Lateral Protein–Protein Interactions at Hydrophobic and Charged

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Lateral Protein-Protein Interactions at Hydrophobic and Charged Surfaces as a Function of pH and Salt Concentration Jana Hladílková, Thomas Hønger Callisen, and Mikael Lund J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.5b12225 • Publication Date (Web): 27 Jan 2016 Downloaded from http://pubs.acs.org on February 7, 2016

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The Journal of Physical Chemistry

Lateral Protein-Protein Interactions at Hydrophobic and Charged Surfaces as a Function of pH and Salt Concentration Jana Hlad´ılková,∗,† Thomas H. Callisen,‡ and Mikael Lund† Division of Theoretical Chemistry, Lund University, P.O.B. 124, SE-22100 Lund, Sweden, and Novozymes A/S, Krogshoejvej 36, DK-2880 Bagsvaerd, Denmark E-mail: [email protected]

Abstract

Surface adsorption of Thermomyces lanuginosus lipase (TLL) – a widely used industrial biocatalyst – is studied experimentally and theoretically at different pH and salt concentrations. The maximum achievable surface coverage on a hydrophobic surface occurs around the protein isoelectric point and adsorption is reduced when either increasing or decreasing pH, indicating that electrostatic protein-protein interactions in the adsorbed layer play an important role. Using Metropolis Monte Carlo (MC) simulations, where proteins are coarse grained to the amino acid level, we estimate the protein isoelectric point in the vicinity of charged surfaces as well as the lateral osmotic pressure in the adsorbed monolayer. Good agreement with available experimental data is achieved and we further make predictions of the protein orientation at hydrophobic and charged surfaces. Finally, we present a perturbation theory for predicting shifts ∗

To whom correspondence should be addressed Lund University ‡ Novozymes †

1

ACS Paragon Plus Environment


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in the protein isoelectric point due to close proximity to charged surfaces. Although this approximate model requires only single protein properties (mean charge and its variance), excellent agreement is found with MC simulations.

Introduction Protein adsorption to surfaces is crucial for phenomena both in industrial applications and in living cells as many enzymatic processes take place at interfaces such as cell membranes, 1 oil-water interfaces, 2,3 and solid surfaces. 4 Solution pH 5,6 and salt concentration 6,7 strongly influence the net protein adsorption by potentially modulating (i) the protein charge distribution; (ii) lateral protein-protein interactions in the adsorbed layer; and (iii) protein-surface interactions. In this study we present an efficient theoretical approach for predicting protein isoelectric point shifts due to surface proximity, i.e., how an external interface affects the charge distribution of proteins, and consequently, their interactions in salt solutions. Unlike commonly used theoretical methods for isoelectric point assessment, 8–10 our approach accounts for salt effects while still being fast enough to be applied to a large variety of surfaces, protein mutations, and salt concentrations. We use statistical thermodynamic perturbation theory to show how different planar surfaces redistribute protonation states of charged residues of a nearby protein. This proton re-ordering, known as charge regulation, was described already in the 1920s, 11 and later applied on protein-protein 12 and protein-surface interactions. 13 We verify the theory by 2D Monte-Carlo simulations and further obtain information about protein orientation and surface coverage at different pH and salt concentrations. Thermomyces lanuginosus lipase (TLL) is chosen as a model protein for demonstrating the susceptibility for surface binding. 14 TLL is a globular enzyme with an average radius of 2.3 nm that adsorbs to both hydrophobic and hydrophilic surfaces. 15,16 The active site, catalyzing the hydrolysis of an ester bond in glycerides, is concealed under an α-helical lid. Upon adsorption or if the dielectric constant of the environment is decreased, the lid 2


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opens, resulting in exposure of the hydrophobic active site at the protein surface. This hydrophobic patch is responsible for strong binding of the enzyme to hydrophobic surfaces. 17 Fluorescence recovery after photo bleach (FRAP) experiments 18 and single-molecule tracking results 19 have shown that TLL interfacial rotational diffusion is modulated by surface polarity, residence time, and the presence of other surface active molecules competing for the surface. The surface distribution of TLL orientations has not been directly determined, but merely inferred from enzymatic activity measurements. 20 The simulation framework presented here capture the sensitivity of the protein to the local charge redistribution and gives additional information about the protein orientation in the adsorbed layer.

