Simple and Flexible Model for Laser-Driven AntibodyâGold Surface

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A simple and flexible model for laser-driven antibody gold surface interactions: functionalization and sensing Bartolomeo Della Ventura, Antonio Ambrosio, Annalisa Fierro, Riccardo Funari, Felice Gesuele, Pasquale Maddalena, Dirk Mayer, Massimo Pica Ciamarra, Raffaele Velotta, and Carlo Altucci ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.6b04449 • Publication Date (Web): 26 Jul 2016 Downloaded from http://pubs.acs.org on July 27, 2016

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ACS Applied Materials & Interfaces

A simple and flexible model for laser-driven antibody - gold surface interactions: functionalization and sensing Bartolomeo Della Ventura,† Antonio Ambrosio,‡,¶ Annalisa Fierro,‡ Riccardo Funari,† Felice Gesuele,† Pasquale Maddalena,†,‡ Dirk Mayer,§ Massimo Pica Ciamarra,⇤,k,‡ Raffaele Velotta,†,? and Carlo Altucci⇤,†,? †Department of Physics, Università di Napoli “Federico II”, via Cintia, I-80126, Napoli, Italy ‡CNR-SPIN, Department of Physics, Università di Napoli “Federico II”, via Cintia, I-80126, Napoli, Italy ¶Harvard School of Engineering and Applied Sciences, Harvard University, 9 Oxford St, Rm 125, Harvard, USA §Peter Grünberg Institute (PGI-8) and Institute of Complex Systems (ICS-8), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany kDivision of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore ?CNISM, UdR Napoli, via Cintia, I-80126, Napoli, Italy E-mail: [email protected]; [email protected] KEYWORDS complex systems, nanoassembly, simulation, surface functionalization, bio-sensing Abstract

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ACS Paragon Plus Environment


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Interactions between bio-molecules and between substrates and bio-molecules is a crucial issue in physics and applications to topics such as biotechnology and organic electronics. The efficiency of bio- and mechanical sensors, of organic electronics systems and of a number of other devices critically depends on how molecules are deposited on a surface so that these acquire specific functions. Here we tackle this vast problem by developing a coarse grained model of bio-molecules having a recognition function, such as antibodies, capable to quantitatively describe in a simple manner essential phenomena: antigen-antibody and antibody substrate interactions. The model is experimentally tested to reproduce the results of a benchmark case, such as (1) gold surface functionalization with antibodies and (2) antibody-antigen immune-recognition function. The agreement between experiments and model prediction is excellent, thus unveiling the mechanism for antibody immobilization onto metals at the nano-scale in various functionalization schemes. These results shed light on the geometrical packing properties of the deposited molecules, and may open the way to a novel coarse-grained based approach to describe other processes where molecular packing is a key issue with applications in a huge number of fields from nano- to bio- sciences.

1

Introduction

The modeling of processes involving complex molecules is an issue relevant to many scientific fields from nano- 1–3 and bio-sciences 4,5 to nonlinear optics 6,7 and organic electronics. 8 In particular, coarse–grained models are needed to get a fundamental understanding of molecular processes involving many molecules and occurring on large spatial and temporal scales, as their molecular level description 9–14 is out of reach, as well as to guide the development of innovative technologies. In this respect, the modeling of molecules that exert a recognition function, such as specific proteins or peptides acting as receptor in a receptor–ligand interaction or antibodies selectively capturing antigens with high specificity is of particular relevance. Indeed, these molecules can be used to functionalize a surface to develop sensors.

