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On the Helix Propensity in Generalized Born Solvent Descriptions of Modeling the Dark Proteome

Intrinsically disordered proteins that populate the so-called “Dark Proteome” offer challenging benchmarks of atomistic simulation methods to accurately model conformational transitions on a multidimensional energy landscape. This work explores the application of parallel tempering with implicit solvent models as a computational framework to capture the conformational ensemble of an intrinsically disordered peptide derived from the Ebola virus protein VP35. A recent X-ray crystallographic study reported a protein-peptide interface where the VP35 peptide underwent a folding transition from a disordered form to a helix-β-turn-helix topological fold upon molecular association with the Ebola protein NP. An assessment is provided of the accuracy of two generalized Born solvent models (GBMV2 and GBSW2) using the CHARMM force field and applied with temperature-based replica exchange dynamics to calculate the disorder propensity of the peptide and its probability density of states in a continuum solvent. A further comparison is presented of applying an explicit/implicit solvent hybrid replica exchange simulation of the peptide to determine the effect of modeling water interactions at the all-atom resolution.

INTRODUCTION
The large conformational heterogeneity and rapid dynamic transitions of intrinsically disordered peptides and proteins (IDPs) present a challenge to experimental boundaries in characterizing their functional form on rugged energy landscapes Dyson, 1999, 2005). From a biological perspective, the broad interest in IDPs draws principally from their fundamental role in the regulation and function of cellular protein networks. Recent experimental studies have begun to unravel the interplay between "ordered chaos" of IDPs and their kinetic transition to a topological funnel of folded states (Arai et al., 2015). The contemporary view of this dynamic process is one that occurs by either an "induced-fit" of the IDP upon molecular association with a protein target or by target "fly casting" of a prefolded state in the disordered conformational ensemble of the IDP (see, e.g., Shoemaker et al., 2000;Arai et al., 2015).
Complementary to experimental studies are computer simulations which offer a powerful set of tools to understand IDPs at the all-atom level and their inherent plasticity to navigate a disordered network of microstates (see, e.g., Zhang and Chen, 2014;Chebaro et al., 2015;Bhowmick et al., 2016;Lee and Chen, 2016). Among the simulation methods, the generalized ensemble sampling technique of temperature-based replica exchange (T-ReX; Sugitaa and Okamoto, 1999;Ishikawa et al., 2001), also known as parallel tempering, has become an increasingly popular approach for exploring the energy landscape of proteins. Algorithms combined with T-ReX to generate protein configurations vary in their theoretical formulations and range from canonical molecular dynamics (MD) simulations to nontraditional methods that accelerate conformational sampling. Of the latter, examples includes coarse replicaexchange molecular dynamics (Peter et al., 2016), accelerated molecular dynamics (see, e.g., Miao et al., 2015), Hamiltonian switch Metropolis Monte Carlo (Mittal et al., 2014), all-atom multicanonical molecular dynamics (Higo et al., 2011) and selfguided Langevin dynamics (SGLD; Wu and Brooks, 2003), among others.
A computational strategy of reducing the complexity of allatom simulations of proteins is the replacement of explicit water interactions with a continuum description of treating implicitly the bulk physical properties of solvation effects. The most common implicit solvent method for protein dynamics simulations is the generalized Born (GB) approximation. GB models are computationally faster than explicit solvent calculations and differ in their accuracy of reproducing Poisson-Boltzmann solvation energies for single protein conformations (see, e.g., Feig et al., 2004b). Application of GB solvent models to studies of IDPs has been reported by several laboratories (see, e.g., Ganguly and Chen, 2009;Click et al., 2010;Chebaro et al., 2015;Ganguly and Chen, 2015). To date the simulation results lack consensus on the accuracy of GB solvent models as a computational framework to capture the fold propensities of IDPs and their probability density of states on a conformational landscape. Particularly missing among the reported studies are comparative assessments of GB models of IDPs with those modeled by explicit all-atom solvent replica exchange simulations.
Given the current interests in characterizing the "Dark Proteome" which consists of "invisible" conformational states within the human, viral and microbial protein fold universe (Perdigão et al., 2015;Bhowmick et al., 2016), this work presents temperature-based replica exchange simulations of modeling an IDP derived from an Ebola virus protein. Ebola viruses are nonsegmented negative sense RNA viruses that cause severe hemorrhagic fever (Sanchez et al., 2006). An Xray crystallographic structure was reported by Amarasinghe and coworkers (Leung et al., 2015) of the Ebola nucleoprotein NP in complex with a 28-residue peptide extracted from Ebola VP35 (peptide designated as NPBP). The NP-VP35 viral assembly is essential for virus replication and offers a protein target for therapeutic development. Experimental data reveals the NPBP peptide binds NP with high affinity and specificity, and acts by blocking NP oligomerization. The peptide undergoes a folding transition from a disordered form free in solution to a helix-βturn-helix fold upon molecular association with NP (Leung et al., 2015).
Two different generalized ensemble sampling methods are applied based on combining T-ReX with the SGLD simulation method (Lee and Olson, 2010) and two different GB solvent models are examined to assess their accuracy in modeling the probability density of states of the NPBP peptide. One of the sampling methods is the conventional application of T-ReX with a static set of temperatures to explore the conformational landscape. The other technique is an adaptive T-ReX where the replica clients dynamically walk in temperature space in search of the optimal population density on a modeled energy function (Katzgraber et al., 2006;Trebst et al., 2006;Lee and Olson, 2011;Olson and Lee, 2014;Olson et al., 2016). The GB models analyzed are GBMV2 (generalized Born molecular volume; Lee et al., 2002Lee et al., , 2003 and the GBSW2 (generalized Born smoothing window; Im et al., 2003). The models differ in their dielectric-boundary descriptions with one of them constructed from an analytical formulation of the molecular volume .
The final simulation model applied to the NPBP peptide is an explicit/implicit solvent hybrid T-ReX/MD method (Chaudhury et al., 2012). The application of this simulation model is to investigate the effect of solvent resolution on the helix propensity and the search of conformational transitions. The idea behind the hybrid model is reducing the number of replica clients needed in explicit solvent simulations by replacing the contribution of explicit solvent energies in the Metropolis exchanges (Metropolis et al., 1953) with those of the GBMV2 solvent approximation. The hybrid model allows the same number of replica clients to be applied as in the GB solvent T-ReX/SGLD simulations of the NPBP peptide while retaining a higher resolution in conformational sampling on an explicit solvent landscape (Chaudhury et al., 2012;Olson and Lee, 2013).

COMPUTATIONAL METHODS
This section provides a brief outline of the computational methods applied in this work of modeling the NPBP peptide taken from the PDB 4YPI (Figure 1). Summarized are the sampling techniques and protocols as well as metrics to evaluate the simulation trajectories.

Replica Exchange Schemes
A general approach for conformational sampling is the application of T-ReX (see, e.g., Ishikawa et al., 2001). Unlike the well-established method of MD simulations at a single sampling temperature, T-ReX is a generalized ensemble method of applying multiple parallel simulations in which each replica is executed at a different temperature. In traditional applications of T-ReX, the temperatures T 1 , T 2 , ..., T n , where n is the number of replica clients, are predetermined by a static (fixed) set of values that span a desired range. It is common to model the set of temperatures by a geometrically spaced sequence (Predescu et al., 2004) using n − 1 intervals from the minimum temperature denoted as T 1 = T min to the maximum T n = T max where T i is the temperature of the ith replica client illustrated in Figure 1. An alternative to Equation (1) is an adaptive replica exchange method of allowing the clients to dynamically walk in FIGURE 1 | Computational strategies of modeling the Ebola virus VP35 peptide (PDB: 4YPI) in its unbound form using temperature-based replica exchange (T-ReX) simulation methods. The methods include: (1) GBMV2 solvent model applied with a traditional (static) set of temperatures spanning a range from a minimum temperature (T 1 ) to the upper extreme (T n ), where n is the number thermal windows (ensemble computing clients); (2) GBMV2 using an adaptive (dynamically walking) set of temperatures between T 1 and T n ; (3) GBSW2 solvent model applied by adaptive sampling; and (4) TIP3P/GBMV2 hybrid replica exchange method. Energies (E i ) used in the replica exchanges are described in the text. Molecular figures were drawn with PyMOL (www.pymol.org).
temperature space (Katzgraber et al., 2006;Trebst et al., 2006;Lee and Olson, 2011;Olson and Lee, 2014;Olson et al., 2016). In implementing the adaptive algorithm, each client is tagged as either "cold" or "hot" depending on the last temperature extreme it visited (Lee and Olson, 2011). Tracing of the clients is made by constructing histograms over temperature space, n cold (T) and n hot (T), where each bin accumulates the number of cold and hot clients visiting each temperature window. The fraction cold, f cold (T), of a client window at temperature T is the number of cold clients visiting that temperature divided by the total number of cold and hot client visits: Using the f cold (T) term, a thermal current is defined (Lee and Olson, 2011) where D (T) is the diffusivity and η (T) is the probability that any client will reside at temperature T. The current j can be maximized by adjusting the temperatures such that f cold (T i ) increases linearly as a function of temperature index, i. Here in this work, a continuous function is constructed from the computed values of f cold (T i ) at the current set of temperatures, T i , and new temperatures are searched for where f cold (T i ) = i/(N−1). To prevent all of the windows from clustering around the same temperature and depleting exchanges at the extremes, a constraint is applied where no neighboring temperatures can be more than two geometric spacing units apart, with the lower and upper values of T i set to T min and T max , respectively.
The exchange of temperatures between neighboring replica clients, a and b, is determined by the Metropolis energy criteria (Metropolis et al., 1953) where β a = 1/k B T a , k B is Boltzmann's constant,T a is the temperature of replica client a, and E a is the potential energy of client a.

