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Vaccine 29 (2011) 9423– 9430

Contents lists available at SciVerse ScienceDirect

Vaccine

jou rn al h om epa ge: www.elsev ier .com/ locate /vacc ine

molecular dynamics study of loop fluctuation in human papillomavirus type 16irus-like particles: A possible indicator of immunogenicity

arshad Joshia, Srinath Cheluvarajaa, Endre Somogyia, Darron R. Brownb, Peter Ortolevaa,∗

Chemistry Department, Indiana University, Bloomington, USADepartments of Medicine, Microbiology and Immunology, Indiana University School of Medicine, Indianapolis, USA

r t i c l e i n f o

rticle history:eceived 26 April 2011eceived in revised form 15 August 2011ccepted 17 October 2011vailable online 24 October 2011

eywords:pitope

a b s t r a c t

Immunogenicity varies between the human papillomavirus (HPV) L1 monomer assemblies of varioussizes (e.g., monomers, pentamers or whole capsids). The hypothesis that this can be attributed to theintensity of fluctuations of important loops containing neutralizing epitopes for the various assem-blies is proposed for HPV L1 assemblies. Molecular dynamics simulations were utilized to begin testingthis hypothesis. Fluctuations of loops that contain critical neutralizing epitopes (especially FG loop)were quantified via root-mean-square fluctuation and features in the frequency spectrum of dynamicchanges in loop conformation. If this fluctuation-immunogenicity hypothesis is a universal aspect of

G loopmmunogenicityuman papillomavirusolecular dynamicsLPaccineomputer-aided vaccine design

immunogenicity (i.e., immune system recognition of an epitope within a loop is more reliable when it ispresented via a more stable delivery structure), then fluctuation measures can serve as one predictor ofimmunogenicity as part of a computer-aided vaccine design strategy.

© 2011 Elsevier Ltd. All rights reserved.

. Introduction

Vaccines for prevention of cervical cancer by human papillo-avirus (HPV) infection have been developed from the L1 major

apsid protein [1]. The active ingredient in these vaccines (e.g.,ardasilTM) is a L1 protein virus-like particle (VLP) [2–6]. TheseLPs are devoid of genetic material, but conformationally resem-le the structure of the intact virus. GardasilTM is composed of L1LPs of HPV types 6, 11, 16, and 18, and is highly effective against

nfection and disease caused by these HPV types [7]. Type-specificntibodies are generated by the immune systems that are capa-le of neutralizing infectious HPV [8]. As more than 40 HPV typeshat cause genital tract disease have been identified, there is a need

o expand the capabilities of current vaccines to target a broaderpectrum of HPV types [9,10]. Considering the high-cost of devel-ping vaccines, the objective of the present work is to develop

Abbreviations: VLPs, virus-like particles; HPV, human papillomavirus; MD,olecular dynamics; fs, femtosecond; ps, picosecond; ns, nanosecond; RMSF, rootean square fluctuation; RMSD, root mean square deviation.∗ Corresponding author at: Department of Chemistry, Indiana University, 800 E.irkwood Ave., Bloomington, IN 47405, USA. Tel.: +1 812 856 6000/855 2717;

ax: +1 812 855 8300.E-mail address: ortoleva@indiana.edu (P. Ortoleva).

264-410X/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.vaccine.2011.10.039

a computer-aided approach to vaccine discovery. Since vaccineefficacy is tied to immunogenicity of VLPs, reliable molecular pre-dictors for achieving computer-aided vaccine design are required.These immunogenicity predictors could be generalized for otherpathogens.