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This surface functionalization process is essential in bio-sensing, 15–20 organic chemistry, 21,22 and immunology. 23 As a relevant case study, here we focus on the anchoring of antibodies on surfaces, which is demanded in sensing and bio-sensing as in the case of quartz-crystal microbalance (QCM) based devices, a type of sensor that has received an increasing interest in recent years with wide applications to liquid samples. 24,25 In these applications, one is primarily interested in the functionalization of gold plates, 25–28 as gold is an inert metal hard to oxidize thus resulting biocompatible, easily cleaned and chemically treated using standard procedures. Previous studies of the interaction of gold with peptide sequences 29–37 have shown the gold-sulfur interaction to be stable, thus representing a useful tool to immobilize molecules onto a gold support, the availability of a thiol group (-SH) being the only prerequisite. The gold-sulfur interaction is also of particular interest in light of the recent development of the Photonic Immobilization Technique (PIT), a process able to break difulside bridges and thus to influence the molecule-surface interaction and the tethering process. PIT functionalized surfaces, both in the case of large and heavy biomolecules such as type G immunoglobulin (IgG), 38 as well as light molecules having a weight of few hundreds Da such as Parathion 19 and Patulin, 20 resulted to have a sensibly increased sensing ability. In this paper we introduce a simple a versatile model to describe the deposition of antibodies on a surface, the effect of the PIT protocol, and the sensing features of the functionalized surface. The model, somewhat ispired to bionanocombinatorics, 39–41 proves to mimic functions and processes typical of biomolecules such as antibodies, by combining simple elemental nanostructures, such as spheres of suitable size properly connected together, a bit reminding the philosophy of the construction machines realized in LEGO-like toys. We first show that the model is able to quantitatively reproduce a variety of experimental measurements concerning the functionalization process, the sensing ability of the functionalized surface, and the effect of the PIT protocol. Then we employ the model to investigate the morphological properties of the functionalized surface, a precious information which is not experimentally accessible. 3



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The importance of this work is twofold. On the one side, we validate the deposition process modeling, by showing that this reproduces all of our experimental results. On the other side, we understand how PIT enhances the efficiency of the immune–recognition function by affecting the spatial localization and orientation of the deposited biomolecules. This investigation allows to understand how one should better pack the molecules on the substrate to improve the efficiency of a possible sensing device.

2

Experimental

QCM Analysis The quartz-crystal microbalance (QCM) is an analytical device used for the detection of an analyte combining biological components with a physical and/or chemical detector and a signal transducer. QCM exploits the piezoelectric effect in quartz to measure changes in the resonant frequency of a quartz oscillator in response to a mass increase due to molecular binding at the sensor surface. 15,42,43 The microbalance is integrated in a microfluidic circuit consisting of a cuvette, containing the sample under study, connected to a fluidic cell through a pipeline where the liquid flows driven by a GILSON peristaltic pump. The cell contains a gold plate electrode deposited onto a chromium layer interfaced to a quartz crystal oscillator. The relation between the frequency shift, adsorbed biomolecules, have

f=

f , and deposited mass of the

m, is given by the Sauerbrey equation 43 from which we typically

K( m), K being a constant that depends on several experimental parameters

(resonance frequency, piezoelectrically crystal active area, quartz density and shear modulus for AT-cut crystal). The QCM oscillator is placed on an electronic console that monitors its resonance frequency. The volume of the circuit is about 300 µL and the flow rate is 3 µL/s. The quartz oscillators (151218) are from ICM, Oklahoma City (USA). They are AT-cut quartz with a fundamental frequency of 10 MHz. The crystal and the gold electrode diameters are 1.37 cm and 0.68 cm, respectively. The gold surfaces are cleaned by immersing 4


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the oscillators for 1 min in a glass becker containing Piranha solution (3:1 ratio between concentrated sulfuric acid and 40% hydrogen peroxide solution). Then, the quartzes are washed with elix water, i.e. a purified water with a certified standard of purity. The whole cleaning procedure is performed in the hood and can be repeated 3–4 times before the quartz needs to be changed. The QCM device is from Novaetech s.r.l., Italy. In the present work we choose as analyte the IgG from mouse and as bio-element the IgG antibody (anti-IgG from goat), because it is characterized by a high specificity for the analyte. Moreover, this complex represents a good case study, since we have used it in previous experiments exploiting standard QCM electrodes. Samples were prepared according to protocols described elsewhere 44 with an IgG diluted concentration ranging in the 0

100 µ g/mL.