Methods and Theory Surface adsorption to hydrophobic surfaces is experimentally studied as a function of pH using surface plasmon resonance (SPR) while theory is used to further investigate the effect of surface charge and electrolyte concentration. We simulate three different surfaces types: (i) neutral (hydrophobic), mimicking the experimental thioacylated gold surface (C18-SAu) or a C18-trichlorosilane modified silica surface (OTS); 20,21 (ii) anionic, representing a hydrophilic silica surface (silica wafers); 20 and (iii) cationic, defined as (ii) but with opposite charge. 22

Experiment The Thermomyces lanuginosa lipase (TLL) was provided by Novozymes A/S (Bagsværd, Denmark). The buffer used throughout the study was (10 mM NaCl, 0.05 mM EDTA, 50 mM glycine, 50 mM Hepes and 1 mM NaN3 ) and all water was of Milli-Q grade. Solution pH was adjusted by HCl or NaOH. To determine the protein concentration, the absorbance at 280 nm was measured, using an extinction coefficient at 280 nm of 38440 cm−1 M−1 . The hydrophobic surface used in the SPR instrument was a commercial HPA chip from Biacore

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made by covalent fixation of a self-assembled monolayer of alkanes on a gold surface (C18S-Au). Adsorption behavior of the lipase was analyzed with SPR according to Sonesson 19 and references therein. Data were acquired by a Biacore 3000 instrument (Biacore, Uppsala, Sweden) operating at 25°C. 60 µl samples at a concentration of 200 nM TLL were injected and adsorption to the hydrophobic HPA surface was monitored under a constant flow of 10 µl/min for 6 minutes, followed by a rinse with buffer solution for 6 minutes. Refractive index variances of the adsorbed film were continuously measured and converted to surface excess (mg/m2 ) using the built-in software, assuming monolayer binding of the protein. Steady-state surface excess data were collected before end-of-injection and corrected for the bulk contribution. Residual adsorption was recorded after rinsing with buffer 4 min after end-of-injection.

Perturbation theory The protonation states of acidic and basic residues on the protein surface enables the molecule to respond to a changing chemical environment. This mechanism can be quantified using the variance, C, of the mean protein net-charge number, hZi, which is the response function to an applied electric potential, φ,

2 2 ∂ Z 1 ∂ Z C=− = Z − Z =− , βe∂φ ln 10 ∂pH

(1)

where C can be regarded as the molecular capacitance 23 of an unperturbed protein (infinite dilution); β = 1/kB T is the inverse, thermal energy; and e is the elementary charge. The mean charge and capacitance are both dependent on the solution pH as well as on the salt concentration. When a protein approaches a surface with an electric potential different than in bulk, the electrostatic perturbation may trigger protein charge regulation. We can estimate the resulting protein mean charge, Z(r), of a mass-center distance r from the

4


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surface, knowing only the protein charge and the capacitance at an infinite distance, i.e., bulk properties of a single molecule: hZi and C,

Z(r) = hZi − φ(r) ·

∂hZi = hZi − βeφ(r)C. ∂φ

(2)

Here φ(r) is the effective electric potential acting on the protein at a distance r from the surface. φ(r) for planar, charged surfaces is estimated using Gouy-Chapman theory (third term in Eq. 3), in which salt dampens the surface potential φ(0) with e−κr , where κ is the reciprocal Debye screening length. In a continuum solvent description, φ(0) = 0 for a hydrophobic surface (reality is slightly more subtle 24,25 ), and charge regulation is thus assumed inactive.

