(phospho)proteome regions with the broad-specificity protease

Subscriber access provided by Gothenburg University Library

Article

Quantifying missing (phospho)proteome regions with the broad-specificity protease subtilisin Humberto Gonczarowska-Jorge, Stefan Loroch, Margherita Dell'Aica, Albert Sickmann, Andreas Roos, and René Peiman Zahedi Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b02395 • Publication Date (Web): 14 Nov 2017 Downloaded from http://pubs.acs.org on November 15, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Analytical Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Quantifying missing (phospho)proteome regions with the broad-specificity protease subtilisin Humberto Gonczarowska-Jorge1,2#, Stefan Loroch1#, Margherita Dell’Aica1, Albert Sickmann1,3,4, Andreas Roos1,5, René P. Zahedi1,6,7*

1

Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V., 44227 Dortmund, Germany.

2

CAPES Foundation, Ministry of Education of Brazil, Brasília - DF, 70040-020, Brazil.

3

Medizinische Fakultät, Medizinische Proteom-Center (MPC), Ruhr-Universität Bochum, 44801 Bochum, Germany. 4

Department of Chemistry, College of Physical Sciences, University of Aberdeen, Aberdeen, AB24 3FX, United Kingdom.

5

The John Walton Muscular Dystrophy Research Centre, MRC Centre for Neuromuscular Diseases, Newcastle University, Newcastle upon Tyne, NE1 3BZ, UK 6

Gerald Bronfman Department of Oncology, Jewish General Hospital, McGill University, Montreal, Quebec H4A 3T2, Canada. 7

Segal Cancer Proteomics Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada. # equal contribution * Corresponding author: [email protected]

Keywords:

sample preparation, missing proteins, phosphoproteomics, Pro-rich regions, novel phosphorylation sites, ERLIC, TiO2, alternative enzymes, iTRAQ

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abstract Despite huge efforts to map the human proteome using mass spectrometry the overall sequence coverage achieved to date is still below 50%. Reasons for missing areas of the proteome comprise protease-resistant domains including the lack/excess of enzymatic cleavage sites, non-unique peptide sequences, impaired peptide ionization/separation and low expression levels. To access novel areas of the proteome the beneficial use of enzymes complementary to trypsin, such as Glu-C, Asp-N, Lys-N, ArgC, LysArginase has been reported. Here, we present how the broad-specificity protease subtilisin enables mapping of previously hidden areas of the proteome. We systematically evaluated its digestion efficiency and reproducibility and compared it to the gold standard in the field, trypsin. Notably, subtilisin allows reproducible near-complete digestion of cells lysates in 1-5 minutes. As expected from its broad specificity the generation of overlapping peptide sequences reduces the number of identified proteins compared to trypsin (8,363 vs. 6,807; 1% protein FDR). However, subtilisin considerably improved the coverage of missing and particularly proline-rich areas of the proteome. Along 14,628 high confidence phosphorylation sites identified in total, only 33% were shared between both enzymes, while 37% were exclusive to subtilisin. Notably, 926 of these were not even accessible by additional in silico digestion with either Asp-N, Arg-C, Glu-C, Lys-C, or Lys-N. Thus, subtilisin might be particularly beneficial for system-wide profiling of post-translational modification sites. Finally, we demonstrate that subtilisin can be used for reporter-ion based in-depth quantification, providing a precision comparable to trypsin – despite broad specificity and fast digestion that may increase technical variance.


Page 2 of 27

Page 3 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Mass spectrometry-based proteomics is still dominated by bottom-up approaches, where proteins are usually enzymatically cleaved into peptides prior to analysis by LC-MS/MS. Although bottom-up proteomics comes along with certain shortcomings as a consequence of protein inference, such as limitations in the discrimination and quantification of proteoforms1, it offers several advantages over the analysis of intact proteins (top-down). These include improved (i) chromatography, (ii) enrichment of post-translational modifications, (iii) analyte ionization and detection, and consequently (iv) sensitivity and proteome coverage. Despite huge efforts to map the human proteome using bottom-up MS2-4 the overall sequence coverage achieved to date is still below 50% (figure 1). Reasons for missing areas of the proteome comprise protease-resistant domains including the lack or excess of enzymatic cleavage sites, non-unique peptide sequences, impaired peptide ionization/separation and low expression levels. Indeed, the large majority of data present in MS repositories such as PRIDE5, ProteomeXchange6 and PeptideAtlas4 is trypsin-based, as it offers high efficiency and specificity7 at comparably low cost and generates peptides that are well-suited for LC separation and MS detection.8,9 However, if cleavage sites are too distant and close, respectively, the reliable identification of generated tryptic peptides is severely hampered. Proline, aspartate, glutamate and phosphorylated residues in the vicinity of a cleavage site may further impair trypsin cleavage efficiency.10-14 Consequently, the sole use of trypsin limits the accessible proteome coverage by preventing the identification of specific protein areas/domains and PTM sites, and thus even of certain proteins.15 To improve the coverage and identify novel areas of the proteome, several studies made complementary use of additional proteolytic enzymes, such as Glu-C, Asp-N, Lys-N, Arg-C15-19 or recently LysArginase20, which all cleave C- or N-terminal of specific amino acid residues. Notably, modern high mass accuracy MS and improved algorithms for false discovery rate (FDR) assessment render the use of broad-specificity proteases feasible, without facing typical constraints arising from huge search spaces in unspecific database searches. Lacking a clear consensus motif, broadspecificity proteases may access previously missing areas of the proteome though potentially generating overlapping peptides that cover redundant sequence information. Proteases with a broad specificity such as subtilisin21-23, elastase24,25 or proteinase K26-28 have been used for qualitative studies to improve the coverage of (membrane) proteomes or purified proteins. For instance Engel et al. could only confirm binding of a novel EGFR inhibitor to a specific Cys residue by LC-MS/MS after subtilisin digestion29, whereas trypsin and Glu-C did not yield any confident peptide identifications, despite including different fragmentation modes such as collision-induced dissociation (CID), higher-energy CID (HCD), electron transfer dissociation (ETD) and EThcD30,31.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

