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Sensors and Materials, Vol. 33, No. # (2021) 1–##
MYU Tokyo
S & M ####

Pattern-recognition-based Identificationof Proteases and Their Complexes by a One-component Array
Composed of a Dansyl-modified Charged Polymer
Shunsuke Tomita1,2* and Ryoji Kurita1,2,3**
1

Health and Medical Research Institute, National Institute of Advanced Industrial Science and Technology,
1-1-1 Higashi, Tsukuba, Ibaraki 305-8566, Japan2
DBT-AIST International Laboratory for Advanced Biomedicine (DAILAB), DBT-AIST International Center forTranslational & Environmental Research (DAICENTER), National Institute of Advanced Industrial Science andTechnology, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8566, Japan
3
Faculty of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan(Received October 22, 2020; accepted December 14, 2020)

Keywords: proteases, biosensors, multivariate analysis, polymers

Pattern-recognition-based protein sensing has recently attracted attention as an alternative
to conventional specificity-based sensing. Herein, we present a simple one-component
array consisting of a charged poly-L-lysine modified with environment-responsive dansyl
fluorophores (PLL-Dnc) for the identification of proteases. An array of PLL-Dnc dissolvedin three buffer solutions with different pH values is capable of producing fluorescent patterns
that are unique to each protease via diverse interactions, allowing the identification of eightproteases by multivariate analysis. Although it is impractical to quantify mixed samples ofproteases and their inhibitor proteins by conventional enzyme-activity-based assays, the present
array has enabled the discrimination of mixtures of trypsin and α1-antitrypsin (ATT) withdifferent compositions. Considering that the PLL-Dnc array has high accessibility and consistsof only a single polymeric material that can be easily prepared, our method is expected to allow
screening for the formation of protein-protein complexes, as well as the detection of diseaseassociated proteases.

1.

Introduction

Proteases have the ability to regulate the physiological functions of proteins throughenzymatic hydrolysis, and are therefore closely involved in biological processes. Abnormal
protease activity causes a variety of diseases, such as cancer and inflammatory diseases;
therefore, the detection of proteases is of critical importance for the diagnosis and treatment of
medical conditions.(1,2) For the detection of proteases, specificity-based methods are generally
used, such as enzyme-linked immunosorbent assays (ELISAs) using antibodies and enzymecatalyzed reactions.(1,3,4) However, it is difficult to develop antibodies or enzyme substrates forhighly specific detection, and the production of these molecules is costly and laborious.
*

Corresponding author: e-mail: s.tomita@aist.go.jp
Corresponding author: e-mail: r.kurita@aist.go.jp
https://doi.org/10.18494/SAM.2021.3074**

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Pattern-recognition-based sensing techniques have attracted considerable attention as analternative approach to the easy and accurate identification of various biological analytes,
including proteins and cells.(5) This sensing strategy utilizes an array of multiple molecules thatare nonspecific but have different affinities, i.e., cross-reactivity, with the analytes. In a typicalform, the molecules are modified with reporter units, such as chromophores and fluorophores,
which change their optical properties upon binding to the analytes. Response readout after
mixing such an array with the analytes produces unique optical patterns of the individual
analytes. The resulting patterns are then processed by multivariate analysis techniques toaccurately identify the analytes. To date, much effort has been devoted to the development ofarrays for protein sensing using various cross-reactive materials, including supramolecularcomplexes(6–8) and nanoparticles.(9,10) Recently, we have developed pattern-recognition-based
sensing systems for protease detection using arrays of fluorophore-modified DNAs withdifferent sequences.(11,12)
To bring the pattern-recognition-based protease sensors closer to practical use, it is desirable
for the sensing system to be easily constructed with fewer materials. Here, we report an arrayconsisting of only a single material for protease detection. The system is based on our recent
studies(13,14) on protein and cell sensing using a poly-L-lysine modified with environmentresponsive dansyl fluorophores (PLL-Dnc; Fig. 1). PLL is a suitable scaffold for the detectionof macromolecules such as proteases because it contains positively charged amino groups(pKa ~ 10.5) and hydrophobic n-butyl groups, which provide the ability to bind strongly andnonspecifically to proteases through multiple electrostatic and hydrophobic interactions. When
PLL-Dnc binds to a protease, the dansyl fluorescence changes and, in many cases, increases
depending on the nature of the binding sites in the protease. As the dominant forces between
PLL-Dnc and proteases differ with the pH value of the solution, an array of PLL-Dnc dissolved
in different buffer solutions produces fluorescence patterns specific to the individual proteases

Fig. 1. (Color online) Schematic representation of the generation of fluorescence response patterns of proteases
using an array of an environment-responsive PLL-Dnc.