Monte Carlo Simulations It follows from experiment 17 that TLL binds strongly to both hydrophobic and hydrophilic surfaces. To mimic the adsorbed layer, 26 we use 2D Metropolis Monte Carlo simulations, where the mass centers of N rigid proteins are confined to a periodic plane, applying the minimum image convention. Each protein is described by a collection of n interaction sites, each representing a single amino acid that can be either neutral, or charged (nch ). Water and dissolved salt are described using the Debye-H¨ uckel approximation 24,27 as is the charged surface, placed one protein radius, RT LL , from the plane. To align the proteins according to hydrophobic residue-surface interactions, we use a simple, linearly decaying potential ranging between zero and ǫphob for residues designated as hydrophobic, nphob , (Ala, Ile, Leu, Met, Phe, Pro, Trp, and Val). The coarse-grained model for TLL is based on the open form of lipase, PDB 1GT6, in which every residue is treated as a spherical bead with a radius derived from the amino acid molecular weight and the common density of 0.9 g/ml. 28 During simulation, a MC swap move is applied where the protonation states of titratable sites are alternated to ensure thermal

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equilibrium with bulk pH, taking into account the local environment. Titratable sites are defined with their pKa values: C-terminus (2.6), Asp (4.0), Glu (4.4), His (6.3), N-terminus (7.5), Tyr (9.6), Lys (10.4), Cys (10.6), and Arg (12.0). The enzymes are further translated and rotated around their mass centers and the usual Metropolis criteria for acceptance is applied using the system energy, n 1 X qi qj −κrij U= e 4πǫ0 ǫr i>j rij n X σij 12 σij 6 − + 4ǫLJ rij rij i>j n X p −1 −κris 2kB T + qi e ρ (8kB T cs ǫ0 ǫr ) sinh ze i

(3)

nphob

−

X

ǫphob (1 − ris /RT LL ) if ris < RT LL

i

+ kB T

np X

pKa,i − pH) ln 10

i

where pKa,i is the intrinsic acid dissociation constant for residue i when isolated in bulk solution; ǫ0 is the vacuum permittivity; ǫr = 80 is the relative dielectric constant for water; ǫLJ = 0.05 kB T is the Lennard-Jones (LJ) interaction strength; 28 σij = (σi + σj )/2 is the LJ diameter; rij is the distance between residue i and j; κ is the inverse Debye length,

λD = κ

−1

=

ǫ 0 ǫ r kB T cs e 2

1/2

,

(4)

with the electrolyte concentration cs ; ρ = ±0.107 C/m2 = ±0.668 e/nm2 is the surface charge density (ρ = 0 for hydrophobic surfaces); ris is the distance between residue i and the planar surface; ǫphob = 10 kB T is the maximum hydrophobic interaction strength (ǫphob = 0 for hydrophilic surfaces). The last term runs over all protonated residues, np . Note that this level of description is expected to be less accurate at high electrolyte concentration and will generally require modifications to correctly describe the effect of multivalent ions. Image

6


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charge effects due to a potentially lower dielectric response in the protein as well as for the planar surface may also modulate the overall protein-surface binding, but are likely to play a lesser role for the lateral electrostatic interactions between protein molecules. 29 All simulations were performed using the Faunus framework 30 with N =1 (bulk properties) or N =35 (adsorbed properties) proteins in the canonical ensemble at 298 K. Scaling up the system size had insignificant effects on the calculated properties. 20.000 sweeps were used for equilibration, followed by 100.000 sweeps for production runs. Each sweep consists of N rotation or translation attempts and np proton swap moves. During simulation we sample the lateral osmotic coefficient,

ϕ=

π

(5)

π ideal

which is a measure of the interaction between adsorbed proteins, 31 and therefore, is inversely related to the experimentally measured surface coverage. The ideal pressure, βπ ideal = N/A, is simply the number density, while the total, lateral pressure also includes the excess arising from forces, fij , between all nN residues, N β βπ = + A 2A

* nN X i>j

f(r)ij · rij

+

,

(6)

whereby proteins attract each other when ϕ < 1 and are repelled when ϕ > 1. P Finally, we sample ensemble averages for the net charge, hZi = h zi i, the capacitance, P P P C = h zi2 i − h zi i2 , and the dipole moment, µ = h ri zi i where ri is the distance vector

from the protein mass center to the i’th residue with valency zi = qi /e.

7



Results and discussion Bulk Properties Fig. 1 shows electrostatic bulk properties of TLL, namely the protein net charge number, hZi, and the capacitance, C, at different pH and salt concentrations. For comparison, we also include values obtained using the semi-empirical propKa method, 8,9 although this neglects the surrounding electrolyte.

