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Cite This: Anal. Chem. XXXX, XXX, XXX-XXX

Analysis of Pore Formation and Protein Translocation Using Large
Biological Nanopores
Hirokazu Watanabe,† Alberto Gubbiotti,‡ Mauro Chinappi,§ Natsumi Takai,† Koji Tanaka,∥
Kouhei Tsumoto,∥ and Ryuji Kawano*,††Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei,
Tokyo 184-8588, Japan‡
Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana 18, Rome 00184, Italy§
Department of Industrial Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome 00133, Italy∥
Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, JapanS Supporting Information*

ABSTRACT: This paper describes the analysis of pore formationand detection of a single protein molecule using a large nanopore
among ﬁve diﬀerent pore-forming proteins. We demonstrate that theidentiﬁcation of appropriate pores for nanopore sensing can beachieved by classifying the channel current signals and performing
noise analysis. Through these analyses, we selected a perforin
nanopore from the membrane attack complex/perforin superfamily
and attempted to use it to detect the granzyme B protein, a serine
protease. As a result, we found that granzyme B might pass throughthe perforin nanopore if it adopts an unfolded structure. Our
proposed analytical approach should be useful for exploring several
types of nanopore as large biological nanopores other than αhemolysin.

N

However, larger molecules, such as proteins, for which thediameter is normally 3−5 nm, cannot be detected by an αHLnanopore because they are too large.
To overcome this limitation, larger nanopores can be
fabricated by using solid-state nanopores. The pore size of asolid-state nanopore can be controlled by the fabrication
procedure, for which protein detection at the single-molecule
level has also been attempted.2,13−17 With regard to biological
nanopores, protein detection still remains a challenge, althoughseveral candidate nanopores other than αHL have beenreported (Table 1). At present, the major issues with biological
nanopores are as follows: (1) Most biological pores are too
small to detect proteins. (2) A stable lipid bilayer is required
because large pores readily result in rupture of the lipid
membrane. Against this background, in this study, we exploredappropriate nanopores for detecting protein molecules fromﬁve pore-forming protein families, namely, the hemolysin(toxin), antimicrobial peptide (antimicrobial), actinoporin(toxin), membrane attack complex/perforin (MACPF) (immunity), and cholesterol-dependent cytolysins (toxin) families(Figure 1). To investigate the pore-forming activity of these
proteins, we employed a high-throughput system that cananopore sensing has emerged as a unique method for
detecting single molecules. One of the most importantand unique features of this method is that it involves electricalmeasurement at the single-molecule level of the ion currents
through a nanopore; when one of the target molecules binds or
passes through the pore, the current clearly decreases, which isrepresented as the blocking current.1 This blocking current andthe duration of analyte binding or translocation reveal thechemical identity and properties at the single-molecule level. In
addition, the frequency of current blocking reﬂects theconcentration of analytes, indicating that this approach canalso be used for quantiﬁcation.2−4 Although this method is
powerful for single-molecule detection, its selectivity essentiallydepends on the size of analytes and the nanopore. For example,
α-hemolysin (αHL) is generally used as a biological nanopore,with a pore of 1.4 nm in diameter at its narrowest point.5−10
This is highly compatible for detecting single-stranded DNA(ssDNA) because its diameter is approximately 1 nm. As such,
DNA detection is a particularly attractive ﬁeld of nanopore
study.11,12 On the other hand, for the detection of small
molecules, a molecular binder that binds to the interior of αHL
and narrows its size has been proposed.1 Alternatively, DNA
aptamer has been used as a component to bind to the target
molecule.3 The speciﬁc sequence of ssDNA can recognize andbind to the target, forming a complex that is larger in size; as aresult, the complex is captured into the αHL nanopore.3© XXXX American Chemical Society

Received: April 27, 2017
Accepted: October 5, 2017
Published: October 5, 2017
A

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Table 1. Biological Nanopores That Have Been Used for Nanopore Sensingaa
αHL is the most common biological nanopore used for nanopore sensing. The diameter of these nanopores is <5 nm.19 All protein structures arefrom Protein Data Bank (PDB).

two diﬀerent pore-forming behaviors by analyzing the noise of
the open-channel current. Finally, using a perforin (PFN)nanopore from the MACPF family, we analyzed the translocation of a single protein, granzyme B (GZB), passing
through it.

■

EXPERIMENTAL SECTION
Reagents and Chemicals. The reagents used in this study
were as follows: 1,2-diphytanoyl-sn-glycero-3-phosphocholine(DPhPC; Avanti Polar Lipids, AL, USA); 1,2-dioleoyl-snglycero-3-phosphocholine (DOPC; Avanti Polar Lipids); 1,2dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE; Avanti
Polar Lipids); 1,2-dioleoyl-sn-glycero-3-phospho-(1′-rac-glycerol) (DOPG; Avanti Polar Lipids); sphingomyelin (SM; brain,
porcine, Avanti Polar Lipids); cholesterol (Avanti Polar Lipids);

Figure 1. Representation of ﬁve types of nanopore reconstituted in alipid bilayer. We selected ﬁve nanopores from diﬀerent pore-forming
protein families and evaluated each pore’s properties.

prepare stable and reproducible lipid bilayers using a droplet
contact method (Figure 2).18 Through our investigation, we
revealed the stability of the open-pore state and the pore size ofﬁve nanopores, from which we propose a method of estimating
B