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3(Fig. 1). Multivariate analysis of the patterns obtained from a simple array consisting of asingle PLL-Dnc successfully identified eight different proteases, as well as the compositions of
mixtures of a protease and its inhibitor.

2.

Materials and Methods

2.1

Materials

Carboxypeptidase A from bovine pancreas (Car), α-chymotrypsin from bovine pancreas(Chy), elastase from porcine pancreas (Ela), papain from papaya latex (Pap), proteinase K from
Tritirachium album (Pro), thrombin from bovine plasma (Thr), trypsin from bovine pancreas(TryB), trypsin from porcine pancreas (TryP), α1-antitrypsin from human plasma (ATT),
3-morpholinopropanesulfonic acid (MOPS), and 2-morpholinoethanesulfonic acid (MES) werepurchased from Sigma-Aldrich, Co., LLC (St. Louis, MO, USA). 3-[4-(2-Hydroxyethyl)-1piperazinyl] propanesulfonic acid (EPPS) was purchased from Dojindo Molecular Technologies,
Inc. (Kumamoto, Japan). Poly-L-lysine trifluoroacetate (PLL; degree of polymerization: 55) was
purchased from Alamanda Polymers, Inc. (Huntsville, AL, USA). The PLL-Dnc polymer (5 outof 55 lysine units per side chain were modified with Dnc groups) has already been reported.(14)2.2

Fluorescence measurements

The concentration of proteases and ATT was determined from their absorbance on a
NanoDrop 1000 spectrophotometer (Thermo Scientific, Inc., Waltham, MA, USA) using
the extinction coefficients given in a previous report.(11) Fluorescence measurements were
performed on a Cytation5 Imaging Reader (BioTek Instruments, Inc., Winooski, VT, USA).
Solutions (120 μL) containing 5.0 μg/mL PLL-Dnc and 0–20 μg/mL Thr in 20 mM MES
buffer (pH = 5.0) were prepared in each well of a 96-well black microplate (Corning Inc.,
Corning, NY, USA) using a PIPETMAX liquid handling system (Gilson Inc., Middleton, WI,
USA). After incubation (35 °C, 10 min), the fluorescence intensity was recorded at 35 °C at
λex/λem = 340 nm/520 nm.
2.3

Pattern-recognition-based sensing

Aliquots (108 μL) of PLL-Dnc (5.5 μg/mL) in 22.2 mM MES buffer (pH = 5.5), 22.2 mM
MOPS buffer (pH = 7.0), or 22.2 mM EPPS buffer (pH = 8.5) were deposited in the wells of a
96-well plate using a PIPETMAX system. After incubation at 35 °C for 10 min, the fluorescenceintensity was recorded in two different channels [λex/λem: 320 nm/520 nm (Ch1) and 360 nm/480
nm (Ch2)]. Subsequently, aliquots (12 μL) of the analytes in distilled water were added to eachwell, and the fluorescence intensity was recorded after 10 min of incubation at 35 °C. This
process was repeated six times to generate a data matrix. This data matrix was processed bylinear discriminant analysis (LDA) using SYSTAT 13 software (Systat Inc., Evanston, IL, USA).

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3.