AFM Analysis Deposited antibodies onto the gold surface of the QCM were analyzed by AFM. AFM images of the deposited antibody height distribution were obtained using an atomic force microscope (XE-100 by Park Systems Corp.) working in tapping mode. The image analysis has been performed using standard software provided by the same company. For characterizing the morphology of the deposited antoibodies, AFM imaging was performed using a Nanoscope Multimode 8 (Bruker) microscope equipped with 120 µm piezoelectric scanner and phosphorus doped Si cantilevers from Veeco (RTESPW). The samples for AFM analysis was prepared by stripping a gold-Si wafer (80 nm Au) and functionalizing the metallic surfaces by using a custom glass and polydimethylsiloxane (PDMS) microfluidic apparatus. The PDMS (Sylgardr 184) was prepared with a 1:10 w/w ratio between the curing agent and the prepolymer. The system consisted of a PDMS layer (about 5 mm) attached to a glass ring covered with a PDMS thin film to confine the liquid onto the surface. The sample was loaded by using a tygon pipe connected to a 1 mL syringe. The volume of the chamber was about 40 µL while the whole circuit was about 100 µL. Surface functionalization was realized by incubating an antibody sample (having a concentration of about 2 µg/mL) with 5



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the metallic substrate for about 90 s.

3

Computational model

Our computational model is based on previous knowledge on the structure and biological function of anti-IgG antibodies and on the PIT technique, we shortly review for completeness. In Fig. 1(a) the typical “Y” structure of the anti-IgG antibody is reported. We evidence the principal domains of this heavy and complex molecule, consisting in variable (green) and constant (blue/cyan) domains. The antigen binding sites are located on the top of the antibody variable regions. In the absence of PIT the functionalization process occurs as described in Sec. 2 “QCM Analysis”. In the presence of PIT antibodies are first irradiated by means of a 257 nm femtosecond laser source for a few minutes in appropriate experimental conditions 44 and then used for a QCM measurement. At a molecular level PIT consists in a photonic activation of Trp/Cys-Cys triads, as resulting from the quaternary threedimensional crystallographic structure of the protein, of a type G immunoglobulin (antiIgG) 45 by means of a single UV photon absorption (257 nm implying about 4.8 eV/photon) mostly by the Tryptophan (Trp) aminoacid due to its high UV absorption cross-section, 46,47 as previously demonstrated in Neves-Petersen et al. 38 and Della Ventura et al. 48 These triads are characterized by the Trp residue having a distance not longer than approximately 5 Å from the disulfide bridges, 49,50 thus allowing an efficient energy transfer from the excited Trp to the bridge with its consequent breackage. This results in the formation of free, reactive thiol groups that will form covalent bonds with thiol reactive surfaces such as gold . 38 It should be stressed that in the present irradiation condition when using fs laser UV pulses of 25 µ J/pulse - 60 s irradiation time and 250 mW average power- we carefully checked 51 that antibodies are not degraded, but rather they are only efficiently photo-activated with no effect on the capability of catching the antigene. Amongst the triads of the entire molecule, i.e. the disulfide bridges having an aromatic residue located nearby, we choose to position

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the effective laser-induced excitation in the top part of the antibody “leg”, below the molecule hinges, not far from the interconnection between variable and constant domains where triads could be more solvent-accessible. This choice is motivated by previous experiments carried on antibody fragments constituted by the only “arm-like” regions of the molecule, the so-called Fab fragments, that identified the region of excitation induced by UV light. 38 Fig. 1 illustrates the philosophy underlying our model, inspired by a modular and simplifying approach typical of a reductionist approach, but at the same time very flexible and capable to simulate many different contexts and even complex interactions. Therefore, we move from the typical representation of an antibody structure highlighting its different domains related to specific antibody functions (a), to (b) our model of the anti-IgG. Here red points indicate the antibody antigen binding sites and the orange cans indicate hotspots where the antibody can preferentially tether onto the metal surface due to disulfide bridge opening induced by laser irradiation. Our approach involves molecular dynamic simulations 52 of the deposition of a model anti-IgG on a model surface, as well as molecular dynamics simulations of the recognition process. As illustrated in Fig. 1(b), we model an anti-IgG through a collection of 10 particles evenly placed along 3 arms, so as to reproduce their typical “Y” shape. These particles are rigidly connected, so that each anti-IgG is considered to be indeformable. The interaction between two anti-IgG is the sum of the interactions of the particles modeling each of them. In our model, this interaction is given by the Weeks-Chandler-Andersen (WCA) potential, 53,54 which is a Lennard-Jones potential

V (r) = 4"