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proteins formed nanopores by reconstitution. When the lipid
bilayer ruptured during this process, we reformed it by tracingwith a hydrophobic stick (typically using pipet chips) at theinterface of the drops. Through this process, no inﬂuence ofcontamination at the droplet fuse was observed in this study.38
Channel Current Measurements and Data Analysis.
Channel currents were monitored using a PICO patch clamp
ampliﬁer (Tecella, Foothill Ranch, CA, USA) connected to an
Ag/AgCl electrode in each chamber. These electrodes were ineach droplet when we added the solution into the chambers.
Nanopores formed in the bilayers allowed ions to pass throughthem under the voltage gradient, so we obtained signals of thechannel current. These signals were detected using a 4-kHz
low-pass ﬁlter at a sampling frequency of 20 kHz. Analysis ofchannel signals was performed using pCLAMP ver. 10.7
(Molecular Devices, CA, USA).
Signal Classiﬁcation. We classiﬁed the current signals into
three types by reference to the shape of the time−current
traces. First, when step-like signals were observed, in which thecurrent sharply increased and then plateaued, we counted one
step of the current increase as one signal. Second, we regarded acurrent increase with continuous ﬂuctuation as multilevelsignals and counted the period from the start to the ﬁnish of thecurrent increase as one signal. The end of the current increase
involved a return to 0 pA or an increase larger than themeasurement limit. Third, when spike signals occurred thatinvolved short increases in current that then momentarily
returned to 0 pA, we counted a series of such events at a similar
amplitude as one signal.39
Calculation of Pore Diameter and the Number ofMonomers. To estimate the pore size and monomer assemblystate of each nanopore, we analyzed the current conductance ofthe step-like signals. The Hille equation was used in the pore
diameter estimation.40

Figure 2. (A) Lipid bilayer prepared using the droplet contact method.
(B) A photograph of two chambers for formation of a lipid bilayer(top view). (C) A photograph of the setup of a device connected witha laptop PC. (D) A photograph of the device for measuring channel
currents.

n-decane (Wako Pure Chemical Industries, Ltd., Osaka, Japan);3-morpholinopropane-1-sulfonic acid (MOPS; Nacalai Tesque,
Kyoto, Japan); KCl (Nacalai Tesque); CaCl2 (Nacalai Tesque);dithiothreitol (DTT; Wako Pure Chemical Industries); αhemolysin (αHL; Sigma−Aldrich); magainin 1 (Mag; LKTLaboratories Inc., MN, USA); perforin (PFN; Kamiya
Biomedical Company, WA, USA); streptolysin O (SLO; Bio
Academia, Osaka, Japan); granzyme B (GZB; R&D System
Inc., MN, USA). FraC was obtained from the Tsumotolaboratory (University of Tokyo). Buﬀered electrolyte solutions
were prepared from ultrapure water, which was obtained from a
Milli-Q system (Millipore, Billerica, MA, USA).
Lipid Bilayer Preparation and Reconstitution of PoreForming Protein. Lipid bilayers were prepared by the“droplet contact method”37 using a device fabricated withmicrofabrication technology. In this method, two lipid
monolayers contact and form a bilayer as shown in Figure
2A. First, 2.3 μL of lipid/oil solution was dropped into eachchamber. Next, 4.7 μL of the buﬀer solution was dropped into
both chambers. Then, two lipid monolayers contacted andformed a bilayer. Pore-forming proteins were added to only one
side of the chambers and spontaneously reconstituted in lipid
bilayer. The detailed procedures of this system have beenpreviously reported.37
We applied various buﬀers and lipid compositions when
performing the measurements on each nanopore. αHL was
dissolved at 0.33 nM in buﬀer solution including 1 M KCl and
10 mM MOPS. Mag was dissolved at 10 μM in buﬀer solution
including 200 mM KCl and 10 mM MOPS. FraC was dissolved
at 10 nM in buﬀer solution including 1 M KCl and 10 mM
MOPS. PFN was dissolved in buﬀer solution including 140 mM
KCl, 0.1 mM CaCl2, and 10 mM MOPS. SLO was dissolved at
16 nM in buﬀer solution including 140 mM KCl, 10 mM DTT,
and 10 mM MOPS. For all measurements, the buﬀer solution
was pH 7.0. The lipid composition for each measurement isshown below. αHL and PFN were reconstituted in DPhPC
membrane. Mag was reconstituted in DOPG/DOPE = 1:3(mol/mol) membrane. FraC was reconstituted in DPhPC/SM= 5:1 (mol/mol) membrane. SLO was reconstituted in
DPhPC/cholesterol = 1:1 (mol/mol) membrane. Within a
few minutes of adding the buﬀer solution, two lipid monolayers
connected and formed a lipid bilayer, and pore-forming⎛
πr ⎞ ρ
R = ⎜l + ⎟ 2⎝
2 ⎠ πrHere, r is the pore radius, ρ is the resistivity of the buﬀered
solution, l is the length of the pore (20 nm for PFN from thepore model41 and 7 nm from the thickness of the lipid bilayer
for other pores), and R is the resistivity of the pore. R is
calculated as V/I, where I is the current through the pore and V
is the applied voltage between two chambers (100 mV). We
calculated the pore diameter of FraC using the equation for acone-like nanopore.42
Using the pore diameter and monomer size from the Protein
Data Bank (PDB), the number of assembling monomers wasmathematically calculated as follows:
⎛
⎞
1
− 1⎟
d = a⎜⎝ sin(π /n)⎠where d is the pore diameter, a is the width of a peptide
monomer from the PDB, and n is the number of assembling
monomers. The arc model is presented in the Supporting
Information.
Granzyme B Detection and Analysis. To achieve thedetection of proteins using the PFN nanopore, we added
granzyme B (∼5 nm, GZB) at a concentration of 1 μM to thecis side. (In this experiment, the cis side was where the potentialof +100 mV was applied and the trans side was grounded.)
GZB is a serine protease that acts in the human immune system
by inducing the apoptosis of target cells.
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The proportion of the step-like signal in αHL was 89%, which
was the highest value among them; this is the key reason for thedomination of αHL in the nanopore measurements. The orderof the other proportions was SLO > FraC > PFN, which wereall >60%. In contrast, the proportion for Mag was 40%; this low
step-like proportion indicated that Mag is not suitable for use in
nanopore sensing. The high proportions for αHL, SLO, FraC,
and PFN reﬂect that they have large outer membrane domains,which stabilize the assembling state with monomers and resultin stable pores.42 On the other hand, Mag is a relatively small
molecule and only has a transmembrane region without theouter membrane domains. Consequently, the monomer of Mag
is more likely to form pores with other monomers and lipids.45
This caused the proportion of step-like signals for Mag to be
the lowest. In addition, the spike signal for Mag was relatively
large, suggesting that this small peptide can penetrate throughthe lipid membrane due to its low molecular weight and fewerhydrophilic domains. From these results, we concluded that
SLO, FraC, and PFN can be applied to nanopore sensing.
The pore diameter was calculated using the conductance ofthe step-like signals and a theoretical model, as listed in Table
2. In this estimation, the Hille model40 was used for the