HPV VLPs are assembled from 72 pentamers arranged in aT = 7 icosahedral fashion [11–13]. The VLP surface has outwardlyprojecting loops containing epitopes that are recognized by theimmune system [14]. These epitopes elicit the production of type-specific antibodies [8]. This type specificity arises from the diversityof epitope conformations within these loops even though thesequence homology between HPV L1 proteins is relatively highcompared to other proteins encoded by the virus [15–17]. Neu-tralization assays of HPV16 VLPs with human sera have identifiedthe following five loop regions: BC, DE, EF, FG, and HI [13,15,16,18](Fig. 1). These loops contain epitopes that elicit documented anti-body responses, and are more flexible than the rest of the L1monomer, showing notable conformational differences across HPVtypes as observed in their X-ray structures. A mouse monoclonalantibody (H.16.V5) binds to an HPV 16 epitope within the FG[17,19]. Moreover, H.16.V5 binds strongly to VLPs having both

FG and HI loops but weakly to mutant VLPs containing only theFG loop [18,20]. Mutagenesis experiments with deletion of cer-tain H.16.V5-binding epitopes mainly from residues in the HI loopand some in the FG loop show that epitope-deleted VLPs are

9424 H. Joshi et al. / Vaccine 29 (2011) 9423– 9430

Fig. 1. L1 HPV 16-monomer in (a) cartoon and (b) surface representation with fiveimportant loop regions highlighted [12]. Color code: red – BC (residues 49–70);yellow – DE (residues 110–154); green – EF (residues 170–189); blue – FG (residues262–291); cyan – HI (residues 347–360). (For interpretation of the references toc

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Fig. 2. Three simulated structures. (a) An isolated HPV 16 L1 monomer in cartoonrepresentation. The protein is depicted in gray while the five important epitopesare highlighted with color code same as in Fig. 1. (b) Cartoon representation ofpentameric assembly of HPV 16 L1 monomers. (c) Space filling representation of the

All-atom explicit solvent MD simulations were performed onthree HPV 16 L1 protein assemblies: (1) an isolated L1 monomer,

olor in this figure legend, the reader is referred to the web version of the article.)

naffected in their reactivity to human HPV sera although theirbility to bind H.16.V5 is strongly reduced [18]. However, epitopeeletion reduces immunogenicity of these VLPs by a factor of at

east 10–20 as compared to wild-type VLPs.The pentameric L1 assembly is stable, and displays the epi-

ope regions within loops described above for VLPs. Thus, L1entamers have been considered as vaccine candidate. An advan-age of these pentameric assemblies is that they can be easily andost-effectively produced in vitro. Pentamers are seen to be sta-le experimentally, but are less immunogenic than the whole VLP21]. The relationship between immunogenicity and the structurend dynamics of epitopes incorporated in various assemblies muste clarified to facilitate the use of L1 pentamers in vaccine design.

X-ray structural studies of HPV L1 pentamers of various HPVypes [11,12] show conformational differences in the loop regionsontaining neutralizing epitopes and provide a molecular basisor type-specificity of HPV antibodies. However, this data onlyrovides an average of the configurations of those regions. Com-arative analysis of X-ray data from HPV16 L1 pentamers andPV16 L1 VLPs failed to discern differences in epitope conforma-

ions between these two systems [12]. However, antibody titersrom mice immunized with L1 pentamers are 20–40 times lowerhan those produced by mice immunized with L1 VLPs [21]. ELISAhows VLP-induced antibodies are more reactive than pentamer-nduced ones. Binding assays of L1 pentamers and VLPs with aariety of mouse monoclonal antibodies also show that VLPs areenerally more reactive than pentamers [21].

These observations suggest that there are important struc-ural dynamic differences between L1 pentamers and L1 VLPs notesolved with the inherently static X-ray data. Since loops FG andI are crucial for binding to H.16.V5, the structural similarity of

hese epitopes in L1 pentamers and L1 VLPs suggests the possi-le importance of dynamic effects on immunogenicity. Therefore,

molecular dynamics (MD) study was undertaken to study behav-ors of L1 assemblies of various sizes. The structural similarity ofhe loop regions in the pentamer and the VLP is hypothesized hereo indicate that immunogenicity differences are related to fluctua-ion, defined here as the temporal variability in the conformation ofoops containing critical epitopes. Sources of variability of loop fluc-uation according to assembly size might include (1) inertial effectsue to the larger versus smaller mass of the host structure; (2) theriction imposed by the host structure on the loop, and (3) atomic

orces that may change according to structure, e.g., the pentamercurvature”.

