Scope and limitations of carbohydrate hydrolysis for de novo glycan sequencing using a hydrogen peroxide/metallopeptide-based glycosidase mimetic

Article abstract of DOI:10.1016/j.carres.2018.01.008

Acidic hydrolysis is commonly used as a first step to break down oligo- and polysaccharides into monosaccharide units for structural analysis. While easy to set up and amenable to mass spectrometry detection, acid hydrolysis is not without its drawbacks. For example, ring-destruction side reactions and degradation products, along with difficulties in optimizing conditions from analyte to analyte, greatly limits its broad utility. Herein we report studies on a hydrogen peroxide/CuGGH metallopeptide-based glycosidase mimetic design for a more efficient and controllable carbohydrate hydrolysis. A library of methyl glycosides consisting of ten common monosaccharide substrates, along with oligosaccharide substrates, was screened with the artificial glycosidase for hydrolytic activity in a high-throughput format with a robotic liquid handling system. The artificial glycosidase was found to be active towards most screened linkages, including alpha- and beta-anomers, thus serving as a potential alternative method for traditional acidic hydrolysis approaches of oligosaccharides.

Full text of DOI:10.1016/j.carres.2018.01.008

Accepted Manuscript
Scope and limitations of carbohydrate hydrolysis for de novo glycan sequencing using
a hydrogen peroxide/metallopeptide-based glycosidase mimetic
Tianyuan Peng, Zachary Wooke, Nicola L.B. Pohl
PII:
S0008-6215(17)30859-5
10.1016/j.carres.2018.01.008
CAR 7512
DOI:
Reference:
To appear in: Carbohydrate Research
Received Date: 9 December 2017
Revised Date: 10 January 2018
Accepted Date: 29 January 2018
Please cite this article as: T. Peng, Z. Wooke, N.L.B. Pohl, Scope and limitations of carbohydrate
hydrolysis for de novo glycan sequencing using a hydrogen peroxide/metallopeptide-based glycosidase
mimetic, Carbohydrate Research (2018), doi: 10.1016/j.carres.2018.01.008.
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Library of monosaccharide
methyl glycosides for
screening
H2O2/CuGGH
OR
Individual screen of sugar
substrates using
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automated liquid handing
system
m/z
Hydrolytic activity
detected by ESI-MS
Oligosaccharide substrates for
cleavage and analysis of constituents

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Scope and limitations of carbohydrate hydrolysis for de novo glycan sequencing using a
hydrogen peroxide/metallopeptide-based glycosidase mimetic
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Tianyuan Peng1, Zachary Wooke1, Nicola L. B. Pohl1,2*
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1. Department of Chemistry, Indiana University, Bloomington, IN, USA
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2. Radcliffe Institute of Advanced Study, Harvard University, Cambridge, MA, USA
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11 * Corresponding author, Simon Hall 120A, 212 S. Hawthorne Drive, Bloomington, IN,
12 47405, npohl@indiana.edu
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24 Abstract
25 Acidic hydrolysis is commonly used as a first step to break down oligo- and
26 polysaccharides into monosaccharide units for structural analysis. While easy to set up
27 and amenable to mass spectrometry detection, acid hydrolysis is not without its
28 drawbacks. For example, ring-destruction side reactions and degradation products, along
29 with difficulties in optimizing conditions from analyte to analyte, greatly limits its broad
30 utility. Herein we report studies on a hydrogen peroxide/CuGGH metallopeptide-based
31 glycosidase mimetic design for a more efficient and controllable carbohydrate hydrolysis.
32 A library of methyl glycosides consisting of ten common monosaccharide substrates,
33 along with oligosaccharide substrates, was screened with the artificial glycosidase for
34 hydrolytic activity in a high-throughput format with a robotic liquid handling system. The
35 artificial glycosidase was found to be active towards most screened linkages, including
36 alpha- and beta-anomers, thus serving as a potential alternative method for traditional
37 acidic hydrolysis approaches of oligosaccharides.
38
39
40 Key word
41 Artificial glycosidase; carbohydrate hydrolysis; high-throughput screening; carbohydrate
42 sequencing
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44 Highlights
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A mix of hydrogen peroxide and a metallopeptide cleaves a range of glycosides.
High-throughput hydrolytic activity screens on a robotic liquid handling platform.