We wondered whether subtilisin might be a valuable addition to the protease toolbox that can be used for in-depth quantitative proteomics and phosphoproteomics. We compared its performance to the enzyme-of-choice, trypsin, considering three major aspects: (i) reproducibility of individual digests, (ii) indepth (phospho)proteome coverage, and (iii) compatibility with reporter ion-based quantification such as iTRAQ32,33 or TMT34. Our data demonstrate that subtilisin provides a surprisingly high level of reproducibility and improves the coverage of the (phospho)proteome, particularly for Pro-rich areas, allowing the identification of numerous novel phosphorylation sites e.g. in transcription factors. From 14,628 high-confidence phosphorylation, 37% were exclusively identified with subtilisin whereas 33% were shared between both enzymes. Despite the higher complexity compared to tryptic digests, we demonstrate that subtilisin digestion is also compatible with iTRAQ-based quantification, providing quantitative ratios with a similar precision as trypsin.

Figure 1. A total of 1,222,862 peptide sequences from Peptide Atlas (October 2017) were mapped to the Uniprot human database to calculate (A) the number of identified peptides per protein and (B) the achieved amino acid sequence coverage per protein.

Experimental Procedures Sample generation HeLa S3 (DSMZ, Germany) and A431 cells (DSMZ, Germany) were grown in 75 cm2 cell culture flask in Dulbecco’s Modified Eagle Medium (DMEM, PAN Biotech), 10% fetal bovine serum, 1% (v/v) penicillin/streptomycin, at 37 °C in a humidified atmosphere with 5% CO2 to a confluence of ~75%. Medium was removed and cells were washed with 5 mL of phosphate saline buffer (PBS) containing 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4. Cells were detached with 2 mL of 0.05% Trypsin, 0.02% ethylenediaminetetraacetic acid (EDTA) in PBS, incubated at 37 °C for 5 min. Trypsin was


Page 4 of 27

Page 5 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


inactivated with 8 mL of fresh medium, suspended cells were transferred to a 15 mL falcon tube, spun down for 5 min at 300 x g and the cell pellet was washed twice with PBS.

Subtilisin and trypsin efficiency under different digestion conditions The HeLa cell pellet was re-supended in 1% (w/v) sodium dodecyl sulfate (SDS), 150 mM sodium chloride (NaCl), 50 mM Tris-Cl (pH 7.8), supplemented with protease inhibitor cocktail Complete Mini and phosphatase inhibitor cocktail PhosSTOP (Roche, Switzerland). The lysate was incubated with 5 µL benzonase nuclease (25 units/µL) (Merck, Germany) for 30 min at 37 °C, followed by centrifugation at 18,000 x g for 30 min at 4 °C. The pellet was discarded. A bicinchoninic acid assay (BCA) (Thermo Scientific) was used to determine protein concentrations. Cysteines were reduced with 10 mM dithiothreitol (DTT) for 30 min at 56 °C and alkylated with 30 mM iodoacetamide (IAA) for 20 min in the dark, at room temperature. The sample was diluted 10-fold with ice-cold ethanol, followed by 1 h of incubation at -40 °C. The sample was centrifuged at 18,000 x g at 4 °C for 30 min and the supernatant was discarded. The sample was re-suspended in 6 M guanidine hydrochloride (GuHCl), and aliquots containing 10 µg of protein were diluted with 50 mM ammonium bicarbonate (ABC) buffer, pH 7.8, to 0.2 M, 0.5 M, 1.0 M, 2.0 M and 4.0 M GuHCl in triplicates and volumes were equated. Samples were either individually digested with trypsin (Sigma Aldrich, Germany) in a ratio 20:1 at 37 °C overnight or pre-heated for 5 min at 56 °C, and individually digested with subtilisin (product number P5380, SigmaAldrich, Germany) in a ratio of 20:1 for 20 min at 56 °C. Digestion was stopped with 1% trifluoroacetic acid (TFA) (Figure S1). In addition, 0.2 M aliquots were digested with subtilisin for 1, 5, 10 and 20 min in triplicate, as described before. Per sample, 1 µg was used for pre and post digestion quality control on a monolithic HPLC system as described previously9 (Figure S2). From each sample an aliquot corresponding to 500 ng was measured by LC-MS/MS as described in Table S1. Additionally, to evaluate the reproducibility between individual digests, 500 ng per trypsin and subtilisin digest triplicate was analyzed by LC-MS/MS, as described in Table S2.