⇣ ⌘ r 12

⇣ r ⌘6

,

(1)

truncated at its minimum, rm = 21/6 , and shifted so that V (rm ) = 0. Accordingly, there is no attractive interaction between the particles, that can be seen as spheres of diameter rm . To reproduce the physical size of the anti-IgG, we fix rm = 5 nm, and the angle between the two

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Figure 1: (a) Schematic structure of an antibody with evidenced its different domains in different colors, related to specific antibody functions such as the antigen binding sites and the hinge region at the conjunctions of the variable (green) and constant (blue/cyan) domains. (b) Our model of the anti-IgG with red points indicating the antibody antigen sites and the orange cans indicating the hotspots where the antibody can preferentially tether onto the metal surface due to disulfide bridge opening induced by laser irradiation. Dimensions reproduce the actual antibody shape. arms so that the antibody has a height of 8.5 nm. The antibodies are deposited on a surface, we model as a collection of point particles on a square grid of length L = 400 nm, with a resolution of 1 nm. The height profile of these particles is exactly that obtained through AFM measurements of the gold lamina. The interaction between these point particles and the antigens is also given by a WCA potential, but in this case the minimum is fixed at rm /2 = 2.5 nm, the radius of the spheres we use to construct the molecules. To model the surface depostion process, we consider that in the experimental system one expects the anti-IgSs to diffuse inside the liquid sample, until they approach the surface. When an anti-IgS approaches the surface, in a random position and with a random orientation, its dynamics becomes dominated by the strong metal–protein attraction, 55 and thermal diffusion becomes negligible. Considering that the number density of the anti-IgG in the experimental system is extremely low, the effect of the metal–protein attraction is well described as a sequential deposition process. Accordingly, we repeatedly simulate the deposition of antigens placed in random positions and with random orientations above the surface. The particle-surface attraction is simply modeled introducing a weak downward pointing force acting on all of the particles forming the antibody. Since thermal motion is

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negligible with respect to the surface-molecule attraction, simulations are conducted at zero temperature and in the presence of a viscous damping force, working in the overdamped limit. Once all of the energy is dissipated, we check whether the antibody is in contact with the surface. If so, we consider the biomolecule as fixed in the rest of the our simulation, assuming that the molecule-surface binding energy is much larger than the thermal energy. If the antibody is not in contact with the surface, then it is sitting on some previously deposited biomolecules. In this case we remove the antibody, thus simulating the effect of the thermal motion which leads to the same effect. The deposition procedure is iterated until the density of deposited antigens saturates. To model PIT, we consider that this creates hot spots, i.e. thiols in localized sites of the antibody which are strongly attracted to the surface. We model this heterogeneity by increasing the downward pointing force to the spheres making up the antibody that are located in the positions of the hot spots (see Fig.1). Specifically, we have doubled this force, but we have also checked that the increase of such a force leads to analogous results. Finally, we have also modeled the IgG anti-IgG recognition process. The IgG is modeled as an anti-IgG, the only difference being the absence of any active receptor. At the molecular level, we consider the recognition to occur when one of the two small receptors located on the extreme of the “arms” of the anti-IgG, i.e. in the variable regions, as in Fig. 1, interact with the “leg” of an IgG. The recognition process is modeled as a sequential deposition process as that described above. We consider an IgG as recognized if it is in contact with a receptor of an anti-IgG, and in this case we consider it as fixed for the rest of the simulation, on the assumption that the anti-IgG/IgG interaction energy is stronger than the thermal energy. On the contrary, if the falling IgG does not touch any anti-IgG, we remove it. Our model makes two key assumptions. Firstly, we neglect thermal motion, considering that the thermal energy is generally much smaller than the binding energy. Secondly, we consider the antibody as a rigid body. This approximation is justified considering that the bending energy of IgG is of the order of 4 kT at room temperature. 56,57 9



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As a final remark, we notice that our model of the effect of PIT process has a wide applicability, as the triads are present in all members of the immunoglobulin superfamily (IgSF). The same geometry of the Trp/Cys-Cys triad is observable in the structures of several IgSF as well as in T-cell Receptors, in major histocompatibility complexes, 58 and various cell–adhesion molecules. This makes the surface functionalization here described very general and the model generalizable to many important classes of biomolecules, such as enzymes (e.g. hydrolases and oxido-reductases), and molecules such as the Prostate Specific Antigen - PSA that can be correlated with the development of prostate cancer and have great biomedical relevance in disease prognosis.