SGLD Simulation Models
For generating trajectories of the NPBP peptide, two methods were combined with T-ReX. The first is based on the SGLD simulation method developed by Wu and Brooks (2003). The SGLD equation of motion is given bẏ whereṗ i defines the rate of change of the momentum of particle i, f i is the force acting on the particle, γ i is the friction constant, R i defines the random force and g i is a memory function, which is scaled by an ad hoc guiding factor λ. The memory function g i is defined by the moving average of momentum over an interval of time, L: where . . . L denotes a local average. The time interval is further defined as L = t L /δt, where t L is the local averaging time and δt the time step along the simulation trajectory. It should be noted that because of the ad hoc force in Equation (6), the sampling algorithm deviates from a canonical ensemble (Lee and Olson, 2010;Wu and Brooks, 2011;Wu et al., 2012Wu et al., , 2016. For this work, the deviation is anticipated to be small for modeling a mini-protein (Lee and Olson, 2010), nevertheless the population distributions can be reweighted to remove the applied bias (Wu and Brooks, 2011).
In the SGLD simulations, solvent was represented by either the implicit solvent model GBMV2 (Lee et al., 2002 or GBSW2 (Im et al., 2003). The most noted difference between the two models is representation of the solvent excluded volume and the treatment of the dielectric interface. The GBMV2 parameters were selected to smooth the molecular volume by setting β s = −12 and P3 = 0.65 (Yeh et al., 2008). The hydrophobic cavitation term was modeled by applying a phenomenological surface tension coefficient set to a value of 0.015 kcal/mol/Å 2 . For applying GBSW2, the model was parameterized to fit the Lee-Richards molecular-surface Poisson results and required w = 0.2 Å, a 0 = 1.2045, and a 1 = 0.1866. The hydrophobic cavitationenergy tension term was set to 0.030 kcal/(molÅ 2 ).
The utilities and programming libraries of the Multiscale Modeling Tools for Structural Biology (MMTSB; Feig et al., 2004a) were used to carry out the T-ReX/SGLD simulations. The CHARMM simulation program (version c35b2) was applied as a modeling platform (Brooks et al., 2009). Simulations were carried out using 24 replica clients and the frequency of exchanges was set to every 1 ps of simulation. Temperatures were set at T min = 300 K and T max = 475 K. Because the implicit solvent models GBMV2 and GBSW2 were originally developed for and have been extensively benchmarked with the CHARMM22 force field, this force field was applied with the CMAP backbone dihedral cross-term extension (Mackerell et al., 2004). An integration time step of 2 fs was used and parameters for SGLD consisted of the friction constant set to γ of 1 ps −1 for all heavy atoms, the guiding factor λ to a value of 1, and the averaging time t L was set to 1 ps. These values were taken from previous studies of the SGLD model Olson, 2010, 2011;Olson and Lee, 2014). Nonbonded interaction cutoff parameters for electrostatics and vdW terms were set at a radius of 22 Å with a 2-Å potential switching function. Covalent bonds between the heavy atoms and hydrogen atoms were constrained by the SHAKE algorithm (Ryckaert et al., 1977). The NPBP peptide was modeled for 200 ns of simulation time per thermal window, generating an ensemble of 4.8 µs.

Hybrid Simulation Model
The alternative method applied for generating trajectories of the NPBP peptide is an explicit/implicit solvent hybrid T-ReX/MD simulation (Chaudhury et al., 2012). In a typical explicit solvent T-ReX simulation the energies are given by where the first term describes the protein potential energy for a CHARMM-based molecular mechanics force field, the second term is the explicit protein-solvent interactions followed by the explicit solvent-solvent interactions. The all-atom solventsolvent energy term requires significant number of replicaexchange clients to achieve adequate Metropolis updates (Chaudhury et al., 2012). In the hybrid T-ReX method, the dynamics of each replica moves on an explicit solvent landscape. During a Metropolis update, all waters are removed from a replica and the solvent energy term of the replica is calculated using the grid-based GBMV2 solvent model where G prot−solv GBMV2 is the free-energy term due to the implicit solvent contribution. After completion of the Metropolis exchanges, the explicit waters in each replica are replaced to their configurations prior to removal and the simulation continues according to Equation (8).
The NAMD code (Phillips et al., 2005) was applied for the 200-ns T-ReX/MD simulation with the CHARMM22+CMAP force field. The simulation cubic box size was set to 53.19 Å 3 and the number of waters was 4796. For modeling the waters the TIP3P potential was applied (Jorgensen et al., 1983). Nose'-Hoover thermostat was applied with a temperature coupling constant of 50 kcal/s 2 . Given that the computational expense of the hybrid model relative to implicit solvent calculations is greater, the NAMD simulation parameters differ slightly from the T-ReX/SGLD simulations in that a smaller cutoff distance of 12 Å was applied with a switching distance of 8 Å. The integration time step remained identical to that used with the SGLD simulations and the SHAKE algorithm was similarly applied. Particle mesh Ewald was applied and combined with periodic boundary conditions.

Evaluation Metrics
To examine the trajectories generated by the simulations, the weighted histogram analysis method (WHAM; Ferrenberg and Swendsen, 1989;Kumar et al., 1992;Gallicchio et al., 2005) was applied to the data sets. The 2D density of states, (q 1 , q 2 ), for a molecular system, where q 1 and q 2 are a set of reaction coordinates of interest, is given by where n j is the number of data points in the jth simulation and β j and T j are Boltzmann's constant and temperature of the jth simulation, respectively. The function N i (q 1 , q 2 ) is the histogram of (q 1 , q 2 ) calculated from the ith simulation, and f j is the scaled free energy obtained by solving the following equations self-consistently, and where P β (q 1 , q 2 ) is the probability density at the inverse temperature β. From a density profile, a potential of mean force is determined from the relationship W T q 1 , q 2 = −RT log P β q 1 , q 2 , where R is the universal gas constant. For calculations presented here, q 1 = fractional helicity (f H ) of the peptide determined from DSSP (Kabsch and Sander, 1983) and q 2 = radius of gyration (R g ).
The trajectories were further analyzed by a Q score for the peptide. Q is the number of side-chain contacts in a generated conformation divided by the total number equivalent contacts in the X-ray crystal structure of NPBP. Values were computed for side-chain center-of-mass pairs (i,j), such that j > i and whose distances are less than a cutoff of 4.2 Å. A sigmoidal function was applied (implemented in MMTSB) to effectively include residue pairs that are slightly further apart with a reduced weight. In addition to a Q score, pairwise Cα root-mean-square-deviation (RMSD) from the starting X-ray structure was computed for each peptide conformation in a generated ensemble of structures.

RESULTS AND DISCUSSION
Bound and Free NPBP Figure 2 illustrates the X-ray crystallographic structure of the NPBP peptide extracted from the Ebola virus VP35 in association with the Ebola NP protein (Leung et al., 2015). The binding of NPBP occupies a functionally critical site on NP required for RNA synthesis. The peptide conformation is stabilized by a network of electrostatic interactions dominated by NP residues Arg240, Lys248, and Asp252. Using the DSSP secondary structure algorithm, NPBP (annotated as residues 20-47) shows segments Trp28 to Thr35 and Val40 to Asp42 as distinct helical conformations. The overall f H is 0.4 and the bound form exhibits an R g of 10.5 Å.
Experimental characterization of the secondary structure of the NPBP peptide free in solution by circular dichroism (CD) spectroscopy is reported to show the peptide as intrinsically disordered (Leung et al., 2015). When added to a solution of 50% trifluoroethanol (TFE), the NPBP peptide transitions from a coil to helical structures of ∼30-40% helicity, thus suggesting a strong underlying secondary-structure propensity. Predictions of secondary-structure without bias of the crystallographic structure estimate the NPBP peptide to encompass a consensus f H ∼0.3 with probabilities >0.9 for helical formation in the sequence segment of Gly27 to Met34 (see, e.g., Kieslich et al., 2016).