Systematic proteome comparison A431 cell pellet was treated as described above. After re-suspension in 6 M GuHCl the sample was 30fold diluted with 50 mM ammonium bicarbonate (ABC) buffer, pH 7.8. One 25 µg aliquot was digested using trypsin (Sigma Aldrich, Germany) in a ratio of 20:1 (protein:enzyme) at 37 °C overnight, in the ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

presence of 2 mM calcium chloride. The second 25 µg aliquot was digested using subtilisin. After preheating to 56 °C for 5 min, subtilisin was added in a ratio of 20:1 and incubated for 20 min at 56 °C. Both digests were stopped by addition of 1% trifluoroacetic acid (TFA). Samples were desalted using SPEC Pt C18AR (Agilent, Germany) as follows: activation with 100 µL 100% acetonitrile (ACN), equilibration with 2 x 100 µL 0.1 TFA, sample loading twice, washing with 2 x 100 µL 0.1% TFA, elution with 2 x 50 µL 70% ACN. Eluates were dried under vacuum. Samples were resuspended in 15 µL of 10 mM ammonium acetate, pH 8.0 (buffer A) and fractionated on an Ultimate 3000 LC (Thermo Scientific). Peptides were separated on a 1 mm × 150 mm C18 (ZORBAX 300SB-C18, pore size 300 Å, 5 µm particle size, Agilent Technologies) column with a 45 min LC gradient ranging from 3 to 45% buffer B (84% ACN in 10 mM ammonium acetate, pH 8.0) at a flow rate of 12.5 μL/min. In total, 20 fractions were collected in concatenated manner using 60 s intervals, starting again with the first after 20 min (Figure S3). Fractions were dried under vacuum and re-suspended in 15 μL of 0.1% TFA for nano-LC–MS/MS analysis.

Systematic phosphoproteome comparison A HeLa cell pellet was processed as described above. After 30-fold dilution with 50 mM ABC, the sample was divided into 8 aliquots à 300 µg. Four aliquots each were digested either with trypsin or subtilisin as described above, desalted as described above and dried under vacuum. Two aliquots digested with trypsin and two aliquots digested with subtilisin were re-solubilized in 1 mL of 80% ACN, 5% TFA and 1 M glycolic acid (buffer 1) and individually subjected to phosphopeptide enrichment using titanium dioxide (TiO2, Titansphere TiO, 5 µm particle size, GL Sciences Inc, Japan), according to Engholm-Keller et al35 with slight modifications. (1) TiO2 beads (6:1, TiO2:peptide (w:w)) were added to the sample, followed by incubation for 10 min on a shaker at room temperature. The sample was centrifuged at 3,000 x g for 30 s. (2) The supernatant was carefully transferred to a second tube containing TiO2 beads (3:1), incubated and centrifuged as before. (3) The supernatant was then transferred to a third tube containing TiO2 beads (1.5:1) incubated and centrifuged as before. The supernatant was carefully discarded, 100 µL buffer 1 was added to the third tube and the TiO2-slurry was transferred to the second tube, mixed and transferred to the first tube, mixed and transferred to a new tube. The sample was centrifuged as before and the supernatant was discarded. Next, the beads were washed with 100 µL buffer 2 (80% ACN, 1% TFA), mixed for 15 s, centrifuged as before and the solution was discarded, followed by another wash step with buffer 3 (10% ACN, 0.1% TFA). TiO2 beads were dried ACS Paragon Plus Environment

Page 6 of 27

Page 7 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


for 10 min under vacuum, followed by incubation with 100 µL of 1% (v/v) ammonium hydroxide (elution buffer), pH 11.3 for 10 min on a shaker at room temperature. Beads were pelleted as before, the eluate was removed and acidified with 8 µL of 100% formic acid (FA) and 2 µL of 10% TFA. Another 30 µL of elution buffer were added to the beads, mixed for 15 s, centrifuged as before and the eluates were pooled. Samples were desalted with self-made stage tips, according to Rappsilber et al36 using C18 3M Empore™ SPE Extraction Disks (Sigma-Aldrich) and 30 µg of OLIGO™ R3 Reversed-Phase resin (Applied Biosystems). Sample desalting was conducted as described above, but elution was performed with 46 µL 98% ACN, 0.1% TFA (HILIC buffer A). HILIC fractionation was performed on an Ultimate 3000 nano RSLC (Thermo Scientific) using a 250 µm x 15 cm TSKgel Amide-80 (Tosoh Bioscience, Japan) column. After injection, the sample was loaded for 20 min at a constant flow of 3 µL/min with 1% buffer B (0.1% TFA). In 2.5 min, B was increased to 10%, followed by an increase to 35% B in 40 min while concurrently decreasing the flow rate from 3.0 to 2.5 µL/min. Next, B was increased to 80% in 5 min. A total of 12 fractions were collected per enzyme. Each fraction was dried under vacuum and re-suspended in 15 μL of 0.1% TFA for nano-LC–MS/MS analysis. For the other four digests phosphopeptides were enriched by ERLIC-SCX/RP as described by Loroch et al.37,38 In brief, samples were separated using an Ultimate HPLC and a Famos autosampler (LCPackings/Thermo Scientific) equipped with a 4.6 x 100 mm PolyWAX column (5 µm particles, 300 Å pore size, PolyLC) using buffer A: 20 mM methylphosphonic acid, 70% ACN pH 2 and buffer B: 200 mM triethylammonnium phosphate, 60% ACN, pH 2. Peptides were injected in 20 µL buffer A and separated using a 3-step gradient: 100% A for 10 min, a linear increase to 100% B in 10 min and 100% B for 10 min at a flow rate of 1 mL/min. 1 min-fractions were collected from retention time (RT) 2-6 min, 2 minfractions were collected from RT 6-20 min and a 6 min-fraction was collected from RT 20-26 min. All fractions were dried under vacuum. Fractions 1-6 were re-dissolved in 200 µL SCX A (20 mM KH2PO4, 20% ACN, pH 2.7), loaded onto an equilibrated Hypersep POROS SCX 10-200 mL cartridges (Thermo Scientific) and the flowthroughs were collected. Fractions 7-12 were re-dissolved in 0.1% TFA and loaded onto equilibrated Hypersep C18-RP 10-200 µL cartridges. After washing with three column volumes of 0.1% TFA, peptides were eluted using three column volumes 60% ACN. All samples were dried under vacuum, re-constitued in 0.1% TFA and up to 50% of each fraction was subjected to nano-LC-MS/MS (Figure S4).