HPV16 (T = 1) VLP consisting of 12 pentamers arranged in T = 1 icosahedral structure.(For interpretation of the references to color in this figure legend, the reader isreferred to the web version of the article.)

The objective of the present study is to relate immunogenicitydifferences to fluctuation differences of HPV 16 loops containingcritical epitopes via MD simulations by measuring quantities thatdistinguish atomic-level details. As MD accounts for all atomic-scale forces and dynamics, this approach accounts for the abovethree factors. In this way, we seek to identify quantitative measuresof immunogenicity-related epitope fluctuation that can serve as aninput to a Quantitative Structure–Activity Relationship (QSAR) [22]or other vaccine design approach. By comparing previously estab-lished immunogenicities of HPV L1 structures of different types,and correlating this data with differences in loop structure, a rela-tionship between the two can be postulated and tested. Such anapproach could lead to more effective, thermally stable, and cost-effective VLPs or other epitope-delivery vaccine systems.

2. Methods

2.1. Assemblies and conditions

(2) pentameric assembly of L1 monomers [12], and (3) the T = 1VLP consisting of 12 pentamers [23] (Fig. 2). Atomic coordinates for

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H. Joshi et al. / Vacc

he monomer were obtained from the crystal structure ([23] PDBode: 1DZL; [24]), while the VLP was constructed from 60 copiesf the monomer using icosahedral symmetry transformationsVIPERDB database [25]). The pentamer was extracted from thisLP construct to maintain structural continuity with the VLP. Eachystem was solvated with water using the solvation feature of theMD software [26]. To match experimental conditions, the systemas solvated in 0.3 M NaCl concentration using VMD autoionize

eature.

.2. Molecular dynamics simulation details

All simulations were performed with the MD software NAMD.7 [27] using the CHARMM27 force field [28] for proteins andhe TIP3P model for water [29]. The simulated systems wereept at constant temperature using Langevin dynamics for allon-hydrogen atoms, with a Langevin damping coefficient of

picosecond−1. A constant pressure of 1 atm was maintainedsing the Nosé-Hoover Langevin piston [30] with a period of00 femtosecond (fs) and damping timescale of 50 fs.

Simulations were performed with an integration time step of fs under a multiple time stepping scheme [31]; bonded inter-ctions were computed every time step, short-range non-bondednteractions every two time steps, and long-range electrostaticnteractions every four time steps. A cutoff of 12 A was used foran der Waals and short-range electrostatic interactions; a switch-ng function was started at 10 A for van der Waals interactions tonsure a smooth cutoff. The simulations were performed undereriodic boundary conditions, with full-system, long-range elec-rostatics calculated by using the particle-mesh Ewald methodith a grid point density of 1/Å. The unit cell was large enough

o that adjacent copies of the system did not interact via short-ange interactions. Prior to simulation, each system was subjectedo 1000 steps of conjugate gradient energy minimization, followedy 100 picoseconds (ps) of equilibration. We then performed all-tom molecular dynamics as described above, at 300 K for 10 ns forach system.

The T = 1 VLP is comprised of 428,770 atoms. Thus the largestystem of T = 1 VLP with explicit solvent environment contains ∼4illion atoms and required massive amount of parallel computing

esources. The large number of atoms in the simulations is mainlyue to the water included to minimize boundary effects. Mini-ization and equilibration were performed on 1024 cores, with

erformance of 750 ps/day in the case of VLP simulation.

.3. Loop structure and fluctuation metrics

To distinguish the behavior in the simulated systems in termsf loop structures and their fluctuations, following indicators wereonsidered.

. Dihedral distribution and fluctuations for individual loops. The dis-tribution of backbone loop dihedral angles [32] was measured.This variable indicates the conformational space explored bythe loops. Similarly, fluctuations of individual atoms from theiraverage value in a given loop type were computed. This metricindicates the conformational freedom available to the loops. Themagnitude of the fluctuations is a measure of the flexibility ofthe loop.