Implicit Solvent T-ReX Simulations
To examine the accuracy of implicit solvent models to counterbalance the network of electrostatic interactions of the viral assembly interface that contribute to the stabilization of the NPBP helical fold and produce a conformational landscape with a predisposed helix propensity in bulk water, replicaexchange simulations were performed using different simulation strategies. The conformational sampling approach of SGLD was explored with two different GB solvent models and two different temperature-based replica-exchange methods. The first simulation model result shown in Figure 2B is the SGLD-GBMV2 with a static (fixed) set of temperatures in defining the replica-exchange protocol. The 2D profile W T f H , R g computed at T = 300 K using WHAM of the full ensemble shows a large manifold of conformational substates with a helix distribution of f H ∼0-0.5. Several representative structures extracted from the basins are illustrated in Figure 2E. The conformational density takes place in R g space of ∼8-11 Å and at the lower end of the population distribution non-structured states are observed to occupy a large range of R g values and show the canonical feature of disorder.
Given the broad population distribution produced by a static set of temperatures in the T-ReX simulations, it is important to test whether the simulation model provided optimal sampling of the basins. To address this issue, an adaptive replica-exchange SGLD-GBMV2 simulation model was applied whereby allowing the clients to walk in temperature space to optimize the efficiency of exchanges between nearest-neighbor thermal windows at potential energy barriers separating conformational basins (Lee and Olson, 2011;Olson and Lee, 2014;Olson et al., 2016). The 2D profile from the adaptive T-ReX is illustrated in Figure 2C for T = 300 K and the result is shown to retain the manifold of transient states similar to those sampled by the static T-ReX method, yet a population shift is observed toward an f H ∼0.5 at the cost of reducing the density of unstructured conformations. The theoretical goal of the adaptive method is to enhance sampling of conformational transitions for a modeled potential energy surface. Early success of the method applied to a sharp phase transition of unfolding-folding of the protein SH3 showed better agreement with the experimental melting temperature than the traditional static approach (Lee and Olson, 2011). In addition, the adaptive method captured with greater accuracy the native state of SH3 extracted from the conformational ensemble. Given these earlier outcomes, and while the NPBP certainly lacks the folding cooperativity of SH3, the result suggests for the CHARMM22+CMAP/GBMV2 potential energy surface a NPBP "native" state of helix propensity near the value observed experimentally for the crystallographic bound conformation. Although the simulation shows a high rate of transitions among different basins, the overall population weight is inconsistent with the CD analysis in free solution. Because the potential energy surface is identical between the static and adaptive T-ReX methods, the less-efficient sampling approach will eventually converge to find a comparable W T f H , R g .
To determine the bias of the GBMV2 solvent approximation on W T f H , R g , adaptive T-ReX simulations were performed with a different implicit solvent model based on the GBSW2 approximation. Of the GB-based solvent models developed for protein dynamics, GBMV2 is one of the most accurate models in reproducing Poisson-Boltzmann theory with a Lee-Richards molecular surface (Feig et al., 2004b). The basis of GBMV2 is an analytical formulation of the molecular volume , while the less accurate but computationally much faster GBSW2 model is based on a smooth dielectric-boundary formulation constructed by applying a superposition of atomic-centered polynomials (Im et al., 2003). The dissimilarities between the two models in conformational sampling are clearly illustrated in Figure 2D. Application of GBSW2 significantly reduces the number of high-probability conformational excursions and leads to a folding funnel at f H ∼0.5. While the "optimized" f H from the two different implicit solvent models is surprisingly similar, the limited disorder from the GBSW2 model in its current parameterization makes this solvent approximation less suitable for modeling IDPs (for an alternative parameterization of GBSW, see, e.g., Chen, 2010). Figure 3 shows the probabilities of observing R g as a function of three sampling temperatures taken from the ensemble. The GBMV2 model produced more compact states of NPBP than the crystallographic bound form, while GBSW2 yielded R g values near the bound conformation. The observed difference between the solvent models can be partly attributed to the distinction in molecular surface representations, where different weights are applied to the surface-tension term that describes the hydrophobic free energy. In general, MD simulations of unfolded states are more compact and tend to favor helical structures than those found experimentally (Piana et al., 2014). By example, an experimental R g for a unfolded 28 amino acids is estimated to be 13 Å (Kohn et al., 2004). Also shown in Figure 3 are the probability profiles of C α -RMSD and the fraction of side-chain contacts similar to the FIGURE 3 | Calculated probability profiles for sampling values of radius of gyration and Cα-RMSD from the starting bound conformation of the NPBP peptide. Plot lines colored blue represent quantities extracted at T = 300 K from the generated conformational ensembles, red represent values at 390 K and green at 475 K. From the top figure to bottom, simulation results are static T-ReX/GBMV2, adaptive T-ReX/GBMV2, and adaptive T-ReX/GBSW2. starting conformation of NPBP. The ensemble average over contacts is denoted as <Q> and values <0.6 are considered unrelated to the starting structure. When combined with the analysis of the 2D profiles, the probabilities provide an interesting picture of the rare event of recognizing (via fly casting) a peptide conformation in the ensemble that is similar to the NPBP bound form. For the GBMV2 model and considering only the last 50 ns of simulation time, the lowest RMSD is 2.9 Å with Q = 0.6, and is clustered in the outer periphery of the highly-populated basin labeled as III in Figure 2C. This sparse cluster of low-RMSD states emerges with an f H of 0.5 and R g approaching 10 Å.
It is also important to understand the configurational stability of IDPs from the simulations and their fold propensities. The thermal unfolding profiles for NPBP are shown in Figure 4A. Consistent with the reduced number of transient states and their populations among the GB models, GBSW2 retains helicity over a greater thermal range. The aggregation of replica clients in the range of 360 K-425 K for the adaptive method (GBMV2 and GBSW2) is the effect of enhanced sampling of unfolding-folding transition points that stabilize helix formation. The statistical errors in the histograms for all model simulations are approximately f H ±0.1 along the temperature profiles. Simulation convergence and the dominance of helix formation in NPBP can be further tested by conducting T-ReX simulations starting from a random coil state rather than the folded conformation. Although these additional simulations were executed only to 100 ns using the adaptive method, Figure 4B shows convergence to a folded state of helical conformations and establishes the strong helix propensity of applying CHARMM22+CMAP/GB descriptions.

Explicit/Implicit Solvent Hybrid T-ReX/MD Simulation
The overweighting of secondary structure biases from the GBMV2 and GBSW2 solvent models is comparable to other studies of using different GB solvent models and parameterizations (Ganguly and Chen, 2009;Click et al., 2010;Chebaro et al., 2015). As a further test of the impact of the GBMV2 solvent model and its mean-field resolution of smearing out the details of the solvent on sampling conformational transitions of NPBP, the final simulation model tested is the explicit/implicit solvent hybrid T-ReX/MD method. This model generates peptide configurations on an explicit solvent (TIP3P) landscape while using the same number of replica clients as in the implicit solvent calculations. The latter is achieved by using the GBMV2 model in the Metropolis exchanges rather than explicit solvent. While the goal is to evaluate the simulation model in terms of a conformational landscape rather than unconstrained folding free energies to high accuracy, it is worth noting that replacement of energies in the Metropolis updates from an all-atom representation to a mean-field approximation can produce errors in the detailed balance required of a canonical ensemble (Chaudhury et al., 2012). Figure 5 shows W T f H , R g at T = 300 K from the WHAM calculation of the hybrid simulation model ensemble and the thermal unfolding profile. Several important observations can be made in comparison to the static GBMV2 model which best corresponds to the non-adaptive hybrid model. The most important distinction between the results is the striking difference in the favorable free energies and the network that shuttles conformations among the helical basins. While both sampling methods show sufficient plasticity among the states, the hybrid model shows a more quantifiable free-energy minimum at f H = 0.26 vs. 0.37 for the static GBMV2, and yields good agreement with secondary-structure predictions. The distinction in the potentials of mean force among the models is illustrated by considering a transition between an unstructured state and the free-energy minimum. For the static GBMV2, the transition (f H = 0; R g = 11 Å) → (f H = 0.37; R g = 8 Å) yields G = −0.1 kcal/mol, whereas for the adaptive model the transition from the same disordered state → (f H = 0.47; R g = 9 Å) G = −1.0 kcal/mol, and for the hybrid model the transition → (f H = 0.26; R g = 9 Å) yields G = −1.7 kcal/mol. Even though the static model exhibits a low-energy reversible transition to unstructured states and would appear to be in better agreement with the CD experiments (Leung et al., 2015), enhanced sampling of P β f H , R g by the adaptive method for this solvent description revealed a more costly transition to the densely populated f H ∼ 0.5.
The lowest RMSD conformer for the hybrid model via the last 50 ns is 3.3 Å with Q = 0.6 and R g = 9.4 Å. This conformer is illustrated in Figure 5B as the first structure depicted for the basin labeled III. The conformation is formed from a helical hairpin of residues Ser26-Met34 and Val40-Phe44. The toprank conformer based on potential energies for the free-energy minimum at f H = 0.26 is illustrated as the first structure for basin I. This structure shows a 5-residue helix of Trp28-Met34. Among the highly populated basins, a distinction between the simulation models is the cluster at f H = ∼0.6, where the hybrid model shows an improved free energy of population. Unlike the other basins, this basin lacks a direct low-energy pathway along the manifold of clusters.
A statistical average of the ensemble for the hybrid model computed from the multiple temperatures of the T-ReX simulation is illustrated in Figure 5C along with a comparison with the static GBMV2 model. Despite the differences in the potentials of mean force between the models, a simple statistical average without reweighting based on free energies shows remarkably similar f H values at 300 K. Because of the lack of instantaneous relaxation of the explicit waters in contrast to GB approximations, the hybrid model shows a reduction in excursions of unfolded states at the upper R g boundaries. Like many MD simulations of unfolded states with explicit solvent (Piana et al., 2014), a residual secondary-structure propensity is observed at 475 K.
The more compact favorable states observed in the explicit/implicit solvent hybrid model than that corresponding to the bound NPBP conformation is unlikely due entirely to the GB model, but rather the additive force field (Piana et al., 2014). As noted above, the CHARMM22+CMAP force field was selected because of extensive benchmarks in reported studies of the GBMV2 and GBSW2 solvent descriptions to successfully model natively folded structures of proteins (see e.g., Yeh et al., 2008;Lee and Olson, 2010). While there are no reported studies of applying either GBMV2 or GBSW2 with the more refined CHARMM36m force field and its parameterization for TIP4P-based explicit solvent simulations (Huang et al., 2017), switching to this description may help reconcile the underestimated R g values with those experimentally determined for unfolded states and reduce the overall weight and stabilization of secondary-structure propensities.

CONCLUSIONS
The current initiative to develop an atomistic understanding of "invisible" conformational states of the human/viral/bacterial proteomes requires an accurate computational framework for modeling conformational transitions within a disordered ensemble and their population density. The work presented here examined the application of temperature-based replica exchange simulations with different sampling methods and solvent descriptions of modeling an intrinsically disorder 28residue peptide from the Ebola virus protein VP35. The Xray crystallographic determination of the VP35 peptide bound to Ebola NP reports a helix-β-turn-helix fold of roughly 40% helical structure, whereas from CD experiments in free solution the peptide is unstructured. The simulations of the unbound peptide showed the selection of a GB solvent model combined with a replica-exchange sampling protocol can have a significant effect on the distribution of sampled populations. Overall, the tested GB models tend to favor a free-energy minimum of roughly 50% helical content for the peptide. The effect of an adaptive temperature-based replica exchange protocol compared to a traditional approach of a static set of temperatures was found to reduce the amount of unstructured states and shifted the ensemble to helical conformations with an extended peptide folding stabilization. A comparison with an explicit/implicit solvent hybrid MD-based replica exchange simulation showed that conformational sampling on an explicit solvent landscape leads to a free-energy minimum of ∼20% helicity, yet the overall conformational network underlying transient states resembles more of a helix-fold propensity in a solvent mixture of TFE-water rather than bulk water. The simulation results can be summarized as a benchmark for the testing of more refined CHARMMbased force fields and different GB model parameterizations.
The ultimate goal is to capture greater heterogeneity in conformational probabilities and reduce the over-stabilization of helix propensities in modeling intrinsically disordered peptides.

AUTHOR CONTRIBUTIONS
The author confirms being the sole contributor of this work and approved it for publication.

FUNDING
Financial support for this work comes from US Department of Defense Threat Reduction Agency grant (DTRA 4.10011_07_RD_B). The opinions or assertions contained herein are the private views of the author and are not to be construed as official or as reflecting the views of the US Army or of the US Department of Defense. This article has been approved for public release with unlimited distribution.