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 27

In-depth quantitative proteomics using chemical peptide labeling strategies (iTRAQ) Eight 150 µg aliquots of an A431 cell pellet were digested with trypsin and 8 additional aliquots with subtilisin, as described above. All 16 samples were desalted as described for the global proteome comparison. Samples were resuspended in 30 µL of 0.5 M triethylammonium bicarbonate buffer (TEAB), pH 8.5. Labeling was conducted with iTRAQ® 8plex (Sciex, Germany) according to the manufacturer’s instructions. One set of labeling reagents (113-119,121) was used to label the 8 trypsin digests, a second one to label the 8 subtilisin

samples.

Next,

subtilisin

samples

were

pooled

in

a

ratio

of

1:2:3:4:5:6:7:8

(113:114:115:116:117:118:119:121; SI), with the respective amounts of peptide for each label: 7.8 µg (113), 15.6 µg (114), 23.3 µg (115), 31.1 µg (116), 38.9 µg (117), 46. 7 µg (118), 54.4 µg (119) and 62.2 µg (121), to obtain a total of 280 µg. The trypsin samples were multiplexed using the same sample amounts, but in a reversed order of 8:7:6:5:4:3:2:1 (113:114:115:116:117:118:119: 121; TI). Pooled samples were desalted as described above for the global proteome. Approximately 500 ng of each multiplexed sample (SI and TI) were subjected to nano-LC–MS/MS analysis (Figure S5). 250 µg of the multiplexed trypsin (TI) and subtilisin (SI) samples, respectively, were subjected to phosphopeptide enrichment based on TiO2/HILIC as described above. Fractions were dried under vacuum and re-suspended in 15 μL of 0.1% TFA for nano-LC–MS/MS analysis (Figure S5). Finally, a combined (STI) sample was prepared by pooling 30 µg of both enzyme samples (SI and TI). 25 µg of the STI sample were separated by high pH reversed phase chromatography. Fractionation was performed as described above and fractions measured by nano-LC–MS/MS analysis (Figure S5).

LC-MS/MS parameters Online LC separations were performed using Ultimate 3000 RSLCnano systems, equipped with trap columns (100 μm × 2 cm, C18 Acclaim Pepmap viper, and main columns (75 μm × 50 cm, C18 Acclaim Pepmap viper; all Thermo Fisher Scientific). All samples were pre-concentrated in 0.1% TFA for 5 min at a flow rate of 20 µL/min followed by separation on the main column at 250 nL/min using binary buffer (A: 0.1% formic acid; B: 84% acetonitrile, 0.1% formic acid) at 60 °C. Gradients are summarized in Table S3.


Page 9 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


MS parameters “systematic proteome comparison”: MS survey scans were acquired in an Orbitrap Fusion Lumos (Thermo Scientific) from 300 to 1500 m/z at a resolution of 60,000, using an AGC target value of 4 x 105 and a maximum injection time (IT) of 70 ms. Data dependent acquisition (DDA) MS/MS scans were acquired in the Orbitrap at a resolution of 30,000. Precursor ions were selected using an isolation window (IW) of 1.0 m/z, an AGC of 3 x 104 and an IT of 150 ms with TopS (3s) option and a dynamic exclusion (DE) of 60 s. HCD was used for fragmentation with a collision energy (CE) of 31. MS parameters “systematic phosphoroteome comparison”: MS survey scans were acquired in a Q Exactive HF (Thermo Scientific) from 300 to 1500 m/z at a resolution of 60,000, using an AGC target value of 1 x 106, 120 ms IT. MS/MS scans were acquired at a resolution of 15,000. For DDA prescursors were selected with an IW of 2.0 m/z, an AGC of 5 x 104 and an IT of 120 ms considering the top 15 ions (Top15) and a DE of 30 s. HCD CE was set to 27. MS parameters iTRAQ samples (SI,TI and STI): MS survey scans were acquired in a Q Exactive HF as above. MS/MS scans were acquired at a resolution of 15,000. For DDA IW was set to 1.0 m/z (0.4 m/z for STI). AGC was 2 x 105, and IT 200 ms, in Top 15 mode. DE was 30 s, HCD CE was set to 30 (32 for phosphoeptides) (summarized in Table S3).

