How Complex Is the Concanavalin AâCarboxypeptidase Y Interaction

Articles Cite This: ACS Chem. Biol. 2019, 14, 1611−1618

pubs.acs.org/acschemicalbiology

How Complex Is the Concanavalin A−Carboxypeptidase Y Interaction? Katarzyna Herman,† Marek Weiss,† Małgorzata Lekka,‡ and Arkadiusz Ptak*,† †

Institute of Physics, Faculty of Technical Physics, Poznan University of Technology, Piotrowo 3, PL-60965 Poznan, Poland Department of Biophysical Microstructures, Institute of Nuclear Physics, Polish Academy of Sciences, PL-31342 Cracow, Poland

‡

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S Supporting Information *

ABSTRACT: Lectin−carbohydrate interactions can be exploited in ultrasensitive biochemical recognition or medical diagnosis. For this purpose, besides the high specificity of the interactions, an appropriate methodology for their quantitative and detailed characterization is demanded. In this work, we determine the unbinding properties of the concanavalin A−carboxypeptidase Y complex, which is important for characterization of glycoproteins on the surface of biological cells. To achieve the goal, we have developed a methodology based on dynamic force spectroscopy measurements and two advanced theoretical models of force-induced unbinding. Our final results allowed excluding both, rebinding processes and the multibarrier character of the interaction potential, as possible explanations of the concanavalin A−carboxypeptidase Y unbinding mechanisms. Such characteristics as the position and height of the activation barrier and the force-free dissociation rate were determined. We hope our paper contributes to a better understanding of the unbinding processes in receptor−ligand complexes.

L

ectin−carbohydrate interactions play a pivotal role in many biological processes.1,2 Carbohydrates can enhance the stability of proteins, serve as the specific binding targets for proteins, anchor proteins into cell membranes, and mediate various cellular activities.3,4 Lectins can bind carbohydrates with high specificity and affinity; therefore the lectin− carbohydrate interactions can be used in ultrasensitive medical recognition and diagnosis.2,5−7 The potential of lectins lies in their ability to induce apoptotic, autophagic, and antiangiogenic effects in pancreatic,8 bladder,9 breast,10 and liver11 cancers. Lectin-based assays such as the use of an antibodylectin sandwich to detect a glycosylated CA15-312 have the potential to be diagnostic markers. For this purpose, the characterization of the energy and kinetics of the interactions should be not only quantitative but also detailed and precise. To obtain such characteristics, an appropriate methodology is necessary, which includes high precision measurements, wellfounded interpretation of the experimental data, and reliable theoretical models enabling extraction of quantitative information. In our article, we study the unbinding properties of the concanavalin A (ConA)−carboxypeptidase Y (CPY) complex (Figure 1). ConA is widely used in biochemistry to characterize glycoproteins on the surface of various cells.13 The detailed knowledge of ConA−CPY and similar lectin− carbohydrate interactions is important for the development of ultrasensitive biochemical recognition and medical diagnosis. To obtain detailed quantitative characteristics of the specific ConA−CPY interaction, we developed a methodology based on dynamic force spectroscopy (DFS), i.e., the measurement © 2019 American Chemical Society

Figure 1. Basic idea of force spectroscopy experiments applied to study the interaction between concanavalin A (ConA; PDB ID: 3CNA) and carboxypeptidase Y (CPY; PDB ID: 1YSC). The presence of a mannose residue in CPY allows binding to ConA (insert). For details, see Figure S3 in the Supporting Information.

of the unbinding force versus the separation speed (usually recalculated into the loading rate). In recent years, such experiments performed with an atomic force microscope (AFM) have become particularly widespread14−18 because they enabled direct probing of the forces acting between a single receptor and its ligand instead of measuring a large ensemble of molecules as in “classical” methods like differential scanning calorimetry,19 surface plasmon resonance,20 analytical affinity chromatography,21 affinity capillary electrophoresis,22 or quartz crystal microbalance23 (QCM). The most common Received: April 27, 2019 Accepted: June 24, 2019 Published: June 24, 2019 1611

