the yeast S. cerevisiae, illustrates the utility of the spectral count parameter. All 1,558 yeast proteins identified from MuDPIT analyses of four parallel +HA and HA samples are plotted. Each protein (each dot) is plotted as averaged +HA sample spectral counts on the x-axis versus averaged HA sample spectral counts on the y-axis. Of the 1,558 total yeast proteins identified, the vast majority are not palmitoylated, but rather contaminant proteins, showing significant representations in both +HA and HA samples and trending, therefore, toward the x, y-diagonal (Fig. 3). These contaminant proteins tend to be proteins of known AKR1 AKR2 ERF2 SWF1 PFA3 PFA4 PFA5 SHR5 YCK2(CCIIS) + + + + + ∆ ∆ ∆ ∆ ∆ + + ∆ + + + + + + + ++ + + ++ + + ++ + ++ + ++ + ∆∆∆ + ∆∆∆ + ∆∆∆ + ∆∆∆ + ∆∆∆ + + + ++ H Swf1 cluster H Erf2/Shr5 cluster Pf noa4 nD Akr1 cluster +– 1 +– 2 ∆– 3 +– 4 +– 5 ++ 6 ++ 7 ++ 8 ++ 9 + + 10 + – 11 24 h 72 h 24 h 24 h 72 h 24 h Yck1 Yck2 Akr1 Ypl199c Ykl047w Meh1 Lcb4 Vac8 Yck3 Lsb6 Ras1 Ras2 Gpa2 Rho3 Ycp4 Psr1 Gpa1 Ste18 Pin2 Sso1 Sso2 Tlg1 Tlg2 Vam3 Syn8 Mnn11 Mnn1 Mnn10 Tat1 Bet3 ∆ + + + + + ∆ ∆ ∆ ∆ + C DHHC proteins Strain genotype yeast cell high abundance11. Many of the PPs (red dots), a group that includes both previously known PPs and PPs newly identified by this analysis, are detected only from the +HA samples and thus map onto the x-axis (Fig. 3). Other PPs map near, but not directly on top of, the x-axis (Fig. 3), reflecting a +HA sample bias that is not fully exclusive. This second class of PPs, which are also detected at low levels from HA samples, would be overlooked by a nonquantitative analysis that simply subtracts the list of HA proteins from the list of +HA proteins. In addition to identifying 12 of the 15 known yeast PPs, our analysis also identified and confirmed palmitoylation for an additional 35 proteins4. The identified PPs encompassed all the known types of PPs—proteins that tether to membranes solely through palmitoylation, proteins that are palmitoylated in addition to being also either N-terminally myristoylated or C-terminally prenylated; as well as many TM PPs. Our spectral count–based approach, in addition to allowing PPs to be distinguished from co-purifying contaminants, may be used to assess the global changes in palmitoylation that can be induced by drugs or mutation. The power of such an approach is illustrated by our recent mapping of the yeast PPs to their cognate palmitoylation enzymes4. The palmitoyl proteome of a wild-type yeast strain was compared to the proteomes of mutant strains deleted for the genes encoding the seven members of the newly identified DHHC (Asp-His-His-Cys) protein acyl transferase (PAT) family12–14. PP substrates of a particular PAT are expected to be lost from the palmitoyl proteome of strains deficient for that PAT; thus, substrates are highlighted by their absence from the DHHC deletion strain palmitoyl proteome (Fig. 4). This analysis finds not only PPs that fully drop out but also PPs with partial under-representations, indicative of a partial requirement for the deleted PAT. Analysis of strains multiply deleted for the different DHHC genes allows the overlapping specificity relationships among the different DHHC PATs to be discerned. Many of the enzyme–substrate relationships uncovered in this comparative proteomic analysis have indeed been confirmed in targeted testings of the palmitoylation of individual PPs in different DHHC mutant backgrounds4. In principle, this approach should allow global changes in palmitoylation to be monitored in response to a wide variety of perturbants and stimuli—e.g., hormonal signals, drugs and changes in growth conditions. Indeed, the mammalian DHHC PAT family is an important set of future drug targets with possible utility in the treatment of cancer and other diseases. Recently, a first generation of compounds with inhibitory effects on palmitoylation has been developed15. Comparing the profiles from drug-treated cells with those of mutants deficient in the individual PATs should provide a means of mapping drugs to their target PATs. The protocol that follows should be applicable to tissue from any source organism with the available sequence data to allow MSbased protein identification. As indicated above, this protocol has been used in yeast both to identify many new PPs and to map substrate PPs with their cognate modifying DHHC PAT4. Recently, we have applied the same approach to the analysis of various mammalian palmitoyl proteomes—from mouse or rat whole brain, and from primary cultures of embryonic rat brain neurons. Similar to our yeast analyses4, our initial mammalian ABE purifications also readily pull out a mix of both known and new PPs; indeed, our analysis of rat embryonic neurons identifies 24 known PPs from among the 100 top-scoring proteins (R. Kang, J.W., A.O.B., J. Yates, N.G.D. and A. El-Husseini, unpublished results). For Depletion periods © 2007 Nature Publishing Group http://www.nature.com/natureprotocols PROTOCOL WT: mutant (fold change) > –20 –10 –5 0 SNAREs 5 10 >20 Figure 4 | Colorimetric depiction of palmitoylation changes seen in strains deficient for the different yeast DHHC protein acyl transferases (PATs). Plushydroxylamine (HA) samples purified from 11 different wild-type and mutant yeast strains were analyzed by multi-dimensional protein identification technology (MuDPIT) with the 30 most prominent palmitoyl proteins (PPs) (listed at the bottom) being compared for relative abundance with the spectral count metric. Wild-type:mutant spectral count ratios for each protein were converted to color, with proteins showing 20-fold or greater mutant sample under-representations shown in red, and proteins with intermediate under-representations depicted by intermediate red shadings (for details on the colorimetric conversion, see ref. 4). Relevant strain genotypes are indicated on the left: for each tested strain, the seven different yeast DHHC PAT-encoding genes are indicated as being wild-type (+), deleted (D) or replaced by a conditional GAL1-driven depletion allele strain with the gene encoding the indicated DHHC PAT fully deleted (D) or, in a few cases, depletion of the indicated PAT was induced via glucose-mediated repression of a GAL1-driven PAT allele (downward arrow; glucose depletion periods are indicated at right). Note that for many PPs, palmitoylation is significantly blocked only in strains concomitantly mutated for multiple DHHC PATs, presumably indicating substantial specificity overlaps among the seven different DHHC PATs. Reprinted with permission from ref. 