ACKNOWLEDGMENTS
The author thanks Michael Lee for earlier collaborations on the adaptive temperature-based replica exchange method and assistance from Evan Olson on the graphics of 2D profiles.


extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy: Chemistry Biology

text:
Nanoplasmonic mid-infrared biosensor for in vitro protein secondary structure detection

Plasmonic nanoantennas offer new applications in mid-infrared (mid-IR) absorption spectroscopy with ultrasensitive detection of structural signatures of biomolecules, such as proteins, due to their strong resonant near-fields. The amide I fingerprint of a protein contains conformational information that is greatly important for understanding its function in health and disease. Here, we introduce a non-invasive, label-free mid-IR nanoantenna-array sensor for secondary structure identification of nanometer-thin protein layers in aqueous solution by resolving the content of plasmonically enhanced amide I signatures. We successfully detect random coil to cross β-sheet conformational changes associated with α-synuclein protein aggregation, a detrimental process in many neurodegenerative disorders. Notably, our experimental results demonstrate high conformational sensitivity by differentiating subtle secondary-structural variations in a native β-sheet protein monolayer from those of cross β-sheets, which are characteristic of pathological aggregates. Our nanoplasmonic biosensor is a highly promising and versatile tool for in vitro structural analysis of thin protein layers.

INTRODUCTION
Plasmonics has revitalized advanced label-free detection techniques by enabling extreme field confinement down to molecular dimensions, providing enhanced light-matter interaction at the nanoscale 1-4 . Engineering of plasmonic nanostructures has emerged as a powerful platform for biosensing through surface-enhanced detection in recent years, promoting new applications in molecular studies. In particular, surface-enhanced infrared absorption spectroscopy (SEIRA) with engineered plasmonic nanostructures has the unique ability to detect distinct IR vibrational signatures of a thin layer of molecules with normal incident far-field measurements [5][6][7][8][9][10][11] . The IR vibrational fingerprints provide atomic-level information on the molecular structures of biosamples such as lipids or proteins for detection, identification and diagnostic purposes [12][13][14][15][16][17] . Metallic nanoparticles, manufactured by top-down nanofabrication techniques, provide well-defined plasmonic resonances in the mid-infrared (mid-IR) range that can be tuned to target vibrational bands of molecules to access their rich spectroscopic information in a highly reproducible manner 18 . This property can enable them to serve as effective spectroscopic biosensors for detection and study of biological samples. Engineered mid-IR substrates with extremely high field enhancements at distinct frequencies enable the ultrasensitive IR detection of small amounts of biomolecules, including proteins 9,19,20 . The chemically specific SEIRA signals of protein amide band signatures extracted with plasmonic nanoantennas have been adopted for detection of protein monolayers and their interactions, due to their vital roles within living organisms 8,21 . Notably, the amide IR fingerprints resulting from protein-backbone vibrations provide comprehensive insight into the secondary-structural properties of proteins, and thus can provide information beyond basic detection limited to merely reporting their presence 22,23 . However, the unique properties of plasmonic biosensors consisting of nanoparticles tuned to molecular vibrational excitations have not been exploited to obtain secondary structure characteristics of protein monolayers in aqueous conditions. So far, the applications of mid-IR nanoplasmonics have been mainly focused on detecting the presence and orientations of molecules dried on the sensor surface, which is an undesirable environment for reliable protein conformational analysis 9,[19][20][21]24 .
Determination of secondary structures of proteins, which are governed by intramolecular and intermolecular hydrogen bonding, is crucial due to their role in major incurable diseases such as type II diabetes, prion and neurodegenerative disorders, including Huntington's (HD), Alzheimer's (AD) and Parkinson's (PD) diseases 25,26 . For instance, structural changes and misfolding of disease-related proteins and formation of nanoscale toxic fibrillar aggregates are considered the primary causes of those neurodegenerative disorders. The molecular mechanisms inducing the changes in secondary structure of such unstable proteins, their progressive accumulation into amyloid deposits and the consequent detrimental impact on cells remain unknown. Current technologies for protein studies, including nuclear magnetic resonance 27 , X-ray crystallography 28 , atomic force microscopy (AFM) [29][30][31] and circular dichroism 32 to name a few, have varied capabilities in accessing different levels of conformational information from secondary to quaternary structure. However, many of these methods face challenges and limitations regarding required sample properties, real-time observations of secondary structure changes under physiological conditions and scalability for multiplexed rapid screening. Some of these techniques demand expensive infrastructure or have strict requirements for preparation of homogeneous samples in large quantities, which restricts investigations on proteins that are costly and difficult to produce, such as amyloids or minute amounts of samples isolated from human tissues or cells. Likewise, some of the conventional techniques are limited to bulk measurements or thick layers of proteins, which are not suitable for mimicking nanometer-thin biological interfaces. Hence, providing nondestructive label-free technologies for in vitro detection of structural information of protein monolayers in their native states and in aqueous solution is highly advantageous in order to introduce a versatile tool for further understanding of the fundamental principles underlying disease-related conformational changes in proteins.
In this work, we determine the secondary-structural characteristics of thin layers of proteins with a mid-IR plasmonic nanoantenna array sensor. Our nanoplasmonic biosensor enables detection of conformational states of proteins in aqueous solution in a label-free, noninvasive manner. To demonstrate the capabilities of our platform, we investigate protein monolayers with different secondary and quaternary structure conformations by analyzing their amide I vibrational signatures, which are sensitive to protein-backbone conformation. The amide I band is highly advantageous for non-destructive structural detection of proteins since various secondary structures, including αhelices and β-sheets, correspond to distinct components of this band with specific vibrational frequencies within its range 22,23,33 . Here, by leveraging the plasmonically enhanced amide I signature from mid-IR nanoantenna biosensors and resolving its spectral content with second-derivative analysis, we extract secondary-structural information of protein layers in aqueous solution. This technique can provide unique possibilities for structural and functional studies on diseaserelated proteins. As a model system, we probe α-synuclein (α-Syn), an intracellular presynaptic protein that can misfold and form cross βsheet rich fibrillar amyloid aggregates, resulting in degeneration of neurons and formation of Lewy bodies (LBs) and Lewy neurites (LNs), the main hallmarks of PD 34 . The cross β-sheet structure of amyloids has unique physical characteristics that differ from the β-sheet structure in native proteins. Our biosensor enables reliable measurements in dry and aqueous conditions with successful detection of conformational variations in disordered (random structured) monomeric monolayer and cross β-sheet structured fibrillated α-Syn protein immobilized on nanoantennas. Furthermore, we show that the acquired secondary structure signals and the extracted frequency range for the structural components are highly reproducible and flexible with the tuning of antenna resonances within the amide I region of protein fingerprints. Finally, we extract the secondary structure information of a monolayer of another model protein, i.e., streptavidin, with predominantly native β-sheet conformation immobilized on the biosensor. Notably, we are able to distinguish between the native and amyloidogenic β-sheet signals of these different proteins by observing the presence of the β-sheet components at higher wavenumbers for the native protein compared to the fibrillated α-Syn sample with pathological cross β-sheets. These results demonstrate the conformational sensitivity of our method in extracting subtle secondary-structural information from thin protein layers. Our experiments highlight the effectiveness of engineered plasmonic substrates as a versatile platform to access the conformational information of proteins in a sensitive and reliable manner in aqueous environment and at the monolayer level. Such a plasmonic biosensor is highly promising for delivering fundamental knowledge on the changes in protein conformations and their relation with disease mechanisms.