RESULTS AND DISCUSSIONClassiﬁcation of Current Signals and Estimation of
Pore Diameter. Five diﬀerent pore-forming proteins could bereconstituted in a planar lipid bilayer with the individual
reconstituted conditions (see Experimental Section), and thechannel currents were observed. Whereas a step-like signal isimperative for nanopore measurement, other shapes of current
signals appeared in all nanopore measurements. Therefore, weclassiﬁed these signals based on the previously proposedclassiﬁcation43 as shown in Figure 3A. Three diﬀerent current

Table 2. Properties of the Reconstituted Nanoporesa

namefamily

αHL
Mag
FraC
PFNL
SLO

hemolysin
AMPs
actinoporin
MACPF
CDC

pore
diameterb
[nm]
1.4
1.1
1.7, 2.6
3.2
1.6, 6.2,
42pore range[nm]

stoichiometry[mer]ratio ofstep
signal
[%]

1.4
0.5−3
1−4
1.4−18
1−104

7
4−10
6−15
4−21
3−116

89
40
62
60
71

a

The pore diameter is calculated from pore conductance. Pore range
indicates the minimum and maximum values of pore diameter.
Stoichiometry is calculated from pore diameter. bThe most probablevalue of pore diameter.

Figure 3. (A) Schematic of three typical types of signals. (B) Signal
classiﬁcation of ﬁve types of nanopore. The numbers in the circulargraphs indicate the percentages of each signal among all signals (n =77−215). Nanopores that show a high proportion of step-like signal
are suitable for nanopore sensing.

estimation of SLO, PFN, and Mag because the pore shapes of
SLO, PFN, and Mag were assumed to be cylindrical. Since
FraC was assumed to feature a cone-shaped pore considering
its crystal structure, Hille’s equation for an asymmetrical model
was used in this case.46 Additionally, the numbers of assembling
monomers were also mathematically estimated using the pore
diameter and width of monomers from the crystal structures.39
The histograms of the conductance and stoichiometry for eachof the protein nanopores are shown in Figure S2. PFN and Mag
showed a single peak, whereas SLO and FraC presented several
peaks (note that a detailed discussion of the conductance of
PFN is presented in the next section). This implies that there
are several stable states of pore formation in SLO and FraC. As
for the results of SLO, it was previously reported that the pore
diameter ranges from 25 to 35 nm with 36- to 125-mer ofassembling monomers.47 Although these values for the SLO
pore were consistent with our conductance results, small pores,of <20 nm, could be observed in our measurements. This small
conductance implies that the monomer of SLO may participate
in slight pore expansion in the process of pore assembly, andﬁnally, it becomes a mature pore, which means a complete ringstructured pore. From the results of FraC, it is already known
from the crystal structure that the pore diameter (for the smallside) is 1.6 nm with an 8-mer form,42 and these values weresignals, namely, step-like, multilevel, and spike, were classiﬁed
from four diﬀerent protein measurements except that of αHLbecause αHL showed only two types of signals (Figure S1).
Studies involving measurement of the current of synthetic
channels or antimicrobial peptides previously reported that thisvariance of the current signals reﬂects the variance of themechanism by which defects in the membrane arise:39 (1) The
step-like signal reﬂects robust and stable pore formation,
resulting in a constant current level. This mechanism is also
known as a “barrel-stave model” in the ﬁeld of antimicrobial
peptides (AMPs).44 (2) The multilevel signal reﬂects unstablecurrent ﬂuctuation after pore opening, indicating that the shapeand size of pores could vary after pore formation. This model
can be applied to random pore defects or the “toroidal model”
of AMPs, which form pores via the assembly of monomers andlipids.44 (3) The spike signal implies the short open-closed
transition of a pore caused by membrane penetration withmonomers or the “carpet model” of AMPs.44 For successful
nanopore sensing, a step-like signal should dominate. The ratioof the appearance of each signal type is shown in Figure 3B.
D