0.5

〈Z〉

protein net charge, 〈Z〉

20 10

0

-0.5 4.7

4.8

25 mM 50 mM 75 mM 100 mM 150 mM propKa

-10 -20 2

4.9

pH

0

3

4

5

7

6

8

9

10

11

pH 6

protein capacitance, C

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25 mM 50 mM 75 mM 100 mM 150 mM propKa

5 4 3 2 1 0 2

3

4

5

7

6

8

9

10

11

pH Figure 1: Average protein charge, Z (top), and capacitance, C (bottom), at different pH and salt concentrations. Obtained using Monte Carlo simulations (fully drawn lines) and with the propKa method 8 (dashed lines). The isoelectric points, pI, are highlighted in the net charge zoom view.

The net charge varies with ±20e in the studied pH interval, with the isoelectric point, 8


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pI=4.8 (pH where hZi=0), which is in close agreement with the propKa prediction as well as with results from other bioinformatic sources (EMBOSS, Grimsley, Solomon, ProMoST). 10 With an increasing salt concentration, the absolute value of the protein net charge increases at the edges of the chosen pH range reflecting a higher number of de/protonations of acidic and basic residues at high and low pH values, respectively. The molecular capacitance (Fig. 1, bottom) peaks at pH 3–4, which follows the high content of aspartates and glutamates. Likewise, positively charged arginines and lysines give rise to a capacitance peak at high pH. The sensitivity to pH becomes more pronounced for higher salt concentrations as the charge fluctuations are more easily achievable in stronger electrolyte where the internal charge repulsion is screened. Since the propKa procedure neglects salt, the resulting capacitance corresponds to rather concentrated salt solutions where internal electrostatic interactions are screened.

Surface Effect on the Isoelectric Point Upon approaching a charged surface, the protein experiences an electric potential that may affect the protonation states. We here investigate how the protein net charge is influenced by a charged, planar surface using two different approaches: (i) Monte Carlo simulations of adsorbed proteins (σp → 0 and σp = 1 mg/m2 ) and, (ii) Perturbation theory, Eq. 2, using previously obtained bulk protein values for hZi and C from Fig. 1. In both cases the surface is placed 3.0 nm from the protein mass centers. As can be seen in Fig. 2, top, the MC simulations and the more approximate perturbation theory are in excellent agreement when the protein coverage is low (σp → 0). At low pH, protein-protein interactions slightly contribute to the charge induction as seen by increasing the coverage to σp = 1 mg/m2 . This is neglected by Eq. 2 where we merely include the potential from the charged surface (third term in Eq. 3). Note that our rigid protein model dismisses unfolding or protein denaturation, which may occur at extreme pH. Fig. 2 shows that at low salt concentration, csalt = 15 mM and medium surface charge 9



Eq. 2 Monte Carlo, σp → 0 mg/m

=

0

2

68

2

nm

0.6

Monte Carlo, σp= 1 mg/m

2

e/

10

68 .6 -0

protein net charge, 〈Z〉surf

20 ρ

0 -10 -20

csalt = 15 mM

2

3

4

7

6

5

8

9

10

11

pH 5.5

5

pI

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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4.5 15 mM 25 mM 50 mM 75 mM 150 mM

4

3.5

-1

0

-0.5

1

0.5 2

surface charge density [e/nm ] Figure 2: Average protein charge when adsorbed onto cationic (ρ = 0.668 e/nm2 ), neutral (ρ = 0), and anionic (ρ = -0.668 e/nm2 ) surfaces as a function of pH. Calculated using perturbation theory (solid lines) and Monte Carlo simulations with low (dashed lines) and high (dotted lines) surface coverages. Note that σp = 1 mg/m2 = 0.02 molecules/nm2 . The vertical arrows indicate the positions of the isoelectric points (pI). Applying Eq. 2, we get the pI values for a variety of charge surfaces and salt concentrations, which is shown in bottom part of the figure.