MATERIALS AND METHODS
Our biosensor platform consists of multiple arrays of rod-shaped gold nanoantennas engineered on a CaF 2 substrate for plasmonic resonant excitations of their dipole mode in the mid-IR frequency range, and the same array layout has been replicated on several CaF 2 chips (see Supplementary Fig. S1 of Supplementary Information for a schematic substrate-layout example). The resonance tuning of the nanorod array is governed by designing the aspect ratio of the antennas and the array period to efficiently reflect the light to the far field and maximize its coupling to vibrational fingerprints for spectroscopic biosensing 8,11,19,[35][36][37] . Here, the antenna width and height are kept at constant values of 200 and 100 nm, respectively, based on our optimized nanofabrication process. This allows us to design their length (L) and the period (P) in different arrays in order to reliably tune the resonance peak and linewidth to provide collective plasmonic excitation at the wavenumber region of interest for incident light polarized along their length. Under such circumstances, the resonance peak frequency can be tuned based on its linear dependence on the antenna length and the refractive index of the surrounding environment according to l P ¼ 2 m À Á n ef f L þ C, where m is the order of the plasmonic mode, n eff is the effective refractive index and C a constant value. The array period can be designed to yield higher field enhancements and optimized quality factor of the resonances by minimizing the radiative damping of the plasmonic response through the coupling of the retarded dipolar fields scattered by the antennas 19,35 . The nanorod antennas can be systematically engineered to have mid-IR plasmonic resonances with bandwidths larger than the absorption bands of proteins (i.e., amide I) in a reliable and reproducible manner (see Supplementary Fig. S3). Furthermore, the required dimensions and design accuracy of mid-IR nanorods are suitable for employing a wide range of reliable conventional nanofabrication techniques, such as nano-stencils 38 or e-beam lithography. Here, cleaned CaF 2 substrates are spin coated with methyl methacrylate (MMA) and polymethyl methacrylate (PMMA) resist double layer followed by electron-beam exposure using Vistec EBPG5000 (Jena, Germany) at 100 KV. Multiple metal nanoantenna arrays of 100 μm × 100 μm size with various values of L and P are identically formed on multiple CaF 2 substrates through a lift-off process by evaporating a thin Cr adhesion layer and 100 nm of Au with a Leybold Optics LAB 600H (Uzwil, Switzerland) e-beam evaporator. E-beam lithography enables reliable and reproducible fabrication of nanorods. Our plasmonic substrates are also highly robust and can be reused by employing appropriate cleaning procedures. Each nanoantenna array is one sensing area that is separately measured. Figure 1a shows an SEM image of a typical antenna array utilized in this work. The fabricated chips are cleaned with O 2 plasma treatment prior to initial Fourier transform infrared (FTIR) measurements 39 . Each nanorod array is illuminated with IR light polarized along the length to provide resonance excitation and the reflected light is used to record the plasmonic response. A Bruker Vertex80V FTIR (Ettlingen, Germany) interferometer equipped with a Globar infrared source and KBr beamsplitter (4000-400 cm − 1 ), coupled to a Hyperion IR microscope with a linear polarizer, a Cassegrain objective (15 , NA = 0.4) and a liquid-nitrogen cooled mercury cadmium telluride (MCT) detector, is used for all IR measurements. The interferometer is maintained under vacuum while the microscope is purged with CO 2 À and humidityfiltered air. Knife-edge apertures at the microscope are aligned with areas of the arrays (100 μm × 100 μm) for each measurement and a thick Au mirror is used for reference. Details of the data acquisition and analysis are reported in the Supplementary Information. As illustrated in Figure 1b and 1d, the interaction of the plasmonic field-enhanced antenna hot spots excited at the tip ends of rods with the protein in their vicinity provides SEIRA signals that are measured in the far field through the reflectance spectrum. A simulated response of an antenna array is used for illustration in Figure 1d, where the reflectance of bare antennas (without absorptive layer in their vicinity) designed for excitation in the amide I range is depicted as a dashed black curve. The reflectance with a thin (5 nm) modeled layer with a complex refractive index on antennas, representing a typical protein layer, is shown as a black solid line. The responses (R) of the array before and after covering with the thin layer are used to access the vibrational signatures of the layer, shown as blue curves, through extraction of the absorbance using the logarithmic ratio of the responses, A = − log(R with protein /R bare ) (Ref. 8) . Finite-difference timedomain simulation was performed with commercial software of Lumerical FDTD Solutions. A plane-wave source with a linearly polarized electric field along the antenna length was defined for an array unit cell with L = 2100 nm and P = 3 μm. The dielectric values of the materials used were obtained from the Palik library for gold, along with a constant refractive index of n = 1.4 for CaF 2 . A modeled complex refractive index was used as an arbitrary thin layer representing a typical protein ( Supplementary Fig. S2).
We use our plasmonic biosensor to probe structural changes associated with the fibrillization of α-Syn. Monomeric α-Syn (WT) (from AnaSpec, Inc.), was diluted in 50 mM Tris HCl buffer with 150 mM NaCl at pH 7.5 and used in dry measurements. To induce fibrillization, a sample from the same stock was incubated on a shaker at 400 rpm at 37°C for 10 days. For measurements of α-Syn in buffer solution, the Au surface of nanoantennas on each substrate was functionalized with COOH-functional alkane thiol self-assembled monolayer (SAM), MHDA (16-mercaptohexadecanoic acid, from Sigma-Aldrich), and synthesized amino-(PEG) 2 -Maleimide linker. In these experiments, α-Syn monomeric samples with a C-terminal and antennas covered with a modeled thin layer (solid black). FDTD simulation is used for this depiction, in which a 5 nm layer of arbitrary complex refractive index (shown in Supplementary Fig. S2) is used to represent a typical protein layer on antennas. Blue curve shows the extracted absorbance signal from the simulated responses before and after covering with the modeled layer, with baseline correction. The bottom graph shows the 2nd derivative of the acquired signature, revealing the spectral components of amide I. The red arrow shows the amide I range in the mid-IR spectrum. The green area represents the approximate range for β-sheet components of secondary structure observable in fibrillated protein samples, whereas the purple area shows the approximate range for components of random structure or helices.

RESULTS AND DISCUSSION
In our experiments, we analyzed the spectral features of the detected plasmonically enhanced signatures within the amide I range of the acquired data. Figure 1b illustrates the dry-condition measurements in which the antennas are illuminated from the top with linearly polarized incident light along their length. The schematic depiction shows the amide I signature of proteins, resulting mainly from the C = O stretching vibration of the peptide bonds (B 80%) with a minor contribution from the C-N stretching vibration 23 . The overall secondary structure of the protein, which critically influences its functionality, represents the local folding of segments of its polypeptides into stable structures, including α-helices and β-sheets, or random coils (disordered). Depending on the secondary structure of the protein, the amide I band can include multiple absorption components with different frequencies within its range (1600-1700 cm − 1 ), as illustrated by an example in Figure 1d 22,23 . The amide I signals of proteins with turns, loops, random coils and helices (in general, non β-sheet strands) have components that are within 1645-1690 cm − 1 with some overlaps among their ranges. However, the β-sheet structured samples have distinguishable components within the 1615-1640 cm − 1 or 1680-1695 cm − 1 ranges. The β-sheets in amyloidogenic proteins in fibrillar states are reported to appear at lower wavenumbers in the first range and at higher wavenumbers in the upper range, compared to native β-sheets 23 . To extract the conformational information of a protein, we apply second-derivative analysis of the amide I fingerprint to resolve its spectral content associated with the secondary structure 40 . The method of evaluating the second derivative of the amide I band has been used for protein spectroscopic studies as it provides a qualitative approach to detect the presence of different secondary structures by separating overlapping component bands of the acquired signals. As shown in the bottom graph in Figure 1d, by determining the frequency positions of the conformational components of the extracted amide I signal through this analysis, we can assess different secondary structure conformations in a sample protein, such as random coils or β-sheets. Here, the arbitrary thin layer is modeled with 2 Lorentzians in amide I range for illustration purposes to represent different structural components of a typical protein. The schematic illustration in Figure 1c represents the distinct conformational changes associated with the misfolding and fibrillization of α-Syn, a process that is central to the pathogenesis of neurodegenerative diseases, including PD and dementia 34 . α-Syn is an intrinsically random structured (disordered) protein and exists as monomers with disordered conformations in solution but can adopt α-helical conformations, for instance, upon membrane binding, or β-sheet structure when it forms amyloid-like fibrillar aggregates, which can be replicated in vitro 41,42 . As schematically represented in Figure 1d, the native random structured monomers and dominantly cross β-sheet structured fibrils of the pathological conformation of α-Syn give rise to spectrally different absorption components within the amide I band that can be extracted with the plasmonic substrate from a thin layer of proteins.
Protein secondary structure detection with plasmonic nanoantennas through amide I signature We initially measured the amide I signal of α-Syn monomers and fibrils from a dried thin film formed by physisorption on gold antenna arrays to validate the extent of the obtained information using our system. For monomeric protein, each plasmonic substrate with multiple arrays was covered with 80 μl of protein solution at 12 μM concentration for an hour, followed by successive rinsing steps with Tris buffer and purified DI water for 10 min to remove excessive unbound proteins from the substrate. The reflectance response of one of the clean (bare) antenna arrays on a CaF 2 substrate (Chip 1) is shown in Figure 2a in black for antennas of length L = 2200 nm with array period P = 2.85 μm. The black curve in Figure 2b shows the response of the same array after physisorption of the α-Syn monomers and subsequent rinsing. The effects of absorption bands of amide I and amide II from the protein layer can be clearly observed in the reflectance response. The resulting absorbance signal for the monomeric α-Syn in Figure 2c (black) shows the acquired amide I and II bands. Successively, to extract the fibril signatures, we induced in vitro aggregation in α-Syn protein stock, as explained in the Materials and Methods section, and similarly acquired the protein spectra using a sample solution containing predominately amyloid fibrillar species of α-Syn with rich β-sheet conformation. For this structural state of protein, another plasmonic substrate (Chip 2) with multiple antenna arrays, including the same array design with L = 2200 nm and P = 2.85 μm, is used, for which the bare response is presented in Figure 2a in red. The two arrays used on these substrates have identical antenna design and bare reflectance to demonstrate the reproducibility of the fabrication and ensure that no variation from the plasmonic response of antennas could impact the resulting signals from the protein in the two states. Red curves in Figure 2b and 2c, show the response after physisorption of the fibrillated sample of α-Syn protein and the obtained absorbance from this array, respectively. The variation in the amide I peak of the α-Syn in the two measurements can be observed in Figure 2c. To analyze the content of protein secondary structure in these measurements, we applied secondderivative analysis on the extracted signals (A) within the amide I range and resolved the components corresponding to each structural conformation 40 . This analysis requires good signal-to-noise ratio of the acquired data, which our plasmonic sensor provides in the amide I region with the tailored resonances optimized for this vibrational band. Figure 2d shows the second-derivative of the resulting logarithmic reflectance ratio (A) for the α-Syn protein in the monomeric (black) and fibrillated (red) states. The results clearly demonstrate the difference between the two conformational states of α-Syn, as expected 43 . The monomeric sample, which exists predominantly in disordered conformation with slight α-helical propensity, has components only at wavenumbers above 1645 cm − 1 . In the case of the Random structured and amyloidogenic β-sheet proteinconformation detection with plasmonic substrates Next, we tested the capabilities of our plasmonic nanoantenna platform to identify the secondary structure content of proteins in aqueous conditions. The overlap of the bending vibration of liquid water molecules with the amide I band of proteins limits highly sensitive mid-IR spectroscopic measurements in conventional IR transmission spectroscopy 47 . We previously demonstrated that our nanoplasmonic substrate, implemented in plasmonic internalreflectance configuration, can overcome this limitation in normal incident microscopic measurements and enable mid-IR spectroscopic detection for a monolayer of proteins, even in the presence of water 8 . In this scheme, shown in Figure 3a, the reflectance responses of the arrays are collected through the back side of the IR-transparent CaF 2 substrate. The excited plasmonic resonance of the antennas provides field enhancements that decay exponentially away from the tip ends of the nanorods into the surrounding background, which results in extreme sensitivity to the antenna vicinity for biological assays. While the proteins are immobilized on the antenna by full coverage of the entire gold surface, the confinement of the plasmonic hot spots at the nanoantenna tip ends enables selective local probing at the tips, and therefore minimizing the interference from the water solution, which is ideal for spectroscopic measurements on thin protein monolayers in an aqueous environment 8 . Due to the increase in the refractive index of the environment in the vicinities of antennas in buffer, the antenna length and the array period are redesigned based on the linear dependence of the resonance on the effective refractive index of their surrounding environment. This allows us to match their plasmonic resonance to the amide I vibrational signature by decreasing the antenna length 8 . Here, the Au nanoantennas are functionalized with an SAM, which is a well-known and widely used method to covalently immobilize proteins. This technique, depicted in Figure 3a and Supplementary Fig. S5, is known for stable and reproducible monolayer formation on gold biosensors 48 . The functionalization procedure,