4. DHHC. both the yeast and mammalian analyses, starting samples are typically scaled to approximately 10 mg of starting total protein, although successful analyses have also been performed on samples that start with as little as 2 mg of total protein. The initial steps of the protocol describe the preparation of starting protein extracts both from a yeast cell culture and from mouse whole brain. For other source tissues, initial homogenization may have to be varied appropriately. However, protocol steps subsequent to these initial homogenization steps should be identical to those detailed below. The protocol is divided into the following sub-sections: (i) preparation of the starting protein extracts (the preparation of lysates from both yeast cultures and mouse brain as well as an optional crude membrane purification step to enrich for PPs is described); (ii) ABE; (iii) affinity-purification of biotinylated proteins; (iv) proteolysis and preparation of samples for MuDPIT analysis; (v) MuDPIT analysis (it is assumed that the MuDPIT analysis will be done collaboratively with a proteomics facility well versed in the procedure; consequently, the MuDPIT portion of the protocol is presented in overview fashion, with the particular parameters that may be unique to our analyses of palmitoylation indicated. For an excellent step-by-step MuDPIT protocol detailing both the set-up and running of MuDPIT, the reader is referred to ref. 16); (vi) spectral count–based quantitation. NATURE PROTOCOLS | VOL.2 NO.7 | 2007 | 1575 PROTOCOL MATERIALS © 2007 Nature Publishing Group http://www.nature.com/natureprotocols REAGENTS . Pepstatin (Sigma) . Antipain (Sigma) . Chymostatin (Sigma) . Leupeptin (Sigma) . Triton X-100 10% solution (Anatrace Anapoe, cat. no. X-100) . NEM (Pierce, cat. no. PI 23030) . Hydroxylamine (Sigma) . HPDP–biotin (Pierce, cat. no. PI 21341) . Streptavidin–agarose (Pierce, cat. no. PI 20349) . b-Mercaptoethanol (Fisher, cat. no. BP176) . Tris(2-carboxyethyl)phosphine (TCEP; Sigma) . Iodoacetamide (Sigma) . Endoproteinase Lys-C (Roche) . Glucose . Peptone . Yeast extract . Trypsin (Roche) . Reversed-phase resins—Aqua C18 (3-mm beads, 100-Å pores) and Aqua C18 (5-mm beads, 300-Å pores) (Phenomenex) . Strong cation exchange resin—Luna SCX (5-mm beads, 100-Å pores; Phenomenex) . Acetonitrile (HPLC grade) . Ammonium acetate . Formic acid . 1 M Tris/Cl, pH 7.4 . 1 M Tris/Cl, pH 8.5 . 0.5 M EDTA, pH 8.0 . 1 M NEM in ethanol m CRITICAL Prepare fresh for every experiment; store on ice. . 1 mg ml 1 pepstatin in methanol m CRITICAL Store at 20 1C. . 10 mg ml 1 leupeptin in water m CRITICAL Store at 20 1C. . 10 mg ml 1 antipain in DMSO m CRITICAL Store at 20 1C. . 10 mg ml 1 chymostatin in ethanol m CRITICAL Store at 20 1C. . 0.1 M phenylmethanesulfonyl fluoride (PMSF; Sigma, cat. no. P7626) in ethanol m CRITICAL Store at 4 1C. . 1 M hydroxylamine, pH 7.4 m CRITICAL Prepare fresh for each experiment; store on ice. . 50 mM HPDP–biotin in DMSO m CRITICAL Store at 20 1C; solution may become somewhat cloudy after 20 1C storage. . 4 mM HPDP–biotin in N,N-dimethyl formamide m CRITICAL Dilute from 50 mM stock just before use; keep on ice until use. . 100 mM TCEP m CRITICAL Store at 20 1C. . 1 M CaCl2 . 0.5 M iodoacetamide m CRITICAL Prepare fresh for every experiment; store on ice. . MuDPIT buffer A (5% acetonitrile, 0.1% formic acid) . MuDPIT buffer B (80% acetonitrile, 0.1% formic acid) . MuDPIT buffer C (500 mM ammonium acetate, 5% acetonitrile, 0.1% formic acid) EQUIPMENT . Coffee grinder (Krups, cat. no. F2037051) . Sorvall (Dupont) RC-5B centrifuge with GSA and HS-4 rotors . 50-ml disposable centrifuge tubes (Sarstedt, cat. no. 62.547.205) . 12-ml screw-cap centrifuge tube (Sarstedt, cat. no. 60.540) . 1.5-ml screw-cap centrifuge tube (Sarstedt, cat. no. 72.692) . IKA tissue homogenizer (T25 basic; Ultra Turrax, cat. no. T25BS1) with S25N-8G blade attachment . Sonicator (Sonic Dismembrator Model 500; Fisher Scientific) with micro-probe (Branson model 1020) . Ultra-centrifuge and fixed angle rotor (optional)—e.g., Beckman L8-M ultra-centrifuge with Type 80Ti rotor (Beckman) . Tube rotator (Thermolyne Labquake, cat. no. 400110) . Micro-centrifuge . Tube rocker (Thermolyne Vari-Mix, cat. no. M48725) . LTQ ion-trap mass spectrometer (Thermo-Finnigan) . HPLC (Agilent) . Fast computer system for analysis of tandem mass spectra and for database searching . MuDPIT microcapillary chromatography set-up (see EQUIPMENT SETUP) . Microfilter assembly (UpChurch Scientific, Oak Harbour, WA) (see EQUIPMENT SETUP) REAGENT SETUP YPD (1% yeast extract, 2% peptone, 2% glucose) For 1 l, dissolve 10 g yeast extract, 20 g peptone in 960 ml water. Autoclave to sterilize, then add 40 ml of sterile 50% glucose. Lysis buffer (LB; 150 mM NaCl, 50 mM Tris, 5 mM EDTA, pH 7.4) For 100 ml, combine 3 ml 5 M NaCl, 5 ml Tris/Cl pH 7.4, 1 ml 0.5 M EDTA and 91 ml water. Supplement with necessary components (e.g., Triton X-100, NEM, PI) to the concentrations indicated in the protocol. 1003 protease inhibitors (PIs) Combine 25 mg ml 1 each of pepstatin, leupeptin, antipain and chymostatin. For 1 ml, combine 250 ml 1 mg ml 1 pepstatin, 25 ml 10 mg ml 1 leupeptin, 25 ml 10 mg ml 1 antipain, 25 ml 10 mg ml 1 chymostatin and 675 ml ethanol. 4% SDS buffer (4SB; 4% SDS, 50 mM Tris, 5 mM EDTA, pH 7.4) For 10 ml, combine 4 ml 10% SDS, 0.5 ml 1 M Tris/Cl pH 7.4, 0.1 ml 0.5 M EDTA and 5.4 ml water. Supplement with necessary components (e.g., NEM) to the concentrations indicated in the protocol. +HA buffer (0.7 M hydroxylamine, 1 mM HPDP–biotin, 0.2% Triton X-100, 1 mM PMSF, 13 PI pH 7.4) For 10 ml, combine 2.5 ml 4 mM HPDP–biotin, 0.2 ml 10% Triton X-100, 0.1 ml 100 PI, 0.1 ml 0.1 M PMSF, 7 ml 1 M hydroxylamine pH 7.4, and 0.1 ml water. HA buffer (50 mM Tris, 1 mM HPDP–biotin, 0.2% Triton X-100, 1 mM PMSF, 13 PI, pH7.4) For 10 ml, combine 2.5 ml 4 mM HPDP–biotin, 0.5 ml Tris/Cl pH 7.4, 0.2 ml 10% Triton X-100, 0.1 ml 100 PI, 0.1 ml 0.1 M PMSF and 6.6 ml water. Low HPDP–biotin buffer (150 mM NaCl, 50 mM Tris, 5 mM EDTA, 0.2 mM HPDP–biotin, 0.2% Triton X-100, 1 mM PMSF, 13 PI, pH 7.4) For 10 ml, combine 0.5 ml 4 mM HPDP–biotin, 0.3 ml 5 M NaCl, 0.5 ml Tris/Cl pH 7.4, 0.2 ml 10% Triton X-100, 0.1 ml 0.5 M EDTA, 0.1 ml 100 PI, 0.1 ml 0.1 M PMSF and 8.2 ml water. 