Structure and Interaction of Nanoparticle-Protein Complexes

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Structure and Interaction of Nanoparticle-Protein Complexes Sugam Kumar, Indresh Yadav, Vinod Kumar Aswal, and Joachim Kohlbrecher Langmuir, Just Accepted Manuscript • DOI: 10.1021/acs.langmuir.8b00110 • Publication Date (Web): 19 Apr 2018 Downloaded from http://pubs.acs.org on April 20, 2018

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Structure and Interaction of Nanoparticle-Protein Complexes

Sugam Kumar†, Indresh Yadav†,#, Vinod Kumar Aswal†,#,* and Joachim Kohlbrecher‡

†

Solid State Physics Division, Bhabha Atomic Research Centre, Mumbai 400 085, India #

‡

Homi Bhabha National Institute, Mumbai 400 094, India

Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institut, CH-5232 PSI Villigen, Switzerland

*Corresponding author. E-mail: [email protected], Phone: +91 22 25594642, Fax: +91 22 25505151 1 ACS Paragon Plus Environment

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ABSTRACT The integration of nanoparticles with proteins is of high scientific interest due to the amazing potential displayed by their complexes, combining the nanoscale properties of nanoparticles with the specific architectures and functions of the protein molecules. The nanoparticle-protein complexes, in particular, are useful in the emerging field of nanobiotechnology (nanomedicine, drug delivery and biosensors) as the nanoparticles having sizes comparable to that of living cells, can access and operate within the cell. The understanding of nanoparticle interaction with different protein molecules is prerequisite for such applications. The interaction of the two components has shown to result in conformational changes in proteins as well as affect the surface properties and colloidal stability of the nanoparticles. In this feature article, our recent studies, exploring the driving interactions in nanoparticle-protein systems and resultant structures are presented. The anionic colloidal silica nanoparticles and two globular charged proteins [lysozyme and bovine serum albumin (BSA)] have been investigated as model systems. The adsorption behavior of the two proteins on nanoparticles is found to be completely different but they both give rise to similar phase transformation from one-phase to two-phase in respective nanoparticle-protein systems. The presence of protein induces the short-range and long-range attraction between the nanoparticles with lysozyme and BSA, respectively. The observed phase behavior and its dependence on various physiochemical parameters (e.g. nanoparticle size, ionic strength and pH of the solution) have been explained in terms of underlying interactions.

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1. INTRODUCTION Nanoparticles due to their small size and large surface-to-volume ratio possess unique and distinct properties from those of their constituent atoms/molecules and bulk materials.1,2 These features are size-dependent and cover a wide range of phenomena having applications in a wide variety of fields including energy, environment, electronics and medicine.3,4 In particular, the special interest in the field of medical science arises from the fact that nanoparticles being small enough interact directly with the cellular machinery and efficiently reach to the otherwise inaccessible targets.5 This fact enables the potential use of nanoparticles in achieving high level targeted drug delivery and realizing extremely sensitive biosensors.6 The majority of these functions require adjoining of some biomolecules on the nanoparticle surface, yielding novel hybrid nanobiomaterials having synergetic abilities desired to probe biological processes that are critical for diagnostics and the modulation of cell functions.7,8 Proteins are the most suitable biomolecules for this purpose as they underpin almost every aspect of biological activity and serve crucial role in essentially all biological processes.9 Nanoparticles having extremely high surface to volume ratio and hence the very active surface chemistry are known to cover immediately by the protein molecules on contact, in most of the cases.10 The interaction of nanoparticles with protein molecules results in the formation of a biological corona on the nanoparticle surface which is considerably different from that adsorbed on a flat surface of the same bulk material, following the same experimental conditions.8 There are considerable amount of studies available in the literature, investigating different aspects of formation, consequences and utilization of this corona in specific nanoparticle-protein systems.11-25 These can be broadly classified in three classes, examining: (i) the effect of nanoparticles on protein conformation,11-16 (ii) influence of protein molecules on nanoparticle stability and surface functionality17-20 and (iii) behavior of nanoparticle-protein composites towards the biological medium.21-25 A literature review summarizing these aspects in addition to some others is presented in Table 1 (including some of the review articles cited later).26-46 Majority of the studies focus on the first issue where it has been shown that the interaction of the nanoparticles can disrupt the native conformation of protein and therefore functional impact (in some cases irreversibly also). It has been shown that the structural and chemical properties of both nanoparticles and proteins along with the degree of the interaction between them play key role in regulating such surface-driven modifications in the native protein 3 ACS Paragon Plus Environment

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structures.47 Further, in a complex nanoparticle-protein system, the presence of nanoparticle may control the interaction between protein molecules, their enzymatic activity as well as protein delivery.11,12,48-50 On the other hand, it is also possible that the interaction of proteins can alter the surface properties and colloidal stability of the nanoparticles.17,18 Proteins may influence the various phase transformations in the nanoparticles, for example, gelation, crystallization, glass transition, and flocculation, which are subject to prepare multifunctional materials.19,20,51 In addition to these, the protein corona has also been shown to influence several other properties of the nanoparticle system such as degradation, accumulation, clearance, inflammation and cellular uptake.22,33 The most important aspect of the nanoparticle-protein interaction is that it can modify the biophysical properties of the nanoparticles which often significantly differ from those of the bare nanoparticles.52 The protein adsorption on nanoparticles confers a new biological identity in the biological milieu, which subsequently controls the biological response of the nanoparticles.31 The protein corona enables the exposure of suitable/target biological entities to transmit avidity effects arising from the interaction of the composites with the novel epitopes of the medium. Even the way in which protein molecules arrange themselves on the nanoparticle surface is also a crucial parameter governing the biological reactivity of the complex at the cellular level.53 The protein adsorption on nanoparticles is usually governed either by direct covalent linkage or by non-covalent interactions between the nanoparticles and proteins.54 Conjugates formed through covalent attachment are irreversible and the resultant systems are highly stable. However, this method requires good command on organic synthesis and often leads the conformal change in protein structure.55 On the other hand, non-covalent interaction (e.g. van der Waals interaction, electrostatic interaction, hydrogen bonding and hydrophobic interaction) at the nano-bio interface provides a complementary strategy to adjoin proteins on nanoparticles.37-43 The most common way to produce non-covalent nanoparticle-protein conjugates is through electrostatic attraction which is known to lead many non-specific associations, particularly important in biological systems.56 Interestingly, even under electrostatically unfavorable conditions, protein adsorption has been observed onto the hydrophobic surfaces due to dominating hydrophobic attraction over the electrostatic repulsion.39 Additionally, the asymmetric charge distribution as well as the presence of hydrophobic patches on protein molecules are also responsible in dictating their interactions with the nanoparticles.57


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In the case when both protein and nanoparticle are charged, their individual stability is dictated by the well-known DLVO theory, which combines the van der Waals attraction with the electrostatic double-layer repulsion.58 However, the overall behavior of the nanoparticle-protein complexes is determined by the interplay of the several non-DLVO interactions (e.g., hydrodynamic, electrodynamics, steric, depletion and solvent bridging).59 The dominance of a particular interaction depends on the intrinsic characteristics (e.g. size, shape, charge, surface functionality, crystallinity, hydrophobicity or hydrophilicity) of the nanoparticles and proteins and finally ascertains the macroscopic properties of the resultant systems.11,14,34,60-62 For instance, the interplay of these interactions decide the stability of the system and may be utilized to give rise or restrict or prevent protein induced aggregation of nanoparticle. Selective adsorption of proteins can enhance the colloidal stability by steric repulsion, whereas non-adsorbing proteins may give rise to depletion-induced aggregation of the nanoparticles.17,63 Also, the proteinnanoparticle and protein-protein interactions governs the protein adsorption on oppositely charged nanoparticles that in turn causes the spontaneous, non-directional and random complexation of nanoparticles through bridging flocculation.43 It has also been observed that the particle interaction induced unfolding of the protein leads to the formation of extended, amorphous protein-nanoparticle assemblies along with the large protein aggregates without embedded nanoparticles.19,36,42 Such protein (or even other biomolecule) interaction induced nanoparticle aggregation has attracted high interest as self-assembly process to construct responsive multidimensional nanoparticle arrays where the physiochemical parameters may be used to tune scale of aggregation.36,64 In nut-shell, it is therefore essential to have in depth understanding of interactions driving the formation of protein corona to successfully design and develop the desired functional as well as physiologically safe and responsive nanoparticleprotein composites.59, 65-70 In this feature article, we summarize some of our recent activity on the study of interaction and structure of nanoparticle-protein complexes.71-74 The anionic silica nanoparticles and two globular proteins [lysozyme and bovine serum albumin (BSA)] have been used as model systems. Both being charged, the interaction is predominantly governed by electrostatic complexion. The conjugation is tuned by choice of protein,74 varying solution conditions (ionic strength and pH)71,73 and size of the nanoparticles.72 The adsorption behaviors of proteins on the nanoparticles were measured using ultraviolet-visible (UV-vis) spectroscopy. The optical 5 ACS Paragon Plus Environment

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Table 1. Different nanoparticle-protein systems, investigated issues and techniques utilized. Nanoparticles SiO2, Al2O3, TiO2, ZrO2,