density of ρ = ±0.668 e/nm2 , the isoelectric point is shifted more than half a pH unit towards acidic or basic pH for the cationic and the anionic surface, respectively, which is due to the significant and relatively unscreened potential experienced by the proteins. Applying Eq. 2, the pI shifts at a variety of salt concentrations and surface charge densities are calculated as shown in Fig. 2, bottom. The higher the surface charge density and the lower the salt concentration, the more the pI value is shifted. For example, 1 pH unit for ρ = 1.336 e/nm2 and csalt = 15 mM. Beyond an ionic strength of 150 mM, the isoelectric point is practically insensitive to charged surfaces due to the salt screening. 10


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Protein-protein interactions in the Adsorbed Layer Above we used MC simulations of adsorbed proteins as well as bulk electrostatic properties to predict how the isoelectric point is influenced by nearby surfaces in an aqueous solution. At the isoelectric point, inter-protein electrostatic repulsion is at a minimum and the largest surface coverage is therefore to be expected, assuming that the protein-surface interaction is of non-electrostatic origin. In this section we use the lateral osmotic coefficient, ϕ, as a measure of in-plane protein-protein interactions. 14

m 68

e/n

10

2

anionic surface hydrophobic surface cationic surface

12

=

0.6

8 ρ

osmotic coefficient

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0 8

.66

6

-0

4 2 csalt = 15 mM

0 2

3

4

5

7

6

8

9

10

11

pH Figure 3: Calculated osmotic coefficients, ϕ, for different surfaces at 15 mM salt concentration and a protein coverage of σp = 1 mg/m2 . Arrows highlight pH values corresponding to ϕ minima and correlate well with predicted pI shown in Fig. 2.

Fig. 3 shows the osmotic coefficient for all three surfaces at low salt concentration, where the surface effects are most pronounced. The ϕ minima, corresponding to maximum surface coverages, correlate well with the isoelectric points since the lateral repulsion between the proteins is at a minimum at pH=pI. 5 Comparing the isoelectric points (Fig. 3) with those predicted by perturbation theory (Fig. 2, bottom), we find an almost perfect match. For the hydrophobic surface, we establish the isoelectric point to 4.6, while for cationic and anionic surfaces it is shifted by ±0.7 pH units, respectively. In the following, we focus on the hydrophobic surface at different salt and protein concentrations. Fig. 4 shows the screening role of the electrolyte: the higher the salt concentration, 11



the less protein-protein repulsion (lower ϕ). Consequently, the osmotic coefficient curve flattens with increasing salt concentrations, meaning that it is possible to tune the ratio between the lowest and highest surface coverage within the studied pH range by altering the salt concentration. Note that one can generally not expect to be able to screen out protein-protein interactions entirely as at high salt concentrations specific ionic effects may set in and, in addition, the Debye-H¨ uckel model becomes less applicable. 7 σp= 1.0 mg/m

osmotic coefficient

6

2

5 4 25

3

mM 50

2

mM

M

75 m

1 0 2

3

4

5

7

6

8

9

10

11

pH 7 csalt = 50 mM

6

mg

/m

2

5 4

2.0

osmotic coefficient

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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3

1.5 1.0 0.5

2 1 0 -1 2

3

4

5

7

6

8

9

10

11

pH Figure 4: Osmotic coefficient, ϕ, variation with salt concentration, cs (top) and protein coverage, σp (bottom) as a function of pH.

Investigating the influence of the protein coverage on the lateral osmotic coefficient ϕ at the hydrophobic surface (Fig. 4, bottom), one notes that both attractive and repulsive inter12