2% SDS buffer (2SB; 2% SDS, 50 mM Tris, 5 mM EDTA, pH 7.4) For 10 ml, combine 2 ml 10% SDS, 0.5 ml 1 M Tris/Cl pH 7.4, 0.1 ml 0.5 M EDTA and 7.4 ml water. Proteolysis buffer (8 M urea, 0.1 M Tris, pH 8.5) For 100 ml, dissolve 6 g urea in 10 ml 1 M Tris/Cl pH 8.5, with water added to volume. EQUIPMENT SETUP MuDPIT microcapillary chromatography set-up (See ref. 16 for additional details). Desalting column is fused silica capillary column (250-mm i.d., 365-mm o.d.; Agilent) filled with 4 cm of Aqua C18 RP resin (5-mm beads, 300-Å pores; Phenomenex, Torrance, CA). Microfilter assembly Analytical microcapillary column (100-mm i.d., with laser-pulled 5-mm orifice) packed with 3 cm of Luna SCX resin (5-mm beads, 100-Å pores; Phenomenex) above 9 cm Aqua C18 RP resin (3-mm beads, 300-Å pores; Phenomenex). Software Xcalibur (Thermo-Finnigan), PARC, SEQUEST (ThermoFinnigan), Excel (Microsoft), DTASelect v1.9. Note: Although we have primarily relied on DTASelect v1.9 for our analyses, the newer version 2.0 utilizes an improved method for evaluating data quality that compares the number of database matches to the number of matches obtained from a decoy database in which protein sequences are reversed. By monitoring the frequency of matches to the decoy database, correlation coefficients and DCn cut-off value are automatically adjusted ad hoc to generate a dataset with the desired false-positive rate (D. Cociorva and J. Yates, personal communication). PROCEDURE Preparation of starting protein extracts 1| Tissue homogenization and cell lysis. Conditions are described for preparation of lysates from either yeast cultures (option A) or freshly resected mouse brain (option B). For other source materials, homogenization conditions may need to be altered. 1576 | VOL.2 NO.7 | 2007 | NATURE PROTOCOLS © 2007 Nature Publishing Group http://www.nature.com/natureprotocols PROTOCOL (A) Yeast lysates (i) Collect by centrifugation (4,000g, 5 min, 4 1C in Sorvall GSA rotor) a 1-l late log-phase culture (A600 ¼ 1.0; 1.5  107 ml 1) of YPD-grown yeast. (ii) Re-suspend cell pellets in 40 ml ice-cold LB with 10 mM NEM, 2 PI, 2 mM PMSF. Transfer to a 50-ml disposable centrifuge tube. Re-pellet cells (5,000g, 5 min, 4 1C). (iii) Aspirate buffer and freeze cell pellet on dry ice. ’ PAUSE POINT Frozen cell pellet may be stored for several days at 80 1C. (iv) Cell lysis. Break open cell pellet–containing centrifuge tubes with one sharp hammer blow. Rapidly transfer the frozen cell pellet to the Krups coffee grinder, together with chunks of dry ice estimated to correspond to one-third of the cell pellet volume. Grind for a total of approximately 30 s (as grinding reaches completion, tone of grinder transitions to higher-pitch whine; continue for an additional 10 s). Scoop cell lysate (viscous, partially frozen slurry) into cold 50-ml centrifuge tube. Incubate, unlidded, at 0 1C, to allow residual dry ice to sublimate. (v) Add 10 ml of cold LB with 10 mM NEM, 1 PI, 1 mM PMSF. (B) Mouse brain homogenate (i) Flash-freeze freshly resected mouse brain in liquid nitrogen. ’ PAUSE POINT Brain may be stored for several days at 80 1C. (ii) Transfer frozen brain to ice-cold 12-ml centrifuge tube. Add 1 ml of ice-cold LB with 10 mM NEM, 2 PI, and 2 mM PMSF. Homogenize for 2 min at 24,000 r.p.m. using an IKA tissue homogenizer with S25N-8G blade attachment. (iii) To further homogenize, sonicate, using micro-probe, with ten duty cycles of 1 s ON and 2 s OFF. 2| (Optional) Collect total membranes by high-speed centrifugation (200,000g, 30 min, 4 1C) in fixed-angle rotor. This membrane purification is intended to enrich for PPs. 3| (Optional) Re-suspend membrane pellet in 3 ml LB buffer with 10 mM NEM, 1 PI and 1 mM PMSF. 4| Detergent solubilization. Add Triton X-100 to 1.7%. Incubate with end-over-end rotation at 4 1C for 1 h. 5| Remove particulates and unbroken cells with low-speed centrifugation (250g, 4 1C, 5 min). 6| Chloroform–methanol (CM) precipitate sample (see Box 1). 7| To each protein pellet, add 300 ml 4SB with 10 mM NEM. Incubate for 10 min at 37 1C with occasional agitation of tube to dissolve pellet. m CRITICAL STEP The protein denaturation accompanying this step facilitates access of NEM to Cys that are buried within the folded protein interior. ? TROUBLESHOOTING 8| To each tube, add 900 ml of LB with 1 mM NEM, 1 PI, 1 mM PMSF, and 0.2% Triton X-100. Transfer to 1.5-ml screw-cap centrifuge tubes. Incubate overnight at 4 1C with gentle rocking. ’ PAUSE POINT Overnight incubation with NEM. Acyl-biotin exchange reactions 9| Remove NEM from samples by three sequential CM precipitations. Transfer samples to 12-ml screw-cap tubes and CM precipitate as described in Box 1. After the first two CM precipitations, add 300 ml 4SB and dissolve protein BOX 1 | CHLOROFORM–METHANOL PRECIPITATION OF PROTEINS Chloroform–methanol (CM) precipitation20 is used extensively in this protocol to move proteins between the different chemical treatment steps. The steps below are all done at room temperature (RT). 1. With 1.2 ml of sample per 12-ml screw-cap centrifuge tube, add 4 volumes (4.8 ml) methanol. Vortex to mix. 2. Add 1.5 volumes (1.8 ml) chloroform. (Use glass pipette.) Vortex to mix. 3. Add 3 volumes (3.6 ml) water. Vortex to mix. 4. Centrifuge [6,000g, RT (22–25 1C), 20 min] in Sorvall HS-4 swinging bucket rotor to separate phases. Should separate into two phases, with protein precipitated at the interphase. m CRITICAL STEP In fixed-angle rotors, the protein precipitate partially adheres to the side of the tube. 5. Remove and discard top aqueous phase (water and methanol) using Pasteur pipette, taking care not to remove the interphase protein. 6. Add 3 volumes (3.6 ml) methanol. Mix gently by tube inversion so that pancake-like protein interface is not fully disrupted. 7. Centrifuge (6,000g, RT, 10 min) in Sorvall HS-4 swinging bucket rotor. Protein should now precipitate as a loose pellet to the tube bottom. 8. Remove and discard supernatant using Pasteur pipette. Remove any remaining liquid with Pasteur pipette with tip pulled to a fine point. m CRITICAL STEP Protein is difficult to re-dissolve if methanol and chloroform are not fully removed. 9. Air-dry for 2–3 min. NATURE PROTOCOLS | VOL.2 NO.7 | 2007 | 1577 PROTOCOL © 2007 Nature Publishing Group http://www.nature.com/natureprotocols pellet by incubating at 37 1C for 10 min with occasional vortex mixing. Then, dilute with 900 ml LB containing 0.2% Triton X-100. m CRITICAL STEP Residual NEM can greatly reduce biotinylation, irreversibly modifying thiols as they become exposed from palmitoylated Cys by hydroxylamine and thus blocking subsequent reaction with thiol-specific biotinylation reagent. ’ PAUSE POINT After first or second CM precipitation, sample may be stored in 4S buffer overnight at 20 1C. The next day, thaw at room temperature (RT; 23–25 1C), and dilute with LB. 10| After the third and final CM precipitation, dissolve protein pellet in 250 ml 4SB, 37 1C, 10 min. At this point the sample is divided into two equal portions (+HA sample and HA sample). To ensure that the two samples are equalized, the entire protein sample is pooled into a single tube and then distributed at 240 ml per 1.5-ml screw-cap centrifuge tube. +HA samples are diluted fivefold with the addition of 960 ml of the hydroxylamine-containing +HA buffer; for HA samples, 960 ml of HA buffer is used. Incubate at RT for 1 h with end-over-end rotation. 