Proteins Lysozyme, BSA

Au, Polystyrene, SiO2

Serum proteins in cell cultured media

Au

BSA

Protein conformation, adsorption behavior and binding constants

Au, SiO2

Lectin, Thyroglobulin, Lysozyme Lysozyme, Ribonuclease A, HSA, Subtilisin Carlsberg, BSA Human plasma proteins

Protein-protein interaction

SiO2, Ag

Polystyrene, SiO2

Issues Adsorption behavior of proteins and colloidal stability of nanoparticles Kinetics of protein corona

Size dependent adsorption of protein and denaturation

Techniques UV-Vis spectroscopy, Zeta potential, DLS, Transmission Electron Microscopy (TEM) UV-vis spectroscopy, DLS, Zeta potential, Differential Centrifugal Sedimentation (DCS), DLS and TEM UV-vis spectroscopy, Circular Dichroism (CD), FourierTransform Infrared Spectroscopy (FTIR), Fluorescence spectroscopy, Gel-electrophoresis UV-vis spectroscopy, Raman spectroscopy, TEM, DLS, simulations CD-spectroscopy, Small-Angle X-Ray Scattering (SAXS), UVVis, TEM

References 39, 40, 18, 41, 43, 64R 13, 31

28, 29

27, 42, 49

11,30, 17, 14, 65R

Influence of surface charge and size of nanoparticles on protein corona Difference in binding of protein on nanoparticles

Mass spectroscopy, DLS, TEM, DCS,

32,33, 65R 34

TEM, Zeta potential, Gel electrophoresis

35, 8R

TiO2,SiO2,ZnO

Human plasma proteins

Au

HAS, BSA

Structure of NP-protein complexes

DLS, Asymmetric Flow Field Flow Fractionation (AF4)

38, 77R

Au Au

Biotin Ferritin, Ad12 knob, 20S proteasome Lysozyme, BSA

Bio-sensing Superlattices

UV-vis spectroscopy TEM, SAXS

26 36, 37

curvature effect of nanoparticles on lysozyme adsorption Formation and function of Protein corona

Simulation

44

SiO2

SiO2, Au

Blood, BSA

Au

Protein NS1

Interaction of nanoparticleprotein complex with biological milieu

Mass spectroscopy, Gel45, 46, 47R, electrophoresis, Analytical 70R, 69R ultracentrifugation TEM, DLS, Zeta potential, 22R, 25, 53 UV−visible, Plasmon resonance light scattering

R: The references having ‘R’ as superscript are the review articles describing the broader aspects of nanoparticle-protein interactions in various nanoparticle and protein systems.


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transmission and dynamic light scattering (DLS) measurements were used to probe the structural evolution and the resultant phase behavior. The interactions governing the structural evolution of the nanoparticle-protein complexes and the morphology of resultant complexes were studied by small-angle neutron scattering (SANS). The article is organized as follows: First, a brief overview of the experimental techniques that are commonly used to study different nanoparticle-protein systems is given. This is followed by the description of geometrical features and surface properties of the nanoparticles and proteins used in our studies. In next sections, the results on protein adsorption on nanoparticles, phase behavior, and evolution of structure and interaction in nanoparticle-protein systems are discussed. The summary and a future outlook of the work are provided in the end. 2. TECHNIQUES FOR THE CHARACTERIZATION OF NANOPARTICLE-PROTEIN COMPLEXES In nanoparticle-protein systems, the interest may aim the investigations on the structural and interactional changes occurring in individual components as well as hybrid characteristics of their complexes.4,8,12,15,21 Based on the interest, such systems may be characterized by several techniques available in general for the characterization of materials, in one or the other way.65,75,76 The resulting information can be refined to yield images or spectra revealing the topographic, geometric, structural, chemical or physical details of the system, as per the requirement. Once the properties of nanoparticle-protein systems are characterized and understood, this information can be used to address important issues such as cause-and-effect and structure-property relationships as well as to make critical decision for their applications.12,14,54 The various techniques used for the characterization of nanoparticle-protein complexes (or materials in general) can be broadly classified as spectroscopic, macroscopic, microscopic and scattering techniques. Spectroscopic techniques such as nuclear magnetic resonance (NMR), Raman, infra-red (IR), UV-vis, circular dichroism (CD) spectroscopy etc. are widely used for elemental analysis of materials and their quantification.65,75-77 While characterizing nanoparticle-protein systems, these techniques generally analyze the structural transitions and chemical bonding between them. For example, The Raman and circular dichroism (CD) spectroscopy have been utilized to examine the extent of the protein deformation on interaction with nanoparticle.11,14,78 Electron paramagnetic resonance (EPR) spectroscopy76 with spin-labeled proteins has been utilized to 7 ACS Paragon Plus Environment

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obtain insight into protein orientation on the nanoparticle surface while size-exclusion chromatography has been used to explore the protein affinity to the nanoparticles.8 UV-vis spectroscopy provide information on protein adsorption on the nanoparticles.72,75 Macroscopic techniques are widely used to measure the abrupt variation of macroscopic properties with respect to variation in some physical parameter.75 The most commonly used macroscopic techniques are zeta-potential measurements, rheology, conductivity measurements etc.13,39 Such techniques explore the different important bulk properties of the system and hence are useful for predicting overall behavior of the nanoparticle-protein systems. For example, the zeta-potential measurements are useful in obtaining the information about surface charge of the particles, nanoparticle stability as directed by proteins and isoelectric point (IEP) of the mixed system.13,40 If protein molecules adsorb on nanoparticles, the extent of nanoparticle coverage has also been determined by zeta-potential measurements.13,76 The microscopic techniques such as optical, electron and atomic force microscopy allow the direct visualization of the system under investigation. Particularly, transmission electron microscopy (TEM) and cryo-TEM have been widely used to image the nanoparticle-protein complexes.18-20 However, the sample preparations for these measurements sometimes require a lot of care and the measurements are not under native conditions. In addition, these techniques are less sensitive to the details of microscopic interactions in the system. The atomic force microscopy (AFM) has been used to characterize protein corona around nanoparticle-surface whereas the structures of bio-conjugates of silver nanoparticles with BSA and lysozyme protein has been investigated by TEM.79,80 Scattering techniques can be used to investigate both the structure and interaction in the samples in situ and under native conditions.18,75,81 Other features of scattering techniques are that they provide fully three-dimensional information and cover many more particles which lead average over large ensembles providing better quantitative information of the structure and dynamics of the system.82,83 The mostly used scattering techniques to probe nanoparticle-protein interactions are, DLS, SAXS and SANS.84,85 In DLS, the structural and interactional information of the system is contained in the diffusion coefficient of the particles, while these two contributions can be separated in SANS and SAXS.82 DLS is widely utilized to measure the collective diffusion of the nanoparticles, proteins and their complexes.13 In DLS, the intensity autocorrelation function (ACF) [g(2)(τ)] of the


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respective monodisperse system is related to the translational diffusion coefficient (D) of the particle and scattering vector (Q) through the relation83,84 g 2 (τ ) = 1 + β exp( − DQ 2τ )

2

(1)

where β is the spatial coherence factor decided by the instrument optics and τ is the delay time in the ACF. Once the diffusion coefficient has been obtained from Equation 1, the corresponding size (hydrodynamic diameter dh) can be calculated by the Stokes-Einstein relation.

dh =

kBT 3πη D

(2)

where kB is the Boltzmann’s constant, T is the absolute temperature and ɳ is the viscosity of the medium. The adsorption of proteins slowed down the diffusion of the nanoparticles and hence increment in size. Concomitantly, increase in effective size of the nanoparticles can be correlated with the protein adsorption thus binding ratio of the nanoparticle-protein complexes can be monitored by DLS.13 The presence of proteins can also leads to the aggregation of the nanoparticles that reflects shift of the diffusive relaxation to longer relaxation times and most prominently to the appearance of additional, slower relaxation modes.71 DLS is fast, relatively cheaper and rather reliable technique. However, it is difficult to disintegrate the contribution of evolution of interaction and/or structure as both affect the data in the same way. On the other hand, small-angle scattering techniques are employed to obtain the size and shape of the individual components (nanoparticles and proteins), the evolution of interaction and morphology of the resultant structure in the nanoparticle-protein systems.41,85-88 These techniques measure the scattering intensity in the absolute scale and hence different levels of information such as particle concentration, internal structure of particle, formation of aggregates and its morphology etc. can be obtained.82 The scattering intensity is related to the coherent differential scattering cross-section per unit volume [dΣ/dΩ(Q)], which for a collection of identical spherically symmetric and interacting particles can be expressed as89,90 2 dΣ (Q) = ϕV ( ρ p − ρ s ) P ( Q ) S ( Q ) + B dΩ

(3)

where ϕ is the volume fraction and V is the particle volume. ρp and ρσ represent scattering length densities of particles and solvent, respectively. P(Q) denotes the orientational average of the


