Global kinetic analysis of proteolysis via quantitative targeted proteomics

Edited by* Robert T. Sauer, Massachusetts Institute of Technology, Cambridge, MA, and approved December 14, 2011 (received for review October 17, 2011)
January 23, 2012
109 (6) 1913-1918

Abstract

Mass spectrometry-based proteomics is a powerful tool for identifying hundreds to thousands of posttranslational modifications in complex mixtures. However, it remains enormously challenging to simultaneously assess the intrinsic catalytic efficiencies (kcat/KM) of these modifications in the context of their natural interactors. Such fundamental enzymological constants are key to determining substrate specificity and for establishing the timing and importance of cellular signaling. Here, we report the use of selected reaction monitoring (SRM) for tracking proteolysis induced by human apoptotic caspases-3, -7, -8, and -9 in lysates and living cells. By following the appearance of the cleaved peptides in lysate as a function of time, we were able to determine hundreds of catalytic efficiencies in parallel. Remarkably, we find the rates of substrate hydrolysis for individual caspases vary greater than 500-fold indicating a sequential process. Moreover, the rank-order of substrate cutting is similar in apoptotic cells, suggesting that cellular structures do not dramatically alter substrate accessibility. Comparisons of extrinsic (TRAIL) and intrinsic (staurosporine) inducers of apoptosis revealed similar substrate profiles, suggesting the final proteolytic demolitions proceed by similarly ordered plans. Certain biological processes were rapidly targeted by the caspases, including multiple components of the endocyotic pathway and miRNA processing machinery. We believe this massively parallel and quantitative label-free approach to obtaining basic enzymological constants will facilitate the study of proteolysis and other posttranslational modifications in complex mixtures.
Apoptosis is a form of programmed cell death that serves to eliminate unnecessary, infected, or tumorigenic cells from eukaryotic organisms. While many intrinsic and extrinsic stimuli can initiate apoptosis, these ultimately converge on the activation of a related family of aspartate-specific cysteine proteases, the caspases, that execute widespread proteolysis and induce noninflammatory death (1). We and others have surveyed N termini that occur in apoptotic cells and collectively reported more than 1,000 caspase-derived cleavages (25). This explosion of proteomic data has defined a vast array of caspase substrates proteolyzed during apoptosis. While these data identify caspase targets, and in some cases the sites of proteolysis, they fail to reveal the relative rates of cleavage, a parameter necessary to establish the order of proteolytic events and their importance in extracts and intact cells.
The recent application of selected reaction monitoring (SRM) methods, traditionally used for metabolite identification, to proteomic studies has enabled the simultaneous label-free quantification of hundreds of peptides (6, 7). Our development of a N-terminal enrichment platform (3) is ideally suited to the application of SRM to apoptotic proteolysis. Using this platform, we have characterized approximately 1,000 caspase-derived peptides from human apoptotic cells forming a basis from which to establish high-confidence mass spectrometric assays for SRM. Additionally, our positive enrichment technology generates samples with less complexity than those derived from the whole proteome, decreasing the likelihood of misassignment. Here, we have applied SRM analysis to the N-terminal isolation technology to determine the time-course of caspase-mediated proteolysis. From these data we calculated catalytic efficiencies for hundreds of caspase substrates in parallel. We believe these data will allow a more quantitative systems-level understanding of the fundamental process of cell death, and move us closer to understanding the global enzymology of posttranslational modifications.

Results and Discussion

To globally assess caspase catalytic efficiencies in complex mixtures, we quantified the appearance of caspase-cleaved N termini in two complementary experiments. First, in cell lysates with endogenous caspases inactivated, we tracked the time-dependent activities of exogenously added executioner caspases-3 or -7, or extrinsic and intrinsic initiator caspases-8 or -9. Second, we compared these results to the rates of appearance of caspase-cleaved peptides in cells treated with different apoptosis-inducing drugs. Experiments in cell lysates reveal the priority of substrate cleavage with a resolution that is unavailable in bulk cellular studies due to the stochasticity of mitochondrial permeablization and caspase activation (8). Additionally, the in vitro experiments allow us to specify the activities of individual caspases, a goal not readily achieved in cellular studies where many caspases are simultaneously activated. Conversely, the cellular studies include exogenous factors, such as subcellular compartmentalization, that may affect cleavage rates.