11| Transfer samples to 12-ml screw-cap tubes and CM precipitate (Box 1). 12| Dissolve each resulting protein pellet in 240 ml 4SB. Dilute with addition of 960 ml low-HPDP–biotin buffer. Incubate at RT for 1 h with end-over-end rotation. 13| To remove unreacted HPDP–biotin before the streptavidin–agarose affinity purification, subject samples to three sequential CM precipitations (Box 1). After the first and second precipitations, dissolve and dilute precipitated proteins as described for Step 10. After the third CM precipitation, dissolve each pellet in 120 ml 2SB at 37 1C, 10 min. ! CAUTION Unremoved HPDP–biotin competes with biotinylated protein for streptavidin–agarose binding. ’ PAUSE POINT After the first or second CM precipitation, sample is typically stored in 4SB overnight at 20 1C. The next day, thaw at RT, before LB addition. Affinity purification of biotinylated proteins 14| Dilute SDS to 0.1% before adding samples to streptavidin–agarose. For this, first pool like samples (e.g., +HA samples with +HA samples), then dilute 20-fold with addition of LB containing 0.2% Triton X-100, 1 PI and 1 mM PMSF. Aliquot 1 ml per 1.5-ml screw-cap centrifuge tube and incubate at RT for 30 min with end-over-end rotation. 15| Centrifuge 15,000g (13,000 r.p.m. in the micro-centrifuge) for 1 min to remove particulates, transferring supernatant to new tubes containing 15 ml streptavidin–agarose, pre-equilibrated with LB containing 0.1% SDS and 0.2% Triton X-100. Incubate at RT for 90 min with end-over-end rotation. m CRITICAL STEP Particulates removed by this pre-spin otherwise would pellet along with the streptavidin–agarose through subsequent wash steps and would nonspecifically contaminate the final purified sample. 16| Remove unbound proteins by four sequential 1-ml washes with LB containing 0.1% SDS and 0.2% Triton X-100. 17| Release bound proteins from the affinity resin through reduction of the protein–biotin disulfide linkages. For this elution, resuspend the resin in each tube in 150 ml LB containing 0.1% SDS, 0.2% Triton X-100 and 1% b-mercaptoethanol. Incubate for 15 min at 37 1C with occasional gentle mixing to resuspend settling resin. 18| Pool like eluants together into 1.5-ml screw-cap centrifuge tubes, then concentrate by trichloroacetic acid (TCA) precipitation: add TCA to 10% (add one-tenth volume of 100% TCA), incubate 20 min on ice then collect precipitates by 15,000g, 10 min, 4 1C centrifugation. Dissolve the pellet in 30 ml 2SB. Then dilute to 150 ml with LB. ’ PAUSE POINT At this point, samples may be stored at 80 1C for up to 1 week. 19| Compare 1% of the final +HA and (e.g., Fig. 2). ? TROUBLESHOOTING HA samples by SDS–polyacrylamide gel electrophoresis (PAGE) and silver-staining 20| Immediately before sending to MS collaborators, clean up the samples further using two sequential CM precipitations to remove residual detergents. For this, the CM precipitation (described above) is scaled down: with 150-ml samples in 1.5-ml screw-cap centrifuge tubes, added methanol, chloroform and water volumes are scaled down proportionately. 21| After the first CM precipitation, dissolve the pellet in 15 ml 2SB, then dilute to 150 ml with LB. Ship the final ‘wet’ protein pellet overnight to MS collaborators on dry ice (i.e., to avoid dislodging the pellet from the tube bottom during transport, the sample is not desiccated). ’ PAUSE POINT At this point, samples may be stored at 80 1C for up to 1 month. 1578 | VOL.2 NO.7 | 2007 | NATURE PROTOCOLS PROTOCOL BOX 2 | MULTI-DIMENSIONAL PROTEIN IDENTIFICATION TECHNOLOGY © 2007 Nature Publishing Group http://www.nature.com/natureprotocols The hallmark feature of multi-dimensional protein identification technology (MuDPIT) is its 2D chromatographic separation of a peptide mixture7,8. The microcapillary chromatography column is packed with strong cation exchange (SCX) resin over reversed-phase (RP) resin and is coupled directly to the tandem MS. Over a 10–12 h time course, peptides are driven through the column by a stepped series of HPLC-driven buffer changes, with eluting peptides being introduced into the tandem MS by electrospray. Each chromatographic step is initiated by a salt bump that drives a subset of the peptides from the SCX resin onto the RP resin, where they are resolved by their relative hydrophobicity, being eluted from the RP by an increasing gradient of acetonitrile. For details on both setting up and running the microcapillary column chromatography, see ref. 16; specifications for the columns and resins used in our analyses are listed under EQUIPMENT. Sample proteolysis 22| Dissolve protein pellets containing 1–20 mg total protein in 50 ml proteolysis buffer. 23| Add TCEP to 5 mM and incubate at RT for 30 min to reduce disulfide bonds. 24| Add iodoacetamide to 10 mM and incubate at RT for an additional 30 min to alkylate free thiols. 25| Add 0.15 mg endoproteinase Lys-C (approximate substrate to enzyme mass:mass ratio of 100:1) and digest proteins for 4 h at 37 1C. 26| Dilute sample fourfold with addition of 100 mM Tris/Cl pH 8.5; add CaCl2 to 2 mM, then 0.5 mg trypsin. Incubate overnight (12–16 h) at 37 1C. ’ PAUSE POINT Samples may be stored at this point for up to 1 month frozen at 80 1C. 27| Just before MS/MS analysis, formic acid is added to 5%, and insoluble particulates are removed by centrifugation (16,000g, 15 min). MuDPIT chromatography and collection of tandem mass spectra 28| Refer to Box 2 for information about MuDPIT. Load peptide samples onto desalting column. Desalt with 5% acetonitrile, 0.1% formic acid. 29| With peptides still retained, couple the desalting column via the microfilter assembly in-line to the analytical column, which is dually packed with SCX resin over RP resin. 