. Power spectra. The power spectrum provides the distribution ofatomic vibration intensity across a range of frequencies. Lowerfrequencies represent slower motions, while high frequenciesrepresent faster ones.

(2011) 9423– 9430 9425

These measures were used to discriminate between the motionsof a given loop type incorporated in an assembly of given size (i.e.,from L1 monomer to T = 1 VLP).

3. Results

Simulations were designed to assess potential differences inbehavior of loops between the three L1 protein assemblies andquantify them to serve as a basis of a computer-aided vaccinediscovery strategy. The study focused on loops known to containcritical epitopes, some of which are neutralizing: EF, FG and HI(Fig. 1).

3.1. Dihedral distribution

Fig. 3 shows the distribution of backbone dihedral angles for EF,FG and HI loops in the three L1 assemblies. In each case, 10,000time points were extracted from MD simulations to construct theprobability distribution of loop conformations. FG followed thehypothesized assembly size trend, i.e., the dihedral angles of thisloop varied over a smaller region for the VLP relative to the pen-tamer, and even smaller for the monomer. This trend suggestedthat the large flexibility of the FG loop observed in the isolatedmonomer diminishes when the monomer resides within largerassemblies. HI maintained more or less its native L1 conformationin the pentamer and VLP, and varied little as can be seen from Fig. 3.In contrast, EF varied over a larger region of conformational spaceas one proceeded from monomer to pentamer to VLP.

3.2. Positional variance

Next, the overall fluctuations of a particular loop from its aver-age configuration were compared. Loop fluctuation is not easilyquantified in X-ray or cryo-EM data. While a structure providesthe most likely or average configuration, its fluctuation measuresthe importance of other configurations away from the average,but which may (according to our immunogenicity hypothesis) befunctionally relevant. As in the introduction, dynamic informa-tion obtained from MD provides advantages over the inherentlyaveraged experimental data. Thus, positional variance of the loopatoms was quantified as another measure of epitope fluctuation.Positional variance was computed by summing the deviation ofindividual backbone atom position and dividing by the numberof backbone atoms in the loop. This measure is slightly differentfrom the usual root mean square fluctuation (RMSF). RMSF mea-sures fluctuation from a fixed reference structure by aligning twostructures, thus eliminating translational and rotational motions. Incontrast, average loop positional variance calculated here containscontributions from overall displacements of the loops and theirmotions relative to the rotation/translation and internal motions ofthe assembly. The overall motions potentially affect epitope loca-tion and orientation within loops; according to our hypothesis,these overall fluctuations also affect immunogenicity and bindingproperties of the monomer or larger assembly. Thus, including theeffect of overall and internal assembly motions on loop fluctuationsprovides a more complete measure of their potential relevance toimmunogenicity.

Positional variance is tabulated for each loop and assembly(Table 1), while plots of individual loop variance are shown (Fig. 4).Individual atomic fluctuations for loop atoms are only shown forEF, FG and HI, known to be central to immunogenicity. These posi-

tional variance measures of loop fluctuation were seen to be largestin the monomer, significantly smaller in the pentamer and evensmaller for the VLP. To show the potential influence of simulationtimes used, averages were taken over 2 ns windows from whole

9426 H. Joshi et al. / Vaccine 29 (2011) 9423– 9430

Fig. 3. Loop dihedral distribution for monomer (left), pentamer (middle) and the T = 1 VLP (right). Distributions are shown for EF (first row), FG (second row), and HI (thirdrow). For each loop the x-axis (ϕ angle) and corresponding y-axis ( angle) specifies positions in the dihedral space. In general, the spread of the distributions decrease asthe assembly size increases.

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ig. 4. Backbone atom loop positional variance for monomer (left), pentamer (midoop (third row). For each loop the x-axis denotes the residue number and corresFig. 3) the positional variance decreases as assembly size increases.

he simulation interval (10 ns) and no significant change in quali-ative behavior was observed (Table 1). Fluctuations of the EF loop

or the pentameric L1 structure and the L1 VLP are significantlymaller than for the monomer. Fluctuations in the monomer rela-ive to that in the larger assemblies greatly exceeded that expected

able 1ositional variance (Å2) for L1 monomer, pentamer, and VLP.