Quantitative Measurement of Caspase-Cleaved Substrates.

Our analysis of proteolysis substrates is based on a previously described N-terminal isolation platform that compares cells or lysates before and after initiating a proteolytic process (3, 9, 10). Briefly, free N termini in lysates are enzymatically labeled with a biotinylated peptide ester, captured on neutravidin beads, and trypsinized to produce N-terminal peptides. The peptides are released by site-specific proteolysis with Tobacco Etch Virus (TEV) protease, and the N-terminal sequence identified via LC-MS/MS. Here, we further optimized the tagging peptide to contain an aminobutyric acid residue at the P1 position instead of the serine-tyrosine tag previously employed (Fig. S1) (4). This improvement resulted in fewer tag-specific fragments and provided a nonnatural mass signature. Cleavages after aspartic acid residues are rare in healthy cell lysates (approximately 1% of N termini), so virtually all aspartic-cleavages identified in apoptotic cells (typically 20–50% of total identified N termini) are due to caspase activities.
To expand this technology to kinetic analysis required quantification of these isolated N termini as a function of time. Traditional isotope encoded approaches to MS-based quantification (e.g. SILAC and iTRAQ) can monitor relative peptide abundance (11, 12). However, these approaches are expensive, challenging to expand to more than a few samples, and often fail to quantify the same peptide across multiple samples due to variable sampling at the MS-level (13, 14). Thus we investigated selected reaction monitoring (SRM) (6), as a targeted label-free quantification approach for tracking caspase-mediated proteolysis. SRM quantification necessitates both prior identification of the species of interest and reliable MS fragmentation patterns, two requirements addressed by previous applications of our N-terminal isolation protocol (3, 7). Additionally, the positive enrichment for N termini significantly decreases sample complexity, facilitating the development of unambiguous peptide quantification assays.
To track hundreds of caspase proteolysis events across many conditions, we developed assays to quantify N-terminal peptides isolated from apoptotic Jurkat cells. Investigation of intrinsic (staurosporine) or extrinsic (TRAIL) inducers of apoptosis identified 1,341 peptides with caspase-like cleavage sites from a total of 3,892 high-confidence peptides (false discovery rate of < 1%) (Fig. 1A and Dataset S1 a and b) (15). We analyzed the same fractions across the same chromatography on a QTRAP mass spectrometer, monitoring up to 10 coeluting parent ion/ fragment ion (Q1/Q3) pairs (transitions) and optimizing the transition’s collision energies (Supporting Information). The presence of at least three coeluting transitions at the retention time observed at the peptide discovery stage qualified the assay for the peptide of interest (Dataset S1c). While we discovered 1,341 substrates via our N-terminal labeling technology, only about half met the strict criteria for unambiguous identification for at least three transitions (Q1/Q3 pairs). Thus, we focused our studies on these 676 caspase-derived peptides for which we had unambiguous identification. To assess the reliability of this method for peptide identification, we monitored an unfractionated sample for the presence of unrelated transitions, transitions with randomized Q1s (m/z ratios), or transitions with altered retention times (± 10 minutes) finding a false discovery rate of 0.9–1.2% for transitions and 0.3–0.6% for peptides (≧3 transitions). We chose the five most intense transitions as qualified assays for each peptide.
Fig. 1.
Targeted proteomics of peptides enriched via subtiligase tagging enables global quantification of proteolysis. (A) Development of optimized transitions: Intense fragment ions from high-mass resolution peptide MS/MS spectra are analyzed via targeted proteomics for coelution at the expected retention time and optimized for ideal collision energies. Abu: L-aminobutyric acid. (B) Validation of peptide quantification: Protein from apoptotic and healthy cell lysates was mixed at 4∶0 (green), 2∶2 (red), or 1∶3 (blue) ratios, and N termini were quantified via our N-terminomics technology. i. The relative intensities of quantifiable peptides are plotted (dashed lines indicate ideal values) against the rank order of the combined peptide intensity for all three samples. ii. Mean intensities for each ratio show a linear dependence on amount of apoptotic cells (r2 > 0.99). (C) Determination of catalytic efficiency i. Integrations of signal intensity over time track the appearance of cleaved N termini. ii. Peptide intensities are fit to pseudo-first-order kinetic equations to determine the kinetic efficiency (kcat/KM) for each substrate. (D) Statistical analysis reveals progress curves (i) below, (ii) within, and (iii) above the measureable range for catalytic efficiencies (2 CV) (black: idealized curves, colors: representative data).
With the qualified transitions in hand, we assessed the ability to quantify caspase-derived N termini in complex mixtures. Lysates from apoptotic and healthy cells were mixed at varying ratios, the N termini were isolated, and the resulting intensity of each peptide was evaluated using our qualified assays (Fig. 1b). The intensities of the apoptotic peptides were linearly correlated to the amount of apoptotic sample across the entire range of measured intensities (r2 > 0.99), indicating that our N-terminal isolation and MS-analysis allow for direct quantitative comparisons.