30| Use the six-step chromatographic program (Box 3) to move the peptides from the desalting column onto and through the analytical column with electrospray-mediated elution, and ultimately into the tandem MS. Buffer changes are effected by the HPLC, driven from the tandem MS using Excalibur software. BOX 3 | MULTI-DIMENSIONAL PROTEIN IDENTIFICATION TECHNOLOGY CHROMATOGRAPHIC PROGRAM 1. An 80-min gradient to 45% buffer B, followed by a 10-min gradient to 100% buffer B, then 10 min of buffer B alone. This step elutes peptides from the desalting column to the strong cation exchange (SCX) resin. Peptides that fail to bind to SCX chromatograph through the reversedphase (RP) resin and elute into the tandem MS. 2–5. Initiated with an increasing series of salt bumps to drive charged peptides off the SCX resin onto the C18 resin. 2. First, 3 min of 25% buffer C, followed by an 80-min gradient to 45% buffer B, then a 10-min gradient to 100% buffer B and finally 10 min of buffer B alone. 3. First, 3 min of 50% buffer C, followed by an 80-min gradient to 45% buffer B, then a 10-min gradient to 100% buffer B and finally 10 min of buffer B alone. 4. First, 3 min of 75% buffer C, followed by an 80-min gradient to 45% buffer B, then a 10-min gradient to 100% buffer B and finally 10 min of buffer B alone. 5. First, 3 min of 100% buffer C, followed by an 80-min gradient to 45% buffer B, then a 10-min gradient to 100% buffer B and finally 10 min of buffer B alone. 6. First, 20 min of 100% buffer C, followed by a 90-min gradient to 45% buffer B, then a 10-min gradient to 100% buffer B and finally 10 min of buffer B alone. Flow rate through the column is maintained at a constant 150 nl min 1. Eluting peptides are introduced into the tandem MS by electrospray at a distally applied spray voltage of 2.4 kV. Column eluate is continuously analyzed by MS/MS over the six-step chromatography program. One full-range mass-scan (400–1,600 m/z) should be followed by the collection of seven data-dependent tandem mass spectra at a 35% collision energy in a continuous loop. NATURE PROTOCOLS | VOL.2 NO.7 | 2007 | 1579 PROTOCOL Data analysis: protein identifications 31| MS/MS spectra are analyzed on an Intel Xenon 80-processor cluster running under the Linux operating system. Use PARC17 to analyze charge state and filter data quality of MS/MS spectra. 32| Search relevant protein database, supplemented with common contaminants (e.g., keratins) using SEQUEST18. 33| Filter SEQUEST results using DTASelect v1.919 with the following parameters: Spectral count–based quantitation 34| Extract spectral counts from DTASelect MS/MS datafiles. Open DTASelect datafile (in .txt format) using Excel. Data fields sort into the rows and columns of the worksheet. 35| Collect DTASelect summary lines. Heading the data for each identified protein within the DTASelect datafile is a summary line reporting various information, including the protein’s unique database ID, its database annotation, its predicted molecular weight and pI, as well as a summary of the MuDPIT data associated with the identification (including the spectral count). DTASelect summary lines are marked at the left with the identifier ‘‘U,’’ which should sort to the leftmost column (column A) of the Excel worksheet. ‘‘Sort’’ worksheet on the basis of column A in ‘‘descending’’ order. Summary lines should now be collected near the top of the worksheet (Fig. 5a). Delete all rows that do not contain the ‘‘U’’ identifier, conserving just the summary line for each identified protein. 36| Delete all columns containing extraneous information (Fig. 5b). 113 YOL127W 114 YOR104W 115 YLLO45C 116 YOLO86C 117 YOR106W 118 YJLO80C 37 36 36 35 35 35 113 114 115 116 117 118 ip n cr ei es D pl ot RPL25 SGDID:S0005487, Chr XV from PIN2 SGDID:S0005630, Chr XV from RPL8B SGDID:S0003968, Chr XII from ADH1 SGDID:S0005446, Chr XV from VAM3 SGDID:S0005632, Chr XV from SCP160 SGDID:S0003616, Chr X from d SC –HA YOL127W YOR104W YLLO45C YOLO86C YOR106W YJLO80C 37 36 36 35 35 35 31 0 19 62 2 42 Expt 2 tiv e W M ro be C um 10.1 5.8 10 6.7 7 5.8 +HA Expt 1 na od fc e ov er ag m on e s C (S nt ou lc tra % 15758 32083 28112 36849 32498 134809 ID SC e 113 114 115 116 117 118 ec c ID 142 282 256 348 283 1222 nSC ID 113 114 115 116 117 118 +HA –HA +HA –HA +HA –HA YOL127W YOR104W YLLO45C YOLO86C YOR106W YJLO80C 31.7 30.9 30.9 30 30 30 41.2 0 25.2 82.4 2.7 55.8 30.2 9.6 12.4 28.9 6.2 7.6 35.8 0 30.2 26.4 0 24.5 31 20.2 21.6 29.4 18.1 18.8 38.5 0 27.7 54.4 1.4 40.2 113 114 115 116 117 118 –HA 41.2 0 25.2 82.4 2.7 55.8 YOL127W YOR104W YLLO45C YOLO86C YOR106W YJLO80C 31.7 30.9 30.9 30 30 30 ID +HA –HA YOL127W YOR104W YLLO45C YOLO86C YOR106W YJLO80C 31 20.2 21.6 29.4 18.1 18.8 38.5 0 27.7 54.4 1.4 40.2 0.8 40.4 0.8 0.5 12.9 0.5 f Avg (1&2) ID +HA +H A ra :–H tio A b 35.20% 39.40% 39.10% 53.40% 35.00% 24.40% 37 36 36 35 35 35 6 10 14 14 13 24 Pr ORFP:YOL127W ORFP:YOR104W ORFP:YLLO45C ORFP:YOLO86C ORFP:YOR106W ORFP:YJLO80C N 113 U 114 U 115 U 116 U 117 U 118 U Sp at ab as e ID N (n um on be -re r o du f p nd ep an tid t) es a ) 37| Use the Excel ‘‘consolidate’’ function to combine spectral count datasets from the MuDPIT runs under comparison onto a single spreadsheet (Fig. 5c). D © 2007 Nature Publishing Group http://www.nature.com/natureprotocols Minimum correlation coefficients of 1.8, 2.5 and 3.5, for singly, doubly and triply charged precursor ions, respectively Minimum DCn of 0.08 Minimum requirement of two peptides per run Avg (1&2) Figure 5 | Example spectral count analysis. Analysis is shown for an arbitrary set of five proteins of similar mid-abundance range identified from published analysis of yeast acyl-biotinyl exchange (ABE) samples4. (a) DTASelect summary lines. After the DTASelect .html file [from multi-dimensional protein identification technology (MuDPIT) of a yeast plus-hydroxylamine (HA) sample] is pasted in its entirety into an Excel spreadsheet, the ‘‘Sort’’ command is used to collect summary lines that are uniquely identified by the ‘‘U’’ in the leftmost column. All other rows are deleted. (b) Extraction of spectral count data. Delete all columns except for the ones containing database IDs and spectral counts. (c) ‘‘Consolidate’’ spectral count data from MuDPIT runs under comparison. (d) Normalize data. Each spectral counts is divided by the total spectral count for the sample (strategy 1 of Box 4; 17,494 for the +HA run and 11,293 for the HA run) then multiplied by the arbitrary multiplier 15,000. (e) Average normalized spectral count data from like experiments. The data from experiment 1 are assembled (using the ‘‘Consolidate’’ command) with data, manipulated as above, from experiment 2. Normalized spectral counts from the two experiments are averaged. (f) +HA: HA ratios identify candidate PPs. Before dividing averaged and normalized +HA spectral counts by the averaged and normalized HA spectral counts, averaged and normalized HA