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square of the form factor [ P (Q ) = F (Q ) 2 ] and provides information about shape and size of the particle.90 S(Q), is the inter-particle structure factor which contains details of the interaction between the particles.91,92 B is a constant accounting for the scattering background. The unique advantage of SANS to study such multi-components system is easy possibility of contrastvariation in this technique.86,87 3. MODEL NANOPARTICLES AND GLOBULAR PROTEINS In order to understand the essentials of the interactions in nanoparticle-protein systems, we have utilized silica nanoparticles and two globular proteins (BSA and lysozyme) as model system. The silica nanoparticles are one of the most commonly used inorganic nanoparticles in a variety of scientific research fields owing to their high chemical and thermal stabilities, good compatibilities with other materials, low toxicity and ability to functionalize with a range of macromolecules. These nanoparticles have a lot of applications in catalysis, pigments, pharmacy, electronic and thin film substrates etc. The silica nanoparticles (Ludox SM30, HS40 and TM40) used in these studies are electrostatically stabilized suspensions of fine amorphous, nonporous, and typically spherical particles in aqueous phase. The two globular proteins lysozyme and BSA are known to be highly stable, available with high purity and easily soluble in water. Both the systems (nanoparticles and proteins) were well characterized using DLS, zeta-potential and SANS.71,72 The details of the sample preparations, experiments and the corresponding data can be found in suitable references.71-74,86-88 The structural models and parameters of nanoparticles and proteins as obtained by SANS are given in Table 2. The silica nanoparticles SM30, HS40, and TM40 are found to have mean sizes of 10.0, 17.6, and 27.6 nm with polydispersities 0.25, 0.16, and 0.13 respectively.72 The SANS data for lysozyme have been modeled by a prolate ellipsoid having semi-major axis (a) of 2.40 nm and a semi-minor axis (b=c) of 1.35 nm while for BSA protein by an oblate ellipsoid having a semi-major axis (a=b) of 4.20 nm and a semiminor axis (c) of 1.50 nm. The measured/calculated values of the charges on the nanoparticle and protein molecules for different pH values can be found in reference 71. The calculated number of lysozyme and BSA molecules per nanoparticle in 1:1 (in wt %) stoichiometry of nanoparticles and protein are presented in Table 2(c). All these parameters are of prime importance while determining the interaction between nanoparticles and proteins and explaining the observed system behavior.


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Table 2. The structural parameters of silica nanoparticles (SM30, HS40 and TM40) and proteins (lysozyme and BSA) in D2O at pH 7 as obtained by fitting the SANS data of 1 wt % solution of each. Reproduced with permission from ref 72. Copyright 2016 American Physical Society. (a) Silica nanoparticles Nanoparticle system

Mean radius

Polydispersity

Surface area

Number

Specific

Curvature

Rm (nm)

σ

per particle

density

surface area

(nm-1)

(nm2)

nNP (m-3)

(m2/g)

0.25

3.1×102

8.59×1021

272

0.20

2

21

155

0.11

99

0.07

SM30

5.00

HS40

8.80

0.16

9.7×10

TM40

13.80

0.13

2.4×103

1.57×10

4.08×1020

(b) Protein systems Protein

Shape

system

Semimajor

Semiminor

Equivalent

Surface area

Number

axis

axis

radius

per molecule

density

a (nm)

b (nm)

Re (nm)

(nm2)

nP(m-3)

Lysozyme

Prolate ellipsoidal

2.40

1.35

1.63

35

4.09×1023

BSA

Oblate ellipsoidal

4.20

1.50

2.98

130

9.07×1022

(c) Calculated number of lysozyme and BSA molecules per nanoparticle in 1:1 (in wt %) stoichiometry of nanoparticles and protein (i.e. 1 wt % nanoparticles mixed with 1 wt % protein) Nanoparticle system

Number of lysozyme molecules

Number of BSA molecules

SM30

47

10

HS40

260

57

TM40

1002

222

4. PROTEIN ADSORPTION ON NANOPARTICLES To examine the protein adsorption on nanoparticles is the first steps towards modeling of their interactions and resultant complex structures. Figure 1 shows the adsorption behavior of both the proteins (lysozyme and BSA) on silica nanoparticles. The protein adsorption on the nanoparticles 11 ACS Paragon Plus Environment

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has been evaluated using UV-Vis spectroscopy, the experimental details of procedure can be found in some of our previous studies.72,93 The free protein in the system is separated from the adsorbed proteins by centrifugation and the concentration of the free protein is obtained from absorbance spectra using the Beer-Lambert law. The adsorption curves thus obtained are expressed in terms of protein adsorbed (wt %) on nanoparticles as a function of the total protein concentration (wt %) added in the system. The lysozyme and silica nanoparticles are oppositely charged and hence protein strongly adsorbs on the nanoparticle surface. In general, the adsorption curve for lysozyme (Figure 1a) is found to show an exponential growth behavior where the amount of adsorbed protein first increases with increasing concentration of added protein and finally saturates at higher protein concentrations. In order to quantify the adsorption, the data are fitted using an exponential equation of the form A = A0[1 – e–kC], where A is the adsorbed protein concentration, C denotes the total protein concentration added in the system, A0 measures the saturation value and k represents the adsorption coefficient.

1

1

(a)

(b) Adsorbed BSA (wt %)

Adsorbed Lysozyme (wt %)

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0.1

0.01 0.0

0.5

1.0

1.5

0

-1

2.0

0

Lysozyme (wt %)

1

2

3

4

5

BSA (wt %)

Figure 1. Adsorption behavior of (a) lysozyme and (b) BSA proteins on 1 wt % HS40 silica nanoparticles at pH7. Lysozyme adsorbs strongly on the nanoparticles, whereas BSA does not adsorb on the nanoparticles. Reproduced with permission from ref 72. Copyright 2016 American Physical Society.

It may be added here that adsorption behavior can also be plotted as adsorption isotherms (adsorbed vs. free protein) and can be well fitted using Langmuir isotherm, provided adsorption is below the monolayer capacity, as usually done by others also.11,94 Though the physical


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representation is different in the two cases, but both describe the same features of the protein adsorption. Unlike lysozyme, BSA protein does not show any adsorption on the nanoparticles (Figure 1b). The electrostatic repulsion due to a similar charge nature of the two components in the pH range of the experiments most likely prevents any adsorption of BSA on the nanoparticles. It may be added here that in spite of similar charge nature, some of the studies have reported the weak adsorption of BSA protein on silica nanoparticles mostly because of the site-specific interactions between them arising from the presence of positive charge patches (non-uniform charge distribution) on BSA surface.14,95 The driving force of the BSA adsorption (if any) in these studies is considered to be related to the structural changes in the adsorbing BSA molecules.96 In such situation, BSA adsorption leads to the formation of a shell around the nanoparticle core which is believed to stabilize the nanoparticles against aggregation by steric and/or electrosteric interactions.17,63,95 This is not consistent with the observed nanoparticle aggregation caused by BSA protein in our studies.71-74,87,97 In fact, the non-adsorption of BSA is found to give rise depletion attraction-induced aggregation of the nanoparticles and is discussed in detail in later parts of the article. It may be of interest to note that the studies claiming the BSA adsorption on silica particles show that it can also be desorbed by changing the pH away from the isoelectric point of the protein and by increasing the ionic strength.96 It has been further observed that protein (hemoglobin or ß-lactoglobulin) adsorption on anionic silica nanoparticles suppress and finally diminishes completely when the charge on the protein changes from positive to sufficiently negative.94,98 Depending on system conditions, various other parameters such as interfacial ion distribution, interfacial charge regulation of amino acids, overall electroneutrality, and mass balance can also play important role in deciding protein adsorption or non-adsorption along with the electrostatic interactions.99 In the case, when the adsorption behavior is predominantly governed by the electrostatic interactions, pH and/or presence of salt provide a useful way to tune it (Figure 2). The pH has been varied for a given size nanoparticle system (HS40) in the range between the IEPs of the lysozyme (IEP lysozyme∼11) and BSA (IEP BSA∼4.6) to maintain the charge nature of the proteins with respect to that of nanoparticles.40,41,71 In the complete pH range of the investigations, the lysozyme remains oppositely charged while BSA remains similarly charged to that of nanoparticles.71,86 The effect of pH on lysozyme adsorption on nanoparticles is depicted in Figure 2a. In this case, the saturation value (A0) increases while adsorption coefficient (k) 13 ACS Paragon Plus Environment

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1

(a)

pH 7 pH 9 pH 11

Adsorbed Lysozyme (wt%)

0,1

1 pH 7 pH 9 pH 11

0,1

5 10 15 Added Lysozyme (mg/m2)

Adsorbed Lysozyme (mg/m2)

(b)

1

Adsorbed Lysozyme (mg/m2)


0,1

1 Lysozyme (wt%)

0,01

1

2

0

1 0 M NaCl 0.05 M NaCl 0.1 M NaCl

0,1

0,01

5

10

15

Added Lysozyme (mg/m2)

1

2

Lysozyme (wt%)

(c)

1000

0,1

SM30 HS40 TM40

Adsorbed Lysozyme/NP

0

10

0 M NaCl 0.05 M NaCl 0.1 M NaCl

0,01


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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100 SM30 HS40 TM40 10 0

200

400

600

800

1000

Added Lysozyme/NP

0,01 0

1

2

Lysozyme (wt%)

Figure 2. Adsorption isotherms of lysozyme protein on 1 wt % HS40 silica nanoparticles for (a) different pH, (b) varying salt concentration and (c)different sizes of the nanoparticles. The pH is kept fixed (pH 7) in (b) and (c). The insets of (a) and (b) show the adsorption plots (adsorbed vs added protein) where the protein amounts are expressed in mg/m2 (normalized to total surface area of 1 wt % nanoparticles). The inset of (c) expresses the adsorption behavior for three sized nanoparticles in terms of number of protein molecules per nanoparticle. In all the cases, lysozyme adsorption shows exponential growth behavior. Adapted with permission from ref 71. Copyright 2017 American Chemical Society. Adapted with permission from ref 72. Copyright 2016 American Physical Society.