Global Enzymatic Analysis of Caspase Activities.

This quantitative analysis was used to generate progress curves for the proteolysis of individual caspase targets as a function of time. From these curves we calculated the catalytic efficiency (kcat/KM) for each substrate from well-established pseudo-first-order kinetic relationship (%Conversion = 1 - e-(kcat/KmEot)) (16). While application of this equation to the progress curve does not allow us to separate kcat from KM in the absence of substrate concentrations, one can calculate the ratio of kcat to KM from the initial enzyme concentration (Eo). We tracked cleavage kinetics in Jurkat-cell lysates inactivated with iodoacetamide to quench both endogenous active and zymogenic caspases. Excess iodoacetamide was reacted with dithiothreitol to ensure it would not inactivate the exogenously added caspases. Evaluation of caspase activities against a fluorescent substrate (DEVD-AFC) under these conditions confirmed that lysate did not competitively inhibit caspase activity (Fig. S2 AC). This control shows that the presence of additional substrates does not alter the relative kcat/KM values, and that this kinetic treatment applies to lysates containing hundreds of caspase substrates.
We added recombinant caspases to the lysates, sampled 6–8 time points over 1–2 h, and evaluated the areas of SRM peaks corresponding to targeted caspase-derived N termini using in-house scripts (Fig. 1C). The relative intensities of these data were fit to a pseudo-first order kinetic equation to allow simultaneous calculation of kcat/KM values for hundreds of substrates. We found the caspase cleavage rates fell into three categories (Fig. 1D and Fig. S2 D and E): (i) substrates that were cleaved below the measurable rate which appear linear (to which we could assign a maximum rate), (ii) substrates cleaved within the measurable range (to which we can calculate a kcat/KM value), and (iii) substrates that are saturated by the first time point (to which we can assign a minimum rate). Optimal progress curves covered approximately one order of magnitude in catalytic efficiencies, and thus we expanded the dynamic range by varying the caspase concentration between 10 and 1,000 nM.
We profiled 676 potential apoptotic substrates for cleavage by the individual caspases -3, -7, and -8, and could determine measurable catalytic efficiencies for 180, 58, and 66 substrates, respectively. Remarkably, the kcat/KM values for substrates for each protease varied over nearly three orders of magnitude (Fig. 2A and Dataset S2). Proteolysis rates for individual substrates analyzed by immunoblot were in good agreement with the rates determined by the SRM method (Fig. 2B and Fig. S3 AD). We also detected proteolysis for many additional substrates but were unable to establish rates due to undetectable levels of N termini at early time points or, rarely, due to nonfirst-order kinetics apparently caused by N-terminal instability and degradation. A recent gel-based study determined that catalytic efficiencies (kcat/KM) for the cleavage of eight purified mammalian substrates of caspase-3 ranged from 104 to 106 M-1 s-1, while the median rate for eleven noncognate Escherichia coli proteins was 2 × 102 M-1 s-1 (17). These studies on purified proteins in vitro are consistent with a wide dynamic range of caspase-cleavage rates we see in a cellular context and suggest quantitative guidelines for assigning likely importance.
Fig. 2.
In vitro activities of the apoptotic caspases. Triton lysates of Jurkat A3 cells were incubated with 10–1000 nM Caspase-3, -7, or -8, and sampled for 6–9 time points over 1–2 h. A. Rank order of catalytic efficiencies and sequence specificity logos for: (i) caspase-3, (ii) -7, and (iii) -8. (B) Lysates treated with 250 nM caspase-3 were analyzed by immunoblot probing for fast (clathrin light chain A (CLCA)), medium (TARBP-2) or slow (ARHGAP4) substrates. (*—cross reactive band) Immunoblot against GAPDH confirms protein loading. (C) Rates for substrates cleaved by both caspase-3 and caspase-7 were compared. No marked correlation was found between the measured rates, though 48% of the observed data points were below the measurable range for both caspase-3 and caspase-7 (2 × 103 and 5.3 × 102 /M/s, respectively) (bottom leftmost point).
Interestingly, treatment with caspase-9 plus APAF-1, a scaffolding protein necessary for caspase-9 activity (18), did not significantly cleave any of the targeted substrates despite being fully capable of cleaving a fluorescent substrate in vitro (Fig. S3E). We note that some substrates are not detected by the N-terminal isolation method because of the small size of the tryptic fragment or inaccessibility of the N terminus for labeling. Such is the case of pro-caspase-3, a known caspase-9 substrate. Nonetheless, it is striking that no caspase-9 substrates were found and our data suggests it has many fewer substrates than the others, including initiator caspase-8.
In aggregate we were able to assess catalytic efficiencies for 20–40% of the targeted substrates within the measurable range (5 × 102 to 2 × 105 M-1 s-1) for individual caspases. Investigation of the primary sequences of the fastest approximately 20% of substrates for each caspase showed specificities similar to those found against synthetic peptide substrates (19). Notably while the primary sequence specificities of caspases-3 and -7 were highly similar, the efficiencies of cleavage by these two enzymes were not highly correlated (Fig. 2C). Interestingly, 60–80% of the targets showed efficiencies less than 2 × 103 M-1 s-1, a cleavage rate within 10-fold the range observed for caspase-3 cleaving noncognate substrates derived from E. coli (17). This may suggest that these poorly cleaved substrates play little role in apoptosis beyond cellular dismantling. Nonetheless, poor rates of hydrolysis alone cannot rule out that limited proteolysis of certain targets may still have important physiological consequences.