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Analytical Chemistrycompatible with the most probable peaks of the histograms(Table 2 and Figure S2).
Summarizing these results, SLO and PFN were shown to beappropriate for protein detection in terms of pore size. This isreasonable because these transmembrane proteins function incell lysis or protein transportation. In addition, pores that areformed in these cases are relatively robust and stable, and the
PFN pore has a current rectiﬁcation ranging from −200 to+200 mV, suggesting the asymmetric geometry of the pore
structure (Figure S3). Considering that the diameter of themost water-soluble proteins is 3−5 nm, we concluded that PFN
would be the most appropriate nanopore for single-protein
detection.
Current Noise Analysis of Channel Recording for
Characterizing the Pore-Forming Process. In the current
measurements of PFN, two types of step-like signal wereobserved: one was small (∼15 pA) and showed stair-likecurrent steps, named as PFNS (Figure 4A); the other one waslarge (∼85 pA) and involved random steps up, named as PFNL
(Figure 4B). We expected that these two distinct current steps
might reﬂect two diﬀerent pore-forming mechanisms. The
small step implies that PFN monomers are added into theprospectively assembled pore one-by-one after initial pore
formation, which is named the “expanding model” in this paper(Figure 4C, left). On the other hand, for the larger case, thesecurrent steps may reﬂect multiple pore formations of thecompleted pores such as αHL pore formation, which is named
the “additional model” here (Figure 4C, right). In addition,
SLO also showed large or small step signals, as mentioned
above. To analyze these two mechanisms, we focused on thecurrent noise of the channel open state because the increase in
the area of the interior of the pores upon their opening should
diﬀer between these mechanisms, and then, the current noise
should be inﬂuenced by the area of the pore wall made ofamino acid residues.
On the basis of this hypothesis, we analyzed the RMS ratiosof the open channel current of PFN, SLO, and FraC andcompared these with αHL and alamethicin (Ala), as typical“additional” and “expanding” models, respectively. For αHL, it
is well-known that assembled prepores bind to a lipid bilayer
and form pores that are membrane-spanning. In contrast, Ala isan AMP that forms pores by assembling monomers in a lipid
bilayer. The ratio of RMS can be calculated as follows:

Figure 4. (A, B) Typical time−current trace of PFN small pore (A)and large pore (B) formation with 100 mV application. (C) Twomodels of current increase. Ala causes a current increase by pore
expansion, and αHL causes a current increase by additional pore
formation. (D) Relationship between the RMA ratio and length ofpore circumference of αHL and Ala, corresponding to additional andexpanding pore formation. (E) Results of RMS analysis of nanopores.
Five nanopores were divided into two models. PFNL, FraC, and αHL
were classiﬁed into the additional model, while PFNS, Ala, and SLO
were classiﬁed into the expanding model.

M

RMS =
RMSratio =∑i = 1 (xi − x ̅ )2
M−1
RMSN − RMS0
RMS1 − RMS0

where M is the number of current values included in one step,xi is each current value included in the step, x̅ is the meancurrent value of the step, RMSN is the RMS value of the Nthstep, and RMS0 is the RMS value at baseline. The relationshipbetween the RMS noise and length of the pore circumferenceof αHL and Ala is shown in Figure 4D. The slopes of αHL and
Ala were clearly diﬀerent at 1.0 and 0.24, respectively. As we
expected, the slope between the RMSratio and length of the pore
circumference is perfectly consistent with each other (Figure
4D).
Therefore, the RMS value can be used to predict whether
pore formation occurs by the “additional” or the “expanding”
mechanism. In the case of the “additional” model, the area ofthe pore interior will linearly increase with the reconstituted
pore number. In the “expanding” model, in contrast, the area ofthe pore interior will increase with increasing area of theinterior of the additional monomers. Figure 4E shows theresults of the RMS value of the ﬁve diﬀerent nanopores as afunction of the number of current steps. The large error bars
were caused by the distribution of pore size even if the same
number of monomers was assembled, as observed using atomic
force (AFM) or transmission electron microscopy (TEM).
E

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From the results of the RMS analysis, the slopes of all
nanopores were close together, with values around 1.4−0.2, aslisted in Table 3. The slope of FraC, the large step of PFN
Table 3. Results of Noise Analysis and Model for Each
Nanoporea
name

slope

mechanism

SLOL
PFNL
FraC
αHL
PFNS
Ala
SLOs

1.4
1.4
1.2
1.0
0.31
0.24
0.21

additional
additional
additional
additional
expanding
expanding
expandinga
The slope of the RMS ratio diﬀers for each nanopore. Five types ofnanopores were divided into two groups depending on the slope value.