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Potential alternative method for acidic hydrolysis of oligosaccharides.
50 Structural analysis of carbohydrates remains a major roadblock in glycobiology studies
51 due to the complex nature of carbohydrates and the resulting limits of current analytical
52 tools [1, 2]. Often, accurate reconstructions of oligosaccharide structures depend on an
53 initial analysis of the monosaccharide constituents in order to significantly limit the
54 possibilities. Such analyses then require carbohydrate analytes to be degraded into
55 monosaccharide units as the first step for de novo glycan sequencing. Progress in
56 monosaccharide identification has now made it possible to distinguish between
57 carbohydrate isomers and enantiomers through mass spectrometry (MS) and ion
58 mobility-mass spectrometry (IM-MS)-based approaches [3-8]. The most common method
59 to date to obtain monosaccharides from a carbohydrate chain is acidic hydrolysis; either
60 trifluoroacetic acid (TFA) or formic acid (FA) is normally used to treat larger glycans
61 before mass spectrometry or liquid chromatography analysis [9-11]. This acidic
62 hydrolysis method is routine with both acids being easily amenable to subsequent
63 analysis. Yet this popular method still has drawbacks. Acidic hydrolysis is often difficult
64 to control, thereby requiring lengthy optimization protocols in an attempt to maintain the
65 structural integrity of the monomeric components [12-14]. Side products that destroy the
66 ring, for example, increase the complexity of subsequent structural analysis [15]. Recent
67 efforts have attempted to improve current acidic hydrolysis methods by varying acid
68 choices from milder ones to solid acidic supports [12, 16]. However, despite these efforts,

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69 no standardized hydrolysis method is yet available for all common carbohydrates that
70 could serve as a protocol for automated analyses.
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72 Glycosidases, enzymes that hydrolytically cleave glycosidic linkages, can of course
73 operate under much milder conditions than standard chemical hydrolysis methods [17].
74 However, their specificity means they lack the universality required for de novo
75 sequencing efforts. Recently, a CuGGH metallopeptide-based artificial glycosidase was
76 reported to have substrate-specific glycosidase function when linked to a fucose-binding
77 domain [18, 19]. This property prompted us to explore the possibility that a more
78 universal “glycosidase” mimic could be obtained in the absence of the specific sugar-
79 binding domain. Such metallopeptide-based artificial enzymes have been developed and
80 used in biological studies for their ability to mimic enzyme-metal cofactor functions [20-
81 22]. Interestingly, this CuGGH-based artificial glycosidase alone without any binding
82 domain has been shown to cleave both para-nitrophenol-fucoside and para-nitrophenol-
83 glucoside substrates with relatively high efficiency [19]. Herein we report studies on the
84 ability of this metallopeptide to cleave a range of glycosidic linkages that are not as
85 activated as para-nitrophenol-containing glycosides and demonstrate this artificial
86 glycosidase is active towards a much broader range of glycosidic linkages through
87 screening experiments with methyl glycosides and polysaccharides substrates.
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89 In order to screen a wide spectrum of carbohydrate linkages, an expanded library of
90 methyl glycosides—designed earlier to interrogate proteinaceous glycosidase function
91 [23, 24]—was used to set up an activity screen of the CuGGH metallopeptide-based

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92 artificial glycosidase in a high-throughput format. A high throughput robotic liquid
93 handling system was used to dispense a small volume of liquid (as low as 1.2 nl) laterally
94 within columns in a 384-well plate with a change of tips in between each transfer step to
95 avoid cross-contamination. In addition to the previously reported ten common
96 monosaccharide substrates[23], three new, commercially available, methyl glycoside
97 substrates (methyl-α-L-rhamnopyranoside, methyl-N-acetyl-α-D-glucosaminide and
98 methyl-β-L-arabinopyranoside) were added to the existing library for an expanded
99 activity screen. All catalytic components (hydrogen peroxide, sodium ascorbate and
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CuGGH metallopeptide) were substituted with deionized water for the negative control.
The hydrolytic activity of substrates was identified via a mass loss of 14 Da (cleavage of
a methyl group) compared between the spectra of the control and the hydrolysis reaction.
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Methyl glycoside library screening results
(Intensity ratio of product/substrate) as their Na+ ions
Substrates
Negative control
0.01
Hydrolysis reaction
Methyl-α-D-glucopyranoside
Methyl-β-D-glucopyranoside
Methyl-α-D-galactopyranoside
Methyl-β-D-galactopyranoside
Methyl-α-D-mannopyranoside
Methyl-β-D-mannopyranoside
Methyl-α-L-fucopyranoside
Methyl-β-L-fucopyranoside
Methyl-α-D-xylopyranoside
Methyl-β-D-xylopyranoside
Methyl-β-L-arabinopyranoside
Methyl-α-L-rhamnopyranoside
Methyl-N-acetyl-α-D-glucosaminide
0.08
0.11
0.08
0.07
0.07
0.11
0.16
0.24
0.29
0.26
0.08
4.74
0.14
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.02
0.00
0.00
0.03
0.01
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Table 1. Screening results of a methyl glycoside substrate library whose components
were individually incubated for 8 h at 37 °C with the CuGGH metallopeptide. All data
are shown as the intensity ratio of the expected product sodium adduct ion peak (m/z 203
for glucose, galactose and mannose; m/z 187 for fucose and rhamnose; m/z 244 for
glucosamine) versus the starting substrate sodium adduct ion peak (m/z 217 for glucose,
galactose and mannose substrates; m/z 201 for fucose and rhamnose substrates; m/z 258
for glucosamine). Xylose and arabinose substrate screening results were obtained through
normal phase LC-MS analysis instead of direct infusion due to contamination peak
overlapping with monosaccharide product peak. (For details on LC-MS set up, see
Experimental 1.5 section.) Data was acquired as an average over 100 individual scans
with 3 microscans each.