DOI: 10.1021/acschembio.9b00337 ACS Chem. Biol. 2019, 14, 1611−1618

ÄÅ l ÅÅ o 0 o ÅÅ o koff kBT exp ΔGβ o ÅÅ kBT o ÅÅ − Fun = 1 ln m ÅÅ ΔG o νxβ o xβrF ÅÅ β o o ÅÅ o o Å Ç n

ACS Chemical Biology analytical approach for DFS data analysis uses the so-called Bell−Evans model (BE). Our methodology relies on the concerted and systematic application of two advanced theoretical models that go beyond the limitations of BE.

( )

■

(1)

Here, Fβ = kBT/xβ is the so-called thermal fluctuation force, kB is the Boltzmann constant, T is the absolute temperature, xβ is the distance between the bound state minimum and the maximum of an activation barrier on the free-energy potential in the absence of external forces, and koff0 is the force-free dissociation rate. BE (eq 1) fitted to DFS data enables extraction of the values of xβ and koff0, and therefore it has been widely applied to study specific interactions between molecules, including the receptor−ligand complexes.26−32 However, its applicability is limited for at least two reasons. First, BE ignores rebinding processes. Second, it reduces all the information about the shape of the interaction potential to a single parameterxβ. Therefore, more complex models have been developed. Friddle et al.33 have considered the contribution of rebinding in the unbinding process described by Bell’s formula. In the frames of the Friddle−Noy−De Yoreo model (FNDY), an additional parameter is defined, namely, the equilibrium force, Feq, i.e., the force at which the system passes from the near-equilibrium regime to the kinetic one: Feq =

2kcΔG bu

RESULTS AND DISCUSSION Force Spectroscopy Experiments. ConA molecules were deposited on mica substrates and CPY molecules on AFM probes after previous surface silanization and glutaraldehyde activation. There was no need to use a polymer linker (which is a common practice for small ligand molecules) because of the large size of CPY. A scheme of the force spectroscopy experiment is shown in Figure 1 (see Methods and the Supporting Information for details). Tens of thousands of various types of force curves were recorded. The first stage of the analysis was to select force curves showing rupture events characteristic for specific binding. Receptor−ligand (including lectin−carbohydrate) complexes usually exhibit characteristic parabolic downward bends on the retraction part of force curves (Figure 2a,b). At the beginning, the receptor−ligand pair is weakly stretched; thus, the recorded cantilever deflection is close to zero. Further stretching of the receptor−ligand pair generally enhances its stiffness. Some conformational changes enforced in the structure of the interacting proteins, particularly in the tertiary one, can violate the stiffness increase. At the end of the stretching process, the stiffness of the receptor−ligand system is approximately constant until the pulling force exceeds the force needed to unbind the molecular complex (Figure 2a). If more than one ConA−CPY complex is formed between the AFM tip and the substrate, then subsequent, individual ruptures are observed (Figure 2b). If no receptor−ligand complex is formed, still nonspecific interactions between the proteins can be recorded (Figure 2c). Figure 2d represents an exemplary force curve with undetectable interactions, neither

Here, kc is the spring constant of the transducer (the cantilever and the molecular complex), and ΔGbu is the free energy of the bound state relative to the unbound (free cantilever) one. The values of Feq, together with xβ and the dissociation rate at the equilibrium forcekoff(Feq), describe an intermolecular bond. The unbinding force is approximated by the equation:

(3)

where γ = 0.577 is the Euler−Mascheroni constant. Dudko et al. have shown that deviations from the logarithmic character of the Fun(rF) dependence can be due to the force-dependent transition state.34 They assumed a stochastic character of the escape process from a potential well and used Kramers’ theory35 receiving the following formula for the unbinding rate constant koff(F): koff (F ) =

(5)