Database search MS raw files were searched with the help of Proteome Discoverer v2.2 (Thermo Scientific), using Mascot v2.4 against the human Uniprot database (downloaded September 2014, 20,194 target sequences). Fractions were searched using the Multidimensional Protein Identification Technology (MudPIT) option. The following search settings were used: Enzyme specificity was set to ‘trypsin’ with a maximum of 2 missed cleavages or ‘none’ with a maximum of 0 missed cleavages, for samples digested with trypsin or subtilisin, respectively. MS and MS/MS tolerances were set to 10 ppm and 0.02 Da, respectively, and carbamidomethylation of Cys (+57.0215 Da) and oxidation of Met (+15.9949 Da) as fixed and variable modifications. For iTRAQ samples iTRAQ-8plex (+304.2054 Da) of N-termini, Lys (both fixed) and Tyr (variable) were considered. Phosphopeptide enriched samples were additionally searched with phosphorylation (+79.9663 Da) of Ser/Thr/Tyr as variable modification. Phosphorylation site probabilities were determined using ptmRS.39 Only phosphorylation sites with site localization probabilities ≥ 99% were considered. Percolator v3.04840 was used for false discovery rate (FDR) estimation and data were filtered at 0.2 were regarded as proline-rich (roughly accounting for at least 25 % of proline residues within 25 amino acids). Proteins were plotted according to the relative positions of the proline dense regions and proline density was indicated as color gradient (figure 2b). Even the presence of multiple Pro residues did not seem to sterically inhibit proteolytic digestion considerably. We further wondered whether subtilisin may also improve the digestion of membrane proteins, which are typically underrepresented in shotgun proteomics experiments, particularly when comprising multiple hydrophobic transmembrane domains. We therefore looked at a specific set of inner mitochondrial membrane protein subunits involved in the electron transport chain, suggested by Ron Beavis’ Global Proteome Machine44,45 (www.thegpm.org) as reference set to specifically ‘check the level-of-detection for integral membrane proteins’, as they include some of the most hydrophobic proteins in the human proteome. For 9 out of 13 proteins we obtained a significantly higher sequence coverage with subtilisin, whereas only two proteins showed a higher sequence coverage with trypsin (Figure 2c). Nevertheless, the distribution of GRAVY indexes for peptides identified with both enzymes (Figure S10) does not support a general gain in the identification of membrane proteins, although this has been reported for other unspecific enzymes.24,26


Page 13 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Using subtilisin we furthermore detected a proteoform of MCOLN3 – a putative ion channel with six predicted transmembrane domains – which is member of the ‘missing proteins’ for which no MS-based evidence in accordance to the guidelines of the Human Proteome Organization (HUPO) has been provided so far.46-48 To achieve the ‘experimental evidence at protein level’ status, the MS data must be reported at 1% FDR at the protein level and at least 2 unique and non-overlapping peptides of nine or more amino acids in length have to be confidently identified.46,47 We identified MCOLN3 in our subtilisin data with three non-overlapping peptides above 9 amino acids length at 1% FDR at the protein, peptide and PSM level (in accordance with the guidelines and using Proteome Discoverer 2.2; 7,082/20,199 proteins passed, 110,186/549,741 peptides passed, 161,508/685,524 PSM passed, no Mascot score cutoff), meeting the established requirements for the identification of missing proteins: 10

SCSSHEEENRCNFN23, 185CFFVEPDEPFHIG197 and 539LEDDPPVSLF548 (Figure S16).

We furthermore compared the sequence coverage of our data (20) to the PeptideAtlas4 repository (www.peptideatlas.org), which contains more than 1500 datasets. Therefore, we downloaded all 1,222,862 peptide sequences (October 2017) and mapped these to our employed Uniprot human database. Thus, we identified novel sequence parts not yet covered by unique PeptideAtlas peptide entries for 3636 different proteins, yielding a 0.40% increase in the total sequence coverage of the human proteome, compared to 1652 proteins and 0.15% for trypsin.

Figure 2. The broad-specificity protease subtilisin provides access to novel areas of the proteome. (a) Proteins identified in A431 cells with at least one unique peptide at 20; 300 µg per enzyme and replicate), 37% were exclusively found with subtilisin (Figure 3 a, Table S5). To assess the contribution of subtilisin to the toolbox of proteases for in-depth phosphoproteomics we furthermore evaluated with in silico digestions which of the novel subtilisin sites could be theoretically covered by Arg-C, Asp-N, Glu-C, Lys-C and Lys-N. Indeed, 9.2% (925) of all 10,238 subtilisin phosphorylation sites were neither present in the trypsin dataset nor could be theoretically covered by one of these enzymes. Moreover, 19.3% of all subtilisin sites were only covered by one additional enzyme, compared to 1.0% (no other protease) and 7.2% (one protease) for trypsin. For instance for the Histone-lysine N-methyltransferase KMT2D we identified a total of 52 phosphorylation sites, 41 of which exclusively after subtilisin digestion and 25 of those not present in the comprehensive PhosphoSitePlus52 database (October 2017, Figure 3b). This massive increase in phosphoproteome coverage comes along with a considerable enrichment of Pro residues in the proximity of exclusive subtilisin sites (Figure 3c). Among these exclusive subtilisin phosphorylation sites were 890 from 377 transcription factors53, of those 351 from 197 transcription factors were additionally not covered by Arg-C, Asp-N, Glu-C, Lys-C and Lys-N. For ZBTB7B for which we identified 9 sites solely with subtilisin, only 2 of those present in PhosphoSitePlus. In contrast, out of 7 exclusive subtilisin sites in LARP1 none was new. This apparent contradiction may arise from the inclusion of more than 1000 phosphoproteomics studies in PhosphoSitePlus, covering ~300,000 sites identified in different cells and tissues under different conditions54,55. Instead, our data is limited to the use of only 300 µg of protein from non-stimulated HeLa cells per enzyme and replicate.