Endocytosis and miRNA Processing are Rapidly Inactivated by Caspase-Cleavage.

In an effort to gain a global perspective into the function of caspase-cleavages we analyzed the gene ontology of rapidly cleaved substrates using GO Miner (20). We found particular enrichment for proteins involved with RNA processing, negative regulators of metabolic processes, and endosomal trafficking. Within our dataset, two components of the miRNA processing pathway, DGCR8 and TARBP2, are rapidly targeted by multiple caspases. Recently, others have reported DICER to be cleaved by caspases (21) and miRNA levels are known to fall during apoptosis (22). Interestingly, several of the endosomal proteins were required for EGFR signaling (Fig. S4). Consistent with this observation, we found that TRAIL-treatment blocked clathrin-mediated endocytosis and altered endocytosis-dependent signaling. By contrast, signaling through AKT, a membrane-associated process was not rapidly disrupted. Recently, Ashkenazi and coworkers reported that cleavage of the clathrin adaptor subunit AP2α is important for sustaining TRAIL signaling by blocking receptor recycling (23). Our data show more targets are cut early in this pathway suggesting this to be a critical early step in apoptosis.

Caspase Activities in Live Cells.

To assess the cellular relevance of catalytic efficiencies determined in cell extracts, we monitored the rate of appearance of caspase-cleaved N termini in Jurkat cells treated with inducers of intrinsic (staurosporine) or extrinsic (TRAIL) apoptosis (Fig. 3A, Figs. S5 and S6 AC, and Dataset S3). Heat map analysis of the cleaved substrates revealed that, after an induction period, there was a consistent monotonic increase in the amounts of N termini generated. Generally the signals reached a maximum between 3–5 h of treatment with staurosporine. The appearance of caspase-cleaved substrates correlates directly with luminescence-based measures of caspase activity and cell viability (Fig. 3B) and with the cleavage rates of the apoptotic caspases and hallmark substrates (Fig. 3C). We assessed the relative priority in cellular cleavages by determining the time to reach half maximal signal (Fig. S6D). These data correlated well with the catalytic efficiencies determined from the addition of individual caspases to extracts which are plotted adjacent to the heat map analysis (Fig. 3A). For example, substrates cleaved at greater than 2.0 × 103 M-1 s-1 by at least one of the apoptotic caspases (corresponding to t1/2 of approximately 1 h at expected physiological conditions of 100 nM caspase) were also among the set of rapidly cleaved cellular substrates. This observation is remarkable as a number of factors could have grossly perturbed this correlation, including stochastic induction of the caspases, subcellular localization, and the possible activities of other caspases and unknown proteases during apoptosis (24). These data suggest that even though the apoptotic caspases are initially activated in the cytosol, they can access cellular compartments very quickly in the apoptotic process. Notably, while cleavage rates in lysates varied by more than 3 orders of magnitude, in cellular studies most substrates are processed with similar rates. Induction of apoptosis is stochastic (8), so analyses of cells treated with apoptotic inducers include cells that have been exposed to the caspases for significantly varying periods of time. As a result, the cellular data do not distinguish between the very fast and the fast substrates, but only the slower substrates that appear later. We believe an advantage of the in vitro cleavage rates is they better reflect the timing of cleavages that occur at the single cell level because the induction of proteolysis is synchronized.