spectral counts of zero are arbitrarily replaced by a value of 0.5 (to avoid division by zero). The highlighted proteins encoded by YDR104W and YOR106W (corresponding to Pin2 and Vam3, respectively) are identified as candidate PPs. 1580 | VOL.2 NO.7 | 2007 | NATURE PROTOCOLS PROTOCOL © 2007 Nature Publishing Group http://www.nature.com/natureprotocols BOX 4 | NORMALIZATION STRATEGIES Strategy 1 normalizes the spectral count data on the basis of the total spectral count yields. This method makes the assumption that in each multi-dimensional protein identification technology (MuDPIT) sample run, peptide identifications are being made at or near the capacity of the tandem MS instrument, which is generally a good assumption for complex samples comprising 10 mg or more of total protein. For data formatted by DTASelect v.1.9, the total spectral count yield is reported in the final summary table as the number of redundant peptide IDs. To normalize, generate a new Excel spreadsheet column that divides the spectral count number associated with each identified protein by the total spectral count yield for that sample and then multiplies each by some arbitrary constant, reflective of the approximate average total spectral count yield, e.g., 10,000. Strategy 1 was used in our published yeast analysis, both for plus- and minus-hydroxylamine (HA) sample comparisons and for the mappings of palmitoyl protein (PP) substrates to the DHHC protein acyl transferase (PAT) enzymes, in this case comparing +HA samples of wild-type and DHHC gene-deleted yeast strains4. However, it has proved less satisfactory in our more recent analyses of samples from neurons (data not shown). Palmitoylation appears to be a much more prominent protein modification in neurons than in yeast and, consequently, acyl-biotinyl exchange (ABE)–processed HA neuronal samples show substantially reduced protein complexity relative to the parallel +HA samples. Thus, our neuronal analyses have relied on an improved strategy: Strategy 2 utilizes abundant proteins from the contaminant protein background common to both +HA and HA samples for normalization. For instance, a-tubulin is an abundant protein contaminant that appears to contaminate both ABE-purified +HA and HA samples from neurons as well as from yeast equivalently. Raw spectral count numbers associated with a-tubulin typically ranged between 200 and 700 counts per run. To normalize spectral counts, a multiplier calculated to equalize a-tubulin counts is applied to all the identified proteins. For instance, if 300 spectral counts were associated with a-tubulin in sample 1 and 600 in sample 2, then normalization is achieved by multiplying the spectral counts associated with each sample 1 protein by a factor of 2.0. 38| Normalization of spectral count data (Fig. 5d and Box 4). Unavoidable variation introduced at the level of the ABE purification and/or the running of the MuDPIT 2D chromatography results in spectral count yields that may vary by as much as twofold to threefold for identical proteins in equivalent samples. Despite these differences in overall spectral count yields, the spectral count ranking of individual identified proteins, particularly for the more abundant sample proteins identified by high spectral count numbers, typically is very well reproduced. Two normalization strategies have been employed to accommodate sample-to-sample spectral count yield variation (see Box 4). 39| Average like data from iterative MuDPIT analyses. Spectral count numbers are statistics, and thus confidence in conclusions derived from spectral count data is increased through repeat analysis. Our identification of yeast PPs was based on the analysis of four paired +HA and HA samples4. Generate a new Excel spreadsheet column that averages the normalized spectral counts for equivalent samples—e.g., a column reporting averaged spectral count numbers from +HA samples and a column reporting averaged HA spectral count numbers (Fig. 5e). 40| Compare the abundance level of each protein within the different samples through ratiometric comparisons of averaged and normalized spectral count data (Fig. 5f). Candidate PPs are identified, for instance, by comparing each protein’s +HA sample representation with its HA sample representation (PPs show higher +HA sample abundance). 41| Generate a new spreadsheet column that divides normalized and averaged counts for each identified protein from the +HA samples by the normalized, averaged counts from the HA samples. Before ratio calculations, to avoid division by 0, replace zeros present in the divisor column with some small arbitrary number (choose a number somewhat smaller than the smallest value present in divisor column). Such replacement is easily automated with the Excel ‘‘IF’’ function. Candidate PPs are identified by their high +HA: HA spectral count ratios. 42| Graphically compare spectral count data using either x, y-scatter plots (see Box 5) or colorimetric depictions of the ratios (see Box 6).  TIMING From start to finish, approximately 2 weeks are required. Day 1—Steps 1–8, extract preparation, culiminating in overnight NEM blockade: 4–6 h Day 2—Steps 9–13, acyl-biotin exchange: 8 h Day 3—Steps 14–18, streptavidin–agarose affinity purification: 8 h Day 4—Steps 19–21, SDS-PAGE analysis of purified samples: 8 h Day 5—Steps 22–27, sample proteolysis: 16 h Days 6 and 7—Steps 28–30, column preparation, loading, and overnight MuDPIT analysis: 18 h Days 8–10—Steps 31–33, computer analysis of tandem MS: 48–72 h (depending on computing power) Day 11—Steps 34–42, spectral count analysis: 4 h NATURE PROTOCOLS | VOL.2 NO.7 | 2007 | 1581 PROTOCOL © 2007 Nature Publishing Group http://www.nature.com/natureprotocols BOX 5 | X, Y-SCATTER PLOTS Each identified protein is plotted using the averaged, normalized spectral counts as x- and y-coordinates. For instance, Figure 3 reports the multi-dimensional protein identification technology (MuDPIT) data from our recent analysis of palmitoylation in yeast4. The 1,557 proteins identified from MuDPIT analysis of three paired plus- and minus-hydroxylamine (HA) samples are displayed: each individual protein is represented by a dot plotted as the averaged, normalized +HA sample spectral count (x-coordinate) against the averaged, normalized HA sample spectral count (y-coordinate). The palmitoyl proteins (PPs) are shown as red dots; these correspond both to the 15 PPs that were known at the outset of this analysis to be palmitoylated and to the 35 new PPs identified and confirmed by this analysis4. The PPs clearly stand out as clustering at