Figure 3. The broad-specificity protease subtilisin provides access to novel areas of the phosphoproteome. (a) Substantially 39

improved access to the phosphoproteome (>99% localization probability ) with subtilisin. (b) Histone-lysine Nmethyltransferase sequence excerpt showing a region defined as Pro-rich in Uniprot as an example for phosphorylation sites exclusively identified with subtilisin. (c) Significant enrichment of Pro-residues around exclusive subtilisin phosphorylation sites.


Page 15 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Icelogo (p10,000 identified subtilisin phosphorylation sites accessible neither with typsin, Arg-C, Asp-N, Glu-C, Lys-C, nor Lys-N. This strong complementarity renders subtilisin a most interesting alternative for detecting unknown PTM sites in in-depth studies, preferably in conjunction with trypsin as these two enzymes are remarkably complementary (only 33% overlap for more than 14,628 high confidence phosphorylation sites). Thus, subtilisin offers exciting opportunities for qualitative proteomic profiling including a considerably improved coverage of Pro-rich regions of the proteome (~2x more amino acids covered in Pro-rich regions of the proteome than with trypsin). We demonstrate that subtilisin enables reporter ion-based quantification which is highly prone to technical variance, despite its broad specificity and extremely fast digestion. With increasing speed and sensitivity of MS, which allows saving sample and analysis time, subtilisin is a quick, cheap and powerful alternative to improve qualitative and quantitative proteome coverage.

Supporting Information Available Supporting Tables S1-7. Supporting Figures S1-16. This material is available free of charge via the Internet at http://pubs.acs.org.

Acknowledgments This study was supported by the Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen, the Senatsverwaltung für Wirtschaft, Technologie und Forschung des Landes Berlin, and the Bundesministerium für Bildung und Forschung. H.G.J. further thanks the CAPES Foundation for financial support. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD008068.6

Conflict of interest disclosure The authors declare no competing financial interests.


Page 19 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


References (1) Smith, L. M.; Kelleher, N. L.; Consortium for Top Down, P. Nature methods 2013, 10, 186-187. (2) Wilhelm, M.; Schlegl, J.; Hahne, H.; Gholami, A. M.; Lieberenz, M.; Savitski, M. M.; Ziegler, E.; Butzmann, L.; Gessulat, S.; Marx, H.; Mathieson, T.; Lemeer, S.; Schnatbaum, K.; Reimer, U.; Wenschuh, H.; Mollenhauer, M.; Slotta-Huspenina, J.; Boese, J. H.; Bantscheff, M.; Gerstmair, A., et al. Nature 2014, 509, 582-587. (3) Kim, M. S.; Pinto, S. M.; Getnet, D.; Nirujogi, R. S.; Manda, S. S.; Chaerkady, R.; Madugundu, A. K.; Kelkar, D. S.; Isserlin, R.; Jain, S.; Thomas, J. K.; Muthusamy, B.; Leal-Rojas, P.; Kumar, P.; Sahasrabuddhe, N. A.; Balakrishnan, L.; Advani, J.; George, B.; Renuse, S.; Selvan, L. D., et al. Nature 2014, 509, 575-581. (4) Desiere, F.; Deutsch, E. W.; King, N. L.; Nesvizhskii, A. I.; Mallick, P.; Eng, J.; Chen, S.; Eddes, J.; Loevenich, S. N.; Aebersold, R. Nucleic acids research 2006, 34, D655-658. (5) Jones, P.; Cote, R. G.; Martens, L.; Quinn, A. F.; Taylor, C. F.; Derache, W.; Hermjakob, H.; Apweiler, R. Nucleic acids research 2006, 34, D659-663. (6) Vizcaino, J. A.; Deutsch, E. W.; Wang, R.; Csordas, A.; Reisinger, F.; Rios, D.; Dianes, J. A.; Sun, Z.; Farrah, T.; Bandeira, N.; Binz, P. A.; Xenarios, I.; Eisenacher, M.; Mayer, G.; Gatto, L.; Campos, A.; Chalkley, R. J.; Kraus, H. J.; Albar, J. P.; Martinez-Bartolome, S., et al. Nature biotechnology 2014, 32, 223226. (7) Olsen, J. V.; Ong, S. E.; Mann, M. Molecular & cellular proteomics : MCP 2004, 3, 608-614. (8) Brownridge, P.; Beynon, R. J. Methods 2011, 54, 351-360. (9) Burkhart, J. M.; Schumbrutzki, C.; Wortelkamp, S.; Sickmann, A.; Zahedi, R. P. Journal of proteomics 2012, 75, 1454-1462. (10) Dickhut, C.; Feldmann, I.; Lambert, J.; Zahedi, R. P. Journal of proteome research 2014, 13, 27612770. (11) Benore-Parsons, M.; Seidah, N. G.; Wennogle, L. P. Archives of biochemistry and biophysics 1989, 272, 274-280. (12) Thiede, B.; Lamer, S.; Mattow, J.; Siejak, F.; Dimmler, C.; Rudel, T.; Jungblut, P. R. Rapid communications in mass spectrometry : RCM 2000, 14, 496-502. (13) Hamady, M.; Cheung, T. H.; Tufo, H.; Knight, R. IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society 2005, 24, 58-66. (14) Vandermarliere, E.; Mueller, M.; Martens, L. Mass spectrometry reviews 2013, 32, 453-465. (15) Giansanti, P.; Tsiatsiani, L.; Low, T. Y.; Heck, A. J. Nature protocols 2016, 11, 993-1006. (16) Giansanti, P.; Aye, T. T.; van den Toorn, H.; Peng, M.; van Breukelen, B.; Heck, A. J. Cell reports 2015, 11, 1834-1843. (17) Golghalyani, V.; Neupartl, M.; Wittig, I.; Bahr, U.; Karas, M. Journal of proteome research 2017, 16, 978-987. (18) Gauci, S.; Helbig, A. O.; Slijper, M.; Krijgsveld, J.; Heck, A. J.; Mohammed, S. Analytical chemistry 2009, 81, 4493-4501. (19) Guo, X.; Trudgian, D. C.; Lemoff, A.; Yadavalli, S.; Mirzaei, H. Molecular & cellular proteomics : MCP 2014, 13, 1573-1584. (20) Huesgen, P. F.; Lange, P. F.; Rogers, L. D.; Solis, N.; Eckhard, U.; Kleifeld, O.; Goulas, T.; Gomis-Ruth, F. X.; Overall, C. M. Nature methods 2015, 12, 55-58. (21) Lu, S.; Fan, S. B.; Yang, B.; Li, Y. X.; Meng, J. M.; Wu, L.; Li, P.; Zhang, K.; Zhang, M. J.; Fu, Y.; Luo, J.; Sun, R. X.; He, S. M.; Dong, M. Q. Nature methods 2015, 12, 329-331. (22) Polzien, L.; Baljuls, A.; Rennefahrt, U. E.; Fischer, A.; Schmitz, W.; Zahedi, R. P.; Sickmann, A.; Metz, R.; Albert, S.; Benz, R.; Hekman, M.; Rapp, U. R. The Journal of biological chemistry 2009, 284, 2800428020.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 27