Fig. 3.
Cleavage of caspase substrates during apoptosis. (A) Jurkat A3 cells were treated with 2 μM staurosporine for 0–5 h, lysed, and N termini were quantified. A heat map analysis of N termini was plotted ranking cleavages by t1/2 from slowest to fastest. Right: The cellular cleavage events are compared to in vitro cleavages. The substrates are distributed into five equally sized bins, and the number of in vitro cleavages less than and greater than 2E3 M-1 s-1 are plotted. (B) Caspase activity and cell viability were monitored over the time-course of staurosporine treatment. (C) Western blot analysis of example caspase substrates during staurosporine treatment. The GAPDH targeted antibody confirms equal protein loading.
Comparing the proteolysis rates between TRAIL- and stuarosporine-mediated apoptosis reveals a modest biphasic correlation (Fig. 4A). This is consistent with cell viability and immunoblot data suggesting that staurosporine-mediated death is slower to initiate, but less stochastic than TRAIL-mediated death (Fig. 3 BC. and Fig. S5). Mapping of the observed cleavage rates onto this cross correlation shows that most of the substrates rapidly cleaved in vitro occur prior to the second phase of staurosporine-mediated death, while slowly cleaved substrates were equally distributed across both phases (Fig. 4b). Interestingly, five of six substrates with half-lives less than 2 h in stuarosporine-mediated death (dashed rectangle Fig. 4A) were involved with mRNA processing, while three of four substrates cleaved rapidly in TRAIL-mediated death but slowly via the intrinsic pathway (circle Fig. 4A) were involved in endocytosis. Thus, while the demolition programs of staurosporine and TRAIL-mediated death are highly similar, each has a set of inducer-specific preferred substrates.
Fig. 4.
Correlation between cleavage rates in staurosporine- and TRAIL-mediated cell death. (A) Time to reach half-maximal signal (t1/2) for peptides identified in stuarosporine and TRAIL-mediated deaths were imaged and approximated by a biphasic fit. Substrates cleaved in staurosporine-mediated apoptosis with a t1/2 less than 2 h are boxed, and those cleaved rapidly in TRAIL-treated cells, but slowly in staurosporine-mediated cells are circled. (B) Substrates cleaved faster than 2.0 × 103 M-1 s-1 during in vitro experiments (red) and those cleaved at less than 2.0 × 103 M-1 s-1 (blue) are plotted.

Conclusions and Perspective.

These studies present a global approach to characterize the kinetics of protease-substrate pairs in their native states allowing us to prioritize substrates for this important class of posttranslational modifications. Interestingly, in addition to their roles in apoptosis, transient caspase activation occurs during cellular differentiation, including the transformations of monocytes to macrophages and pluripotent stem cells to more specialized cell types (25, 26). Perhaps these rapidly cleaved substrates are also important during transient caspase activation in differentiation. The use of label-free SRM technologies to characterize enzyme-substrate kinetics and the dynamics of cellular modifications has tremendous potential to prioritize posttranslational modifications and provide fundamental enzymological constants for systems-level cellular modeling.

Methods Summary

Cell Culture and Lysate Preparation.