(PFNL), and SLO (SLOL) were classiﬁed into the same groupas αHL as the “additional” model. FraC forms a prepore andmakes a transmembrane pore similar to αHL, as previously
proposed. On the other hand, the slope of the small step of
PFN (PFNS) and SLO (SLOS) corresponded to that of Ala as
the “expanding” model. From these results, PFN has two poreforming mechanisms at variance with αHL, FraC, and Ala. To
discuss the diﬀerence between PFNL and PFNS, we analyzed
the duration of the pore-opening state. The median values ofthe duration of PFNL and PFNS were 9.7 s (n = 154) and 15 s
(n = 27), suggesting that the process of pore formation inwhich monomers participate was much slower than thecomplete pore-forming process of the “additional” model atthis concentration.
Pore Formation Model and Pore Size of PFN from theCurrent Conductance. As mentioned above, the stepcurrents of PFNL and PFNS were approximately ∼85 and∼15 pA, respectively. According to AFM or TEM observationsof the PFN pore, arc-shaped incomplete pores and complete(circular) pores were observed. An AFM observation showedthat the diameter of the complete pore was approximately 6.1
nm (Figure 5A).48 In the case of the diameter of PFNL from thecurrent conductance, the calculated diameter using Hille’s
equation was approximately 3.4 nm (Figure 5B). This size wasslightly diﬀerent from the value from the AFM observation due
to the cylindrical assumption of pore geometry in Hille’s model.
The ranges of the histograms from the AFM observation andcurrent conductance were similar, with pore diameter being
approximately 10 nm (e.g., 2−12 nm in Figure 5B). On theother hand, the pore size of PFNS (∼1.5 nm in diameter fromthe Hille equation) was too small to form the complete pore
because the width of the PFN monomer was determined to beapproximately 2.9 nm from the crystal structure.41
Recent studies reported that PFN cannot form a complete
pore with a diameter of <5 nm and instead forms incomplete(arc-shaped) pores.48,49 Therefore, we modiﬁed the Hille
equation for correct estimation of the size of incomplete pores.
Speciﬁcally, we established a more precise model for the arcshaped pores, which is reported in detail in the Supporting
Information. PFNS represented the constant current steps
probably due to the monomer addition, as summarized as thehistograms of conductance (Figure 5C) and its variation to thecurrent steps (Figure 5D). Using our model based on the
conductance, pore areas for PFNS were from 0.3−3 nm2(smallest section) to 9−70 nm2 (largest section; see the

Figure 5. (A) A histogram of PFN pore diameter calculated from
AFM imaging from a recent report.48 (B) A histogram of PFNL pore
diameter and pore area calculated using the Hille equation from theresults of channel current measurement. (C) A histogram of PFNS
pore conductance from channel current measurement. (D) Poreconductance of expanding PFNS pore. (E, F) A histogram of PFNL
pore conductance from channel current measurement (E) andstoichiometry calculated from the pore diameter (F).

Supporting Information). In our current recordings, two typesof pore-forming behavior could be observed, suggesting that
small pores may initially form and then become mature andcomplete pores.
Single-Protein Detection Using a PFN Nanopore. We
attempted single-protein detection of GZB using a PFN
nanopore. GZB is a serine protease with a diameter ofapproximately 5 nm, and it has already been proposed that
GZB would be transported through the PFN pores in a living
system.50 Figure 6A−C shows the typical current blocking
events through PFN without or with GZB and the associatedscatter plots. Interestingly, current blocking was only observedin PFNS pores, whereas the pore size of PFNL was suﬃcient toallow the passage of GZB. A similar phenomenon waspreviously reported for a mitochondrial pore-forming protein.51,52 We will discuss this phenomenon following theblocking event analysis on PFNS.
The smallest value of the cross-sectional area of PFNs pores
was lower than 3 nm2 for every step (see the Supporting
Information). Therefore, we considered that the translocating
events would reﬂect the translocation of unfolded GZB. A
histogram of the blocking currents is presented in Figure 7A.
Several groups of blocking events were observed and named
peak 1 to peak 4. These four diﬀerent levels of current blocking
imply four diﬀerent translocation or pore-blocking states. The
histograms of the duration (time of current blocking) of eachpeak are shown in Figures 7B, and the blocking current andduration are summarized in Table 4. On the basis of theseF

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Figure 6. (A, B) The typical current−time traces of PFNs nanopore without (A) and with (B) GZB. (C) Scatter plots of current blocking andduration caused by GZB translocation. We deﬁned the blocking current to be between the open and blocking levels.

Figure 7. (A, B) Histograms of the blocking currents of GZB with PFNS (100 mV application) (A) and duration of peaks 1−4 (B). (C) The possible
current blocking model of each of the four peaks. Peaks 1−4 correspond to collision of folded protein, translocation of unfolded protein,translocation of unfolded protein, and folding and translocation of unfolded protein, respectively. These data were taken from three diﬀerent PFN
pores (N = 3).