(23) MacCoss, M. J.; McDonald, W. H.; Saraf, A.; Sadygov, R.; Clark, J. M.; Tasto, J. J.; Gould, K. L.; Wolters, D.; Washburn, M.; Weiss, A.; Clark, J. I.; Yates, J. R., 3rd. Proceedings of the National Academy of Sciences of the United States of America 2002, 99, 7900-7905. (24) Rietschel, B.; Arrey, T. N.; Meyer, B.; Bornemann, S.; Schuerken, M.; Karas, M.; Poetsch, A. Molecular & cellular proteomics : MCP 2009, 8, 1029-1043. (25) Wang, B.; Malik, R.; Nigg, E. A.; Korner, R. Analytical chemistry 2008, 80, 9526-9533. (26) Wu, C. C.; Yates, J. R., 3rd. Nature biotechnology 2003, 21, 262-267. (27) Zybailov, B. L.; Florens, L.; Washburn, M. P. Molecular bioSystems 2007, 3, 354-360. (28) Schlosser, A.; Vanselow, J. T.; Kramer, A. Analytical chemistry 2005, 77, 5243-5250. (29) Engel, J.; Richters, A.; Getlik, M.; Tomassi, S.; Keul, M.; Termathe, M.; Lategahn, J.; Becker, C.; Mayer-Wrangowski, S.; Grutter, C.; Uhlenbrock, N.; Krull, J.; Schaumann, N.; Eppmann, S.; Kibies, P.; Hoffgaard, F.; Heil, J.; Menninger, S.; Ortiz-Cuaran, S.; Heuckmann, J. M., et al. Journal of medicinal chemistry 2015, 58, 6844-6863. (30) Frese, C. K.; Altelaar, A. F.; van den Toorn, H.; Nolting, D.; Griep-Raming, J.; Heck, A. J.; Mohammed, S. Analytical chemistry 2012, 84, 9668-9673. (31) Mommen, G. P.; Frese, C. K.; Meiring, H. D.; van Gaans-van den Brink, J.; de Jong, A. P.; van Els, C. A.; Heck, A. J. Proceedings of the National Academy of Sciences of the United States of America 2014, 111, 4507-4512. (32) Pichler, P.; Kocher, T.; Holzmann, J.; Mohring, T.; Ammerer, G.; Mechtler, K. Analytical chemistry 2011, 83, 1469-1474. (33) Kocher, T.; Pichler, P.; Schutzbier, M.; Stingl, C.; Kaul, A.; Teucher, N.; Hasenfuss, G.; Penninger, J. M.; Mechtler, K. Journal of proteome research 2009, 8, 4743-4752. (34) Yu, P.; Petzoldt, S.; Wilhelm, M.; Zolg, D. P.; Zheng, R.; Sun, X.; Liu, X.; Schneider, G.; Huhmer, A.; Kuster, B. Analytical chemistry 2017, 89, 8884-8891. (35) Engholm-Keller, K.; Birck, P.; Storling, J.; Pociot, F.; Mandrup-Poulsen, T.; Larsen, M. R. Journal of proteomics 2012, 75, 5749-5761. (36) Rappsilber, J.; Mann, M.; Ishihama, Y. Nature protocols 2007, 2, 1896-1906. (37) Loroch, S.; Zahedi, R. P.; Sickmann, A. Analytical chemistry 2015, 87, 1596-1604. (38) Loroch, S.; Schommartz, T.; Brune, W.; Zahedi, R. P.; Sickmann, A. Biochimica et biophysica acta 2015, 1854, 460-468. (39) Taus, T.; Kocher, T.; Pichler, P.; Paschke, C.; Schmidt, A.; Henrich, C.; Mechtler, K. Journal of proteome research 2011, 10, 5354-5362. (40) Kall, L.; Canterbury, J. D.; Weston, J.; Noble, W. S.; MacCoss, M. J. Nature methods 2007, 4, 923-925. (41) Dorfer, V.; Pichler, P.; Stranzl, T.; Stadlmann, J.; Taus, T.; Winkler, S.; Mechtler, K. Journal of proteome research 2014, 13, 3679-3684. (42) Moruz, L.; Hoopmann, M. R.; Rosenlund, M.; Granholm, V.; Moritz, R. L.; Kall, L. Journal of proteome research 2013, 12, 5730-5741. (43) Kyte, J.; Doolittle, R. F. Journal of molecular biology 1982, 157, 105-132. (44) Fenyo, D.; Eriksson, J.; Beavis, R. Methods in molecular biology 2010, 673, 189-202. (45) The Global Proteome Machine, P. d. a., reuse and validation for biological and biomedical research. http://www.thegpm.org/. 2017. (46) Baker, M. S.; Ahn, S. B.; Mohamedali, A.; Islam, M. T.; Cantor, D.; Verhaert, P. D.; Fanayan, S.; Sharma, S.; Nice, E. C.; Connor, M.; Ranganathan, S. Nature communications 2017, 8, 14271. (47) Omenn, G. S.; Lane, L.; Lundberg, E. K.; Beavis, R. C.; Nesvizhskii, A. I.; Deutsch, E. W. Journal of proteome research 2015, 14, 3452-3460. (48) Gaudet, P.; Michel, P. A.; Zahn-Zabal, M.; Britan, A.; Cusin, I.; Domagalski, M.; Duek, P. D.; Gateau, A.; Gleizes, A.; Hinard, V.; Rech de Laval, V.; Lin, J.; Nikitin, F.; Schaeffer, M.; Teixeira, D.; Lane, L.; Bairoch, A. Nucleic acids research 2017, 45, D177-D182.