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(2)

ij yz rF zz Fun ≅ Feq + Fβ lnjjjj1 + e−γ z j Fβkoff (Feq) zz k {

kBT

ÉÑν | ÑÑ o ÑÑ o o ÑÑ o o ÑÑ o ÑÑ } o ÑÑ o o ÑÑ o o ÑÖ o ~

Here, ΔGβ is the free energy of activation in the absence of external forces, and the parameter ν is related to the shape of the free-energy potential. The authors suggest the use of ν = 2/ 3, appropriate for the linear-cubic free-energy potential, as a universal value in their proposed model. The Dudko− Hummer−Szabo model (DHS)in contrast to BEenables extraction of ΔGβ additionally to koff0 and xβ. For ν = 1, eq 5 reduces to the Evans formulaeq 1. Although DHS is more complex than BE, it has been successfully applied to the analysis of the interactions occurring between an AFM tip and self-assembled monolayers36−39 as well as ligand−receptor interactions.40 We have performed the analysis of the DFS data in various loading rate ranges, with the two advanced models: FNDY and DHS. The methodology allowed systematic examination of possible reasons for the deviations from the predicted (by BE) logarithmic dependence, like the influence of the interaction potential shape, the existence of an inner activation barrier, and the contribution of rebinding processes. We have extended the range of the loading rate toward higher values (about 1 order of magnitude relative to the standard single-molecule DFS measurements) to obtain more reliable results. Finally, we determined such parameters of the ConA−CPY complex as the force-free dissociation rate, the position of the activation barrier on the free-energy potential, the height of the activation barrier, and the energy difference between the unbound and bound states.

THEORETICAL MODELS OF FORCE-INDUCED UNBINDING Evans and Ritchie have shown,24 using Bell’s formula for the unbinding rate constant,25 that the unbinding force (Fun) depends logarithmically on the loading rate (rF): ij r yz Fun = Fβ lnjjjj F0 zzzz j Fβkoff z k {

ΔGβ

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∫ e E(x) − Fx / kBT dx∫barrier e−E(x) + Fx / kBT dx

0 well koff

∫well e E(x)/ kBT dx∫barrier e−E(x)/ kBT dx (4)

The authors specified the free-energy potential shape and obtained the following expression for the most probable unbinding force: 1612


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ACS Chemical Biology

fitted with the log-normal function (Figure 3) instead of the widely used Gaussian function or the distribution functions derived from some theoretical models of unbinding.19 In our opinion, the log-normal function describes better real data distributions, which are asymmetrical as a rule. The left tail of the distributions is limited by zero, since the unbinding force cannot be negative, whereas the right tail is theoretically unlimited and practically includes also events that are not true single-bond ruptures. And this is not predicted by the theoretical models. Some nonspecific interactions and hidden multiple ruptures, which are difficult to distinguish from true single-bond ruptures,41 are often such outliers increasing the average unbinding force. However, the most probable value in the log-normal distribution fitting, which is used in further analysis, is marginally affected by the outliers, which can be considered an advantage of the log-normal distribution function over other distribution functions, in particular symmetrical or with the opposite asymmetry. Dynamic Force Spectrum and Its Interpretation. The most probable values of the unbinding force were determined for each loading rate and used to plot the so-called dynamic force spectrum (Figure 4). The spectrum for the ConA−CPY complex resembles a typical one for a receptor−ligand interaction.26−32 The unbinding force increases with the elevating loading rate, and this increase becomes steeper above some threshold loading rate: 50 000 pN/s in the case of the ConA−CPY complex (Figure 4a, curve 3). Two ranges of dynamic force spectra are often identified in a standard analysis, and the common interpretation of such data is based on the assumption that the receptor−ligand complex goes through two activation barriers during unbinding: the outer one, which determines the dependence at low loading rates (below ∼50 000 pN/s; Figure 4a, curve 2), and the inner one, which determines the dependence at higher loading rates (over ∼50 000 pN/s; Figure 4a, curve 3). The inner barrier can be revealed at high loading rates because the outer one is strongly lowered by external force at the moment of forceinduced unbinding (Figure 5a). Then, one of the models of thermally activated unbinding under load (usually BE) is fitted