described in Supplementary Information, involves attachment of an amino-(PEG) 2 -maleimide linker through activation with sulfo-NHS/ EDC chemistry (see Supplementary Information and Supplementary
Figs. S5-S7 and S9 for details) 49 . The α-Syn protein, prepared with a terminal cysteine residue at its C-terminus to provide a sulfhydryl group for binding, was then immobilized on the nanoantennas through a thiol-maleimide Michael addition reaction with the maleimide linker 50 . This selective immobilization ensures the formation of a monolayer of protein samples with a stable covalent bond without introducing any detected additional absorption bands in the amide I region (shown in Supplementary Fig. S9). The reflectance responses of one of the antenna arrays (L = 1850 nm and P = 2.6 μm) on a plasmonic substrate with the alkane thiol SAM and after the immobilization of monomeric α-Syn with linker, are measured in aqueous environment (i.e., Tris buffer) and shown in Supplementary  Fig. S8a (Chip 3). Next, another plasmonic substrate (Chip 4) with the same layout of multiple arrays including an identical antenna array (L = 1850 nm and P = 2.6 μm) was measured before and after immobilization of fibrillated α-Syn with the same procedure as described above; the reflectance responses for this array are shown in Supplementary Fig. S8b. To extract the conformational information of these protein layers measured in buffer, the second derivative of the logarithmic ratio of the reflectance responses before and after protein immobilization (A) was calculated as shown in Figure 3b, where the blue and red curves show the secondary structure components of the α-Syn for the two cases before and after inducing fibrillization in the protein sample, respectively. As expected, the blue curve of the monomeric α-Syn protein indicates the presence of secondary structure components, above 1645 cm − 1 , representing disordered (random) conformations without distinct β-sheet content. However, a distinguished β-sheet structure is detected in the red curve representing the structural content and conformation in the cross-β state of fibrillated α-Syn. While the water absorption can be observed in the reflectance response of the antenna arrays measured in buffer (Supplementary Fig. S8 and Figure 4a), the acquired signals at the amide I range with plasmonic substrate enable reliable spectroscopic measurements. These results indicate that our biosensor can successfully resolve the two distinct secondary structure components for the β-sheet and the non-β-sheet strands (random, turns or loops) in a thin layer of protein samples measured in buffer solution in a reliable and reproducible manner (see Supplementary Fig. S12 for more replications of the data).

Effect of resonance-excitation conditions on acquired protein structural information
The signal enhancements achieved with our plasmonic nanoantennas for detection of protein absorption signatures depend on the spectral matching of their resonances with the vibrational bands of the biomolecules 19 . In this regard, it is important to determine the sensitivity of the acquired amide I components to the resonancetuning conditions for reliable detection of conformational information. We experimentally investigated the detected structural components of the proteins acquired from arrays with peak resonances throughout and beyond the amide I region in aqueous solution. These results are shown in Figure 4a and 4b for secondary structure components of the fibrillated α-Syn extracted from arrays tuned in a wide wavenumber range and with varied linewidths of the resonances. Figure 4a shows the reflectance of the fabricated antenna arrays functionalized with alkane thiol SAM in the buffer solution before immobilization of protein, demonstrating a broad tuning range of the resonance peaks, as large as 200 cm − 1 , and different resonance Mid-infrared nanoplasmonics detecting protein conformation D Etezadi et al linewidths from 350 to 500 cm − 1 . This resonance tuning is obtained by designing multiple arrays with antenna lengths ranging from 1850 to 2050 nm and array periods from 2.3 to 2.6 μm. Figure 4b shows the resulting components of the amide I through second-derivative analysis of the content of the acquired data for each antenna array after protein immobilization as described before. As shown here, all the extracted signals are similar to one another with minor variations, which indicates that the multiple components of the secondary structure can be reliably resolved with our biosensor despite slight variations of the plasmonic resonance-peak position within the amide I region. To further emphasize this property of our biosensor, we extracted the locations of structural components of the α-Syn sample from multiple antenna arrays tuned within the amide I range (L = 1850, 1900, 1950, 2000, 2050, 2100 nm and P = 2.3, 2.4, 2.5, 2.6 μm). Our experimental observations are summarized in Figure 4c, in which the acquired secondary structure components of monomeric and fibrillated α-Syn proteins in buffer from 21 arrays with different resonance conditions are shown. The graph represents the detected frequency of each component from the acquired protein amide I second-derivative data versus the resonance peak of the antenna array used in each corresponding measurement. To extract the resonance peak from each reflectance spectrum, a Lorentzian fit is used (see Supplementary Fig. S10). The circular data points in multiple colors represent the different components extracted from the measurements of the aggregated fibrillar α-Syn protein, for which three distinguished components could be observed in all of the measurements, demonstrating the presence of β-sheet and non-βsheet (random, turns or loops) strands. The data points presented as blue triangles show that in the monomeric α-Syn sample, one component representing disordered (random) structure (with αhelical propensity) can be detected, even with variation in the resonance condition of the plasmonic biosensor. As observed here, despite a large tuning range of the resonances beyond the amide I range, the detected β-sheet component of the fibrillated protein sample exhibits only minor variations within 5 cm − 1 , and the additional non-β-sheet components can be resolved successfully.
Our results indicate that while optimized matching of our nanoantenna-array resonance peak to the vibrational amide bands provides maximum enhanced absorption signals for spectroscopic detection, the multiple components of the secondary-structural conformation can be reliably acquired through second-derivative analysis of the extracted amide I signals without strong dependence on the conditions of resonance matching and coupling with this vibrational band, even in the presence of water. These properties indicate the reliability of our biosensing platform of nanoantenna arrays, even in the potential case of minor manufacturing variations induced by any employed nanofabrication technique. Distinguishing native from pathological β-sheet protein conformations Conformational studies of proteins require sensitive detection of structural content in order to enable their evaluation and extract subtle differences in their secondary structure. For instance, β-sheet conformations exhibit variations in different proteins that reflect their functional diversity in health and diseases. Therefore, it is important for a detection technique to sensitively access and distinguish detailed structural information of proteins in order to elucidate their characteristics. To evaluate this capability, we employed our platform to extract the secondary-structural information of another protein monolayer, streptavidin (SA), with a rich native β-sheet conformation 51 . The SA protein is immobilized on our nanoantenna biosensor through its high binding affinity to biotin. First, the plasmonic substrate is functionalized by incubation in a solution of biotinylated-PEG-alkane thiol (BAT), followed by rinsing, before initial measurement in phosphate buffer saline (PBS). Next, SA protein solution at 50 μg /ml in PBS was immobilized on all the arrays of the substrate, followed by rinsing with PBST (PBS with 0.05% Tween 20) and PBS to remove unbound proteins before measuring the arrays with protein monolayer in buffer. The secondderivative of the extracted amide I signatures from an array with L = 1900 nm and P = 2.5 μm in this experiment is shown in Figure 5.
Here, the secondary structure components in the amide I range correspond to β-sheet structures observed within the 1635-1640 cm − 1 and 1690-1695 cm − 1 ranges, in addition to a component of β turns in the range of 1670-1675 cm − 1 and some indications of a small presence of coils in this protein. Notably, the extracted signal detected in this native β-sheet protein is observed at slightly higher wavenumbers compared to the cross β-sheet component of the aggregated α-Syn protein (shown in Figure 3b and 4b). This result was consistently observed, even with intentional tuning of the nanoantenna design for resonances within the amide I range (data shown in Supplementary  Fig. S14). This is in agreement with previous observations in which the redshift of this component is observed for fibrillar β-sheets within amyloid aggregates relative to native proteins 23,52,53 . These results demonstrate that our biosensor can enable the distinction between native and pathological β-sheet conformations and indicate that our technique has the capability to extract detailed structural information of protein monolayers for conformational investigations.

CONCLUSION
The effectiveness of the engineered plasmonic nanoantennas in producing signal enhancements to many orders of magnitude in specific mid-IR vibrational bands of interest has been employed in the detection of different biomolecules, including proteins and lipids. However, previous studies did not incorporate investigation of the spectral content within individual absorption bands of plasmonically detected functional groups. In particular, the amide I signatures of proteins consisting of multiple overlapping components contain important information on the protein secondary-structural properties.
In this work, we have expanded the sensing capabilities of metallic mid-IR nanostructures to sensitively probe and differentiate the secondary-structural compositions in protein monolayers. Using second-derivative analysis of the accessed amide I vibrational signatures from thin layers of protein on nanoantennas in dry conditions, we resolved the secondary-structural components and confirmed the detection of a conformational change from random coil to cross β-sheet as a result of aggregation in α-Syn protein. Furthermore, we implemented our sensor in a plasmonic internal-reflectance scheme and successfully detected the conformational information of a monolayer of α-Syn protein in an aqueous environment. Based on our experimental results, we can distinguish the monomeric α-Syn protein from the fibrillated α-Syn sample, including multiple conformational components of β-sheets, turns and random structures. These results are confirmed to be tolerant to the tuning of the resonance peak and linewidth of nanoantennas within the amide I range. Moreover, we demonstrated the capability of our sensor to resolve variations of conformational details by distinguishing the secondary structure information of another protein with predominantly native β-sheet conformation from that of the aggregated cross β-sheet α-Syn. Our experiments with different protein secondary structures and immobilization conditions indicate that this plasmonic biosensor is a highly promising platform, applicable to sensitive detection and a broad range of investigations of protein conformational properties. It is also important to emphasize that the acquired secondary structure signals of monolayer proteins from each nanoantenna array correspond to minute amount of proteins immobilized on the antenna surface. For instance, for the case of α-Syn monomers, the extracted structural information presented in Figure 3b corresponds to a monolayer covering the gold surface of each antenna (i.e., B 0.78 μm 2 ) in one array. Considering the size of an α-Syn monomer, with an approximate radius of gyration of 3 nm, and assuming full coverage of all the gold surfaces of the antennas, the presented result corresponds to α-Syn monomers estimated in the femtomole range measured in buffer. Our technique sensitively determines different secondary structure components of proteins in aqueous solution in a highly reproducible and reliable manner and offers the potential to observe minor secondary-structural changes induced in minute amounts of protein samples (see Supplementary Fig. S13), for instance, upon biomolecular interactions. Employing the unique features of engineered mid-IR nanophotonic substrates in plasmonic excitations, our chip-based sensor can enable new possibilities to deterministically tailor resonances to reveal the spectral content of detected absorption bands with high spectral resolution. For instance, the resonant characteristics of plasmonic substrates can be adapted to incorporate other important absorption bands besides amide I in proteins for in-depth analysis of structural properties and provide insight into higher-order conformational characteristics. This surface-sensitive nanoplasmonic biosensor can provide a robust and highly scalable tool for applications demanding rapid screening, such as drug discovery and pharmacology. In addition, the cost of our method could be largely scaled down by using inexpensive nanofabrication techniques to manufacture plasmonic nanorods. Furthermore, our nanoplasmonic sensor can be integrated with compact and low-cost on-chip FTIR spectrometers 54 or tunable mid-IR sources such as quantum cascade lasers, as well as advanced microfluidics, to exploit the rich information content of the mid-IR spectrum and have significant impact on numerous fields ranging from biochemistry to materials science.