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Table 3. Fitted parameters of adsorption curves of lysozyme protein on 1 wt % silica nanoparticles with varying pH, salt concentration and nanoparticle size. Reprinted with permission from ref 71. Copyright 2017 American Chemical Society. Adapted with permission from refs 72. Copyright 2016 American Physical Society. (a) pH effect (HS40 Nanoparticle system)

Saturation

Adsorption

value

coefficient

A0 (wt %)

К (1/wt %)

7

0.5

2.5

9

0.7

2.1

11

0.8

1.8

pH

(b) Salt effect (HS40 Nanoparticle system and pH 7)

[salt]

Saturation

Adsorption

(M)

value

coefficient

A0 (wt %)

К (1/wt %)

0

0.5

2.5

0.05

0.5

2.5

0.1

0.5

2.5

(c) Nanoparticle size effect (pH7 and no salt)

Nanoparticle

Saturation

Adsorption

system

value

coefficient

A0 (wt %)

К (1/wt %)

SM30

0.67

2.25

32

2.5

0.48

HS40

0.50

2.49

130

3.2

0.79

TM40

0.31

2.63

310

3.3

0.86

NP

Ms

PF

(mg/m-2)

Np, Ms and PF are number of adsorbed lysozyme per nanoparticle, mass of adsorbed lysozyme normalized to total surface area of 1 wt % nanoparticles and packing fraction, respectively at saturation value. 15 ACS Paragon Plus Environment

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decreases with the increase in pH of the solution (Table 3a). The variation in the saturation value is explained by the interplay of two interactions: (i) the attraction between nanoparticles and lysozyme and (ii) mutual repulsion between protein molecules. The amount of protein adsorption approaches a maximum value near the IEP of the protein. Because the net charge on protein is minimum near the IEP as a result the molecules can attain relatively closer packing at the nanoparticle surface. On the other hand, the decrease in the adsorption coefficient with increasing pH is attributed to the decrease in the strength of attraction between nanoparticles and protein due to lower charge on proteins molecules near the IEP. The adsorption behavior of both the proteins do not modify in the presence of small amount of salt (0.1 M NaCl) (Figure 2b and Table 3b). In the case of lysozyme, though the interactions between different components (nanoparticle-nanoparticle, protein-protein and nanoparticle-protein) within the system are altered in presence of salt, but it seems that competing interactions (nanoparticle-protein attraction and protein-protein repulsion) somehow balance resulting in to no significant change in the adsorption of protein. It may be pointed out here that the higher amount of salts is expected to change the parameters such as saturation value and adsorption coefficient but not the basic features of the adsorption curves for both the proteins. Moreover, the presence of salt on adsorption level is reported to have maximum impact near IEP of the proteins.94 Figure 2c shows the effect of nanoparticle size, which is again a crucial parameter to govern the lysozyme adsorption on nanoparticles. In this case, the saturation value (A0) decreases whereas adsorption coefficient (k) increases with increasing the size of the nanoparticles (Table 3c).72,86 The higher value of saturation for smaller size nanoparticles (SM30) is due to the availability of the larger total surface area (large number density for fixed wt % concentration) of the nanoparticles. Apart from surface area, the curvature of the nanoparticles also plays an important role. On increasing nanoparticle size [decrease in the nanoparticle curvature (Table 2a)], the adsorption coefficient increases because of increase in the contact area of the protein with nanoparticle. The importance of curvature is also reflected while calculating the number of adsorbed lysozyme molecules per nanoparticles, mass of adsorbed protein per unit area of nanoparticles, and packing fraction of adsorbed lysozyme, presented in the Table 3(c). As expected, the number of adsorbed protein molecules per nanoparticle increases with increasing nanoparticle size, but this increase is not scaled to the change in the 16 ACS Paragon Plus Environment

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surface area due to the additional curvature effect, which is also reflected in the reduction in the adsorbed protein mass per unit area and packing fraction of adsorbed protein on the nanoparticles with decreasing particle size.11,72 It is important here to add that adsorption behavior of BSA (Figure 1b) remains same (non-adsorbing), irrespective of similar variation in pH, ionic strength and nanoparticle size.

1.5 1 wt % HS40+Lysozyme 1 wt % HS40+BSA Transmission

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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1.0

III

II

I

0.5

0.0 -4

10

-3

10

-2

10

-1

10

0

10

1

10

Proteins Concentration (wt %)

Figure 3. Transmission of light through 1 wt % HS40 silica nanoparticles with varying concentration of lysozyme and BSA proteins. Reproduced with permission from ref 74. Copyright 2014 American Physical Society.

5. PHASE BEHAVIOR OF NANOPARTICLE-PROTEIN COMPLEXES To compare the macroscopic effect of the different adsorption behavior of the two proteins, the phase behavior of nanoparticle-protein systems has been examined in Figure 3.72,74 The figure actually depicts the variation in the transmission of light through the 1 wt % HS40 silica nanoparticles with varying concentrations of lysozyme and BSA proteins. It is interesting to observe that the transmission decreases dramatically beyond a critical protein concentration (CPC) for both proteins. The CPC for BSA is much higher (almost two orders) than that for the lysozyme. Based on the features, the phase behavior for the two proteins can be divided in three protein concentration regimes. In the first regime corresponding to the very low protein concentrations (1 wt %), BSA also shows low transmission similar to the case of lysozyme. The transmission of the light through these systems usually reflects the structural evolution in the system where the decrease in the transmission indicates the formation of larger structures. Therefore the observed variation of the transmission shows the transformation of the 18 ACS Paragon Plus Environment

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system from clear one-phase (individual nanoparticles) to turbid two-phase system (nanoparticle aggregates) for both the proteins but with very different CPC values. The adsorption of oppositely charged lysozyme on nanoparticles causes the protein to mediated bridging aggregation of nanoparticles which finally leads to the decrease in transmission.18,74,93 However, the nanoparticle-BSA system surprisingly, renders on a similar behavior despite having repulsive interaction between the two components. This suggests that the mechanism leading to the similar phase behavior in the two cases is very different. It is also important to mention here that the trends in the phase behavior remains same (Figure 4) but the CPC values changes with respect of the different parameters such as pH of the solution71, nanoparticle size72 or ionic strength73of the system due to the tuning of the respective interactions governing the systems. The CPC value is found to be increasing for lysozyme, whereas decreasing for BSA as pH approaches the respective IEPs [Figure 4a(I,II)].71 The change in CPC is more pronounced in the case of BSA compared to that observed for lysozyme. The presence of electrolyte though does not alter the adsorption behavior but shows significant modifications in the phase behavior for both the proteins [Figure 4b(I,II)]. The CPC values are suppressed on addition of an electrolyte again being more prominent for BSA than lysozyme. In the case of varying nanoparticle size, the CPC is lowered for the larger sized nanoparticles for both proteins [Figure 4c(I,II)].72 6.

EVOLUTION

OF

INTERACTION

AND

RESULTANT

STRUCTURE

IN

NANOPARTICLE-PROTEIN COMPLEXES DLS and SANS measurements have been carried out in the three regions of the phase behavior to understand the mechanism of the interaction of silica nanoparticles with lysozyme and BSA proteins and to probe the structures of the complexes formed under different solution conditions. 6.1. Strongly Adsorbing Protein on Nanoparticles (Silica Nanoparticle-Lysozyme System) The DLS data (ACF) of the nanoparticle-lysozyme system and the corresponding effective size distributions for fixed 1 wt % HS40 silica nanoparticles with varying lysozyme concentration at pH7 are depicted in Figure 5. As can be seen in the size distributions, that the mean size shifts towards larger values with increase in lysozyme concentration in the transition regime I to II of the phase behavior, indicating the formation of nanoparticle-protein complexes [Figure 5(b-II)].


Langmuir

Interestingly, the size distribution becomes bimodal for a given concentration range of lysozyme corresponding to the regime II of phase behavior [Figure 5(b-III)]. At higher lysozyme concentrations, the DLS data shows existence of particles in the range of a few hundred nanometers with a mono-modal size distribution suggesting the two-phase (nanoparticle aggregates) formation [Figure 5(b-IV)]. 20

1.0

( a) 1 wt % HS40 + C wt % Lysozyme 0 0.01 0.02 0.03 0.04 0.06

0.6 0.4

pH7

0.2

0

0

1

10

2

10

3

4

10

10

5

10

0 .0 2 w t % L y s o z y m e

(b -II) 10 0

0 .0 4 w t % L y s o z y m e

(b -III) 10 0 10

0.0 10

0 w t % L yso zym e

(b -I) 10

No. of Nanoparticles (%)

0.8

g2(τ )-1

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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0 .0 6 w t % L y s o z y m e (b -IV )

0 1 10

10

2

10

3

10

4

D ia m e te r (n m )

Delay Time (µS)

Figure 5. (a) Autocorrelation functions (ACFs) of 1 wt % HS40 silica nanoparticles in the presence of varying lysozyme concentration as obtained by DLS at pH 7 and (b) Calculated particle size distribution for some of the protein concentrations for which ACFs are shown in (a). Reproduced with permission from ref 72. Copyright 2016 American Physical Society.