Figure 2. Exemplary types of the most common force curves observed for ConA−CPY interaction: (a) an individual rupture of a single, specific ConA−CPY complex; (b) subsequent, individual ruptures of specific ConA−CPY complexes; (c) nonspecific interactions present between the CPY modified AFM probe and ConA functionalized mica surface; (d) no detectable interaction, neither a specific nor a nonspecific one. The horizontal arrow indicates the direction of AFM probe retraction; Fun is the unbinding force.

specific nor nonspecific ones. Force curves indicating a single specific rupture event, which accounted for about 30% of all force curves, were qualified for further analysis. In the reference experiment, in which ethylenediaminetetraacetic acid (EDTA) inhibited the specific interaction, the number of such force curves decreased below 8% (Table S1 in the Supporting Information). Each force curve showing a specific rupture event was analyzed in search for unbinding force value (Figure 2a). All values were collected into histograms, made separately for each loading rate (14 together). The distribution of events was

Figure 3. Four exemplary histograms (of the set of 14) of the unbinding force obtained for the ConA−CPY complex at the following loading rates: (a) 3100 pN/s, (b) 31 000 pN/s, (c) 232 500 pN/s, and (d) 542 500 pN/s. The histograms are fitted with the log-normal distribution function (solid line). The most probable values (maxima of the fitting curves) were used in further analysis. 1613


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Figure 5. Conceptual interaction potentials: force-free, E(x) (solid line); deformed by external force, E(x) − Fx (dashed line), describing a specific binding characterized by two activation barriers (a) and one barrier with possible rebinding as a back transition (b). xβ, the distance between the bound state and the maximum of an activation barrier; ΔE0, the difference between the height of the barriers; ΔGbu, the free energy of the bound state relative to the unbound (free cantilever); ΔGβ, the free energy of activation in the absence of external forces.

Figure 4. Dynamic force spectrum, i.e., the dependence of the unbinding force on the loading rate, for the ConA−CPY complex. Each point (black square) denotes the most probable value of the unbinding force. The data were fitted with three models: (a) BE for the whole experimental range of loading rates (curve 1, red solid line), the low range (curve 2, blue solid line), the high range (curve 3, violet solid line), and the very high range (curve 4, green solid line). (b) FNDY for the whole experimental range (curve 1, red solid line), the range without the lowest loading rate point (curve 2, green solid line), the range without the two lowest loading rate points (curve 3, blue solid line), the range without the three lowest loading rate points (curve 4, cyan solid line), the range without the four lowest loading rate points (curve 5, magenta solid line). (c) DDHS for the same ranges as FNDY. Exact values of the ranges are specified in Tables 1−3. The experimental uncertainties were estimated as a double standard error.

Table 1. Kinetic and Energetic Parameters Describing the Unbinding of Single ConA−CPY Complex Obtained from BEa rF [pN/s] 775−620 000 (whole range) 775−50 000 (low range) 50 000−620 000 (high range) 400 000−620 000 (very high range)

to DFS data separately for both ranges of the loading rate (Figure 4a, curves 2 and 3). As a result of its application, such parameters as koff0 and xβ for each of the barriers as well as the difference between the heights of the barriers (ΔE0) can be obtained (Figure 5a). It is important to note that the absolute height of the barrier cannot be determined with such a model. Analysis with the Models of Force-Induced Unbinding. We have fitted BE to the experimental curves for the whole experimental range of loading rates: 775−620 000 pN/s (Figure 4a, curve 1, red solid line), the low range: 775−50 000 pN/s (Figure 4a, curve 2, blue solid line), and the high range: 50 000−620 000 pN/s (Figure 4a, curve 3, violet solid line). The goodness of the fits was estimated with the adjusted coefficient of determination (Radj2), which is usually used to compare fitting models with different numbers of predictors, as it is for BE, FNDY, and DHS. In the case of BE fits, Radj2 is higher for the two distinct ranges (Figure 4a, curves 2 and 3; Table 1) than for the whole range (Figure 4a, curve 1; Table

a

curve (Figure 4a)

koff0 [s−1]

xβ [nm]

Radj2

1

6.4 ± 2.1

0.327 ± 0.023

0.948

2

1.4 ± 0.3

0.515 ± 0.017

0.995

3

118 ± 34

0.180 ± 0.015

0.956

4

780 ± 100

0.091 ± 0.008

0.982

Errors at the parameter values denote the quality of the fits.