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As shown in Table 1, the CuGGH metallopeptide-based artificial glycosidase has
hydrolytic activity with all of the methyl glycosides substrates that were screened. The
hydrolysis is most potent for deoxysugar substrates such as fucosyl, xylosyl and
rhamnosyl linkages as evidenced by the rhamnose monosaccharide product/substrate
intensity ratio rising to 4.74 as compared to 0.03 in the negative control sample. In
addition to neutral sugar substrates, the hydrolysis is also effective towards amine-
containing sugar substrates as shown in the case of methyl-N-acetyl-α-D-glucosaminide.
This initial screening result confirmed our hypothesis that this artificial glycosidase has
hydrolytic ability towards a broader range of carbohydrate substrates in the absence of a
binding tag, but that the motif still had an inherent preference for linkages that are often
easier to cleave using acidic conditions.
Given this finding of expanded hydrolytic activity for the CuGGH metallopeptide, a
small sample of natural sugar substrates were screened with the high-throughput liquid
handling system. Multiple glucose-, galactose- and mannose-containing
disaccharide/trisaccharide substrates were selected for screening. (For structures of all
substrates, see supporting information Figure S1-S4). The deoxysugar linkages appear to
be more prone to catalytic cleavage with this artificial glycosidase mimetic; whether an
oxidative or hydrolytic mechanism is at play is unclear [25, 26]. Although a complete
mechanistic study is beyond the scope of this work, a single negative control without the
CuGGH metallopeptide was set up for all screened sugar substrates to investigate the
catalytic/oxidative role of hydrogen peroxide with this artificial glycosidase.

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Screening results for disaccharide and trisaccharide substrates showed that hydrogen
peroxide alone is itself a mild hydrolytic reagent for carbohydrates. The addition of the
CuGGH metallopeptide, however, increased the hydrolysis activity of most substrates by
2- to 5-fold as observed from the signal intensity comparison between negative control
samples and those containing the metallopeptide. For example, the galactose
monosaccharide sodium adduct ion peak intensity is 15.6% of the intensity of the
substrate β-1,4-galactobiose substrate sodium adduct ion peak in the negative control
reaction spectrum (Fig. 1A). The same ratio increased to 42.1% with the addition of the
A
B
with only H2O2, negative control
artificial glycosidase hydrolysis
β-1,4-galactobiose substrate peak
m/z 365
β-1,4-galactobiose substrate peak
m/z 365
galactose monosaccharide product peak
m/z 203
galactose monosaccharide product peak
m/z 203
m/z
m/z
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
C
With hydrogen peroxide only
With added CuGGH
Sugar Substrates
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Fig. 1 Screening results for disaccharide & trisaccharide substrates: (A): ESI-MS