The DLS observations thus suggest that the changes in the phase behavior of nanoparticlelysozyme system can be understood in terms of evolution of attractive interaction and/or formation of larger structures (e.g. aggregates). However, by DLS, it is difficult to disintegrate the contributions of structure and interaction, as both of these influences the data in the same manner. On the other hand, SANS can separate these two contributions as the scattering crosssection in SANS is proportional to the form factor and structure factor providing the information on structure and interaction, respectively.90,100 Therefore, to unfold these two contributions, SANS measurements were carried out on

nanoparticles-lysozyme protein systems at pH7

(Figure 6).74 The important features of the data can be seen in to three concentration regimes, similar to phase behavior: (i) the low protein concentration (0.0-0.02 wt %) regime (Figure 6a) corresponding to Figure 5(b-II), (ii) the intermediate protein concentration (0.05-0.2 wt %) regime (Figure 6b) where the DLS data shows a bimodal distribution [Figure 5(b-III)] and 20 ACS Paragon Plus Environment

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100

1 wt % HS40 + C wt % Lysozyme

-1

dΣ /dΩ (cm )

0.0 0.01 0.02

(a)

10

1

0.1

0.01 0.005 0.01

0.1

-1

0.3

Q (Å )

100

100

1 wt % HS40 + C wt % Lysozyme (b)

1 wt % HS40 + C wt % Lysozyme 10

1

0.1

0.01 0.005 0.01

(c)

0.0 0.5 1.0 2.0 5.0

-1

-1

0.0 0.05 0.1 0.2

dΣ /dΩ (cm )

10 dΣ /dΩ (cm )

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

Langmuir

1

0.1

-1

0.1

0.01 0.005 0.01

0.3

Q (Å )

-1

0.1

0.3

Q (Å )

Figure 6. SANS data of 1 wt % of HS40 silica nanoparticles in the presence of varying lysozyme concentration (a) low protein concentration (0.0-0.02 wt %), (b) intermediate protein concentration (0.05-0.2 wt %), and (c) high protein concentration (0.5-5 wt %). Reproduced with permission from ref 74. Copyright 2014 American Physical Society.

(iii) the higher protein concentration (0.5-5 wt %) regime (Figure 6c) where the DLS size distribution shows the presence of large structures [Figure 5(b-IV)]. It may be observed in Figure 6a that the addition of small amounts of lysozyme with silica nanoparticles gives scattering buildup in the low-Q region with almost no changes in the intermediate and high-Q values. Similar to the variations in autocorrelation functions, such scattering buildup in the SANS data originates also from the evolution of attraction between nanoparticles and/or by the formation of larger structure.91,100 Being oppositely charged in nature lysozyme, immediately adsorbs on nanoparticles and therefore, mediates an attraction between them leading to their 21 ACS Paragon Plus Environment

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Page 22 of 50

aggregation.41,71,93 In the low protein concentration (≤0.02 wt %) regime, the system is characterized by the nanoparticles experiencing the protein induced attraction (Figure 6a) and hence the corresponding S(Q) has been modeled by the two-Yukawa (2Y) potential.81,91 The fitted potentials (details of which are provided in next paragraph) give a signature for the existence of short-range attractive interaction in the system.74 The fitted parameters are given in Table 4a. The SANS data in the intermediate protein concentration regime (Figure 6b) corresponds to the region of phase behavior where the bimodal particle size distribution [Figure 5(b-III)] has been observed in the DLS data. In this lysozyme concentration range, the system may be considered consisting of nanoparticle aggregates, coexisting with individual nanoparticles undergoing attractive interaction. It has been found that the linearity of scattering at the low-Q values (log-log scale) increases with increasing the concentration of lysozyme (Figure 6b). This linearity in the scattering profiles

indicates the fractal nature of the

aggregates.92 The mass fractal aggregates together with some individual nanoparticles interacting with the 2Y potential have been considered to analyze the scattering data for lysozyme concentration (0.05-0.2 wt %). In this protein concentration range, the amount of protein is not sufficient to bind every nanoparticle in the fractal structure. The remaining particles form flocculates, which interacts via protein-mediated short-range attraction. On increasing the protein concentration, the fraction of aggregates increases (Table 4b). For protein concentration (0.5-5 wt %), as depicted in Figure 6c, all the nanoparticles have become fractal aggregates. The fractal dimension (Df) is obtained to be about 2.4 indicating the diffusion limited aggregation (DLA) kind of fractal morphology. The scattering buildup in the high Q region in this data set suggests the presence of excess protein existing along with the nanoparticle aggregates.72-74 The protein-mediated interaction between nanoparticles is fitted using following two-Yukawa (2Y) potential91 exp[ − Z1 ( r / σ − 1)] exp[ − Z 2 ( r / σ − 1)] V2Y ( r ) = − K1 + K2 k BT r /σ r /σ

(4)

where r and σ represent the inter-particle distance and hard sphere diameter of the interacting particles, respectively. kB is Boltzmann constant and T is the absolute temperature. The first term of the potential serves for the interparticle attraction, while the second one is to account for the interparticle repulsion. The two-Yukawa (2Y) potential disintegrates the attractive and repulsive parts of the total interaction potential, which allows the determination of the magnitude and the 22 ACS Paragon Plus Environment

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range of the respective parts of the potential without any pre-defined assumption or limitations. Moreover, the present system is primarily governed by the electrostatic interactions, hence the choice of the 2Y potential is expected to present the system in most suitable physical manner.81,91,100,101 Further, it has been shown that the 2Y potential is also able to take account of most popular theory of colloidal stability, i.e. DLVO theory, by simulating the van der Waals potential by a short-range attractive Yukawa potential.91,100 Considering all these points, we believe that the 2Y potential could be a proficient option to represent such systems without losing generality. There are four unknown parameters in the potential: Ki and Zi (i= 1, 2), which represent strength and range (σ/Z) of the respective parts of the potentials. To limit the fitting parameters, the parameters of the repulsive part (K2, Z2) are obtained from the concentrated solutions of the pure nanoparticles since S(Q) contribution is almost negligible [S(Q) ̴ 1] in the 1 wt % nanoparticle solutions prepared in buffer. The SANS data, therefore, were collected for different silica nanoparticle concentrations, and fitted to get the parameters (K2, Z2) for 1 wt % by the extrapolation, the details of the calculations can be seen elsewhere.74 These parameters accounting for the repulsive interactions were fixed in the analysis of the SANS data, and those corresponding to the protein induced attraction between nanoparticles were obtained by fitting procedure. It should also be noted that the lysozyme adsorption may reduce the overall charge of the nanoparticle-protein complex and hence the repulsion between them. However, such reduction in the repulsion alone cannot take account of rise in scattering intensity. Therefore, any protein-induced attraction between nanoparticles is considered in the attractive term (K1, Z1) of the two-Yukawa potential while keeping the parameters of repulsion fixed. Interestingly, the protein mediated attractive interaction between nanoparticles is found to be short-range compare to the long-range electrostatic repulsion between them. The interaction seems to be governed by distance over which the protein molecule is effectively bridging the interaction between two nanoparticles. There are other studies also which could successfully explain the scattering data using other short-range attractive potentials (e.g. square well potential) in silica nanoparticlelysozyme protein system.18 However, in the cases where the interaction is relatively long-range (e.g. electrostatic attraction in a system having very low ionic strength or depletion attraction), the use of 2Y potential can give a more realistic representation to the system.


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Page 24 of 50

Table 4. Fitted parameters of 1 wt % HS40 + C wt % Lysozyme. Reprinted with permission from refs 72, 74. Copyright 2016, 2014 American Physical Society.

