Reversible Lysine Derivatization Enables Improved Arg-C (iArg-C

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Reversible Lysine Derivatization Enables Improved Arg-C (iArgC) Digestion, a Highly Specific Arg-C Digestion Using Trypsin Zhen Wu, Jichang Huang, Jianan Lu, and Xumin Zhang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04410 • Publication Date (Web): 20 Dec 2017 Downloaded from http://pubs.acs.org on December 22, 2017

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Analytical Chemistry

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Reversible Lysine Derivatization Enables Improved Arg-C (iArg-C)

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Digestion, a Highly Specific Arg-C Digestion Using Trypsin

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Zhen Wu1, Jichang Huang1, Jianan Lu1, Xumin Zhang*,1

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1

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of Life Sciences, Fudan University, Shanghai 200438, China

State Key Laboratory of Genetic Engineering, Department of Biochemistry, School

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Keywords:

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Citraconylation / proteomics / LC-MS/MS / Arg-C / Lys-C / trypsin

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*Corresponding author:

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Dr. Xumin Zhang

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E-mail: [email protected]

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Tel: +86 21 51630575

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Abstract

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Bottom-up proteomics approach has become an important strategy in diverse areas of

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biological research, and the enzymatic digestion is essential for this technology.

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Endopeptidase Arg-C catalyzing the hydrolytic cleavage of peptide bonds C-terminal

5

to arginine could be an important protease in bottom-up proteomics. However, it has

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been seldom applied due to its low specificity and high cost. In this report, the

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reversible amine derivatization method (citraconylation and decitraconylation) was

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introduced and optimized towards a real Arg-C digestion using trypsin. Combination

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of the reversible derivatization and trypsin digestion (termed iArg-C digestion for

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improved Arg-C digestion) resulted in 64.2% more peptide identification (11,925 ±

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199 vs 7,262 ± 59) and significantly higher cleavage specificity (95.6% vs 73.6%)

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than the conventional Arg-C digestion. Comparison of iArg-C digestion with the

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widely used trypsin and Lys-C digestion revealed that iArg-C performed slightly

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better than Lys-C although not comparable to trypsin. Therefore, the well-established

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iArg-C digestion method is a promising approach for proteomics studies and could be

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used as the prior alternative digestion method to trypsin digestion in order to achieve

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higher proteome coverage. Data are available via ProteomeXchange with identifier

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PXD007994.

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Analytical Chemistry

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Introduction

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During the latest two decades, proteomics has become the core technology for

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high-throughput protein characterization and quantification. However, unlike

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genomics studies, proteomics studies often suffer from the low coverage at both

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protein and amino acid level.1

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In bottom-up proteomics analysis, samples are enzymatically digested, subsequently

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fractionated and analyzed by liquid chromatography coupled with tandem mass

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spectrometry (LC-MS/MS).2 Trypsin is always the most frequently used protease in

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bottom-up proteomics studies because it results in MS-favored proteolytic peptides

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with high specificity and reasonable cost.3-5 It was revealed that the priority of the

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most used proteases in proteomics studies is: trypsin > Lys-C > chymotrypsin >

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Glu-C > pepsin, and 96% studies utilized trypsin for protein digestion.4 However,

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trypsin also has some limitations. E.g., more than half of tryptic peptides are too small

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(≤ 6 residues) for MS identification, and thus trypsin alone cannot achieve high

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sequence coverage.6

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Multi-enzyme digestion approaches have been developed to improve proteome

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coverage.7-10 Lys-C cleaves peptide bonds C-terminal to Lys,11,12 whereas Arg-C

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cleaves peptide bonds C-terminal to Arg.13,14 Since these two proteases produce

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peptides possessing basic residues at C-termini, they could get more chance to be used

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as alternatives to trypsin. In addition, both proteases produced longer and

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higher-charged peptides than trypsin, which are favored by ETD analysis and

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middle-down proteomics.15 However, Arg-C gained rather fewer applications than

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Lys-C due to the unsatisfactory specificity and the high cost (> 30 times more

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expensive than trypsin).5,14 Amine derivatization approaches were adopted to

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Arg-C-like digestion utilizing trypsin and the most used strategies are irreversible,

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including dimethylation,16,17 propionylation18-20 and acetylation21,22.