Results and Discussion

3.1

Pattern-recognition-based protease sensing

In this study, we employed PLL-Dnc in which 5 out of 55 units of lysine side chains weremodified with dansyl groups, on the basis of the results of our previous study.(14) The numberof dansyl groups introduced was determined by considering the background fluorescence andthe magnitude of the signal of the analytes.(14) Dansyl fluorophores exhibit weak fluorescencein aqueous solutions, which intensifies when the polarity of the surrounding environment
decreases upon binding to proteins.(15,16) First, we examined whether PLL-Dnc exhibits
fluorescence responses against proteases. Although Thr (pI = 7.1; Table 1) was positively
charged at a weakly acidic pH of 5.5, mixing with cationic PLL-Dnc resulted in an increase indansyl fluorescence [(Fig. 2(a)]. This result suggests that PLL-Dnc can bind to Thr despite theexpected electrostatic repulsion between them, probably owing to the hydrophobic interactions
and the heterogeneous amino acid distribution on the Thr surface.
Once we had confirmed that proteases can be detected using PLL-Dnc, we subsequently
attempted to discriminate eight different proteases (Table 1). In this sensing procedure, asolution containing each protease was mixed with PLL-Dnc (5.0 μg/mL) to a final proteaseconcentration of 20 μg/mL in three buffer solutions (pH = 5.5, 7.0, and 8.5). The net charge ofthese proteases varied over a selected range of pH values, which was expected to result in achange in affinity with PLL-Dnc. The fluorescence responses were recorded as I–I0, where I
and I0 are the fluorescence intensities after and before the addition of proteases, respectively,
using two detection channels [λex/λem = 320 nm/520 nm (Ch1) and 360 nm/480 nm (Ch2)],given that signal detection at different wavelengths is an easy and effective way to enrich theinformation available for analyte identification.(17,18) As a result, a dataset of 6 replicates × 3 pHvalues × 2 channels × 8 proteases was generated.

Table 1
Profiles of proteases and the protease inhibitor used in this study.
AnalyteSourceAbbreviationpI
Protease
Carboxypeptidase A
Bovine pancreas
Car
6.0
Thrombin
Bovine plasma
Thr
7.1
Elastase
Porcine pancreasEla
8.5
α-Chymotrypsin
Bovine pancreas
Chy
8.8
Proteinase K
Tritirachium album
Pro
8.9
PapainPapaya latex
Pap
9.6
Trypsin
Bovine pancreas
TryB
10.1
TrypsinPorcine pancreas
TryP
10.2
Protease inhibitor
Human serum
ATT
5.4
α1-Antitrypsin

Mw [× 103]
34
38
26
25
29
23
24
23
44

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(a)

5

(b)

(c)

Fig. 2. (Color online) Discrimination of proteases by an array of PLL-Dnc. (a) Binding isotherm for 5.0 μg/mL
PLL-Dnc upon addition of Thr in 20 mM MES (pH = 5.5). λex = 340 nm, λem = 520 nm. Values are shown as meanvalues ± 1 SD (n = 3). (b) Heat map and bar graph of the fluorescence response patterns of 20 μg/mL proteases.
Six replicates are shown for each analyte. (c) Discriminant score plot for 20 μg/mL proteases. Ellipses represent
confidence intervals (± 1 SD) for the individual analytes.

The fluorescence responses of the PLL-Dnc array are summarized in the form of a heatmap [Fig. 2(b)], which is a color-scaled representation of the fluorescence intensities. As
expected, various fluorescence changes were observed depending on the type of protease. The
fluorescence patterns were then subjected to LDA to display the complex response patterns as
two-dimensional graphics [Fig. 2(c)]. LDA is a supervised multivariate analysis offering insight
into data clustering and exhibits classification ability.(5) In a linear discriminant plot, each point
represents the response pattern of a single protease analyte in the PLL-Dnc array. Althoughslight overlapping of Pap, Pro, and Ela clusters was observed in the plot, there are wellseparated clusters for the other proteases. In order to examine the reliability of the PLL-Dnc
array for pattern-recognition-based protease identification, a leave-one-out cross-validation test
was performed. For this test, one fluorescence response pattern was removed from the dataset
and treated as test data, which was then assigned to the cluster with the closest Mahalanobis
distance calculated using the remaining data. This procedure was repeated for all the data. Of
the 48 analytes, only two Ela analytes were incorrectly assigned (one to Pap and the other toPro), providing an accuracy of 98%. This indicates that the PLL-Dnc system has high reliability
even for the identification of proteases.