1). It could support the “inner barrier” interpretation. However, it is obvious that the split of the experimental range into further, narrower ranges (e.g., 400 000−620 000 pN/s, Figure 4a, curve 4, green solid line) can improve to some extent the fittings (Table 1, curve 4). But this does not necessarily mean the existence of further inner barriers and cannot be considered a sufficient argument. Instead, there are counterarguments against the “inner barriers” interpretation. Our recent experiments for various hydrophobic selfassembled monolayers at near zero relative humidity, when 1614


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Table 2. Kinetic and Energetic Parameters Describing the Unbinding of Single ConA−CPY Complex Obtained from FNDYa rF [pN/s]

curve (Figure 4b)

775−620 000 (whole range) 1550−620 000 2300−620 000 3100−620 000 9300−620 000

1 2 3 4 5

koff(Feq) [s−1] 295 372 546 720 990

± ± ± ± ±

xβ [nm]

70 84 120 190 260

0.193 0.183 0.170 0.157 0.139

± ± ± ± ±

Feq [pN]

0.016 0.015 0.015 0.017 0.017

35.9 38.3 42.5 45.7 49.6

± ± ± ± ±

1.7 1.8 2.1 2.8 2.8

ΔGbu [kBT]

Radj2

± ± ± ± ±

0.989 0.990 0.991 0.989 0.991

5.5 6.2 7.7 8.9 10.5

0.9 0.9 1.0 1.2 1.2


a

Table 3. Kinetic and Energetic Parameters Describing the Unbinding of Single ConA−CPY Complex Obtained from DHSa rF [pN/s]

curve (Figure 4c)

775−620 000 (whole range) 1550−620 000 2 300−620 000 3100−620 000 9300−620 000

1 2 3 4 5

koff0 [s−1] 3.91 4.07 4.21 4.25 4.25

± ± ± ± ±

0.53 0.52 0.59 0.61 0.67

xβ [nm] 0.4737 0.4725 0.4722 0.4730 0.4725

± ± ± ± ±

0.0003 0.0003 0.0003 0.0002 0.0003

ΔGβ [kBT]

Radj2

± ± ± ± ±

0.983 0.985 0.983 0.982 0.979

9.84 9.79 9.76 9.74 9.74

0.35 0.32 0.35 0.35 0.37


a

would be more affected by rebinding than those at higher loading rates. The final values of the kinetic and energetic parameters were calculated as a mean of five values obtained with DHS for various loading rate ranges: koff0 = 4.1 ± 0.4 s−1, xβ = 0.473 ± 0.002 nm, ΔGβ = 9.77 ± 0.11 kBT = 24.2 ± 0.3 kJ/mol (for T = 293 K), where the measurement uncertainties are standard deviations multiplied by a Student’s t coefficient (2.571) for the 95% confidence level. Additionally, we calculated ΔGbu as a mean of the values obtained from the fittings with FNDY for the three widest regions: ΔGbu = 6.5 ± 1.6 kBT = 16.0 ± 3.9 kJ/mol (for T = 293 K). The measurement uncertainty was determined in the same way as for the parameters from DHS fittings. Although ΔGbu is burdened with a large uncertainty, it delivers complementary information to ΔGβ, i.e., the energy difference between the unbound and bound states instead of the height of the activation barrier (Figure 5b). Conclusions. Although such detailed characteristics have been obtained for the first time for the ConA-CPY complex (as well as for any lectin−carbohydrate complex), some of the parameters can be compared with earlier reported values. Those of xβ, obtained using BE, ranged from 0.228 to 0.27 nm.43−45 The values of xβ obtained by us with BE for the whole range are slightly higher, and for the high ranges, smaller than the above results. However, the value obtained with DHS (xβ = 0.473 nm) is almost twice higher than the values obtained with BE. A similar difference between the values obtained with both models has been observed by Dudko et al. for simulated parameters of the titin unfolding,34 by Hane et al. for the parameters of amyloid-β-dimer unbinding,40 and by us for xβ of syndecan−antibody complexes.46 Since the free energy of activation could not be obtained directly with BE, we compared our result with those obtained with QCM measurements for the free energy of the bound state relative to the unbound one: ΔGbu = 13.3 kBT43 and ΔGbu = 36.40 ± 0.11 kJ/mol ≈ 14.9 ± 0.1 kBT.44 In this case, the difference is not significant. Much larger differences occur for the values of dissociation rate constant: koff0 = 0.095 s−139 and koff0 = 0.026 s−1.40 However, one should remember that the values obtained from DFS experiments concern a single molecule unbinding, whereas QCM results are from volume measurements and can be affected by a number of nonspecific interactions as well as rebinding. Analyzing the results obtained