or near the x-axis. In contrast, contaminant proteins, which are equivalently purified into both the +HA and HA samples, tend to fall near the x, y-diagonal. This graphical approach also gives a sense of the statistical significance of candidate PP identifications; note that at low spectral count numbers the PP cluster begins to merge and overlap the x, y-diagonal contaminant protein cluster. In addition to its utility in identifying candidate PPs, the x, y-scatter plot approach should prove useful in other binary comparisons, particularly for highlighting PPs that are affected by palmitoylation-perturbing conditions (e.g., plus and minus drug treatment). ? TROUBLESHOOTING Troubleshooting advice can be found in Table 1. TABLE 1 | Troubleshooting table. Step 7 Problem Protein fails to fully dissolve in 4% SDS buffer (4SB) Possible reasons Too much protein Solution Increase 4SB volume as well as the number of tubes used in processing each sample 19 Indistinguishable gel profiles for proteins purifed into plus- and minus-hydroxylamine (HA) samples Palmitoyl modifications lost either before or during purification Maintain tissues or cells in cold before purification Check buffer pH (thioesters are most stable at neutral pH) Insufficient N-ethylmaleimide (NEM) blockade of free thiols Increase NEM concentration in Step 8 Change buffering agent of lysis buffer (LB) used in Steps 1–7. In addition to reacting with thiols, NEM also reacts with primary amines at low level. Thus, Tris may be consuming NEM. Substitute HEPES or phosphate buffer for Tris in LB Residual NEM may be blocking thiols as they become exposed by hydroxylamine Increase number of chloroform–methanol precipitations Failure to release biotinylated proteins from streptavidin–agarose Increase b-mercaptoethanol concentration Try 50 mM DTT Non-specific streptavidin–agarose binding Reduce amount of streptavidin–agarose used ANTICIPATED RESULTS PPs should be substantially over-represented in +HA versus HA samples and thus should be highlighted by the ratiometric comparison of +HA and HA sample spectral counts for each identified protein. For abundant sample proteins (i.e., those identified by high spectral count numbers), the bona fide PPs should be cleanly resolved from the background of co-purifying contaminant proteins. Contaminant proteins tend to be proteins of known high abundance, e.g., cytoskeletal proteins, chaperones, ribosomal proteins and glycolytic enzymes, that generally are purified in hydroxylamine-independent fashion and thus show plus-to-minus spectral count ratios near 1. At lower spectral counts, distinguishing PPs from contaminants becomes a little more problematic; at these low numbers, some non-palmitoylated contaminant proteins show skewed plus-to-minus spectral count ratios owing purely to chance. Spectral count statistical variation Spectral counts correlate with, but do not directly measure, abundance10. For proteins identified by low spectral count numbers, substantial run-to-run spectral count variation may be seen in parallel MuDPIT runs of identical samples, a reflection, 1582 | VOL.2 NO.7 | 2007 | NATURE PROTOCOLS PROTOCOL © 2007 Nature Publishing Group http://www.nature.com/natureprotocols BOX 6 | COLORIMETRIC DEPICTIONS OF RATIOS This approach, extrapolated from the genomic analysis of microarray data, when applied to spectral count data, allows sample-to-sample changes in individual protein abundance to be depicted. Figure 4 is a colorimetric depiction of the relative abundance of the top 30 yeast palmitoyl proteins (PPs) in plus-hydroxylamine (+HA) samples from wild-type yeast versus a series of mutant yeast strains deficient for members of the DHHC family of protein acyl transferases (PATs)4. The different strains have the DHHC PATs mutated either individually or in combination (Fig. 4; strain genotypes are indicated to the left). For each protein, the ratio of its wild-type sample spectral counts to its mutant sample spectral counts is mapped onto a red-green colorimetric gradient where red corresponds to proteins with 20-fold or greater spectral count under-representations in the purified mutant strain +HA acyl-biotinyl exchange (ABE) samples relative to equivalent wild-type samples. A green coloration would indicate mutant strain over-representation (Fig. 4; as one would expect, none of the proteins show substantial mutant strain over-representations). Black, the absence of green or red, indicates proteins with no appreciable abundance change between wild-type and mutant strain samples. Intermediate ratios, reflective of intermediate mutant strain sample under- or over-representations, are reported by intermediate red or green shadings. The Figure 4 heat map provides both a sense of the overall participation of the DHHC PAT family in palmitoylation and a first sense of DHHC PAT specificity, yielding a first crude enzyme–substrate map that links the individual PPs to their cognate, modifying PAT. Similar to the x, y-scatter plot, this approach should prove useful in identifying the specific changes in palmitoylation brought on by a variety of perturbants. presumably, of variations in both the peptidyl chromatography and peptide sampling by the tandem MS. Ultimately, spectral count is a statistic, and confidence in a result is reinforced by repeat analyses and also, perhaps, by statistical tests of significance. For our analyses to date, we have tended to derive conclusions mainly from the set of proteins that are identified by high spectral count numbers. For instance, our mapping of PP substrates with DHHC PATs (Fig. 5), which relied on comparing spectral count scores from +HA samples derived from DHHC PAT mutant yeast strains with scores from wild-type yeast +HA samples, focused on just the 30 top-scoring yeast PPs. Spectral count reductions of tenfold or more specific to the DHHC mutant strain samples easily highlighted the links between substrate PPs and DHHC PATs. Confidence in detected palmitoylation changes in the two- to fivefold range may require repeat analyses coupled with statistical tests of significance. False positives Two classes of false-positive proteins typically are seen. One class, ‘statistical false-positives’, result from the spectral count statistical variation discussed above, i.e., proteins that show high plus-to-minus spectral count ratios simply owing to chance under-detection from the HA samples. Such false positives become increasingly dominant as one proceeds further down the list to proteins identified by low spectral count numbers. Repetition of the MuDPIT analysis reduces this