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A special reversible amine derivatization approach has been reported using

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citraconylation and decitraconylation.23-25 The citraconyl groups can react with

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amines at high pH (pH > 8) and the reaction can be reversed at low pH (pH < 4). In

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proteomics application, proteins are first citraconylated and digested using trypsin,

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and then the peptide solution is adjusted to acidic pH to remove the citraconyl groups

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(Figure 1). By this mean, trypsin works as exactly as Arg-C and Lys remains its

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original form, facilitating the studies on Lys modifications, ETD analysis,

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middle-down proteomics and also the consecutive proteolytic digestion similar to the

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method described by Wisńiewski et al.26 The reversible derivatization had been tested

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by a few groups.27,28 However, these studies focused on a single protein using

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matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) analysis,

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and the reaction conditions varied greatly (10 µL/mL solution vs 3.0 g/g protein), one

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even using Tris-HCl buffer,27 which is obviously not compatible with amine

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derivatization reaction. Therefore, it lacks a systematic investigation on the reaction,

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especially for proteome-scale studies.

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In this study, we systematically optimized the reversible derivatization method and

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evaluated iArg-C digestion in large-scale proteomics studies. We revealed that iArg-C

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digestion performs much better than conventional Arg-C digestion and slightly better

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than Lys-C digestion. We concluded that iArg-C digestion can be applied as the prior

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alternative digestion method to the most used trypsin digestion.

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Analytical Chemistry

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EXPERIMENTAL SECTION

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Materials and Chemicals

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Microcon YM-10 (10-kDa cutoff) was purchased from Merck-Millipore (Bedford,

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MA). Dithiothreitol (DTT), acrylamide, triethylammonium bicarbonate (TEAB),

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guanidine hydrochloride, citraconic anhydride and trifluoroacetic acid (TFA) were

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purchased from Sigma-Aldrich (St. Louis, MO, USA). Sodium hydroxide was from

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Sinopharm shares (Shanghai, China). Mass spectrometry grade trypsin was obtained

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from Promega (cat. no. V528A) (Madison, WI), Lys-C MS grade was from Wako

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Chemicals (cat. no. 129-02541) (Osaka, Japan) and Arg-C was obtained from Roche

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(cat. no. 11.370.529.001) (Indianapolis, IN). All other reagents and solvents were used

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without further purification.

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Citraconylation reaction

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After reduction and alkylation, E. coli or HeLa cell proteins in lysis buffer (4 M

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guanidine hydrochloride and 100 mM TEAB, pH 8.7) were submitted to

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citraconylation. Citraconic anhydride and NaOH were added five times to the solution

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to accomplish the citraconylation reaction. E.g., to reach a final concentration of 200

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mM citraconic anhydride, 5 µL of 2M citraconic anhydride (dissolved in acetonitrile

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(ACN)) was added to 200 µL protein sample and immediately followed by the

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addition of 5 µL 4M NaOH, then the solution was kept at 25 °C in a Thermomixer

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with a vortex of 800 rpm for 10 min. The procedure was repeated for another four

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times. Finally the solution was kept for 1 h to accomplish citraconylation reaction.

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Digestion

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The FASP method was adapted for digestion in Microcon YM-10 filters.29 After

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three-time buffer displacement with digestion buffer (100 mM TEAB, pH 8.0),

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digestion was carried out at 37 °C for 12 h using trypsin (enzyme/protein as 1:50).

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After digestion, the solution was filtrated out and the filter was washed twice with 10%

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ACN, and all filtrates were pooled and vacuum-dried to remove ACN.

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Decitraconylation reaction

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TFA was added to reach a final concentration of 1% (~pH 2.0) and the acidified

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solution was kept at room temperature for 2 h to accomplish decitraconylation

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reaction.