(a) Low protein concentration regime where the nanoparticles feel protein induced attraction. The parameters of repulsive interaction K2 =9.0, Z2=7.0 were kept fixed. Concentration C (wt %)

K1

Z1

0.01

18.0

9.0

0.02

20.0

9.0

(b) Intermediate protein concentration regime where the nanoparticle aggregates coexist with interacting unaggregated nanoparticles. The fractal dimension of nanoparticle aggregates Df = 2.4 and parameters of repulsive interaction K2 = 9.0, Z2 = 7.0 were kept fixed. Lysozyme

Building block

Concentration

radius

unaggregated

Rb(Å)

nanoparticles

Z1

K1

Fraction of

C (wt. %)

φunp (%)

0.05

90.0

26.5

10.0

40

0.1

94.5

29.5

13.0

30

0.2

93.2

40.0

14.5

10

(c) High protein concentration regime where nanoparticle aggregates coexist with excess free proteins. Concentration

Fractal

Building block

Fraction of free

dimension

radius

protein

C (wt. %)

Df

Rb (Å)

φfp (%)

0.5

2.4

94.2

-

1.0

2.5

95.2

35

2.0

2.4

93.1

70

5.0

2.4

94.0

85


Page 25 of 50

6.2. Non-adsorbing Protein with Nanoparticles (Silica Nanoparticle-BSA System) Figure 7 presents the DLS data of the nanoparticle-BSA system. The auto correlation functions (ACF) of the nanoparticles as a function of BSA concentration and the calculated size distributions at certain BSA concentrations are shown in Figures7a and 7b, respectively. There are no significant changes in ACF function of the nanoparticle-BSA system observed up to 0.2 wt % of BSA protein (inset of Figure 7a). This is in support of the fact that BSA protein is nonadsorbing to the nanoparticles. The non-adsorption of BSA induces the depletion attraction between the nanoparticles, leading to the observed changes in ACF at higher BSA concentrations.102 It is known that the strength of the depletion interaction increases with the increase in the depletant (BSA) concentration which causes the apparent enhancement in the size of the nanoparticles.72 The further increase the BSA concentration, causes sufficient attraction to lead nanoparticle aggregation as indicated by the very high mean size in the distribution.

1.0

20

1.0 0 0.01 0.05 0.1 0.2

0.8

2

g (τ )-1

0.8

0.6 0.4

(b )

0.2

0.6

0.0 0 10

1

10

2

10

3

10

2

Delay Time (µS)

0.4

0 0.5 1 2 3

0.2

0 w t % BSA

10 No. of Nanoparticles (%)

(a)

g (τ )-1

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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0 1 w t % BSA 10 0 2 wt % BSA 10 0 3 wt % BSA

20 10

0.0 0

10

1

10

2

10

3

10

4

10

0 1 10

5

10

Delay Time (µS)

10

2

10

3

10

4

D ia m e t e r ( n m )

Figure 7. (a) Autocorrelation functions (ACFs) as obtained by DLS of 1 wt % HS40 silica nanoparticles with varying BSA concentration at pH7 and (b) calculated particles size distribution for some of the data presented in (a). The ACFs of the system at low BSA concentrations are presented in the inset of Figure (a) indicating no change in the system. Reproduced with permission from ref 72. Copyright 2016. American Physical Society.

The SANS data of the 1 wt % silica nanoparticles in presence of varying BSA concentration are depicted in Figure 8.74 Similar to that done for nanoparticle-lysozyme system, data in this case also are divided in three BSA protein concentration regimes: (i) the low 25 ACS Paragon Plus Environment

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concentration regime (0.0-0.2 wt %) (Figure 8a), where the system is observed to be transparent in the phase behavior (Figure 3), (ii) the intermediate concentration regime (0.5-1 wt %) representing the transition regions II to III of the phase behavior and (iii) the high protein concentrations regimes (2-5 wt %), (Figure 8c) where the system becomes two-phase in the region III of phase behavior. The scattering profiles of the silica nanoparticle-BSA system are found to have very distinct features compare to those for the silica nanoparticle-lysozyme system. The data in Figure 8a (BSA concentration ≤ 0.2 wt %) do not show any significant changes with respect to that of 1 wt % of silica nanoparticles on addition of BSA. Consistent to the phase behavior, in this protein concentration range, there is no indication of the formation of any larger structure in the system in the SANS data also and hence the data were fitted by summing the scattering contributions from the individual nanoparticles and the free protein. Unlike the silica nanoparticle-lysozyme system, here, both silica nanoparticle and BSA protein exist as individual entities in the system. Any possibility of the site-specific adsorption of BSA on the silica nanoparticle surface leading to the formation of protein corona (core-shell structure) is also not supported by the SANS analysis and hence endorsing the observed adsorption isotherms (Figure 1b). We therefore, believe that the strong electrostatic repulsion (both components being similarly charged) prevents any protein adsorption on the nanoparticles.39 However, changes in the scattering data in Figure 8b may be attributed to another kind of forces in the system originating from the non-adsorption of the BSA, for example, depletion interaction. The effect of depletion interaction could not be observed in Figure 8a as the BSA concentration may not be enough to give notable attraction. On increasing BSA concentration (≥0.5 wt %), the scattering buildup at low-Q as well as at high-Q regions can be observed (Figure 8b). The scattering buildup at low-Q, cannot be explained by considering nanoparticle aggregates (no change in the transmission in this protein concentration range in the phase behavior). Therefore, the data in Figure 8b have been fitted by 2Y potential attributing the scattering buildup to the attractive depletion interaction (Table 5). In this case, the attractive interaction is found to be relatively long-range, compare to that induced by lysozyme protein. As shown in phase behavior, the system becomes turbid at higher BSA concentrations (2-5 wt %), as the sufficient amount of non-adsorbing BSA causes the depletion induced aggregation of the nanoparticles. SANS data (Figure 8c) in this BSA concentration regime also show linearity at low-Q values, suggesting the


Page 26 of 50

Page 27 of 50

100

1 wt % HS40 + C wt % BSA

-1

dΣ /dΩ (cm )

0.0 0.1 0.2

(a)

10

1

0.1

0.01 0.005

0.01

0.1

-1

Q (Å )

100

0.3

100



0.0 0.5 1.0

1

0.1

0.01 0.005

0.0 2.0 5.0

(c)

10 -1

-1

(b)

dΣ /dΩ (cm )

10 dΣ /dΩ (cm )

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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1

0.1

0.01

-1

0.1

0.3

0.01 0.005

0.01

0.1

0.3

-1

Q (Å )

Q (Å )

Figure 8. SANS data of 1 wt % HS40 silica nanoparticles with varying BSA protein in three concentration regimes (a) low concentration (0.01-0.2 wt %) (b) Intermediate concentration (0.5-1 wt %) and (c) high concentration (2-5 wt %). Reproduced with permission from ref 74. Copyright 2014 American Physical Society.

formation of fractal-like aggregates in the system, similar to the silica nanoparticle-lysozyme system. Therefore, SANS data in Figure 8c were fitted by the fractal structure of the nanoparticle aggregates having fractal dimension of about 2.4, coexisting with the free proteins. The depletant (BSA protein) concentration is known to govern the degree of the aggregation as well as the size of the aggregates in such cases, and hence the value of the transmission is observed to be different for 2 and 5 wt % of BSA. As we do not observe any lower Q cut-off in the SANS data, the overall size of the aggregates are expected to be much larger (as also reflected in DLS measurement) than that can be determined in the present Q-range (2π/Qmin~ 100 nm). This may be the possible reason for the almost similar SANS data in the low Q region at 2 and 5 wt % of 27 ACS Paragon Plus Environment

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Page 28 of 50

BSA. The determination of the size of the aggregates by SANS would need data to be measured in much lower Q range also. It has been observed that BSA and lysozyme proteins both give rise to the nanoparticle aggregation but through entirely different mechanisms. The comparison of the protein (lysozyme and BSA) induced attractive interaction with the electrostatic repulsion is depicted in Figure 9.74 It is clear from the figure that the dominance of the attractive interaction over the repulsion leads to the nanoparticle aggregation in the presence of both the proteins, however the mechanisms involved are completely different. The lysozyme driven nanoparticle aggregation results from the charge neutralization of the nanoparticles and their bridging by the protein molecules. The lysozyme induced attractive interaction causing particle aggregation is found to be relatively short-range. On the other hand, BSA induced particle aggregation arises from the non-adsorption of the BSA protein on the nanoparticles, which gives rise a relatively long-range depletion attraction between nanoparticles. The understanding of such interaction potential provides useful guidelines for predicting the phase stabilities of the system as well as for developing hybrid functional materials having multi-responsive properties with respect to easily tuneable parameters. A schematic showing the structural transitions in silica nanoparticles with lysozyme and BSA proteins is shown in Figure 10.

Table 5. Fitted parameters of 1 wt % HS40 nanoparticles in presence of BSA in the intermediate protein concentration range, where the nanoparticles experience depletion attraction before to their aggregation. The parameters of repulsion K2 = 9.0, Z2 = 7.0 were kept constant. Reprinted with permission from ref 74. Copyright 2014 American Physical Society.

Concentration

K1

Z1

0.5

10.5

4.0

1.0

26.5

3.0

C (wt %)


Page 29 of 50

10

10

(a)

(b)

1 wt % Silica + 0.1 wt % Lysozyme

1 wt % Silica + 1.0 wt % BSA 5

V/KBT

5

V/KBT

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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Figure 9. The calculated interaction potentials (along with repulsive and attractive parts) causing aggregation of silica nanoparticles in the presence of (a) lysozyme and (b) BSA proteins. Reproduced with permission from ref 74. Copyright 2014 American Physical Society.

Figure 10. A schematic representing the mechanisms responsible for the aggregation of silica nanoparticles in presence of lysozyme and BSA proteins.