Page 21 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(49) Pinkse, M. W.; Uitto, P. M.; Hilhorst, M. J.; Ooms, B.; Heck, A. J. Analytical chemistry 2004, 76, 39353943. (50) Thingholm, T. E.; Jorgensen, T. J.; Jensen, O. N.; Larsen, M. R. Nature protocols 2006, 1, 1929-1935. (51) Alpert, A. J. Analytical chemistry 2008, 80, 62-76. (52) Hornbeck, P. V.; Zhang, B.; Murray, B.; Kornhauser, J. M.; Latham, V.; Skrzypek, E. Nucleic acids research 2015, 43, D512-520. (53) Vaquerizas, J. M.; Kummerfeld, S. K.; Teichmann, S. A.; Luscombe, N. M. Nature reviews. Genetics 2009, 10, 252-263. (54) Sharma, K.; D'Souza, R. C.; Tyanova, S.; Schaab, C.; Wisniewski, J. R.; Cox, J.; Mann, M. Cell reports 2014, 8, 1583-1594. (55) Batth, T. S.; Francavilla, C.; Olsen, J. V. Journal of proteome research 2014, 13, 6176-6186. (56) Ting, L.; Rad, R.; Gygi, S. P.; Haas, W. Nature methods 2011, 8, 937-940. (57) Savitski, M. M.; Mathieson, T.; Zinn, N.; Sweetman, G.; Doce, C.; Becher, I.; Pachl, F.; Kuster, B.; Bantscheff, M. Journal of proteome research 2013, 12, 3586-3598. (58) Erickson, B. K.; Jedrychowski, M. P.; McAlister, G. C.; Everley, R. A.; Kunz, R.; Gygi, S. P. Analytical chemistry 2015, 87, 1241-1249. (59) Kim, J. S.; Monroe, M. E.; Camp, D. G., 2nd; Smith, R. D.; Qian, W. J. Journal of proteome research 2013, 12, 910-916. (60) Zhang, Y.; Fonslow, B. R.; Shan, B.; Baek, M. C.; Yates, J. R., 3rd. Chemical reviews 2013, 113, 23432394. (61) Geyer, P. E.; Kulak, N. A.; Pichler, G.; Holdt, L. M.; Teupser, D.; Mann, M. Cell systems 2016, 2, 185195.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

For TOC only


Page 22 of 27

Page 23 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1 57x39mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60



Page 24 of 27

Page 25 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60





1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 4 149x125mm (300 x 300 DPI)


Page 26 of 27

Page 27 of 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


TOC 165x136mm (300 x 300 DPI)


(phospho)proteome regions with the broad-specificity protease

Recommend Documents