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Nanoflow LC-ESI-MS/MS

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LC-ESI-MS/MS analysis was performed using a nanoflow EASY-nLC 1000 system

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coupled to an LTQ Orbitrap Elite mass spectrometer. A two-column system was

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adopted for all analyses. Samples were first loaded onto an Acclaim PepMap100 C18

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Nano Trap Column (5 µm, 100 Å, 100 µm i.d. × 2 cm, (Thermo Fisher Scientific,

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Sunnyvale, CA)) and then analyzed on an Acclaim PepMap RSLC C18 column (2 µm,

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100 Å, 75 µm i.d. × 25 cm (Thermo Fisher Scientific, Sunnyvale, CA)). The mobile

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phases consisted of Solvent A (0.1% formic acid) and Solvent B (0.1% formic acid in

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ACN). The peptides were eluted using the following gradients: 2-5% B in 3 min, 5-28%

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B in 160 min, 28-35% B in 5 min, 35-90% B in 2 min and 90% B for 10 min at a flow

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rate of 200 nL/min. Data-dependent analysis was employed in MS analysis: the 15

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most abundant ions in each MS scan were automatically selected and fragmented in

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HCD mode. All experiments were carried out in duplicate.

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Data Analysis

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The raw data were analyzed by Proteome Discoverer (version 1.4, Thermo Fisher

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Scientific) using an in-house Mascot server (version 2.3, Matrix Science, London,

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UK).30 E. coli protein database (20161228, 4,304 sequences) and Human protein

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database (20160213, 20,186 sequences) were downloaded from UniProt. Data were

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searched using the following parameters: up to two missed cleavage sites were

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allowed; 10 ppm mass tolerance for MS and 0.05 Da for MS/MS fragment ions;

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propionamidation on cysteine as fixed modifications; oxidation on methionine as

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variable modifications. Additional enzyme-specific parameters were as follows: for

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citraconylated samples, Arg-C/P as the enzyme and protein N-terminal citraconylation,

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citraconylation on lysine as variable modifications; for Arg-C, Lys-C or trypsin

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Analytical Chemistry

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digested samples, Arg-C/P, Lys-C/P or trypsin/P as the enzyme. For the analysis of

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wrong cleavage sites, the enzymes were changed to the corresponding semi-enzyme.

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The incorporated Target Decoy PSM Validator in Proteome Discoverer and the

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mascot expectation value was used to validate the search results and only the hits with

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FDR ≤ 0.01 and MASCOT expected value ≤ 0.05 were accepted for discussion. The

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mass spectrometry proteomics data have been deposited to the ProteomeXchange

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Consortium via the PRoteomics IDEntifications (PRIDE) partner repository with the

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dataset identifier PXD007994.31,32

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RESULTS AND DISCUSSION

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Optimization of citraconylation and decitraconylation reactions

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To determine the favored concentration of citraconic anhydride, E.coli proteins were

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derivatized by citraconic anhydride with different concentrations: 0, 100, 200 and 400

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mM.

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It is inappropriate to judge the derivatization efficiency by the percentage of identified

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citraconylated Lys since citraconyl groups are probably lost during LC-MS/MS

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analysis (~pH 2.7). Considering that the citraconylated Lys cannot be cleaved by

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trypsin, the number of K-end peptides in well-derivatized sample should be far lower

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than in underivatized sample. Therefore, the percentage of K-end peptides was used to

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evaluate the derivatization efficiency.

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Data were searched using trypsin as enzyme with four missed cleavage sites (higher

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than four missed cleavage did not lead to more identified peptides). As summarized in

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Figure 2A, 200 and 400 mM citraconic anhydride resulted in 1.4% and 0.9% K-end

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peptides, respectively, much lower than that in underivatized sample (42.5%),

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indicating that high citraconylation efficiency can be achieved when citraconic

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anhydride concentration is not lower than 200 mM.

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The identification capacity of desired peptides was also examined. For this purpose,

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underivatized sample was searched using trypsin as enzyme and derivatized samples

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were searched using Arg-C as enzyme; and for all samples up to two missed cleavage

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sites were allowed. As shown in Figure 2B, 200 mM citraconic anhydride resulted in

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the highest identification number among the derivatized samples. Therefore, 200 mM

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citraconic anhydride was chosen for derivatization.

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It is well known that the citraconyl groups could be completely removed at acidic pH.

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After digestion, TFA was added to the solution to reach a final concentration of 1%

26

(v/v). The acidified solution was incubated at different temperatures (25 and 37 °C)

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for different time scales (1 and 2 h). Figure 2B illustrates the removal efficiency.