Table 4. Blocking Currents (Mean) and Duration (Mean ±SE) of Peaks 1−4a

These are all possible models given that several reports havereported the translocation of unfolded proteins throughbiological nanopores.53 In addition, we examined the voltage
dependence with changing from 100 to 120 mV.54 Probably
due to the faster events, the number of peaks declined in
number to become three peaks (Figure S4). Because it wasdiﬃcult to assign these three peaks against the previous fourpeaks, the mean duration was compared to the previous data.
As the results, the mean duration became slightly shorter under
120 mV (∼1.1 ms).
We were not able to observe the translocation of GZB
through the PFNL nanopore, as mentioned above. We
considered that there were two possible explanations for this.
One is that translocation of the folded GZB occurred too
rapidly to be observed in our experimental conditions. A similar
explanation was proposed in a previous paper.51 Anotherexplanation is that the translocation of folded GZB requires
cofactors, such as the translocon or supporting proteins. These
cofactors may interact with GZB or PFN to support the transferfrom one side to the other. This type of translocation has
already been observed in parasite infections.55 In contrast to the
PFNL nanopore, GZB in an unfolded state can pass through the
PFNS nanopore upon the application of voltage. The blocking
current signals can reﬂect the translocation behavior at thesingle-protein level.

peak no.
blocking [pA]
duration time [ms]

1

2

3

4

4.8
1.4 ± 0.3

6.7
1.3 ± 0.2

8.2
1.6 ± 0.9123.7 ± 2.8a

The values of blocking and duration were the peak top value of eachhistogram, as shown in Figure 7A,B.

results, we propose a possible model for explaining peak 1 topeak 4 behavior as GZB binding or translocating to the PFN
nanopore, as shown in Figure 7C. (1) Peak 1 showed thesmallest current blocking and almost always had the shortest
duration; this result may indicate the collision with the porerumen of the folded GZB. (2) Peak 2 showed the second small
blocking and almost always had the shortest duration; thisbehavior may reﬂect the collision with the pore rumen of theunfolded GZB. (3) Peak 3 represented the third current
blocking state and had a slightly longer duration than peaks 1
and 2; it may represent translocation of the unfolded GZB
through the pore. (4) Peak 4 showed the deepest current
blocking and the longest duration, which was double that ofpeak 3; this can represent translocation with the unfolding
process.
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Analytical Chemistry■(2) Wanunu, M.; Dadosh, T.; Ray, V.; Jin, J. M.; McReynolds, L.;
Drndic, M. Nat. Nanotechnol. 2010, 5 (11), 807−814.
(3) Kawano, R.; Osaki, T.; Sasaki, H.; Takinoue, M.; Yoshizawa, S.;
Takeuchi, S. J. Am. Chem. Soc. 2011, 133 (22), 8474−8477.
(4) Hiratani, M.; Ohara, M.; Kawano, R. Anal. Chem. 2017, 89 (4),2312−2317.
(5) Song, L. Z.; Hobaugh, M. R.; Shustak, C.; Cheley, S.; Bayley, H.;
Gouaux, J. E. Science 1996, 274 (5294), 1859−1866.
(6) Deamer, D. W.; Branton, D. Acc. Chem. Res. 2002, 35 (10), 817−
825.
(7) Rosen, C. B.; Rodriguez-Larrea, D.; Bayley, H. Nat. Biotechnol.
2014, 32 (2), 179−181.
(8) Nivala, J.; Mulroney, L.; Li, G.; Schreiber, J.; Akeson, M. ACS
Nano 2014, 8 (12), 12365−12375.
(9) Di Marino, D.; Bonome, E. L.; Tramontano, A.; Chinappi, M. J.
Phys. Chem. Lett. 2015, 6 (15), 2963−2968.
(10) Asandei, A.; Schiopu, I.; Chinappi, M.; Seo, C. H.; Park, Y.;
Luchian, T. ACS Appl. Mater. Interfaces 2016, 8 (20), 13166−13179.
(11) Kasianowicz, J. J.; Brandin, E.; Branton, D.; Deamer, D. W. Proc.
Natl. Acad. Sci. U. S. A. 1996, 93 (24), 13770−13773.
(12) Branton, D.; Deamer, D. W.; Marziali, A.; Bayley, H.; Benner, S.
A.; Butler, T.; Di Ventra, M.; Garaj, S.; Hibbs, A.; Huang, X. H.;
Jovanovich, S. B.; Krstic, P. S.; Lindsay, S.; Ling, X. S. S.; Mastrangelo,
C. H.; Meller, A.; Oliver, J. S.; Pershin, Y. V.; Ramsey, J. M.; Riehn, R.;
Soni, G. V.; Tabard-Cossa, V.; Wanunu, M.; Wiggin, M.; Schloss, J. A.
Nat. Biotechnol. 2008, 26 (10), 1146−1153.
(13) Han, A. P.; Schurmann, G.; Mondin, G.; Bitterli, R. A.;
Hegelbach, N. G.; de Rooij, N. F.; Staufer, U. Appl. Phys. Lett. 2006, 88(9), 093901.
(14) Garaj, S.; Hubbard, W.; Reina, A.; Kong, J.; Branton, D.;
Golovchenko, J. A. Nature 2010, 467, 190−193.
(15) Wanunu, M. Phys. Life Rev. 2012, 9 (2), 125−158.
(16) Goyal, G.; Lee, Y. B.; Darvish, A.; Ahn, C. W.; Kim, M. J.
Nanotechnology 2016, 27 (49), 495301.
(17) Balme, S.; Coulon, P. E.; Lepoitevin, M.; Charlot, B.;
Yandrapalli, N.; Favard, C.; Muriaux, D.; Bechelany, M.; Janot, J. M.
Langmuir 2016, 32 (35), 8916−8925.
(18) Funakoshi, K.; Suzuki, H.; Takeuchi, S. Anal. Chem. 2006, 78(24), 8169−8174.
(19) Ying, Y. L.; Cao, C.; Long, Y. T. Analyst 2014, 139 (16), 3826−
3835.
(20) Faller, M.; Niederweis, M.; Schulz, G. E. Science 2004, 303(5661), 1189−1192.
(21) Butler, T. Z.; Pavlenok, M.; Derrington, I. M.; Niederweis, M.;
Gundlach, J. H. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (52), 20647−
20652.
(22) Iacovache, I.; De Carlo, S.; Cirauqui, N.; Dal Peraro, M.; van derGoot, F. G.; Zuber, B. Nat. Commun. 2016, 7, 12062.
(23) Pastoriza-Gallego, M.; Rabah, L.; Gibrat, G.; Thiebot, B. n. d.;
van der Goot, F. o. G.; Auvray, L. c.; Betton, J.-M.; Pelta, J. J. Am.
Chem. Soc. 2011, 133 (9), 2923−2931.
(24) Subbarao, G. V.; van den Berg, B. J. Mol. Biol. 2006, 360 (4),750−759.
(25) Alcaraz, A.; Ramirez, P.; Garcia-Gimenez, E.; Lopez, M. L.;
Andrio, A.; Aguilella, V. M. J. Phys. Chem. B 2006, 110 (42), 21205−
21209.
(26) Chen, M.; Khalid, S.; Sansom, M. S. P.; Bayley, H. Proc. Natl.
Acad. Sci. U. S. A. 2008, 105 (17), 6272−6277.
(27) Locher, K. P.; Rees, B.; Koebnik, R.; Mitschler, A.; Moulinier, L.;
Rosenbusch, J. P.; Moras, D. Cell 1998, 95 (6), 771−778.
(28) Mohammad, M. M.; Iyer, R.; Howard, K. R.; McPike, M. P.;
Borer, P. N.; Movileanu, L. J. Am. Chem. Soc. 2012, 134 (22), 9521−
9531.
(29) Bayrhuber, M.; Meins, T.; Habeck, M.; Becker, S.; Giller, K.;
Villinger, S.; Vonrhein, C.; Griesinger, C.; Zweckstetter, M.; Zeth, K.
Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (40), 15370−15375.
(30) Hoogerheide, D. P.; Gurnev, P. A.; Rostovtseva, T. K.;
Bezrukov, S. M. Nanoscale 2017, 9 (1), 183−192.