4

Results

4.1

Experiment v.s. simulation: deposition & recognition

We compare in Fig. 2 experimental and numerical results concerning the evolution of the surface density of deposited and recognized antibodies during the deposition process, conducted both without (black dashed) and with (red) PIT protocol. Both in the experiment and in the simulations, we measure the densities as number of antibodies (nAB) for square centemeter. First we consider results for the density of deposited anti-IgS. Panels (a) and (b) illustrate how the density of deposited anti-IgS increases as a function of time in the experiment, and as a function of the number of attempted depositions in the simulation. We observe the density of deposited anti-IgG to saturate to a value which is unaffected by PIT, and which is similar in the experiment and in simulation, being 6

7 ⇥ 1012 cm

2

and 5.5

6 ⇥ 1012 cm 2 ,

respectively. This demonstrates that our model nicely reproduces the interaction between anti-IgG and gold lamina both in the presence and in the absence of the PIT procedure application. We then consider the capability of the model to simulate the interaction between IgG 10


Experiment

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Figure 2: Test of the model to simulate the functionalization of a gold surface, and its sensing ability. (a,b): density of anti-IgG deposited on the gold lamina with and without the PIT protocol. (d,e): density of IgG caught by anti-IgG deposited on the gold lamina with and without the PIT protocol, as in (a,b). (g,h): density of anti-IgG caught by IgG deposited on the gold lamina with and without the PIT protocol. The analysis of these results (see text) suggests the molecules arrange as schematically illustrated in panels c,f,i (anti-IgG, black, full; IgG, red, dashed).

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and anti-IgG in the immune-recognition phase and subsequent antibody-antigen binding formation. We thus compare experiments with simulations concerning the density of captured IgG onto the gold lamina of the QCM, functionalized with anti-IgG anchoring as shown in (a) and (b), in Fig. 2 (c) and (d), respectively. The agreement between theory and experiment is excellent. The density at saturation of caught IgG in the experiment is 5 ⇥ 1012 cm

2

and ' 3.7 ⇥ 1012 cm 2 , with and without PIT, respectively, being 4.9 ⇥ 1012 cm

2

and ' 3.7 ⇥ 1012 cm 2 , respectively, in the corresponding simulations. This implies that our model reproduces very well the interaction of IgG with anti–IgG molecules as well as the effect of the PIT protocol onto surface functionalization. The latter point is corroborated by perfectly reproducing in the simulations the measured increase in the QCM sensor efficiency of about a 35% factor, when the PIT process is applied. Finally, in order to check whether the model was capable to correctly describe the operation of the QCM-based biosensor, which asks for a powerful bionanocombinatoric tool as it involves at the same time the interaction between gold and anti-IgG, the antibody-antigen interaction, and the laser-induced functionalization of anti-IgG we contrived a “countertest” experiment. In this experiment, first we deposit the IgG on the gold lamina, both with and without PIT, and then consider whereas the deposited molecules are able to recognize the anti-IgG. When IgG and anti-IgG are exchanged, the deposition process is unaffected as the molecules have the same structure. Indeed, our results for the density of deposited IgG (not shown), are analogous to that reported in Fig. 2a and b for the density of deposited anti-IgG. However, the recognition process is strongly affected by the exchange, as consistently shown by our experimental and numerical results of Fig. 2e and f. In absence of PIT, the density of anti-IgG recognized by the deposited IgG, is analogous to the density of IgG recognized by the deposited anti-IgG in Fig. 2a and b. Conversely, PIT greatly reduces the density of recognized anti-IgG. We interpret these results considering that the recognition process involves the receptors of the anti-IgG, that are located on its ‘harms’, and the ‘leg’ of the IgG, and assuming that PIT influences the orientation of the functionalizing molecules in 12


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such as way that their harms points preferentially upwards. If this is the case, when the deposited molecules are of anti-IgG type more receptors are exposed and the recognition ability of IgG in enhanced. Conversely, when the deposited molecules are IgS, PIT preferentially exposes their harms, which are inactive, rather than their ‘legs’, which bind with the anti-IgG. Accordingly, the recognition ability is suppressed. This interpretation suggests that PIT affects the orientation of the deposited molecules, as schematically illustrated in Fig. 2c,f,i. The results of Fig. 2 strongly support our modeling approach, that quantitatively reproduces experimental findings obtained using different protocols.