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Pattern-recognition-based discrimination of mixed analytes of a protease and its
inhibitor protein

After the successful identification of the proteases, we subsequently investigated whether
the PLL-Dnc system could be used to discriminate a composition of mixtures of TryB and
its inhibitor protein ATT (Table 1). Because trypsin, which regulates the pancreatic exocrine
function, is associated with various diseases,(19) the detection of trypsin and its complex with
inhibitor proteins is of diagnostic and clinical significance.(20) Trypsin detection is typically
based on the catalytic degradation of the substrate peptides,(21–23) but accurate determinationof the concentration is difficult if its activity is altered by the presence of inhibitor proteins.
Therefore, we attempted the discrimination of eight different TryB/ATT mixtures, including
those with concentration ratios at which the protease activity was completely inhibited by theformation of a 1:1 stoichiometric complex between TryB and ATT [20 μg/mL TryB (~0.85 μM)
and 50 μg/mL ATT (~1.14 μM)],(11) using the PLL-Dnc array [Fig. 3(a)]. At pH = 5.5 and 7.0,
only slight changes were observed with the addition of TryB compared with the fluorescencein the absence of proteins, while at pH = 8.5, the addition of 10 μg/mL TryB reduced thefluorescence intensity (0/0 to 0/10), with the further addition of TryB producing almost nochange (0/10 to 0/30). The addition of 50 μg/mL ATT markedly increased the fluorescenceintensity of PLL-Dnc at all pH values, which may be mainly due to the electrostatic interaction
between the anionic ATT (pI = 5.4; Table 1) and cationic PLL-Dnc. Interestingly, when TryB
was added to a solution containing ATT, the PLL-Dnc signals decreased regardless of the pH
value.

(a)(b)

Fig. 3. (Color online) Discrimination of mixtures of ATT and TryB by an array of PLL-Dnc. (a) Heat map and bar
graph of the fluorescence response patterns of various compositions of ATT and TryB. Six replicates are shown foreach analyte. (b) Discriminant score plot for the mixtures. Ellipses represent confidence intervals (±1 SD) for theindividual analytes.

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In the LDA plot of the first two discriminant scores [Fig. 3(b)], the cluster position in theabsence of ATT changed little as the concentration of TryB increased (0/0 to 0/30), while
the ATT cluster shifted significantly in the negative direction of Score (1) and in the positive
direction of Score (2) with an increase in TryB concentration (50/0 to 50/30). This result may be
due to a decrease in the exposed surface area as a result of the formation of a complex of ATT
and TryB. Despite the overlap of TryB clusters in the plot, the leave-one-out cross-validation
test afforded a relatively high value of 90%, suggesting that our PLL-Dnc array is capable ofextracting information on the events of the ATT/TryB complex formation as response patterns,
although it is currently unable to completely discriminate these eight analytes. Thus, our onecomponent array can be used to discriminate the composition of mixed analytes of a proteaseand its inhibitor protein. We previously reported the discrimination of ATT/TryB compositions
using an array of four fluorophore-modified ssDNAs with different sequences.(11) In thisstudy, we have succeeded in discriminating them using only a single material by utilizing
pH-dependent affinities. In addition to the use of different pH values, the addition of salts toattenuate electrostatic interactions may improve the detection accuracy of the PLL-Dnc systemfor protease sensing, as we have previously demonstrated.(13) In the case of complex matrices
such as serum and urine, PLL-Dnc also interacts nonspecifically with the nontarget proteins
contained therein. Although it is still possible to extract target protein information and toaccurately identify them using pattern-recognition-based sensing techniques,(24) the introductionof specific recognition units into probes(13,25) or the removal of contaminants by purification
will improve the identification accuracy.

4.

Conclusions

We applied a pattern-recognition-based sensor using PLL-Dnc to the discrimination ofproteases. Although our array has high accessibility and consists of only a single material
that can be easily prepared, it allowed the identification of various kinds of proteases andthe detection of protease/inhibitor protein complexes. Considering that this system can beeasily extended through the selection of more diverse solution conditions, e.g., different ionicstrengths,(13) our method can potentially be used to screen for the formation of protein-protein
complexes, including proteases, as well as to detect disease-associated proteases.

Acknowledgments
We would like to thank S. Ishihara (Health and Medical Research Institute, National Instituteof Advanced Industrial Science and Technology) for technical assistance with the experiments.
This work was supported by JSPS KAKENHI grant JP20H02774.

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