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spectrum for negative control (no CuGGH) reaction of β-1,4-galactobiose; (B): artificial
glycosidase hydrolysis (with CuGGH) sample of β-1,4-galactobiose; (C): Screening
results for all substrates after blank control deduction, all substrate structures are shown
in supporting information S1-S4. Data is shown as the sodium adduct ion peak intensity
ratio of monosaccharide product versus substrates. Red column: substrate reaction with
artificial glycosidase; Blue column: negative control, substrate reaction with only
hydrogen peroxide. All data were acquired as an average of 100 individual scans with 3
microscans each.
CuGGH metallopeptide for the artificial glycosidase hydrolysis reaction (Fig. 1B). A 3-
fold increase was seen with only a catalytic amount of CuGGH. The same trend was
observed for all screened sugar substrates as shown in Fig. 1C, making it clear that
CuGGH serves as an activator/facilitator to increase the power of hydrogen peroxide.
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Based on these results, the potential hydrolysis of even larger oligosaccharide species
was probed, namely maltotetraose, maltopentose and maltohexose. Since reaction
mixtures could get complicated to resolve due to various degrees of completion of the
hydrolysis of bigger oligosaccharides, a nanoLC-MS instrument was used before mass
spectrometry analysis. The results showed that the expected glucose monosaccharide
product has the largest area under the peak for all three oligosaccharides after artificial
glycosidase treatment (Table 2) as compared to substrate dominant control samples. The
remaining area percentages for each oligosaccharide substrate are in the range of 1-3.5%,
a marked decrease compared to around 90% in the negative control sample. This result
indicated that this hydrogen peroxide-based, CuGGH-activated artificial glycosidase
method can degrade larger oligosaccharides into their smaller monosaccharide subunits,
to the extent of above 90% hydrolysis yield as calculated based on area percentage of
substrate peak before and after hydrolysis, thereby making it potentially amenable for
inclusion in a de novo carbohydrate sequencing workflow.

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Glucose Oligomer
Peaks
Maltohexaose
Maltohexaose
Hydrolysis
61.3
Maltopentaose Maltopentaose
Maltotetraose
Control
5.2
Maltotetraose
Hydrolysis
63.8
Control
Control
Hydrolysis
6.0
3.1
60.3
m/z 203 Monomer
m/z 365 Dimer
4.0
0.3
13.7
12.9
6.0
NA
NA
NA
96.9
16.8
13.1
5.9
NA
NA
26.0
9.2
m/z 527 Trimer
NA
0.9
94.8
1.0
m/z 689 Tetramer
m/z 851 Pentamer
m/z 1013 Hexamer
2.6
4.0
88.8
3.5
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Table 2 Peak area table for glucose oligosaccharide hydrolysis: all peak area calculated
using integrated area under the peak from extracted-ion chromatogram (EIC) of each
glucose oligomer peak. See supporting information Figure S9-S12 for extracted-ion
chromatograms of maltohexaose reactions.
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In addition to our screening, we also employed a MS-based quantification method to
quantify the hydrolytic yield for a few disaccharide substrates [27]. Increasing amounts
of monosaccharide were doped into a fixed amount of substrates to acquire a series of
ratio data points. A calibration curve was plotted based on the measured data points and a
linear relationship was obtained. The hydrolysis yield was measured in the range of 5-
75% for most substrates, with the α-1,2-galactosyl linkage being the most vulnerable with
a 74% hydrolysis yield. (For details of calibration curve and yield, please see the
Supporting Information Figure S5-S8, Table S1.)
In summary, we have demonstrated that the cleavage ability of hydrogen peroxide
towards carbohydrate linkages can be strengthened with the addition of the
metallopeptide CuGGH as an artificial glycosidase. Although the current results are still

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at a preliminary stage for use in de novo sequencing, this glycosidase mimetic was able to
break down oligosaccharide substrates into individual monosaccharide units as a potential
first step for monosaccharide analysis. Other than hydrolytic ability, no side reaction
product was observed for this method even after an 8-hour incubation period. Although
future work will be needed to test the scope of these conditions with diverse and larger
glycan samples, we envision this new hydrogen peroxide-based hydrolysis method to be
a potential alternative or complement to the current norm of acidic hydrolysis in the
development of carbohydrate analysis protocols.
Acknowledgements
We would like to thank Dr. Jon Trinidad and the Biological Mass Spectrometry Facility
at Indiana University, where all mass spectrometry experiments were performed, as well
as Joan and Marvin Carmack Chair funds for partial support of this work and a U.S.
National Institutes of Health grant (5U01GM116248) for the high-throughput screening
robot. NP also thanks the Radcliffe Institute for Advanced Study for the Edward, Frances,
and Shirley B. Daniels Fellow position. We also appreciate comments on the manuscript
from Dr. Gabe Nagy (Pacific Northwest National Laboratory).
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1. Experimental
1.1 Materials
Methyl-α-D-glucopyranoside (>99%), methyl-α-D-mannopyranoside (>99%) were
purchased from Sigma Aldrich (St. Louis, MO, USA); methyl-α-D-galactopyranoside