the van der Waals forces were major interactions between the AFM tip and the samples, resulted in a similar shape of the adhesion force−loading rate dependences.36,42 In those cases, the “inner barriers” interpretation could not be applied. The force-free interaction potential can be expressed as a Lennard− Jones type potentialwithout any barriers. A single barrier appears but during the deformation of the interaction potential caused by external force. Therefore, the “inner barriers” interpretation cannot be treated as universal, and different explanations were considered. One of them assumes the influence of rebinding processes, i.e., back transitions through the activation barrier (Figure 5b) at low loading rates and hence the weak dependence of the unbinding force in this range. Since BE ignores the rebinding processes, we have applied the FNDY model and fitted it to the DFS data of the ConA− CPY complex (Figure 4b). The obtained parameters (Table 2) generally differ from those obtained with BE (Table 1). However, the contribution of the rebinding processes should be negligible at high loading rates, and therefore, the results obtained with BE for this region should agree with those obtained with FNDY, which is based on Bell’s equation (eq 1). For this particular case, some agreement has been found (Table 1, curves 3 and 4 versus Table 2). Nevertheless, the fitting parameters in FNDY are sensitive to the range of loading rates. If the range is narrowed by subtracting the points at the lowest loading rates, the calculated parameters change significantly: the values of koff and ΔGbu increase, whereas the value of xβ decreases with the increase of the lower limit of the fitting range. It does not support the hypothesis of the significant influence of rebinding on the shape of the dynamic force spectrum. If FNDY, which takes into consideration rebinding processes, properly describes the unbinding of the ConA−CPY complex, then the parameters should be independent of the loading rate range. Finally, we have applied the DHS model that works better at high loading rates than BE. Although DHS ignores rebinding, the fitting with this model is not sensitive to the range of experimental points (Figure 4c). The obtained values of the fitting parameters are almost independent of the loading rate range (Table 3). This supports the conclusion that the rebinding processes do not influence significantly the unbinding force. Otherwise, the data at lower loading rates 1615


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ACS Chemical Biology with the three models, one can notice that the values of koff0 obtained with BE and FNDY are strongly dependent on the loading rate range (even more than the values of xβ and ΔGβ). Moreover, the FNDY koff0 values are much higher than usually measured for receptor−ligand interactions.28 In conclusion, our methodology, based on dynamic force spectroscopy measurements and the systematic analysis of DFS data for different loading rate ranges with advanced models of force induced unbinding, allowed rejection of rebinding processes and the multibarrier character of the interaction potential as possible explanations of the ConA-CPY unbinding mechanism. The approach delivered detailed, quantitative, and consistent characterization of the receptor−ligand interactions at the single molecule level.

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chosen for further analysis. All force curves were analyzed using AtomicJ software.51

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acschembio.9b00337.