6.3. Effect of pH of the Solution Since the protein adsorption and the phase behavior both show modifications on varying the pH of the solution, it becomes interesting to know the effect of the pH on evolution of interaction and structures in the silica nanoparticle-lysozyme system. The DLS data for 1 wt % silica nanoparticles with 0.03 wt % lysozyme (as a representative concentration) with varying pH


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Figure 11. (a) Autocorrelation functions of 1 wt % silica nanoparticles + 0.03 wt % lysozyme concentration as a function of varying pH, (b) effective hydrodynamic size of the system (1 wt % HS40 + C wt % lysozyme) with varying pH at different lysozyme concentrations, (c) SANS data of 1 wt % HS40 + 0.02 wt % lysozyme at different pH values and (d) calculated total interaction potentials from SANS data along with repulsive and attractive components (inset) between the nanoparticles. Reproduced with permission from ref 71. Copyright 2017 American Chemical Society.

and corresponding variation of the effective mean size of the system at different concentrations as a function of pH are shown in the Figure11.71 The autocorrelation function becomes systematically narrow with increasing pH and finally almost matches to that of the pure nanoparticles at pH11 (Figure 11a). This suggests that the protein-mediated aggregation of the nanoparticles is suppressed as the charge on the lysozyme decreases as the pH approaches its IEP. A systematic decrease in the mean size of the system can be observed on increase in the pH at a given lysozyme concentration (Figure 11b). Interestingly, for pH values above the IEP of 30 ACS Paragon Plus Environment

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lysozyme (e.g. pH12), the mean size of the system become equal to that of pure nanoparticles as the protein becomes non-adsorbing, and this free protein contributes negligibly in the DLS measurements. The SANS data of the nanoparticle-lysozyme system at different pH values for particular lysozyme concentrations (0.02 and 0.05wt %) corresponding to regime I and III of the phase behavior [Figure 4(a-I)] are utilized to examine the influence of pH. The data at 0.02 wt % lysozyme concentration show the evolution of the interaction (Figure 11c) and those at 0.05 wt % compares the structures formed at different pH values (Figure 12). At 0.02 wt % protein concentration, as described earlier, system is characterized by individual nanoparticles undergoing attractive interaction due to adsorbed protein at all the pH values. The rise in scattering intensity (Figure 11c) with decreasing pH suggests the pH-dependent intensification of the attractive interaction. The interactions have been modeled again using the two-Yukawa potential, taking account of attractive as well as repulsive parts (Figure 11d). One can observe that there is a slight decrease in the magnitude of the repulsion at pH 11, compare to that at pH 7 and 9, in accordance with the measured zeta potential (surface charge) of the nanoparticles. The range of the repulsion (σ/Z2) being governed by the same ionic strength of the system at all pH values remains constant. The magnitude of the attraction (K1) is decided by the charge on the protein molecules and hence is increased with decreasing pH, while the range (σ/Z1), as determined by the size of the protein molecule, remains almost unchanged. The sum of these attractive and repulsive potentials results in to a total potential which become more and more attractive with decreasing pH away from the IEP.41,71,86 The higher charge on protein molecules at smaller pH makes them more effective for mediating an attraction between nanoparticles. The pH of the solution provides an easy and controlled way to tune the morphology of the nanoparticle-protein aggregates as can be seen in Figure 12, where the SANS data of 1 wt % nanoparticles system with 0.05 wt % lysozyme (in the two-phase region) at three pH values (5, 7 and 9) are compared.103 The scattering intensity in this case shows a systematic decreases in lowQ region with increasing pH (inset of Figure 12a) and also the slope of the linearity decrease. At pH 5, a Bragg peak can be observed which is absent at higher pH values (7 and 9). The slope of the linearity and the presence of the Bragg peak at pH 5 suggest that the aggregates formed at pH 5 are surface fractals in nature with a closed ordered packing of the particles. The aggregates lose their packing with increasing pH and finally transform in to mass fractals. The fractal dimension decreases with increase in pH on approaching IEP. The resultant structures at three pH values 31 ACS Paragon Plus Environment

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can be explained by considering the interplay of the nanoparticle-protein attraction and proteinprotein repulsion. The enhanced adsorption of protein at higher pH (e.g. pH 9) gives rise to a net positive protein shell around nanoparticle. The mutual repulsion of these protein shells of the neighboring nanoparticles does not allow the close packing of the particles (as observed at pH 5) within the aggregates.86,103 We have also observed that the ionic strength of the solution due to its effects charge-charge interactions, have similar impact on morphology of the aggregates at low protein concentrations.73 It should be added here that not only the morphology but the degree of aggregation can also be tuned using the pH of the system. It has been shown that the critical balancing of the pH near isoelectric point of the protein may suppress the protein mediated nanoparticle aggregation and individual nanoparticles with adsorbed protein can be observed. In this context, these structures may be termed as pH sensitive where the morphology as well as degree of aggregation both can be varied with pH. Now a days, the use of pH sensitive nanoparticles or their complexes with biomolecules have gained interest as many body organs, tissues, subcellular structures as well as their pathophysiological states can be characterized by their pH level.56 32 ACS Paragon Plus Environment

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Figure 13. Data of 1 wt % HS40 silica nanoparticles in the presence of BSA: (a) ACF at a fixed lysozyme concentration (0.03 wt %) with varying pH, (b) effective hydrodynamic size of the nanoparticles and aggregates of nanoparticles with varying pH at different lysozyme concentrations, (c) SANS data at fixed lysozyme concentration (0.02wt %) but different pH values and (d) corresponding calculated total interaction potentials from SANS data along with repulsive and attractive components (inset) between the nanoparticles. Reproduced with permission from ref 71. Copyright 2017 American Chemical Society.

The influence of pH on the nanoparticle-BSA interaction is investigated in Figure 13. Figure 13a shows the variations in autocorrelation function of 1 wt % HS40 with 0.05 wt % BSA as a function of pH values.71 The calculated effective hydrodynamic sizes in the system for varying BSA concentrations at different pH values are depicted in Figure 13b. Similar to the case of lysozyme, the effective size of the system here also decreases with increasing pH at a given BSA concentration. This happens because the charge on the nanoparticles and BSA protein, both reduces with decreasing pH, suppressing the electrostatic repulsion (nanoparticle-nanoparticle 33 ACS Paragon Plus Environment

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and protein-protein) and eventually strengthens the depletion attraction between the particles. The SANS data of 1 wt % HS40 nanoparticles in the presence of 1 wt % BSA as a function of pH have been plotted in Figure 13c. The scattering intensity show a systematic increase in the low-Q region with decreasing pH, but now towards IEP of BSA (opposite to the trend in lysozyme). The fitted total interaction potentials along with their individual parts are shown in Figure 13d. The variation in the charge on both the components with pH plays the key role in deciding the magnitude of the attractive interaction. The charge on the nanoparticles and protein both decreases with decreasing pH, leading to a suppression of nanoparticle-nanoparticle as well as BSA-BSA repulsion.71 This reduction in BSA-BSA repulsion on approaching pH towards IEP, enhances magnitude of the depletion attraction (K1) by increasing the excluded volume gain for BSA molecules. Therefore, the two parameters (decrease in the nanoparticle-nanoparticle repulsion and increase in the BSA-induced depletion attraction) together causes the observed decrease in the CPC value with decrease in pH. 6.4. Role of Addition of an Electrolyte and Nanoparticle Size Effect The electrolyte and nanoparticle size effects in terms of interaction potentials for nanoparticleprotein systems are compared in Figure 14.72,73 The observed reduction in the CPC (Figure 3) on addition of salt (0.1 M NaCl), in silica nanoparticle-lysozyme system is primarily governed by the reduced strength and range of the electrostatic repulsion between nanoparticles (Figure 14a). The parameters of the repulsion were obtained in accordance of Debye-Huckel theory by suitably taking into account the modifications in ionic strength of the system.104 However, the protein induced attraction between the nanoparticles remains almost unchanged on addition of salt. On the other hand, it has been found that the presence of electrolyte not only causes the obvious (similar to the case of lysozyme) alteration in electrostatic repulsion but also modifies the depletion attraction in the system (Figure 14b). The reduced electrostatic repulsion in the presence of salt further give rise to the increase in the strength of the depletion interaction by enhancing the excluded volume gain, as explained in the previous section (6.3). These modifications in total interaction potentials are found to be responsible for the distinct changes in phase behavior of the nanoparticles with two proteins.