Loop L1 monomer Pentamer T = 1 VLP

EF 8.54 ± 1.83 2.15 ± 0.81 1.89 ± 0.96FG 6.69 ± 2.87 1.41 ± 0.64 0.85 ± 0.36HI 10.5 ± 5.66 1.67 ± 0.48 1.12 ± 0.53

nd the T = 1 VLP (right). Plots are shown for EF (first row), FG (second row), and HIng y-axis gives the magnitude of fluctuation in Å2. Like the dihedral distributions

simply from the inertial effects of the larger assembly; this suggestsimportant differences in internal fluctuations between the assem-blies. The extent of motion of individual loops was then measuredby RMSD from the initial structure for the L1 monomer, pentamer,and VLP at the end of simulation trajectory (10 ns) (Table 2). RMSDdata in Table 2 showed that, over a 10 ns MD simulation, epitopefluctuations were largest for the monomer and smallest for the VLP.

3.3. Power spectra

Power spectra from the loop conformation time series provideda third measure of fluctuation (Fig. 5). The power spectrum is a mea-sure of the energies of the different frequency motions present in a

H. Joshi et al. / Vaccine 29 (2011) 9423– 9430 9427

Fig. 5. Power spectrum for fluctuations in three loops: EF, FG, and HI. Frequency of the loop conformational fluctuations is indicated on x-axis while their averaged squaremagnitude is indicated on y-axis. Spectra for L1 monomer, pentamer and T = 1 VLP are indcolor in this figure legend, the reader is referred to the web version of the article.)

Table 2RMSD (Å) of structures in the loop regions from their initial structures at the endof 10 ns MD simulations of VLP, pentamer, and L1 monomer are shown in the firstthree columns. The RMSD decreases for each epitope as we move across the table.

Loop L1 monomer Pentamer T = 1 VLP

EF 4.13 3.81 3.11FG 4.28 2.74 2.37

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ignal and is a commonly used to understand electrical and chemi-al signals. The power spectrum provides a way to identify types ofotions (i.e., high versus low frequency). All loops in the monomer

howed increased power in the low frequency region versus thoseor the pentamer and VLP (Fig. 5). At higher frequency, the power forhe three cases was similar because short time-scale fluctuationsre due to spatially localized processes (e.g., bond oscillations) andre similar in the three structures.

To measure relative flexibility of L1 pentamers within and out-ide the VLP, the RMS deviation was computed in two ways: (1) thepread of the RMS deviation from the initial structure between aonomer and a pentamer in a pentamer simulation (black and red

urves, respectively, Fig. 6 upper panel), and (2) the spread of theMS deviation from the initial structure between a monomer and

pentamer in a VLP simulation (black and red curves, respectively,ig. 6 lower panel). Comparison of (1) and (2) showed the spreadas larger in the pentamer than in the VLP, i.e., the flexibility of theentamer is greater when it is in isolation than within the VLP.

. Discussion

Any molecule or virus particle containing a structure recognizedr bound by an immunoglobulin is known as an antigen [33]. Theortion of the antigen bound to the immunoglobulin is known ashe antigenic determent, or epitope. Antibody binding to epitopes

ay or may not result in neutralization of a virus [34]. Neutraliza-ion has been defined as “the loss of infectivity which ensues whenntibody molecule binds to a virus particle, and usually occurs with-ut any other agency” [35]. Antibody neutralization often dependsn the strength of antibody binding to surface epitopes. In the casef influenza virus, this has been shown that neutralizing antibody

inds primarily to the HA epitope [36]. Higher affinity of antibodyinding to HA correlates directly with neutralization of influenzairus.

icated in black, red and green, respectively. (For interpretation of the references to

Burton has argued for a simple occupancy model of viral neutral-ization in which virus is neutralized “when a fairly large proportionof available sites on the virion are occupied by antibody, whichleads to inhibition of virus attachment to host cells or interferencewith the entry (fusion) process” [37]. He argues that an impor-tant prediction of this model is “that the neutralizing efficacy of anantibody should be related to its affinity for antigen on the virionsurface” [37]. Therefore, it is argued that the most effective vaccinesshould be those that elicit antibodies with high binding affinitiesto neutralizing epitopes, the portion of the antigen bound to theneutralizing immunoglobulin.