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Clearly all conditions worked very well and there was no significant difference. To 8

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Analytical Chemistry

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avoid unexpected side-effect at high temperature, we decided to choose 25 °C and 2 h

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as the decitraconylation condition. All identification results can be found in Table S-1.

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Reactions of citraconylation on Ser, Thr and Trp were also evaluated. About 2.0%, 1.5%

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and 0.2% peptides were identified with citraconylated Ser, Thr and Trp, respectively,

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with two orders of magnitude smaller than corresponding unmodified peptides in

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terms of peak area. Therefore, these side reactions would also be reversible under the

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current experimental conditions and the effects are negligible.

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Using the optimized conditions, we employed iArg-C digestion using HeLa cell

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proteins. Similar to that from E. coli sample, the percentages of K-end peptides and

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citraconylated Lys were 0.7% and 1.3%, respectively, suggesting that the reaction

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conditions worked well regardless of sample origins. HeLa cell proteins were used for

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the following experiments. Detailed iArg-C digestion protocol can be found in SI.

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iArg-C performs better than Arg-C in proteomics analysis

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Subsequently we carried out a systematic comparison of iArg-C and Arg-C for

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proteomics analysis. The identification results can be found in Table S-2. We

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evaluated the digestion performance on the basis of three aspects: identification

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capacity, digestion efficiency and specificity.

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As shown in Figure 3A and 3B, it is evident that iArg-C digestion performs much

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better in identification capacity and cleavage specificity. iArg-C digestion identified

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11,925 ± 199 peptides corresponding to 2,779 ± 36 proteins, about 64.2% more

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peptides and 19.9% more proteins than Arg-C digestion. Moreover, iArg-C digestion

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led to very low wrong cleavage rate (4.4%) due to the high specificity of trypsin, and

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the superior specificity would be highly beneficial for the high confidence in

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large-scale identification. While Arg-C digestion led to a rather high wrong cleavage

25

rate (26.4%).

26

In terms of digestion efficiency, iArg-C digestion resulted in 93.3% completely

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digested peptides (zero missed cleavage site), lower than 96.9% by Arg-C digestion. It

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seems that Arg-C has better cleavage ability than trypsin, however, iArg-C still 9

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resulted in considerably more completely digested peptides than Arg-C (11,129 ± 189

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vs 7,038 ± 37).

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Therefore, taking advantage of reversible amine derivatization and high specificity of

4

trypsin, iArg-C demonstrated much higher specificity and resulted in considerably

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more peptide/protein identification than Arg-C.

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iArg-C performs slightly better than Lys-C in proteomics analysis

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Next we compared iArg-C with two most used proteases: trypsin and Lys-C (Table

8

S-3). Figure 4A depicts the identification performance of the three proteases. Lys-C

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identified 11,312 ± 30 peptides corresponding to 2,652 ± 14 proteins, while trypsin

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identified 19,656 ± 141 peptides corresponding to 3,129 ± 35 proteins. Trypsin as

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expected outperformed the other two proteases, and iArg-C identified slightly more

12

peptides and proteins than Lys-C.

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Figure 4B shows the digestion efficiencies of different proteases. Lys-C and trypsin

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resulted in 96.5% and 81.1% completely digested peptides, respectively. The lower

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digestion efficiency of trypsin is ascribed to its low cleavage ability on Lys.33,34 It was

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revealed that 76.2% of all missed cleavage sites are contributed by Lys.

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Proteases other than trypsin are often used to acquire results complementary to that

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from trypsin digestion in order to increase proteome coverage. As illustrated in Figure

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4C, the identified protein number and sequenced amino acids increase as more

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proteases are included. Averagely iArg-C identified 416 proteins, which were not

21

identified by trypsin, while Lys-C identified 307 proteins. Combination of three

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proteases together led to identification of total 3,758 ± 4 proteins and 427,256 ± 380

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amino acids, 20.1% and 60.5% more than trypsin alone.