One-pot synthesis of polythiol ligand for highly bright and stable hydrophilic quantum dots toward bioconjugate formation

A fast and efficient one-pot synthesis of thiol-terminated poly(vinylpirrolidone-co-maleic anhydride-co-ethylene glycol dimethacrylate) based heterobifunctional polymer (PTVP) has been developed. The polymer was used for the modification of quantum dots (QDs) to prepare water soluble and stable QDs with emission quantum yield as high as 80%. Using carbodiimide method, PTVP-capped red light-emitting QDs were conjugated to model monoclonal antibodies specific to glycoprotein B (gB) of Aujeszky’s disease virus (ADV) and successfully used in the lateral flow assay (LFA) for the detection of ADV gB in biological fluids. A comparative analysis of the sensitivity of the method was carried out using three types of QDs emitting in the red and far-red region.

QDs with the near infrared (NIR) emission range are particularly important, because NIR light is poorly absorbed by the components of body tissues and markedly distant from the Advances in Natural Sciences: Nanoscience and Nanotechnology One-pot synthesis of polythiol ligand for highly bright and stable hydrophilic quantum dots toward bioconjugate formation autofluorescence of biological materials [20][21][22] and nitrocellulose, which is the basic material of test strips for immunochromatography [23].
The use of QDs as fluorescent labels requires the solution of two interlinked problems: the formation of the hydrophilic capping of nanocrystals with appropriate groups to ensure their water-solubility, and conjugation with biomolecules [24]. Advantages and disadvantages of different approaches to the solution of these problems were discussed in a number of reviews [25][26][27][28][29][30].
An overall strategy of the fabrication of water-soluble QDs includes the substitution of original hydrophobic ligands (alkylphosphines, alkylphosphine oxides and others) by ligands, which have, on the one hand, chemical groups capable of binding to the surfaces of nanocrystals (thiol, amine, phosphine, carboxyl, pyridine), and, on the other hand, hydrophilic groups for good water solubility (e.g. hydroxyl). An alternative approach is based on the preservation of the original ligands and implies formation of a shell of amphiphilic molecules or polymers with hydrophobic groups attracted to the original ligands and hydrophilic groups providing water solubility. However, such shells are typically not sufficiently stable, when subjected to biological media [30,31].
It should be noted, that monodentate ligands, such as thiols, are readily available, but chemically unstable due to oxidation of -SH groups; this eventually leads to QDs aggregation. Contrariwise, bi-and polydentate thiols provide long-term stability of QDs [32]. Carboxyl-terminated ligands provide stability at high pH, while amine-terminated ones are stable in low pH media; silicon oxide and polymer/phospholipid membranes provide less toxicity and stability over a wide pH range but increase size of QDs from 4-10 nm to 20-30 nm, which makes impossible their use in Förster resonance energy transfer (FRET) detection [32,33]. A variety of multidentate ligands is known including peptides, oligomeric phosphines, hydrophilic polythiols containing polyethylene glycol (PEG), amino or carboxyl groups. Mattoussi used a low molecular weight polyacrylic acid functionalized with methoxy, amino, azido, thioctic and PEG groups [36]. In the work, fragments of dihydrolipoic acid, obtained from thioctic acid by reduction, provided efficient interactions with QD surface, PEG ensured water solubility, while amino and azide groups were used for further functionalization.
Two main parts are present in the structure of most of the QD ligands: the anchor group(s) and hydrophilic group(s). Many ligands also contain a third functional group for conjugation with biological molecules. Synthesis of the heterobifunctional ligands is typically a multistaged process that includes polymerization followed by a series of chemical transformations (acylation, reduction and others) [34][35][36][37][38][39]. Accordingly, the search of simple synthetic routes to produce ligands is a significant problem.
The ideal ligand for biological applications of QDs must meet the following criteria: (1) high affinity to the surface of QDs; (2) colloidal stability in a wide range of pH and salt concentration; (3) small hydrodynamic diameter; (4) minimal nonspecific binding to biological molecules; (5) minimum toxicity; (6) affordability (commercial availability or ease of synthesis); (7) the functional groups available for bioconjugation and (8) minimal immunogenicity [19,24].
Conjugation of QDs to biopolymers can occurs electrostatically, through the biotin-avidin interaction, covalently (e.g. activated with carbodiimide binding of amino and carboxyl groups, catalyzed maleimide linking with amino and sulfhydryl groups, linking between aldehyde and hydrazine functions), binding via the polyhistidine-tag [40,41]. Carbodiimide conjugation method is simple and versatile. However, conjugation may change the optical properties of the QDs and the biochemical activities of conjugated enzymes and antibodies [42].
Therefore, there is a need for development of ligands for QDs that maximally fulfill the above criteria. It is important that a synthetic approach is simple and cost efficient. To satisfy this urgent need, we report here a one-pot synthesis of polythiol ligand, and demonstrate its practical utility by modification of QDs surface, conjugation with antibodies and further testing on LFA test strips.

PTVP synthesis
A 250 ml three-neck flask was charged with 15 ml of VP (140 mmol), 5 g (51 mmol) of MAN in mixture of 10 ml of acetic acid and 15 ml of THF. MAN was dissolved under stirring and then 0.66 g (2.4 mmol) of ABCVA was added. Mixture was flushed with argon and refluxed for 15 min under stirring. Then 2 ml (10.6 mmol) of EGDMA and 0.3 g (1.2 mmol) ABCVA dissolved in 20 ml of THF was added by syringe to above mixture. After 10 min of additional refluxing the reaction was quenched by addition of HT (15 ml, 102 mmol) in 20 ml of THF after 10 min at reflux. Then heater was removed and solution cooled down to room temperature. About 3 volumes of diethyl ether were added for mixture and precipitate of PTVP was formed. Polymer was separated by centrifugation at 10 000 g and dissolved in DMF. Dissolution/precipitation cycle was repeated twice and obtained white powder was rinsed with diethyl ether. Following vacuum drying led to 7.7 g of PTVP (chemical yield ~35%).

QDs hydrophilization
Briefly, 4 g of PTVP was dissolved in 10 ml of 30% DMF in THF. Colloidal solution of 1 g trioctylphosphine/trioctylphosphine oxide (TOP/TOPO) capped QDs in 10 ml of CHCl 3 was slowly added to above mixture under fast stirring. After 1 h at 25 °C a saturated solution of TMAOH in DMF containing 10% of water was dripped thereto until opacity occurred. Stirring of reaction continued for 1-1.5 h and then flakes of PTVP capped QDs appeared. Stirring was stopped and precipitate was separated by centrifugation at 10 000 g.
After two washes with diethyl ether QDs were dried under vacuum and dissolved in deionized water to give clear colloidal solution. Concentrations of prepared QDs samples were calculated using the method presented by Yu et al [51]. For mixed CdTeSe cores extinction coefficient was estimated using weighted coefficients for pure semiconductors CdSe and CdTe as follows: where E CdTeSe is experimentally determined energy of the first peak of exciton absorption of CdTeSe, E CdSe and E CdTe are calculated energies for pure CdSe and CdTe with the same size QDs respectively. Thus, weighting coefficients reflects contribution of each type of semiconductor with considering of energies proximity to pure ones.

Purification of QDs and their conjugates by gel-filtration
Size-exclusion chromatography was performed on superose 6 sorbent using manufacturer's suggested protocol. 10 ml of sorbent was used per 100 mg of sample. Elution was performed using 5 mM TEA at 1 ml min −1 for QDs-PTVP and phosphate buffere saline (PBS) pH 8.0 for QDs-PTVP-Ab. We discarded about 15% of initial fluorescent volume and about 20% of latter fractions. Detection of fluorescence was obtained using a hand-held UV lamp. The collected QDs or QDs bioconjugates solution was filtered with a syringe filter (0.22 µm) to remove gel fragments and the sample was stored at 4 °C prior to further analysis.

Agarose gel-electrophoresis
QDs-PTVP of different types and QDs-PTVP-Ab conjugates were analyzed using 0.4% agarose gel-electrophoresis. The electrophoresis conditions were Tris/borate/ethylenediaminetetraacetate buffer (0.5× concentrate, pH 8.0) at 70 V for 20 min. Fluorescence images of the gel were obtained using BioRad Gel Doc XR (Hercules, CA) with UV light.

Covalent conjugation of quantum dots to proteins
The conjugation of protein with purified QDs-PTVP using 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) was conducted. NHS was also added into the reaction at the final concentration of 1.5 mM since NHS has been known to stabilize the EDC intermediate [52] but the concentration of NHS was not changed in the reaction. Briefly, the 500 µl of 3 µM QDs-PTVP solution was mixed with NHS first and then EDC was added to the reaction mixture. After approximately 15 min, a 2 µM solution of Ab was added, followed by incubation in the dark for 2 h at 25 °C with gentle shaking. Excess of active species was neutralized by addition of 100 µl Tris (10 mM). All conjugation reactions occurred at pH 7.5 in PBS buffer, followed by purification by either gel-filtration or gelelectrophoresis as mentioned above. Additional control in same conditions without Ab was performed. For the preparation of cell lysates, the uninfected and ADV-infected cells were treated with 1% Triton X-100 for 1 h at 37 °C, the solution was clarified by centrifugation for 20 min at 10 000 g. The gB-antigen was purified by immunoaffinity chromatography as described [53]. The purity of gB was assessed using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) performed using the method of Laemmli [54]. The concentration of gB in the cell lysates were determined by the gB-specific two-site 'sandwich' assay [43] using purified gB-antigen for creation of calibrating curve.