Jurkat clone A3 and U87-MG (ATCC) were cultured as per ATCC guidelines. For in vitro cleavage experiments Jurkat cells were lysed (2 × 108 cells/mL) in triton X-100 (0.1%), with HEPES (100 mM pH 7.4), and protease inbibitors [EDTA (1 mM), AEBSF (1 mM), PMSF (1 mM), and iodoacetamide (10 mM)] for 15 min at 4 °C, quenched by the addition of DTT (20 mM) for 15 min 4 °C. For caspase-9 assays, KCl (10 mM), MgCl2 (1.5 mM), and sucrose (1.5%) were included in the lysis buffer. The lysates were clarified by centrifugation (4,100  × g, 5 min, 2x). For analysis of cellular cleavages, cells were treated with TRAIL (2 nM, Peprotech) or Staurosporine (2 μM, LC labs) for 0–6 h, lysed (8 × 108 cells/mL) in SDS (4%), Bicine (400 mM pH 8.0), with protease inhbitors [EDTA (1 mM), AEBSF (1 mM), PMSF (1 mM), E-64 (0.1 mM), and zVAD-fmk (0.1 mM)], and DNA was fragmented via probe sonication. The lysates were diluted with triton X-100 followed by water (final concentrations 1% SDS, 1% triton X-100, 100 mM Bicine), pelleted to remove any insoluble material (7,500  × g, 15 min), reduced by treatment with TCEP hydrochloride (2 mM) at 95 °C for 10 min, and alkylated with iodoacetamide (4 mM) in the dark for 1 h.

In Vitro Cleavage Assays and N-terminal Isolation.

Lysates were treated with recombinant caspases-3, -7, -8, or -9 plus APAF-1, for the indicated periods of time at RT, and quenched with zVAD-fmk (0.1 mM). TEVest4B (Fig. S3E) (10 mM in DMSO) was added to the lysates (10% v/v), followed by subtiligase (1 μM) and incubated at RT for 1 h. Samples were precipitated in CH3CN (80% v/v), resolubilized in guanidine hydrochloride (8 M, 10 mg/mL final protein concentration) and N termini were isolated as described (9).

LC/MS/MS.

For discovery experiments, peptides were analyzed via two-dimensional reversed-phase LC/MS/MS. Samples were fractionated by offline high pH reversed-phase chromatography with a 70 min gradient on a 1 × 150 mm XBridge C18 3.5 μM with a flow rate of 75 μL/ min. Fractions were analyzed by low pH reversed-phase chromatography with a 90 min gradient on a 0.1 × 100 mm column and a flow rate of 1 μL/ min on a nanoACQUITY UPLC system (Waters). The capillary column was coupled to a QSTAR Elite mass spectrometer (Applied Biosystems) for peptide identification or a QTRAP 5500 mass spectrometer (AB Sciex) for transition qualification. For analysis of cleavage kinetics, samples were analyzed via coupling of the same system to a QTRAP 5500 mass spectrometer.

Data Analysis.

Peptide identification was performed as described.(3) Prospective transitions were generated using in-house scripts and fractionated peptides were qualified by monitoring for > 3 coeluting transitions within 2.5 min of the expected retention time (Supporting Information). Integrations for qualified peptides were performed using Supplementary Script 4, and normalized via a set of qualified peptides monitoring endogenous (noncaspase-cleaved) N termini. For cellular cleavage experiments, peptides with significant cleavage (Integrationfinal > 4 ∗ Integration0) were analyzed. For in vitro cleavage experiments peptides with significant cleavage were fit to pseudo-first-order kinetic equations via Graphpad Prizm. Spectra from peptides with statistically significant fits (r2 > 0.9) were manually validated to investigate the presence of interfering peaks, or peak mis-assignment.

Immunoblotting of Caspase Cleavages and Cell Viability Assays.

Small portions of the mass spectrometry samples were reserved prior to biotinylation for immunoblot analysis. Lysates were normalized to approximately 4 mg/mL prior to analysis by SDS-PAGE and western blot. The antibodies used in western blots included: anti-Casp-3 (9662), anti-Casp-7 (9494), anti-PARP (9542), anti-GAPDH (2118) from Cell Signaling, anti-DGCR-8 (H00054487-A01), anti-ARHGAP4(H00000393-M01), and anti-TARBP2 (H00006895-M01) from Novus Biotech, and anti-CLCA (X16), a generous gift from Professor Frances Brodsky. Cell viability and caspase activity assays were performed with Cell Titer glo and Caspase glo (Promega) per the manufacturers recommendations.