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Figure 4D shows the wrong cleavage rates of different proteases. Trypsin again

25

outperformed iArg-C and Lys-C. Although iArg-C digestion also utilizes trypsin, its

26

wrong cleavage rate is about twice as much as that in trypsin digestion (4.4% vs 2.1%)

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because it has only half desired cleavage sites of trypsin. Lys-C performed the worst

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with 8.6% wrong cleavage rate. 10

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Analytical Chemistry

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The reproducibility of different digestion approaches was also examined. 68%, 60%,

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66% and 61% peptides could be identified by both experiments using iArg-C, Arg-C,

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Lys-C

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reproducibility of iArg-C method. Among the co-identified peptides, 86%, 80%, 78%

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and 85% were found with relative standard deviation (RSD) < 20% in terms of peak

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area for iArg-C, Arg-C, Lys-C and trypsin digestion approaches, respectively.

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Therefore, iArg-C approach resulted in comparable reproducibility with trypsin in

8

both qualitative and quantitative proteomics studies.

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Taken together, iArg-C performs slightly better than Lys-C in terms of identification

and

trypsin,

respectively,

indicating

the

acceptable

identification

10

capacity, complementarity to trypsin and digestion specificity.

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Cleavage specificity of different proteases

12

The specificity of iArg-C, Arg-C, Lys-C and trypsin were further investigated (Figure

13

5). As expected, iArg-C and trypsin demonstrated a very similar amino acid

14

distribution with the exception of Lys. In iArg-C results, Lys ranked third after Phe

15

and Asn, accounting for 11% of total wrong cleavage sites, and it ranked sixth after

16

Phe, His, Tyr, Asn and Met when the background was considered. Considering the

17

K-end peptides are more prone to MS identification, its portion could be far lower

18

than observed. These observations certainly confirm the high efficiency of

19

citraconylation reaction.

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In Lys-C digestion, Arg contributed for the majority of the wrong cleavage sites,

21

indicating a certain cleavage ability of Lys-C at Arg residues (Figure 5C). Similar

22

situation was observed for Lys in Arg-C digestion (Figure 5D).

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CONCLUSIONS

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In this report, we systematically optimized the reversible amine derivatization method,

3

citraconylation and decitraconylation, and successfully applied it to iArg-C digestion,

4

a real high-specificity and low-cost Arg-C digestion using trypsin. Although not

5

comparable to trypsin, iArg-C performed slightly better in all tested aspects when

6

compared with Lys-C, the second most used protease, and much better than the

7

conventional Arg-C digestion. Therefore, iArg-C could be used as the prior alternative

8

digestion method to trypsin digestion. In addition, it could get more chances in studies

9

on Lys modifications, ETD analysis, middle-down proteomics and also the

10

consecutive proteolytic digestion.

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The well-developed iArg-C digestion offers researchers a new digestion possibility

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and would become an important component in our digestion toolbox for different

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proteomics studies.

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Analytical Chemistry

1

ASSOCIATED CONTENT

2

Supporting Information

3

iArg-C digestion protocol (PDF)

4

Tables of detailed identification results (XLSX)

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AUTHER INFORMATION

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Corresponding Author

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*E-mail: [email protected]. Tel.: +86 21 5163 0575.

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ORCID

9

Xumin Zhang: 0000-0002-2810-6363

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Notes

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The authors declare no competing financial interest.

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ACKNOWLEDGEMENTS

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This work was supported by National Natural Science Foundation of China

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(31470806), the starting funding for Xumin Zhang from Fudan University and the

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Research Fund of the State Key Laboratory of Genetic Engineering, Fudan

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University.