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actions are more pronounced for higher surface coverages. At a low surface concentration, σp = 0.5 mg/m2 , there is almost no pH effect as bound proteins are distributed farther than the Debye length and thus unaffected by each other. No additional stabilisation by attractive interactions is gained at the iso-electric point and, hence, ϕ ∼ 1. For higher coverages, the repulsion at high and low pH becomes more significant, 32 which is similar to the discussed effect of electrolyte. For pH near the isoelectric point, additional stabilizing protein-protein interactions play an important role, whereby the osmotic coefficient decreases significantly below unity. For even higher coverages, all the described effects become more pronounced, however, the osmotic coefficient drops below zero around the isoelectric point, which suggests a phase separation under the experimental conditions. Increasing the coverage further, we . obtain the minimum achievable osmotic coefficient (ϕ = -1) at pH 4.5, corresponding to the calculated maximum possible surface coverage (σmax = 2.7 mg/m2 ). Beyond this point, the osmotic coefficient again increases as the proteins are forced into close proximity. Interestingly, all curves cross at two pH values, which surround the pH region with an overall attractive regime. The origin of this attraction is mainly due to the van der Waals forces, but also charge fluctuations of acidic and basic amino acid residues allow additional stabilization within the attractive regime (lower ϕ values), which was proven by comparison with MC simulations of proteins with fixed charges. Consequently, the highest protein surface coverage is expected in the limiting pH range 3.6–6.3.

Experimental results In addition to theoretical modeling, we have used SPR to measure the TLL adsorption on a hydrophobic surface (C18-S-Au) in the pH range 2 to 11 – see Fig. 5. Results are shown as average of double determination of each data point. 19 Variation for each adsorption response data point is typically ±0.1 mg/m2 . The maximum observed coverage, σp = 1.9 mg/m2 , is reached near pH 4, which is in very good agreement with our theoretical predictions of a minimum in osmotic coefficient near the isoelectric point. Considering the maximum, 13



theoretical surface coverage, σmax = 2.7 mg/m2 , the hydrophobic surface is thus covered by approximately 70%. Lowering pH, the adsorption drops to a minimum value of σp = 0.9 mg/m2 . With increasing pH, the surface coverage decays more slowly, while the minimum measured adsorption σp = 0.6 mg/m2 is observed for pH 9–11. It should be noted that the degree of ionization contributes to the ionic strength which consequently varies between 10–60 mM over the experimental pH range, see inset of Fig. 5.

2

ionic strength [mM]

2.4

surface coverage [mg/m ]

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2.1 1.8 1.5 1.2

60 50 40 30 20 10 0

2

4

8 6 pH

10

0.9 0.6 0.3 0

2

3

4

5

6

7

pH

8

9

10

11

Figure 5: SPR measurements of the steady-state adsorption, σp , of TLL on a hydrophobic surface. The inset shows the estimated ionic strength variation due to the buffer ionization state using the acid dissociation constants, pKa , 2.35, 9.78 (glycine); 7.56 (Hepes); and 1.8, 12.2 (EDTA).

Let us now attend the osmotic coefficient using a mean field description of the lateral osmotic pressure. Via the Langmuir adsorption isotherm, the experimental data in Fig. 5 can be related to the perpendicular Gibbs binding free energy,

∆G = −kB T ln K + const

(7)

where K=

θ ∝ qxy qz cp (1 − θ)

(8)

and θ = σp /σmax is the fractional coverage; cp is the bulk protein concentration. The binding constant is proportional to the partition function product of the proteins in the bound state, 14


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where qxy = e−βε is the partition function due to lateral protein interactions, while −kB T ln qz is the perpendicular protein-surface binding energy, here assumed independent of pH and other solution conditions. Similar slitting of the Langmuir binding free energy into individual contributions has previously been done to account for cooperativity of protein sorption to core-shell microgels. 33 We now define the constant in Eq. 7 such that ∆A=0 when ε=0, occurring when pH∼pI when there are only weak or no lateral interactions. Rescaling ∆A with the surface number density, n′s , at the experimental conditions, the mean field energy per particle in a 2D layer of density ns is ns ε= 2

Z

∞

2πrw(r)dr ≈ ∆G · d

ns n′s

(9)

where w(r) is the effective pair-potential with surrounding proteins and d = 2RT LL is the protein diameter. In the generalized van der Waals (gvdW) approach, the lateral osmotic coefficient is given by 34 ϕ=

1 + βε. 1 − ns d 2

(10)