Preparation of LFA strips
Glycoprotein B (gB)-specific antibodies (clone 34/2) were immobilized on a nitrocellulose membrane backed on a polyester card (HiFlow Plus 180, Merck-Millipore, Darmstadt, Germany). The detection zone consisted of a test line and a control line. For the preparation of the test line, gB-specific monoclonal antibodies (MAb) 34/2 were brought to a concentration of 1 µl cm −1 in phosphate buffered saline (PBS) and dispensed on the membrane at a rate of 1 l cm −1 using a BioJet XYZ 3000 Dispenser (Biodot Inc., Irvine, CA, USA). The control line was prepared using rabbit anti-mouse IgG antibodies. The membranes were dried, soaked in PBS containing 1% bovine serum albumin (BSA) (PBS-BSA), washed in PBS containing 0.05% Tween 20 (PBS-T), stabilized in PBS containing 2% sucrose (PBS-S), and dried. The cards were assembled so that the glass fiber sample pad, the glass fiber conjugate pad, and the absorbent pad (Merck-Millipore, Darmstadt, Germany) were attached to the gluecovered plastic support, and all the membranes and filters lightly overlapped in order to allow a continuous lateral flow of the liquid sample. The cards were cut into 4-mm strips using an automatic programmable cutter CM4000 (Biodot Inc., Irvine, CA, USA).

gB-LFA procedure
The assay was performed in the 'wet' format without drying the gB-directed QD conjugates. The ADV gB was diluted with the assay buffer (PBS-BSA-T containing 0.2% Triton X-100 and 3% BSA). The diluted samples in a volume of 30 µl (contains 200 ng of ADV gB) were mixed with gB-directed QD conjugates and pipetted onto the sample pad. After 10 min, 30 µl of assay buffer was pipetted on the sample pad and allowed to migrate for further 10 min.

Evaluation of results
In order to obtain quantitative results of LFA, the intensities of the control lines and background were measured using fluorimeter equipped with solid-sample holder accessory. Integration of PL signal was performed in the range λ em ± 0.5 FWHM, where λ em is QDs emission maximum and FWHM is corresponding full width at half maximum as it is shown in table 2. Repeated measurements of the samples were carried out after irradiation by UV light (312 nm) 225 mW cm −2 in 1-160 min time range.

Results and discussion
We realized a multistage one-pot synthesis for heterobifunctional ligand, which provides water-solubility, colloidal stability, biocompatibility, simple conjugation with QDs with proteins. We used copolymer of N-vinylpyrrolidone (VP) with maleic anhydride (MAN) as starting materials. VP and MAN can easily react to produce water soluble and biocompatible copolymers [55,56]. First, we synthesized copolymer of VP and MAN (copolymer 1, figure 1) in the presence of the initiator 4,4ʹ-azobis(4-cyanovaleric acid) (ABCVA) followed Adv. Nat. Sci.: Nanosci. Nanotechnol. 9 (2018) 015002 by addition of ethylene glycol dimethacrylate (EGDMA) as a branching agent in reaction mixture without isolation of the intermediates. Following addition of an excess of 1,6-hexanedithiol (HT) leads to the radical thiol-ene reaction with remaining double bonds forming thiol-containing residues of the copolymer. Further modification of QDs and hydrolysis in an aqueous medium in the presence of tetramethylammonium hydroxide (TMAOH) as the strong base produced free carboxyl groups that could be used for further conjugation with proteins. Remarkably, the whole process takes less than 1 d. Thus, proposed synthetic approach is several times faster than the recently published state-of-the art procedures [34][35][36]57].
It should be noted that the optimized polymerization conditions led produced only minimal yellowing of the final products. Yellowing was significant in case of increased amount of MAN and in the absence of acetic acid as a part of solvent mixture. Corresponding absorption spectra (PTVP 1 and 2) are shown in supporting information (see figure S1 (stacks. iop.org/ANSN/9/015002/mmedia)). The colored byproduct could be the anionic form of MAN, and its formation was negligible in presence of acetic acid.
IR spectrum of the copolymer in comparison with the spectrum of PVP is shown in figure 2.
Formation of polymer-coated water soluble QDs was accomplished in two stages. At the first, initial TOP/TOPO ligands were substituted by PTVP in THF/DMF/CHCl 3 mixture. After stirring at room temperature for about 30 min, excess of concentrated base (TMAOH) solution was added to the reactor. Successive stirring of the above-mentioned mixture led to flocculated precipitate of QDs. After centrifugation supernatant was discarded and precipitate was rinsed by THF to remove low Mw byproducts. Residue was dispersed in DI water. Principal scheme of QDs hydrophilization is shown in figure 3.
Only very small amount of aggregates was formed (less than 10%) under condition of synthesis, and they were easily removed by centrifugation. Thus, these procedures are very simple and fast and gives water soluble QDs with good yield.
The size distribution of QDs-PTVP was determined by dynamic light scattering (figure S2). The average hydrodynamic diameters of the resultant particles in comparison with intrinsic diameter of semiconductors represented in table 1.
The optical properties of QDs were fully unchanged after hydrophilization, except the small diminution of PLQY, which is the maximum for QDs with emission at 600 nm (see table 2 and figure 4). Probably, the quantum yield decreased in the case of short-wave QDs due to the absorption of the PTVP at the shorter excitation wavelength (520 nm). This wavelength was used for rhodamine 101 fluorescent standard when we tested PLQY of 600 nm emitting QDs. QDs with emission at 640 nm and 680 nm was measured using oxazine 170 perchlorate and magnesium phthalocyanine as standards (corresponding to maximum cross section with QDs emission spectra). When wavelength was increased, the absorption of the polymer became insignificant, and, therefore, it did not affect PLQY of the QDs with longer wavelength emission, which were exited above 550 nm.
The data concerning ζ-potential of hydrophilic QDs is shown in table 1. There is a substantial negative charge on particles at pH 8.0 in PBS, which indicates the stability of the colloidal dispersion under these conditions [61]. It should be noted that dispersions maintained they colloidal and optical properties for more than 6 months at room temperature.
In addition, QDs-PTVP was characterized by agarose gelelectrophoresis ( figure 5(a)). The analysis shows clearly that all QDs have similar electrophoretic mobility. This data is in a good agreement with the measured sizes and charges of the particles (see table 1).
To demonstrate the ability of functionalized QDs to couple to amino-containing biomolecules, PTVP-capped red lightemitting QDs were conjugated to model monoclonal antibodies specific to glycoprotein B (gB) of Aujeszky's disease virus (ADV).
Conjugation was performed by the carbodiimide method using N-(3-dimethylaminopropyl)-Nʹ-ethylcarbodiimide hydrochloride (EDC) and N-hydroxysuccinimide (NHS) as described earlier [62]. In order to optimize coupling procedure, different ratios QDs:Ab:EDC were used for the conjugation. The conjugation efficiency was evaluated by gel-electrophoresis in the agarose gel ( figure 5(b)). Hydrodynamic diameter of conjugates is shown in table 1, see also figures S2(d), (f) and (h). The formation of conjugated QDs-MAbs was accompanied by a shift of electrophoretic mobility; the shift was not observed without Ab (figure S3). Highest electrophoretic homogeneity of products and the lack of unconjugated QDs-PTVP was obtained at the Ab:QDs ratio of 10:1 and QDs:EDC ratio of 1:2000. The shift in electrophoretic mobility of conjugates decreased in the series QDs 600 nm > QDs 680 nm > QDs 640 nm which correlated with the sizes of the QDs-PTVP. A larger particle size corresponds to a larger area of its surface, which leads to fewer changes in its properties after conjugation. Conjugation has no significant effect on the PLQY of QD-PTVP except that for QD 680 nm (table 2).
To demonstrate the applicability of QDs-PTVP as a fluorescent label in LFA, QDs-PTVP-Ab conjugates based on MAbs directed to ADV gB were used for the detection of ADV gB. The ADV gB was detected by the two-site 'sandwich' LFA in the lysate of ADV-infected cells. Conjugates with different fluorescence peaks were used in the assay. In the presence of 200 ng ADV gB, a fluorescence appeared in the test line area (figure 6(a), line 2). To quantify the results, a ratio of integral fluorescence intensity of test line to that of the background was used. The values of 1.27, 2.77, and 2.67 were obtained for the conjugates with emission peaks of 600 nm, 640 nm, and 680 nm, respectively. Thus, the conjugates QDs-PTVP-Ab    with fluorescence maximum in a range of 640-680 nm were most suitable in LFA for the detection of ADV gB since the background intensity in this area of spectrum is low [48].
Since the photostability of QDs-PTVP-Ab conjugates is important for the LFA, we evaluated the fluorescence of the conjugates irradiated by UV for a long time. It was demonstrated that fluorescence intensity did not decrease for several hours of UV irradiation. Moreover, we observed an increase of the intensity in the case of the QDs-PTVP-Ab conjugates emitting at 680 nm (figure 6(b)). A possible explanation of this observation is that this type of QDs demonstrated a considerable decrease in quantum yield in the conjugation process (table 2). This can be related with particular etching of the surface of semiconductor. Perhaps, under the action of UV these defects are partially cured. The best stability of the analytical signal was observed for the QDs 640 nm.

Conclusion
We developed time-effective one-pot synthetic procedure for a copolymer of VP, MAN and EGDMA with pendant thiol groups. The ligand can be used to produce water soluble QDs with good chemical yield and high PLQY. Copolymer was used for the functionalization of CdSe/CdS/ZnS, CdTeSe/CdS/ ZnS, CdTeSe/CdS/CdZnS/ZnS QDs. High colloidal stability of PTVP-modified QDs was shown using ζ-potential measurement. Hydrophilic PTVP-capped QDs were conjugated with monoclonal mouse antibodies and demonstrated optical properties similar to QDs-PTVP. The conjugates, of particular those emitting in the far-red spectrum, demonstrated highly specific activity in the two-site LFA in the detection of ADV gB. We hope that the developed method will be used in a great variety of analytical applications.