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REFERENCES (1) Gstaiger, M.; Aebersold, R. Nat. Rev. Genet. 2009, 10, 617-627. (2) Zhang, Y. Y.; Fonslow, B. R.; Shan, B.; Baek, M. C.; Yates, J. R. Chem. Rev. 2013, 113, 2343-2394. (3) Olsen, J. V.; Ong, S. E.; Mann, M. Mol. Cell. Proteomics 2004, 3, 608-614. (4) Tsiatsiani, L.; Heck, A. J. FEBS J. 2015, 282, 2612-2626. (5) Giansanti, P.; Tsiatsiani, L.; Low, T. Y.; Heck, A. J. R. Nat. Protoc. 2016, 11, 993-1006. (6) Swaney, D. L.; Wenger, C. D.; Coon, J. J. J. Proteome Res. 2010, 9, 1323-1329. (7) Choudhary, G.; Wu, S. L.; Shieh, P.; Hancock, W. S. J. Proteome Res. 2003, 2, 59-67. (8) Biringer, R. G.; Amato, H.; Harrington, M. G.; Fonteh, A. N.; Riggins, J. N.; Hühmer, A. F. Brief Funct. Genomic Proteomic 2006, 5, 144-153. (9) Guo, X. F.; Trudgian, D. C.; Lemoff, A.; Yadavalli, S.; Mirzaei, H. Mol. Cell. Proteomics 2014, 13, 1573-1584. (10) Wisniewski, J. R. Anal. Chem. 2016, 88, 5438-5443. (11) Jekel, P. A.; Weijer, W. J.; Beintema, J. J. Anal. Biochem. 1983, 134, 347-354. (12) Raijmakers, R.; Neerincx, P.; Mohammed, S.; Heck, A. J. R. Chem. Commun. 2010, 46, 8827-8829. (13) Mitchell, W. M.; Harrington, W. F. J. Biol. Chem. 1968, 243, 4683-4692. (14) Krueger, R. J.; Hobbs, T. R.; Mihal, K. A.; Tehrani, J.; Zeece, M. G. J. Chromatogr. 1991, 543, 451-461. (15) Molina, H.; Horn, D. M.; Tang, N.; Mathivanan, S.; Pandey, A. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 2199-2204. (16) Hsu, J. L.; Huang, S. Y.; Chow, N. H.; Chen, S. H. Anal. Chem. 2003, 75, 6843-6852. (17) Boersema, P. J.; Raijmakers, R.; Lemeer, S.; Mohammed, S.; Heck, A. J. Nat. Protoc. 2009, 4, 484-494. (18) Garcia, B. A.; Mollah, S.; Ueberheide, B. M.; Busby, S. A.; Muratore, T. L.; Shabanowitz, J.; Hunt, D. F. Nat. Protoc. 2007, 2, 933-938. (19) Sidoli, S.; Yuan, Z. F.; Lin, S.; Karch, K.; Wang, X. S.; Bhanu, N.; Arnaudo, A. M.; Britton, L. M.; Cao, X. J.; Gonzales-Cope, M.; Han, Y. M.; Liu, S. C.; Molden, R. C.; Wein, S.; Afjehi-Sadat, L.; Garcia, B. A. Proteomics 2015, 15, 1459-1469. (20) Golghalyani, V.; Neupartl, M.; Wittig, I.; Bahr, U.; Karas, M. J. Proteome Res. 2017, 16, 978-987. (21) Choudhary, C.; Kumar, C.; Gnad, F.; Nielsen, M. L.; Rehman, M.; Walther, T. C.; Olsen, J. V.; Mann, M. Science 2009, 325, 834-840. (22) Baeza, J.; Dowell, J. A.; Smallegan, M. J.; Fan, J.; Amador-Noguez, D.; Khan, Z.; Denu, J. M. J. Biol. Chem. 2014, 289, 21326-21338. (23) Dixon, H. B. F.; Perham, R. N. Biochem. J. 1968, 109, 312-314. (24) Habeeb, A. F. S. A.; Atassi, M. Z. Biochemistry 1970, 9, 4939-4944. (25) Shetty, J. K.; Kinsella, J. E. Biochem. J. 1980, 191, 269-272. (26) Wisniewski, J. R.; Mann, M. Anal. Chem. 2012, 84, 2631-2637. (27) Kadlcik, V.; Strohalm, M.; Kodicek, M. Biochem. Bioph. Res. Co. 2003, 305, 1091-1093. (28) Son, Y. J.; Kim, C. K.; Kim, Y. B.; Kweon, D. H.; Park, Y. C.; Seo, J. H. Biotechnol. Progr. 2009, 25, 1064-1070. (29) Wisniewski, J. R.; Zougman, A.; Nagaraj, N.; Mann, M. Nat. Methods 2009, 6, 359-360. (30) Perkins, D. N.; Pappin, D. J. C.; Creasy, D. M.; Cottrell, J. S. Electrophoresis 1999, 20, 3551-3567. (31) Vizcaino, J. A.; Deutsch, E. W.; Wang, R.; Csordas, A.; Reisinger, F.; Rios, D.; Dianes, J. A.; Sun, Z.; 14