That is, gvdW provides a simple correction to the ideal surface pressure by taking into account entropy loss due to excluded volume as well as lateral intermolecular interactions. As can be seen in Fig. 6, the experimental data converted to ϕ, closely follows the simulated osmotic coefficients in Fig. 4 with predominant repulsion (ϕ > 1) at extreme pH and an attractive window (ϕ < 1) surrounding the iso-electric point. At alkaline pH, the observed repulsion levels off while in the MC simulations, the repulsion increases with increasing net charge. This is likely due to the varying ionic strength of the buffer (see inset of Fig. 5) which reaches 60 mM at high pH and electrostatic screening thus counteracts the protein-protein repulsion. Hence, we are able to qualitatively reproduce the functional essence of the experimental results by straight forward computational methods with a coarse-grained protein model. Moreover, the most dense layers of TLL at a hydrophobic surface were measured in the pH

15



6 2

csalt = 10 - 60 mM

2.0 mg/m

5

osmotic coefficient

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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4

1.5

3

1.0

2

0.5

1 0 2

3

4

5

7

6

8

9

10

11

pH Figure 6: Osmotic coefficient, ϕ, at different coverages, extracted from experimental SPR adsorption data (Fig. 5) using gvdW theory. At the minima we assume that the mean protein energy, β∆G = -1 due to short-ranged, protein-protein attraction, see text.

range matching the region with the estimated osmotic coefficient smaller than one, i.e., for pH 4, 5, and 6. These results corroborate that our computational approach complements experimental techniques and render additional molecular insight. For instance, we can assess the orientation of enzymes at the surface, which has not previously been reported.

Orientation of Adsorbed Proteins The preferred orientation of TLL at a hydrophobic and a hydrophilic surface has previously been discussed on the basis of surface diffusion and enzymatic activity measurements. 20 Results showed that TLL is more active towards a water-soluble substrate on a hydrophilic surface, while the opposite was found for activity on the hydrophobic surface. We here investigate the protein orientation in MC simulations with all three surface types. As discussed before, TLL binds to both hydrophobic and hydrophilic surfaces. TLL has a strong electric dipole moment, which varies for different pH values, for example, at csalt = 50 mM, it reaches its minimum (60 e˚ A) at pH 3 and its maximum at pH 6 and 11 (90 e˚ A). Due to this pH dependence, the dipole moment – showed as a red arrow in 16


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dipole moment and the hydrophobic patch fluctuates (due to proton degrees of freedom) around 40 degrees. Fig. 7 shows the TLL orientation at hydrophobic, anionic, and cationic surfaces at pH 4 (full lines), 8 (dashed lines), and 10 (dotted lines), 50 mM salt concentration, and with the enzymatic concentration of σp = 1.0 mg/m2 , corresponding to 40% surface coverage. The strongest alignment is observed at the purely hydrophobic surface which has the narrowest probability distribution. As expected, the hydrophobic patch is strictly oriented towards the hydrophobic surface which may block the access of a water-soluble substrate to the active site. This is clearly seen from the bottom view of the hydrophobic surface as no black area is visible, rendering the active sites sterically unreachable from the solution. This observation correlates well with experimental activity measurements, 20 in which the conversion of a water-soluble substrate is significantly slowed down. 14 The protein orientation is practically insensitive to pH variations as the dominant interaction between the hydrophobic patch and the hydrophobic surface remains unchanged. For the anionic surface, the probability distribution of the protein orientation is much wider, despite that the mean value of the distribution differs only slightly from the hydrophobic surface. This is because the dipole moment is similarly oriented as the vector of the hydrophobic interaction. The protein is in this case more likely to reorient and be stabilized by point charge fluctuations of titratable residues. Consequently, the black areas are recognized in the bottom view of the anionic surface, which points to the possibility of a substrate accessing the active site more easily. The cationic surface orients the proteins in the opposite way, with the probability width being similar to the one for the anionic surface. Here, most of the molecules face the solution with their active site, making it potentially more accessible for substrates to bind. pH influences the probability distribution for both charged surfaces as the electric dipole moment, which leads the interactions with a charged surface, varies (see Fig. 1, bottom). Therefore, we obtain slightly less oriented distribution for pH 8 and altered distribution for pH 10. Although the distribution widths are clearly

18


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dependent on the empirical binding parameters ǫphob and ǫLJ in Eq. 3, the overall agreement with available experimental data for TLL adsorption as well as for other proteins, 28 suggests that these are adequately chosen.