4.2

Experiment v.s. simulation: morphology 0.4

P(∆z)

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Figure 3: Analysis of the antibody height distribution onto the gold surface. a) Height normalized distribution, as measured with AFM microscopy of surfaces functionalized without (dashed, black) and with (bold, red) PIT protocol application. b) Simulation corresponding to the experimental result reported in a). To further validate our model, we consider its ability in reproducing morphological properties of the functionalized surface. We have performed AFM characterizations of the surface height z after the deposition, both with and without PIT (See the Sec. 2 for details on the AFM measurements), on seven 5 µ m ⇥ 5 µ m (resolution 512 ⇥ 512 points). Fig. 3a shows the distribution of the fluctuations

z=z

hzi of samples prepared with and without PIT

protocols. The figure evidences that PIT induces a reduction of the roughness, the standard deviations of the distributions being 1.77 ± 0.06 nm (w/o PIT) and 1.30 ± 0.04 nm (w/ PIT). In the simulations we model the AFM measurement assuming the height measured in 13



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position x, y to be the lowest value of z at which we can place a particle of radius R, without this interacting with the deposited anti-IgG. Here z is measured from a fixed arbitrary reference level, and R ' 20 nm corresponds to the size of the AFM tip used in the experiments. Fig. 3b shows that the numerical results nicely reproduce the experimental ones, and confirm that the PIT protocol consistently reduces height fluctuations. It is worth remarking that we have checked the role played by the AFM tip radius by calculating the height distribution, both with and without PIT, for different radii of the tip. From this study we conclude that such a parameter does not affect substantially the obtained result in the 15

4.3

25 nm range.

Simulation: a closer look at the morphology

The dependence of the height distribution on the functionalization process, as well as the results of Fig. 2, suggests that PIT enhances the recognition by affecting the morphological properties of the deposited anti-IgG. This calls for a deeper investigation of the morphological properties of the functionalized surfaces. Besides explaining the microscopic origin of the increased efficiency, this investigation might also allow to identify packing structures that further enhance the recognition ability. Since it is quite challenging to gain structural information at a molecular level on the orientation of the antibodies from experiments, we take advantage of our model, which is expected to yield meaningful results as it proved able to quantitatively reproduce all of our experimental results. We have performed two different analyses to gain insight into how the molecules pack on the surface that allows one to enrich the previous understanding of packing density and order of biomolecules and gold nanoparticles. 59 First we consider that, by projecting the segment of connecting the two receptors of each molecule on a constant z plane, we map our system in a series of small rods, as in Fig. 4, opening the way to the investigation of ordering properties in the xy plane. Here panel (a) refers to the system prepared without the PIT procedure, panel (b) to that prepared with PIT. The figure suggests that PIT might 14


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Figure 4: Nematic order of the deposited antibodies. The top panels show how the surfaces prepared without (a) and with (b) PIT appear when observed from above, if each molecule is represented by the segment connecting its two receptors. Correlation functions of the angle ⌘ the segments make with an arbitrary direction without (c) and with (d) PIT.

0.02 w/o PIT w/ PIT

P(θ) 0.01

0 0

100

50

150

θ

Figure 5: Probability distribution of the angle ✓ between the molecular axis and the normal to the surface without and with PIT.

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change the morphological properties by affecting the nematic order, i.e. promoting the local alignment of the rods. We have investigated this scenario computing the correlation function of the cosine of the angle ⌘ the rods make with a fixed direction, e.g. the x axis,

C(r) = hcos ⌘(r) cos ⌘(0)i

hcos ⌘i2 .