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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. Nat Biotechnol 2014, 32, 223-226. (32) Vizcaino, J. A.; Csordas, A.; del-Toro, N.; Dianes, J. A.; Griss, J.; Lavidas, I.; Mayer, G.; Perez-Riverol, Y.; Reisinger, F.; Ternent, T.; Xu, Q. W.; Wang, R.; Hermjakob, H. Nucleic Acids Res. 2016, 44, D447-D456. (33) Glatter, T.; Ludwig, C.; Ahrne, E.; Aebersold, R.; Heck, A. J. R.; Schmidt, A. J. Proteome Res. 2012, 11, 5145-5156. (34) Huesgen, P. F.; Lange, P. F.; Rogers, L. D.; Solis, N.; Eckhard, U.; Kleifeld, O.; Goulas, T.; Gomis-Ruth, F. X.; Overall, C. M. Nat. Methods 2015, 12, 55-58.

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Figure legends

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Figure 1. Workflow of the iArg-C digestion approach. After citraconylation at high pH

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and digestion using trypsin, the resulting peptides are decitraconylated at low pH. By

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this mean, trypsin works as exactly as Arg-C.

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Figure 2. Optimization of citraconylation and decitraconylation reactions. (A) The

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number of identified peptides (the left y axis) and the percentage of K-end peptides

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(the right y axis). (B) The number of identified peptides (the left y axis) and the

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occurrence of citaconylated Lys (the right y axis).

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Figure 3. Comparison of iArg-C and Arg-C digestion. (A) The number of MS/MS

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scans and peptide spectrum matches (PSMs) (the left y axis) and the number of

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unique peptides and proteins (the right y axis). (B) Proportion of identified peptides

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with 0, 1 or 2 missed cleavage sites. (C) The number and percentage of wrong

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cleavage sites.

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Figure 4. Comparison of iArg-C, Lys-C and trypsin digestion. (A) The number of

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MS/MS scans and PSMs (the left y axis) and the number of unique peptides and

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proteins (the right y axis). (B) Proportion of identified peptides with 0, 1 or 2 missed

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cleavage sites. (C) The number of proteins (the left y axis) and sequenced amino acids

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(the right y axis) identified by trypsin or the three proteases combined. (D) The

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number and percentage of wrong cleavage sites.

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Figure 5. The distribution of wrong cleavage sites of the four different proteases. (A)

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iArg-C digestion, (B) trypsin digestion, (C) Lys-C digestion and (D) Arg-C digestion.

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For TOC only

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Figure 1. Workflow of the iArg-C digestion approach. After citraconylation at high pH and digestion using trypsin, the resulting peptides are decitraconylated at low pH. By this mean, trypsin works as exactly as Arg-C. 83x85mm (300 x 300 DPI)

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Figure 2. Optimization of citraconylation and decitraconylation reactions. (A) The number of identified peptides (the left y axis) and the percentage of K-end peptides (the right y axis). (B) The number of identified peptides (the left y axis) and the occurrence of citaconylated Lys (the right y axis). 175x63mm (300 x 300 DPI)

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Figure 3. Comparison of iArg-C and Arg-C digestion. (A) The number of MS/MS scans and peptide spectrum matches (PSMs) (the left y axis) and the number of unique peptides and proteins (the right y axis). (B) Proportion of identified peptides with 0, 1 or 2 missed cleavage sites. (C) The number and percentage of wrong cleavage sites. 175x125mm (300 x 300 DPI)

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Analytical Chemistry

Figure 4. Comparison of iArg-C, Lys-C and trypsin digestion. (A) The number of MS/MS scans and PSMs (the left y axis) and the number of unique peptides and proteins (the right y axis). (B) Proportion of identified peptides with 0, 1 or 2 missed cleavage sites. (C) The number of proteins (the left y axis) and sequenced amino acids (the right y axis) identified by trypsin or the three proteases combined. (D) The number and percentage of wrong cleavage sites. 160x114mm (300 x 300 DPI)

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Figure 5. The distribution of wrong cleavage sites of the four different proteases. (A) iArg-C digestion, (B) trypsin digestion, (C) Lys-C digestion and (D) Arg-C digestion. 160x112mm (300 x 300 DPI)

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