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(>97%), methyl-β-D-galactopyranoside (>98%) were purchased from TCI America
(Portland, OR); methyl-β-D-glucopyranoside, methyl-β-D-mannopyranoside, methyl-α-
D-fucopyranoside, methyl-β-L-fucopyranoside were ordered from CarboSynth
(Berkshire, UK); methyl-N-acetyl-α-D-glucosaminide was ordered from Sigma Aldrich
(Milwaukee, WI USA). All D-galactose, D-Glucose and D-mannose-containing
substrates (kojibiose, maltose, isomaltose, maltotetraose, maltopentose, maltohexose,
nigelose, trehelose, raffinose, maltotriose, α-1,1-trehelose, α-1,2-galactobiose, α-1,3-
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galactobiose, α-1,4-galactobiose, α-1,2-mannobiose, α-1,4-mannobiose, β-1,4-
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mannobiose, β-1,4-galactobiose) were purchased from Carbosynth (Berkshire, UK) or
Sigma Aldrich (Milwaukee, WI, USA) without further purification. The GGH copper
binding tripeptide was purchased from Sigma Aldrich (Milwaukee, WI, USA).
1.2 Artificial glycosidase screening
CuGGH stock solution is prepared by titrating CuCl2 stock solution (1M) to GGH
tripeptide solution (20 mM) till a final 1:1 ratio. The final concentration of CuGGH stock
solution was diluted to 5 mM using deionized water. The formation of a CuGGH
metallopeptide complex was confirmed by ESI-MS (m/z 166) and absorption at 250 nm
and 525 nm. The final 5mM CuGGH stock solution should be purple in color.
Mosquito HTS robotic liquid handling system (TTP Labtech Inc, Cambridge, MA) was
programmed to mix freshly prepared sodium ascorbate solution (100 mM, 5 µl), freshly
prepared hydrogen peroxide (100 mM, 5 µl), methyl glycoside stock solution (20 mM, 5
µl), sodium phosphate buffer (pH 7.0, 250 mM, 5 µl) together with CuGGH stock

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solution (5 mM, 1 µl) together in 384-well plate. For CuGGH single negative control: no
CuGGH stock solution was mixed in; For normal negative control: methyl glycoside
stock solution (20 mM, 5 µl), sodium phosphate buffer (pH 7.0, 250 mM, 5 µl) and 10 µl
deionized water was mixed instead of catalytic component. After 8 hours incubation at 37
°C, 80 µl 50/50 water/methanol solution was added into each sample well for a better
ionization in ESI-MS.
1.3 ESI-MS
Mass spectrometry conditions used were: 5 kV spray voltage, 0 V capillary voltage, 150
°C capillary temperature, 40 V tube lens voltage, 20 units sheath gas flow rate, 0 units
sweep gas flow rate, 10 units aux gas flow rate, with 100 scans consisting of three
microscans for each experiment at a flow rate of 10 µL/min performed on a Thermos
Scientific LTQ Velos Pro instrument with only the ion trap portion used.
1.4 NanoLC-MS
The nanoLC conditions used are: a 30-minute gradient of 100% to 10% 0.1% formic acid
water mobile phase A, 0-90% 0.1% formic acid acetonitrile mobile phase B using C18
reverse phase nanoLC column. The injection volume is 10 µL for each sample with a 30-
minute blank run in between each sample run. Total flow rate is at 300 nl/min.
Mass spectrometry conditions used were as follows: scan range: 150-1200 m/z, normal
scan rate, full scan type, positive mode, centroid data type, 5 kV spray voltage, 0 V
capillary voltage, 150 °C capillary temperature, 40 V tube lens voltage, 20 units sheath
gas flow rate, 0 units sweep gas flow rate, with 10 units aux gas flow rate. All

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experiments were performed with a Thermos Scientific LTQ Velos Pro instrument
coupled with an Eksigent nanoLC-2D instrument.
1.5 Normal phase LC-MS
Liquid chromatographic separation of the pentoses from the ascorbate buffer was
performed on an Agilent 1200 Infinity II HPLC system (Santa Clara, CA, USA) equipped
with a Kinetex HILIC column (4.6 mm ID x 250 mm, 5 µm particles) from Phenomenex
(Torrance, CA, USA). Mobile Phase A and B consisted of 0.1% (v/v) formic acid in
water or acetonitrile, respectively. Separation was performed by holding at 99% mobile
phase B for the first 5 minutes followed by a gradient to 80% mobile phase B over 20
minutes at a flow rate of 1.25 mL/min. Column temperature was kept at 60°C. A flow
splitter was used to reduce the flow rate to 150 µL/min before entering the mass
spectrometer. Mass spectrometric analysis was performed with a Thermo Scientific LTQ
Velos Pro linear ion trap mass spectrometer with an electrospray source in positive ion
mode (San Jose, CA, USA). Mass spectrometry data was recorded from Thermo
Scientific’s Tune Plus software.
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