For HPV 16, although specific amino acid residues characterizingthe neutralizing epitopes have not been completely elucidated, itis known that such epitopes exist within the FG and HI loops of L1protein [15]. The FG and HI loops are hypervariable regions locatedat the tip of the capsomere that contain surface-exposed epitopes[15]. Amino acids 266 through 297 constitute the HPV 16 FG loop.The mouse monoclonal antibody H16.V5 binds to a major epitopewithin the FG loop, and neutralizes HPV 16 with a high degree ofefficacy [15,38,39].

Ryding, et al., performed studies to characterize the epitopebound by H16.V5 monoclonal antibody [18]. They showed thatmutation of L1 residues 270 and 285 led to failure of H16.V5 binding[18]. Christensen et al., transferred H16.V5 binding by transferringthe HPV16 FG loop, suggesting that these two amino acid residuesare included in the H16.V5 binding site [20]. The mAb H16.E70 isalso neutralizing for HPV16, and appears to be directed against anepitope within FG [13]. Amino acid 282 appears to be importantin the epitope bound by the H16.E70 mAb. In addition, Sadeyan,et al., showed that insertion of a six amino acid epitope of hepatitisB into the FG loop at amino acid position 266 led to lower levels ofneutralizing antibody [40]. Thus, studies on VLP immunogenicityas well as antibody binding have underscored the important roleplayed by epitopes within the FG and HI loops of L1.

Differences in FG and HI loop fluctuations between the three L1structures (monomers, pentamers, and T = 1 VLPs) are likely a resultof intra- and cross-monomer interactions of specific amino acidresidues making up critical epitopes. In the pentamer, HI from onemonomer is confined by loops EF and FG from its counterclockwiseneighbor. In contrast, loop FG interacts with loops DE and HI from itsclockwise neighbor. Furthermore, interaction of intra-monomer FG

them. These spatial relationships and interactions manifest in thebreadth of the dihedral variation of these loops. HI samples a nar-row distribution of conformations in the VLP and the pentamer but

9428 H. Joshi et al. / Vaccine 29 (2011) 9423– 9430

Fig. 6. (a) Comparison of RMSD of protein (black) and pentamer (red) from a pentamer simulation over a 10 ns time-course. (b) Comparison of RMSD of protein (black) andp tweenw the re

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entamer (red) from a T = 1 VLP simulation over a 10 ns time-course. The spread beithin T = 1 VLP. (For interpretation of the references to color in this figure legend,

broad spectrum in the monomer (RMSD for HI increases 10 foldn monomer compared to pentamer and VLP), suggesting strongnteractions between this loop and the neighboring monomer. HItructures obtained at the end of the pentamer and VLP simulationsave small deviation from their initial one (Table 2).

FG fluctuations, on the other hand, reduce systematically inoing from monomer to pentamer to VLP, and this is a result ofG–HI interactions. Table 2 shows this higher flexibility of loop FGn pentamer as compared to VLP. Antibody binding studies in ELISAssays indicate that FG is stabilized by HI by binding to antibodieshen HI is present but only weakly binding in its absence. Varia-

ions in FG and HI fluctuations, in going from L1 monomeric proteino pentamer, indicate the effect of intra- or cross-monomer interac-ions with these loops. The change in HI fluctuation is less than thatn FG, suggesting its relative lack of sensitivity to local structure.his insensitivity of HI fluctuations across the various assemblies,s predicted by the simulations, is consistent with the observedmportant role of HI in antibody binding in the ELISA assays.