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The nanoparticle size dependent total potential and its individual components, for lysozyme and BSA proteins at concentrations near their respective CPC values, are plotted in Figures 14 (c) and (d).72 For lysozyme, the results show that the strength of repulsion (K2) increases with the size of the nanoparticles (Figure 14c). This can be understood from the DLVO theory, according to which the magnitude of the repulsion between the nanoparticles stabilized by same zeta potential is proportional to their size.105 The range as decided by the ionic strength does not depend on the nanoparticle size. Surprisingly, despite of increased magnitude of repulsion between nanoparticles for larger size nanoparticles, the CPC follows the opposite trend. The reason for this is the simultaneous enhancement of the attraction with the increase in 35 ACS Paragon Plus Environment

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the size of the nanoparticles. Indeed, the CPC value in these systems also depends on the number of adsorbed protein molecules per particle (2, 2.6 and 3 protein molecules per SM30, HS40 and TM40 particle, respectively) mediating strong attraction between nanoparticles.72 The lowering of the CPC for the higher size therefore probably originates due to the dominance of proteinmediated short-range attractive interaction over the long-range repulsion between the nanoparticles (Figure 14c).72 For different sized nanoparticle-BSA system, the variations in potentials (Figure 14d) suggest that the strength of attraction increases with almost no change in the range with increasing nanoparticle size. The increased nanoparticle size enhances the excluded volume effect, thereby leading to the increased depletion attraction. Therefore, for both the proteins, the total interaction potential responsible for the two-phase formation is found to be more attractive for larger sized nanoparticles. The simplest explanation for this could be the particles number density effect as the number of particles is less for the larger size nanoparticles than smaller ones at a constant concentration and therefore requires a larger interaction to aggregate them. However, the nanoparticle size is known to have larger impact on nanoparticleprotein interactions as it affects the structure and enzymatic activity of the adsorbed proteins. The greater loss of α-helix content for the lysozyme adsorbed on larger silica nanoparticles under otherwise similar conditions has been observed.11 More detailed investigations can be carried out in order to correlate these conformational changes in the adsorbed protein with the parameters of nanoparticle-protein interactions. It should be noted that such potential plots can also be utilized to tune or restrict the large scale aggregation in the system to give rise stable small sized clusters. In general, the origin of such equilibrium cluster phases is in the competition between the short/long range attractive and repulsive forces where the attraction promotes the aggregation at short length scale while repulsion prevents the infinite growth of these aggregates.106 Therefore, the understanding of these potentials can suggest the suitable variations in the physiochemical parameters in such a way that the attractive and repulsive interactions can balance on average distance after the formation of microscopic size aggregates. The equilibrium cluster phase has been observed in pure protein solutions arising from the interplay of the electrostatic repulsion with the partial ion clouding induced attraction between protein molecules.106,107 The emergence of stable hybrid clusters in gold nanoparticles and hemoglobin as well as BSA protein systems has been reported.42,108 In the present system of silica nanoparticle and lysozyme protein also, such 36 ACS Paragon Plus Environment

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equilibrium cluster phase can be obtained by appropriately enhancing the region II of the phase behavior in the light these potential parameters. Overall, these results thus demonstrate that the characteristics of nanoparticles and proteins as well as solution conditions can be employed to control the interaction between them and hence properties of nanoparticle-protein complexes. 7. SUMMARY AND FUTURE OUTLOOK The conjugation of nanoparticles with proteins leads to the formation of versatile hybrid multifunctional materials having promising applications in the field of the nanobiotechnology. Most of these applications require controlled interaction of nanoparticles with proteins. In this feature article, we highlight the different scientifically important issues related to nanoparticle-protein systems along with a brief description of the various characterization techniques to unfold them. We summarize some of our recent studies, understanding the interactions and resultant structures in the model nanoparticle-protein systems of colloidally dispersed anionic silica nanoparticles and two globular charged proteins (anionic BSA and cationic lysozyme). The adsorption behavior of the protein on nanoparticles and the subsequent phase behavior were investigated to correlate the macroscopic observations with microscopic interactions. The overall interaction potential has been disintegrated into electrostatic repulsion and the protein induced attraction between nanoparticles as well as quantified in terms of magnitude and range of the respective parts of the potential. In addition, the effects of different physiochemical parameters on the phase behavior have been presented and the observed changes are elucidated in terms of the modification of underlying interactions (nanoparticle-protein, nanoparticle-nanoparticle and protein-protein). On the basis of these, we propose the potential use of these interaction mechanisms to predict the phase stability of the nanoparticle-protein complexes, to incorporate tunability in them with respect to characteristic parameters and to generate multiscale equilibrium clusters. Approaches may be designed to control switching between protein adsorption and non-adsorption and induce reversibility in the system with the help of selective additives such as surfactants and/or multivalent ions. It will be further interesting to develop a more detailed and general phenomenological model for nanoparticle-protein interactions taking into account the combined role of various other small but important contributions e.g. effects of interfacial ion distributions, non-uniform charge distribution on protein molecules, entropic or thermodynamic contributions etc. 37 ACS Paragon Plus Environment

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(15) Duan, Y.; Liu, Y.; Shen, W.; Zhong, W. Fluorescamine Labeling for Assessment of Protein Conformational Change and Binding Affinity in Protein-Nanoparticle Interaction. Anal. Chem. 2017, 89, 12160-12167. (16) Dennison, J. M.; Zupancic, J. M.; Lin, W.; Dwyer, J. H.; Murphy, C. J. Protein Adsorption to Charged Gold Nanospheres as a Function of Protein Deformability. Langmuir 2017, 33, 7751-7761. (17) Gebauer, J. S.; Malissek, M.; Simon, S.; Knauer, S. K.; Maskos, M.; Stauber, R. H.; Peukert, W.; Treuel, L. Impact of the Nanoparticle-Protein Corona on Colloidal Stability and Protein Structure. Langmuir 2012, 28, 9673-9679. (18) Bharti, B.; Meissner, J.; Findenegg, G. H. Aggregation of Silica Nanoparticles Directed by Adsorption of Lysozyme. Langmuir 2011, 27, 9823–9833. (19) Mcmillan, R. A.; Paavola, C. D.; Howard, J.; Chan, S. L.; Zaluzec, N. J.; Trent, J. D. Ordered Nanoparticle Arrays Formed on Engineered Chaperonin Protein Templates. Nat. Mater. 2002, 1, 247-152. (20) Mann, S.; Shenton, W.; Li, M.; Connolly, S.; Fitzmaurice, D. Biologically Programmed Nanoparticle Assembly. Adv. Mater. 2000, 12, 147-150. (21) Monopoli, M. P.; Bombelli, F. B.; Dawson, K. A. Nanoparticle Coronas Take Shape. Nature Nanotech. 2011, 6, 11-12. (22) Monopoli, M. P.; Aberg, C.; Salvati, A.; Dawson, K. A. Biomolecular Coronas Provide the Biological Identity of Nanosized Materials. Nature Nanotech. 2012, 7, 779-786. (23) Salvati, A.; Pitek, A. S.; Monopoli, M. P.; Prapainop, K.; Bombelli, F. B.; Hristov, D. R.; Kelly, P. M.; Aberg, C.; Mahon, E.; Dawson, K. A. Transferrin-Functionalized Nanoparticles Lose their Targeting Capabilities when Biomolecule Corona Adsorb on the Surface. Nature Nanotech. 2013, 8, 137-143. (24) Phillips, R. L.; Miranda. O. R.; You, C. C.; Rotello, V. M.; Bunz, U. H. F. Rapid and Efficient

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Structure and Interaction of Nanoparticle-Protein Complexes Sugam Kumar†, Indresh Yadav†,#, Vinod Kumar Aswal†,#,* and Joachim Kohlbrecher‡ †

Solid State Physics Division, Bhabha Atomic Research Centre, Mumbai 400 085, India #

‡

Homi Bhabha National Institute, Mumbai 400 094, India

Laboratory for Neutron Scattering and Imaging, Paul Scherrer Institut, CH-5232 PSI Villigen, Switzerland


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Biography

Sugam Kumar did his M.Sc. in Physics from Aligarh Muslim University in 2008 and joined Bhabha Atomic Research Centre, Mumbai as Scientific Officer in 2009. He obtained his Ph.D. from Homi Bhabha National Institute, Mumbai in 2015 under the guidance of Prof. V. K. Aswal. He is currently working as postdoctoral fellow in Prof. Germán Salazar Alvarez’s group at Stockholm University, Sweden. His research interest includes studies of structure and interaction in nanoparticle-macromolecule systems.

Biography

Indresh Yadav received his M.Sc. in Physics in 2012 from Indian Institute of Technology Delhi, New Delhi. He completed his Ph.D. in 2017 under the supervision of Prof. V. K. Aswal from Homi Bhabha National Institute, Bhabha Atomic Research Centre, Mumbai. Currently he is doing postdoc with Prof. Johan R.C. van der Maarel in National University of Singapore. He has been working in the area of nanoparticle-protein complexes to understand the evolution of interaction and structure in these systems using scattering techniques.


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Biography

Vinod Kumar Aswal is presently Head, Small Angle Scattering Section, Solid State Physics Division, Bhabha Atomic Research Centre, Mumbai. He is also Professor, Homi Bhabha National Institute, Mumbai. He has been working as a scientist at the Bhabha Atomic Research Centre since 1993. He is M.Sc. in Physics from IIT Bombay (1992), Ph.D. from Bombay University (1999) and Post-doctorate from Paul Scherrer Institut, Switzerland (2001-2003). He is expert in the field of neutron scattering techniques for its applications to soft matter, nanomaterials and biological systems.

Biography

Joachim Kohlbrecher received his BSc(Hon) in Physics from the University of Osnabrück (Germany) in 1993 and a Ph.D. degree in material science from the Technical University in Berlin. Afterwards he has moved to the Paul Scherrer Institut in Switzerland where he has served at the neutron spallation source SINQ since 1996. Currently he is the group leader for small angle scattering and reflectometry in the Laboratory for Neutron Scattering and Imaging at the Paul Scherrer Institut.


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