Conclusions We have studied the pH and salt dependent adsorption of TLL to cationic, anionic, and hydrophobic surfaces using a combination of experiment, computer simulations, and statistical mechanical perturbation theory. As for the latter, we have presented a simple theory for predicting shifts in the isoelectric point due to the presence of charged interfaces. The model is sensitive to the ionic strength, surface charge density and requires knowledge of single protein properties – the net charge and the capacitance – obtained (measured or calculated) in bulk solution in the absence of the interface. Results are in excellent agreement with Metropolis Monte Carlo simulations of coarse grained proteins in contact with charged interfaces. MC simulations were used to estimate the influence of pH and salt concentration on the protein binding at the hydrophobic surface. Using the lateral osmotic pressure, we qualitatively predicted the surface coverage as a function of pH and electrolyte concentration. Moreover, additional stabilization due to attractive protein-protein interactions, was observed for pH between 3.6 and 6.3. The simulation results follow the experimentally obtained surface coverage, which is at a maximum in the pH range 4 to 6. At the same time, the least coverage is observed for pH 9–11, which correlates very well with the computational results showing the largest osmotic pressure in this range. Finally, the simulations show that the hydrophobic patch found in the active, open form of TLL, strongly aligns the active site towards the hydrophobic surface. The active site is therefore unreachable for water-soluble substrates. In contrast, the hydrophobic patch is less hidden at charged interfaces where electrostatic interactions induce a wider range of possible

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orientations, thus making the patch more accessible from solution. The outcome of the theoretical approach add insight to previous experimental studies on TLL surface binding. 20 Moreover, this joint information allows for better understanding and design of enzymatic solutions for technical applications such as enzymatic detergency. 19

Acknowledgement For financial support we thank the Swedish Research Council and the Swedish Foundation For Strategic Research.

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(16) Sonesson, A. W.; Elofsson, U. M.; Brismar, H.; Callisen, T. H. Adsorption and mobility of a lipase at a hydrophobic surface in the presence of surfactants. Langmuir 2006, 22, 5810–5817. (17) Verger, R. Interfacial activation’ of lipases: facts and artifacts. Biotechnology 1997, 15, 32–38. (18) Sonesson, A.; Callisen, T.; Brismar, H.; Elofsson, U. Lipase Surface Diffusion Studied by Fluorescence Recovery after Photobleaching. Langmuir 2005, 21, 11949–11956. (19) Sonesson, A. W.; Callisen, T. H.; Brismar, H.; Elofsson, U. M. A comparison between dual polarization interferometry (DPI) and surface plasmon resonance (SPR) for protein adsorption studies. Colloids and Surfaces B: Biointerfaces 2007, 54, 236–240. (20) Sonesson, A. W.; Callisen, T. H.; Brismar, H.; Elofsson, U. M. Adsorption and activity of Thermomyces lanuginosus lipase on hydrophobic and hydrophilic surfaces measured with dual polarization interferometry (DPI) and confocal microscopy. Colloids and Surfaces B: Biointerfaces 2008, 61, 208–215. (21) Fahlman, B. D.; Ram´ırez-Porras, A. Surface-Functionalized Porous Silicon Wafers: Synthesis and Applications. Advances in Chemical Sensors 2012, Prof. Wen Wang (Ed.). (22) Kumar, M.; Sameti, M.; Mohapatra, S.; Kong, X.; Lockey, R.; Bakowsky, U.; Lindenblatt, G.; Schmidt, C. H.; Lehr, C.-M. Cationic silica nanoparticles as gene carriers: synthesis, characterization and transfection efficiency in vitro and in vivo. Journal of nanoscience and nanotechnology 2004, 4, 876–881. (23) Lund, M.; Jönsson, B. Charge regulation in biomolecular solution. Quarterly reviews of biophysics 2013, 46, 265–281. (24) Israelachvili, J. N. Intermolecular and surface forces: revised third edition; Academic press, 2011. 22


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Lateral Protein–Protein Interactions at Hydrophobic and Charged

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