statistical noise. A second class of false positives comprises proteins that are not palmitoylated, but that are strongly and specifically detected by ABE none the less. As ABE detects PPs through detection of the palmitoyl–cysteinyl thioester linkage, it is not surprising that some proteins that utilize thioesters for chemistries other than palmitoylation also are purified by ABE. Two examples of this class, prominently detected by our yeast analysis, are Pdx1 and Lat1, which are both subunits of the mitochondrial pyruvate dehydrogenase complex. In the decarboxylation of pyruvate, these two subunits transiently accept acetyl moieties in thioester linkage to lipoic acid prosthetic groups. Although strongly detected by ABE, Pdx1 is not palmitoylated and it is not labeled in experiments that assess palmitoylation through metabolic [3H]palmitic acid incorporation4. Other thioester-utilizing false positives detected in our yeast proteomic analysis include Gcv3, which uses the lipoic acid prosthetic group for glycine decarboxylation, the E2 ubiquitin conjugase Ubc1, which transiently accepts ubiquitin moieties in thioester linkage, and the acyl-carrier protein Acp1, which carries growing fatty acyl chains in thioester linkage to a phosphopantetheinyl prosthetic group. An orthologous set of thioester-utilizing false positives also are prominently detected in mammalian analyses (R. Kang, J.W., J. Yates, N.G.D. and A. El-Husseini, unpublished results). These thioester-utilizing false positives are always seen; indeed, their detection provides some measure of the efficacy of the ABE reactions and purifications. Furthermore, as their ABE labeling is palmitoylation independent, these proteins should remain unchanged in analyses aimed at the global palmitoylation effects of perturbants, e.g., mutation or drugs, and may prove useful as a standard for normalizing sample-to-sample spectral counts (Box 4). Particularly useful in this regard are two strongly detected lipoic acid–utilizing subunits of the mitochondrial pyruvate dehydrogenase complex, namely, the dihydrolipoamide S-acetytransferase and the lipoyl-containing component X (in yeast, Lat 1 and Pdx1, respectively). Candidate testing To confirm palmitoylation and to eliminate false positives, the newly identified PP candidates should be independently tested for palmitoylation. Statistical false positives can be eliminated in small-scale experiments that use ABE chemistry to detect palmitoylation. Such a scaled-down ABE protocol involves a scaled-down ABE processing of extracts and immunoprecipitation of an epitope-tagged version of the protein under consideration, with a final western blot detection using anti-biotin antibodies4. NATURE PROTOCOLS | VOL.2 NO.7 | 2007 | 1583 PROTOCOL However, the alternative approach, the direct testing of the candidate protein incorporation of a label from [3H]palmitic acid via classic metabolic labeling4, may be preferred: it has the added advantage of eliminating the thioester-utilizing class of false positives. © 2007 Nature Publishing Group http://www.nature.com/natureprotocols ACKNOWLEDGMENTS We thank John R. Yates 3rd (The Scripps Research Institute, La Jolla, CA) for his help in the original development of this protocol. The MuDPIT proteomic technology developed by his laboratory proved indispensable to this analysis in terms of its high capacity and the facile quantitative analysis that it affords. The development of this technology was supported by NIH GM065525 (N.G.D.) and NIH RR11823 (John R. Yates 3rd). COMPETING INTERESTS STATEMENT The authors declare no competing financial interests. Published online at http://www.natureprotocols.com Rights and permissions information is available online at http://npg.nature.com/ reprintsandpermissions 1. Huang, K. & El-Husseini, A. Modulation of neuronal protein trafficking and function by palmitoylation. Curr. Opin. Neurobiol. 15, 527–535 (2005). 2. Resh, M.D. Palmitoylation of ligands, receptors, and intracellular signaling molecules. Sci. STKE 2006, re14 (2006). 3. Linder, M.E. & Deschenes, R.J. Palmitoylation: policing protein stability and traffic. Nat. Rev. Mol. Cell Biol. 8, 74–84 (2007). 4. Roth, A.F. et al. Global analysis of protein palmitoylation in yeast. Cell 125, 1003–1013 (2006). 5. Roth, A.F., Wan, J., Green, W.N., Yates, J.R. & Davis, N.G. Proteomic identification of palmitoylated proteins. Methods 40, 135 (2006). 6. Drisdel, R.C. & Green, W.N. Labeling and quantifying sites of protein palmitoylation. Biotechniques 36, 276–285 (2004). 7. Link, A.J. et al. Direct analysis of protein complexes using mass spectrometry. Nat. Biotechnol. 17, 676–682 (1999). 8. Washburn, M.P., Wolters, D. & Yates, J.R. 3rd. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247 (2001). 9. Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003). 1584 | VOL.2 NO.7 | 2007 | NATURE PROTOCOLS 10. Liu, H., Sadygov, R.G. & Yates, J.R. 3rd. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–4201 (2004). 11. Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003). 12. Roth, A.F., Feng, Y., Chen, L. & Davis, N.G. The yeast DHHC cysteine-rich domain protein Akr1p is a palmitoyl transferase. J. Cell Biol. 159, 23–28 (2002). 13. Lobo, S., Greentree, W.K., Linder, M.E. & Deschenes, R.J. Identification of a Ras palmitoyltransferase in Saccharomyces cerevisiae. J. Biol. Chem. 277, 41268–41273 (2002). 14. Smotrys, J.E. & Linder, M.E. Palmitoylation of intracellular signaling proteins: regulation and function. Annu. Rev. Biochem. 73, 559–587 (2004). 15. Ducker, C.E. et al. Discovery and characterization of inhibitors of human palmitoyl acyltransferases. Mol. Cancer Ther. 5, 1647–1659 (2006). 16. Link, A.J., Jennings, J.L. & Washburn, M.P. Analysis of protein composition using multidimensional chromatography and mass spectrometry. in Current Protocols in Protein Science, (eds. Coligan, J.E., Dunn, B.M., Speicher, D.W. & Wingfield, P.T.) chapter 23, 23.1.1–23.1.25 (John Wiley and Sons, Inc., Hoboken, New Jersey, 2003). 17. Bern, M., Goldberg, D., McDonald, W.H. & Yates, J.R. 3rd. Automatic quality assessment of peptide tandem mass spectra. Bioinformatics 20 (Suppl 1): I49–I54 (2004). 18. Eng, J.K., McCormack, A.L. & Yates, J.R. 3rd. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994). 19. Tabb, D.L., McDonald, W.H. & Yates, J.R. 3rd. DTASelect and contrast: tools for assembling and comparing protein identifications from shotgun proteomics. J. Proteome Res. 1, 21–26 (2002). 20. Wessel, D. & Flugge, U.I. A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Anal. Biochem. 138, 141–143 (1984).