CONCLUSION
In summary, we explored large biological nanopores that weresuitable for detecting a single protein from ﬁve diﬀerent familiesof pore-forming proteins. Classiﬁcation of the current signals of
the protein nanopores was shown to be useful for estimating
the adequacy of protein-targeted nanopore measurements. In
this estimation, two types of pore formation were observed:
additional or expanding pore formation. They could bediscriminated using noise analysis. After this assessment, weselected the PFN nanopore because its size was the mostsuitable among the ﬁve nanopores. This nanopore showed two
diﬀerent types of pore formation: the additional and expanding
models for large or small pore formation. Using PFN
nanopores, we examined the availability of these nanoporesfor single-protein detection using GZB, which is 5 nm indiameter. The results showed that the blocking current signals
were only observed in the PFNS pores, whereas the small pores
were much smaller than the GZB protein. We concluded that
unfolded GZB translocated through the PFNS pores, but somecofactors may be needed for transport through the PFNL pores.
It should be noted that molecules that are smaller than
nanopores will not always translocate through biological
nanopores. For the extensive use of protein nanopores fornanopore sensing, further investigation of their selectivity will
be required. Our analytical method of classiﬁcation or noise
analysis should be useful for such investigation.

■

ASSOCIATED CONTENT

S Supporting Information*

The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/acs.analchem.7b01550.
Typical current−time traces for ﬁve nanopores; pore
diameters calculated from Hille model; I−V curve of asingle PFNL pore; histogram of the blocking current
signal of GZB with PFNs; model for low conductance
perforin pores (PDF)■

AUTHOR INFORMATION

Corresponding Author*E-mail: rjkawano@cc.tuat.ac.jp.
ORCID

Ryuji Kawano: 0000-0001-6523-0649
Author Contributions

H.W. and R.K. conceived and designed the project. H.W. and
N.T. performed the nanopore measurements. A.G. and M.C.
conducted the theoretical analysis for the arc-shaped nanopores. K. Tanaka and K. Tsumoto expressed FraC protein andanalyzed the structure. H.W, M.C., and R.K. analyzed the data
and wrote the manuscript.
Notes

The authors declare no competing ﬁnancial interest.