(2)

Here the brackets indicate an average over the sample. The data illustrated in the top panels c,d show that, in the presence of PIT (d), the correlation function has a higher peak at r = 5nm (the width of the molecules), and a small minimum at r = 10nm. This implies that PIT slightly enhances the local nematic order. However, this effect is tiny, as both correlation functions decay to zero at a very short distance. This result is consistent with the observation reported in Fig. 2, where we show that PIT does not influence the number of molecules deposited on the surface. Indeed, nematic order would allow to pack the molecules more efficiently, and thus to deposit more molecules. In this respect we notice that given the size of the molecules it is possible to estimate the maximum density of molecules one could deposit on the surface, e.g. by depositing the molecules in an ordered pattern. This ordered density turns out to be roughly ten times the density of molecules we actually deposit. This implies that devising deposition procedures able to increase the nematic order of the molecules one could still greatly enhance the efficiency of the sensor. We finally have investigated the emergence of ordering along the z direction, investigating the distribution of the angle ✓ identified by the molecular axis and the normal to the surface: ✓ = 0 if the molecule is normal to the surface with the receptors exposed, ✓ = 180 if the receptors are in contact with the surface. Fig. 5 shows that PIT strongly influences the distribution of this angle. Indeed, in the absence of PIT the angle distribution is essentially flat. Conversely, we see that PIT strongly enhances the probability of observing angles smaller than 60 degrees. These results clarify the action mechanism of PIT onto antibodies: no remarkable or-

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dering in the xy-plane, namely in the surface plane, whereas preferential orientation of the anti-IgG with their receptors exposed is induced in the vertical direction. This type of order allows to rationalize the actual microscopic origin of the increased recognition efficiency. In particular, it explains why PIT enhances the sensing efficiency (Fig. 2d,e) without affecting the number of molecules deposited on the surface (Fig. 2a,b).

4.4

Single molecule deposition

Figure 6: Configurations of the tethered antibodies onto the gold lamina sensor. Sketch of the four main configurations of the tethered anti-IgG (a) and related AFM images (b): Tailon (1), Head-on (2), Side-on (3), and Flat-on (4). 60 c: definition of the ✓ and angles that characterize an antibody configuration onto the lamina. It is easily seen that 0 < ✓ < 180 and, given 0 < ✓ < 90 , 0 < < ✓ , whereas given 90 < ✓ < 180 , then 0 < < 180 ✓. Panels c, d and e show a density field representation of the probability P (✓, ) obtained using our coarse–graining model, respectively without and with PIT protocol. As a striking evidence of the power and flexibility of our model, we are also able to mimic very well the characteristic morphologies of antibodies deposited onto rigid surfaces. This is another type of interaction we can study and reproduce, which is observed in the single molecule–surface interaction regime, rather than in saturation as for the above QCM sensor. This a further evidence of the wide applicability of our coarse grain model. It is, in fact, known the attitude of the antibodies to anchor onto a dielectric surface such as mica, preferentially in one of the four configurations shown in the top panel of Fig. 4 of Ref., 60 17



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i.e. Tail-on, Head-on, Side-on and Flat-on, as the result of non-specific adhesion interactions on mica. These four configurations are schematically reproduced in Fig. 6 a. Within each morphology some variability in the geometrical configuration is due to the flexibility of some antibody regions. We first experimentally verified by AFM microscopy that these four morphologies are found also when antibodies are tethered onto a metallic surface such as gold (Fig. 6 b). This finding is very interesting and somewhat unexpected, given also the much higher surface energy of gold (1540 mJ/m2 ) compared to that of mica (⇠ 350 mJ/m2 ). We believe this to be an important indication to generalize our model to include micaantibody interactions, and more in general dielectric crystalline-antibody interaction, where different adsorption mechanism might be involved. 61 Successively, in order to specifically test the capability of the model to describe the antibody anchoring and the effect of the PIT protocol application, we have characterized a generic antibody position onto the sensor surface by two angles, as shown Fig. 6c. ✓ is the angle between the molecular axis (the straight line connecting the Fc region to the center of the molecule) and the normal to the surface.

is defined as the angle between the straight segment (dx, dy, dz) connecting

the so-called variable two Fab domains, where the antibody receptors are located, and the projection (dx, dy, 0) of this segment on the surface. Obviously, 0 < ✓ < 180 , whereas simple q tan2 ✓ geometrical considerations (it is easy to show that cos = 1 1+⇠+tan 2 ✓ ) show that, when 0 < ✓ < 90 , then 0

Simple and Flexible Model for Laser-Driven AntibodyâGold Surface

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