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METHODS

Structure and function of carboxypeptidase Y and concanavalin A, specific binding site between D-mannose and ConA residues, and detailed description of the reference measurement (PDF)

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

Proteins and Their Interactions. CPY (Sigma−Aldrich) from baker’s yeast (Saccharomyces cerevisiae) belongs to the family of serine proteases. It is a monomer containing a single polypeptide chain (Figure S1 in the Supporting Information) and four asparagine-linked oligosaccharide chains.47 The carbohydrate moiety of CPY is composed of mannose residues, whose presence allows binding to ConA.48 ConA (Sigma−Aldrich) is a lectin isolated from Canavalia ensiformis bean seeds, known wider as Jack bean. Unlike CPY, it is a metalloprotein containing Mn2+ and Ca2+ ions. The presence of the metal ions stabilizes the conformation of the protein and is necessary for saccharide-binding activity.49 ConA may bind to mannose as well as interact with immunoglobulins, lipoproteins, or rhodopsin. See the Supporting Information for details on CPY, ConA, and their specific interactions. Sample Preparation. A small piece (about 2 cm2) of atomically flat muscovite mica was mechanically exfoliated. Then, the mica substrate was placed for 1.5 h in a desiccator along with a Petri dish filled with 1 mL of 3-aminopropyltriethoxysilane (APTES; Sigma− Aldrich) for gas phase surface silanization. Subsequently, the silanized mica substrate was immersed in 2.5% (v/v) glutaraldehyde (Sigma− Aldrich) aqueous solution for 45 min. After these steps, the substrate was gently rinsed with tris-buffered saline (TBS; Sigma-Aldrich, T5030) solution. ConA molecules were deposited on the prepared mica substrate via a drop of 0.1 mg mL−1 protein solution. After 30 min, the sample was thoroughly rinsed with TBS solution in order to remove the excess unbound material. Supersharp, V-shaped, and low spring constant silicon nitride cantilevers (SNL-D, Bruker AFM Probes) were chosen for force spectroscopy measurements. They were functionalized with CPY by immersing them in 0.1 mg mL−1 solution after previous surface silanization and glutaraldehyde activation. Force Spectroscopy. Agilent 5500 AFM (Keysight Technologies Inc.) operating in contact mode was used for the force spectroscopy. All the measurements were made in a liquid cell filled with TBS solution (pH 7.6) at RT. The spring constant of SNL-D cantilevers was determined before the functionalization process by the reference method.50 The reference cantilever was CLFC-NOBO-C (Bruker AFM Probes), and the resulting relative uncertainty of the SNL-D cantilevers’ spring constants did not exceed 4%. Force curves were recorded as a function of the separation rate in the range from 0.05 μm/s to 20.0 μm/s. The respective loading rate, calculated as a product of the effective spring constant and the separation rate, ranged from 775 pN/s to 620 000 pN/s. The effective spring constant was determined from the slope of the retract part of the force− displacement curve recorded just before the rupture of a specific bond. The force curve recording process was carried out in cycles. Each cycle comprised measurements for a 32 × 32 mesh of points localized on a 100 μm2 surface area. The cycles were carried out pseudorandomly versus the separation rate. Altogether, more than 5000 force curves were recorded for each separation rate. A series of measurements made with one cantilever for all separation rates was

ORCID

Małgorzata Lekka: 0000-0003-0844-8662 Arkadiusz Ptak: 0000-0001-8570-0863 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The experimental support from L. Majchrzycki (The Wielkopolska Centre of Advanced Technologies, Poznan, Poland) is gratefully acknowledged. The visualization of proteins (Figure 1,) has been made with ArgusLab 4.0 (M. Thompson, Planaria Software LLC, Seattle, WA). The research was supported by the National Science Centre of Poland (NCN) within project no. UMO-2014/15/B/ST4/04737. A.P. and M.W. acknowledge the Ministry of Science and Higher Education in Poland for financial support within Project No. 06/62/SBAD/1923 realized at Faculty of Technical Physics, Poznan University of Technology.

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REFERENCES

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