■■

ACKNOWLEDGMENTS
This work was partly supported by KAKENHI (Grant Nos.
25708024, 15H00803, and 16H06043) from MEXT, Japan.
REFERENCES(1) Gu, L. Q.; Braha, O.; Conlan, S.; Cheley, S.; Bayley, H. Nature
1999, 398 (6729), 686−690.
H

DOI: 10.1021/acs.analchem.7b01550
Anal. Chem. XXXX, XXX, XXX−XXX

Article

Analytical Chemistry(31) Dgany, O.; Gonzalez, A.; Sofer, O.; Wang, W. X.; Zolotnitsky,
G.; Wolf, A.; Shoham, Y.; Altman, A.; Wolf, S. G.; Shoseyov, O.;
Almog, O. J. Biol. Chem. 2004, 279 (49), 51516−51523.
(32) Wang, H.-Y.; Li, Y.; Qin, L.-X.; Heyman, A.; Shoseyov, O.;
Willner, I.; Long, Y.-T.; Tian, H. Chem. Commun. 2013, 49 (17),1741−1743.
(33) Simpson, A. A.; Tao, Y. Z.; Leiman, P. G.; Badasso, M. O.; He,
Y. N.; Jardine, P. J.; Olson, N. H.; Morais, M. C.; Grimes, S.;
Anderson, D. L.; Baker, T. S.; Rossmann, M. G. Nature 2000, 408(6813), 745−750.
(34) Geng, J.; Wang, S. Y.; Fang, H. M.; Guo, P. X. ACS Nano 2013,
7 (4), 3315−3323.
(35) Mueller, M.; Grauschopf, U.; Maier, T.; Glockshuber, R.; Ban,
N. Nature 2009, 459 (7247), 726−730.
(36) Soskine, M.; Biesemans, A.; Moeyaert, B.; Cheley, S.; Bayley, H.;
Maglia, G. Nano Lett. 2012, 12 (9), 4895−4900.
(37) Kawano, R.; Tsuji, Y.; Sato, K.; Osaki, T.; Kamiya, K.; Hirano,
M.; Ide, T.; Miki, N.; Takeuchi, S. Sci. Rep. 2013, 3, 1995.
(38) Kawano, R.; Tsuji, Y.; Kamiya, K.; Kodama, T.; Osaki, T.; Miki,N.; Takeuchi, S. PLoS One 2014, 9 (7), e102427.
(39) Watanabe, H.; Kawano, R. Anal. Sci. 2016, 32 (1), 57−60.
(40) Hille, B. Ion channels of excitable membranes, 3rd ed.; Sinauer:
Sunderland, Mass., 2001; p xviii, 814 p.
(41) Law, R. H. P.; Lukoyanova, N.; Voskoboinik, I.; Caradoc-Davies,
T. T.; Baran, K.; Dunstone, M. A.; D’Angelo, M. E.; Orlova, E. V.;
Coulibaly, F.; Verschoor, S.; Browne, K. A.; Ciccone, A.; Kuiper, M. J.;
Bird, P. I.; Trapani, J. A.; Saibil, H. R.; Whisstock, J. C. Nature 2010,
468 (468), 447−451.
(42) Tanaka, K.; Caaveiro, J. M. M.; Morante, K.; Gonzalez-Manas, J.
M.; Tsumoto, K. Nat. Commun. 2015, 6, 6337.
(43) Chui, J. K. W.; Fyles, T. M. Chem. Soc. Rev. 2012, 41 (1), 148−
175.
(44) Melo, M. N.; Ferre, R.; Castanho, M. Nat. Rev. Microbiol. 2009,
7 (3), 245−250.
(45) Cruciani, R. A.; Barker, J. L.; Durell, S. R.; Raghunathan, G.;
Guy, H. R.; Zasloff, M.; Stanley, E. F. Eur. J. Pharmacol., Mol.
Pharmacol. Sect. 1992, 226 (4), 287−296.
(46) Heins, E. A.; Siwy, Z. S.; Baker, L. A.; Martin, C. R. Nano Lett.
2005, 5 (9), 1824−1829.
(47) Sekiya, K.; Akagi, T.; Tatsuta, K.; Sakakura, E.; Hashikawa, T.;
Abe, A.; Nagamune, H. Microbes Infect. 2007, 9 (11), 1341−1350.
(48) Leung, C.; Hodel, A. W.; Brennan, A. J.; Lukoyanova, N.; Tran,S.; House, C. M.; Kondos, S. C.; Whisstock, J. C.; Dunstone, M. A.;
Trapani, J. A.; Voskoboinik, I.; Saibil, H. R.; Hoogenboom, B. W. Nat.
Nanotechnol. 2017, 12 (5), 467−473.
(49) Praper, T.; Sonnen, A.; Viero, G.; Kladnik, A.; Froelich, C. J.;
Anderluh, G.; Dalla Serra, M.; Gilbert, R. J. C. J. Biol. Chem. 2011, 286(4), 2946−2955.
(50) Trapani, J. A.; Smyth, M. J. Nat. Rev. Immunol. 2002, 2 (10),735−747.
(51) Shimizu, S.; Ide, T.; Yanagida, T.; Tsujimoto, Y. J. Biol. Chem.
2000, 275 (16), 12321−12325.
(52) Aksoyoglu, M. A.; Podgornik, R.; Bezrukov, S. M.; Gurnev, P.
A.; Muthukumar, M.; Parsegian, V. A. Proc. Natl. Acad. Sci. U. S. A.
2016, 113 (32), 9003−9008.
(53) Cressiot, B.; Braselmann, E.; Oukhaled, A.; Elcock, A. H.; Pelta,
J.; Clark, P. L. ACS Nano 2015, 9 (9), 9050−9061.
(54) Stefureac, R. I.; Trivedi, D.; Marziali, A.; Lee, J. S. J. Phys.:
Condens. Matter 2010, 22 (45), 454133.
(55) Spielmann, T.; Gilberger, T. W. Trends Parasitol. 2015, 31 (10),514−525.

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DOI: 10.1021/acs.analchem.7b01550
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