Bioinformatics.

Cleaved substrates were analyzed by GO Miner using the collection of all substrates with measureable or limited rates as the total dataset, and substrates known to be cleaved at > 5.3 × 102 M-1 s-1 as the enriched dataset. Substrates not annotated in GO Miner were manually evaluated for references to the indicated functional categories.

Analysis of Endocytosis.

Analysis of endocytosis in Jurkat A3 cells was performed as described (23). To assess phosphorylation of U87-MG cells they were grown in serum-free media for 12 h, treated with or without TRAIL (10 nM) for 2.5 h, and EGF (3 ng/mL, Cell Signaling) was added. Cells were lysed in 1% SDS with sonication, and probed with appropriate antibodies. Antibodies used include anti-p-Tyr-100 (9411), anti-AKT pT308 (4056), and anti-ERK1/2 pT202/pY204 (9106) from Cell Signaling and anti-pEGFR pY1173 (101668) from Santa Cruz Biotechnology.

Acknowledgments.

We would like to thank David Wildes, Prof. Frances Brodsky, and the University of California, San Francisco Mass Spectrometry for invaluable discussions. We would like to thank Julie Zorn and Prof. Dennis Wolan for providing the recombinant caspases. This work has been supported by National Institutes of Health F32 AI077177 (N.J.A.), R01 GM081051 (J.A.W.). Mass spectrometry was performed at the Bio-Organic Biomedical Mass Spectrometry Resource at UCSF (A.L.B., Director) supported by the Biomedical Research Technology Program of the National Institutes of Health National Center for Research Resources, NIH NCRR P41RR001614 and 1S10RR026662.

Supporting Information

Supporting Information (PDF)
Supporting Information
SD01.xlsx
SD02.xlsx
SD03.xlsx

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Information & Authors

Information

Published in

The cover image for PNAS Vol.109; No.6
Proceedings of the National Academy of Sciences
Vol. 109 | No. 6
February 7, 2012
PubMed: 22308409

Classifications

Submission history

Published online: January 23, 2012
Published in issue: February 7, 2012

Keywords

  1. apoptosis
  2. caspase
  3. enzymology
  4. mass spectrometry
  5. selected reaction monitoring

Acknowledgments

We would like to thank David Wildes, Prof. Frances Brodsky, and the University of California, San Francisco Mass Spectrometry for invaluable discussions. We would like to thank Julie Zorn and Prof. Dennis Wolan for providing the recombinant caspases. This work has been supported by National Institutes of Health F32 AI077177 (N.J.A.), R01 GM081051 (J.A.W.). Mass spectrometry was performed at the Bio-Organic Biomedical Mass Spectrometry Resource at UCSF (A.L.B., Director) supported by the Biomedical Research Technology Program of the National Institutes of Health National Center for Research Resources, NIH NCRR P41RR001614 and 1S10RR026662.

Notes

*This Direct Submission article had a prearranged editor.

Authors

Affiliations

Nicholas J. Agard
Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94114;
Department of Biocatalyst Characterization and Design, Codexis Inc., 200 Penobscot Drive, Redwood City, CA 94063-4718;
Sami Mahrus
Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94114;
Department of Biomarker Research, Genentech, 1 DNA Way, South San Francisco, CA 94010;
Jonathan C. Trinidad
Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94114;
Aenoch Lynn
Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94114;
Duke Translational Medicine Institute, Duke University Medical Center, Durham, NC 27710; and
Alma L. Burlingame
Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94114;
James A. Wells1 [email protected]
Department of Pharmaceutical Chemistry, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94114;
Department of Cellular and Molecular Pharmacology, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94114

Notes

1
To whom correspondence should be addressed. E-mail: [email protected].
Author contributions: N.J.A., S.M., J.C.T., A.L.B., and J.A.W. designed research; N.J.A., S.M., and J.C.T. performed research; N.J.A., S.M., J.C.T., A.J.L., and A.L.B. contributed new reagents/analytic tools; N.J.A., S.M., J.C.T., and J.A.W. analyzed data; N.J.A. and J.A.W. wrote the paper.

Competing Interests

The authors declare no conflict of interest.

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