At first the behavior of EF, with its wider dihedral distribu-ion, seems to contradict the notion that fluctuation decreases withssembly size. Comparison of dihedral distribution of EF with thatf FG, HI (Fig. 3), shows that EF exhibits different configurations inhe larger assemblies than in the monomer, while FG and HI do not.F also has negligible cross-monomer interactions, and resides in anrea which is at the monomer–monomer interface for the pentamernd VLP. Therefore, it explores a different volume of configurationpace in pentamer compared to monomer (Fig. 3). Going from pen-amer to VLP, EF explores different regions of configuration space.his may arise from the fact that when two pentamers assemble toorm VLP (Fig. 1), the close proximity of EF to regions from otherentamers prompts it to sample a different ensemble of configu-ations, relative to that for the isolated pentamer. Effectively, theuctuation breadth for EF increases systematically from monomero pentamer to VLP. This likely explains why this loop illustrates auctuation trend opposite to that from FG and HI. Thus, the seem-

ngly contradictory trend can be attributed to a structural transitionn the EF loop induced by the interactions between monomers in thearger assemblies. Such structural transitions are not apparent inther loops. The above results suggest the importance of loop loca-ion in mediating the dependence of fluctuation on assembly sizend therefore, via the proposed hypothesis, on immunogenicity.

Interactions between loops in the monomer do not involveydrogen bonds as the loops are well separated in space, but hydro-en bonds and direct electrostatic interactions between adjacent

onomers in the pentamer and VLP have a stabilizing effect on

he protein backbone and hence on the epitopes contained in theoops. In the VLP, helix h4 (posterior to HI) from one pentamer hasydrophobic interactions with helices h2 and h3 from pentamers

two curves is less in (b) than in (a) indicating the reduced flexibility of pentamerader is referred to the web version of the article.)

adjacent to it at points of threefold symmetry. These interactionsmake the pentamer less flexible inside the VLP than when it isisolated, and thereby reducing fluctuations (Fig. 6).

The fluctuation–immunogenicity hypothesis was furtherprobed via fluctuation power spectra. Thus, for higher immuno-genicity, the loops within any L1 assembly should exhibit a powerspectrum similar to that observed in the VLP (Fig. 5). Thus, thereduced immunogenicity of a pentamer relative to the VLP may belinked to differences between the red and green curves in Fig. 5.

While the T = 7 (∼2.5 million atoms) VLP is the active con-stituent in the Gardasil vaccine, in this study the T = 1 structure(428,770 atoms) was chosen to minimize computational costs.However, the T = 1 assembly has pentamer-level structural simi-larity to the T = 7 assembly. Furthermore, it was a natural step inthe sequence of assembly sizes, and since the proposed hypoth-esis is the relationship between assembly size, loop fluctuation,and immunogenicity, the authors believe it provides an adequatebasis for evaluating this hypothesis. There are also immunogenicitystudies using T = 1 structure that validate the approach [41].

5. Conclusions

The simulations presented here showed that the fluctuationchange in these two loops is significantly more than in other loopsas one goes from one assembly to the other, underscoring theuse of the fluctuation of these loops as a potential indicator ofimmunogenicity. According to the proposed low-fluctuation/high-immunogenicity hypothesis, methods to minimize the fluctuationsof FG and HI (similar to those observed in the VLP) could leadto higher immunogenicity of pentamer-based vaccines or thoseinvolving other scaffolds as epitope-delivery systems.

The analysis indicates important subunit interactions. For exam-ple, HI shows minimal change in fluctuations between the L1pentamer and VLPs, whereas FG shows significant differencebetween the two cases. This suggests HI is mainly stabilizedby intra-pentameric interactions whereas FG is stabilized to alarge extent by inter-pentameric interactions. Thus differencesin loop–loop and loop–monomer interactions in the variousassemblies can be taken as an additional potential predictor ofimmunogenicity.

Results presented here have implications for QSAR andcomputer-aided vaccine discovery strategies. The “loopfluctuation–immunogenicity hypothesis” suggests that for similarimmunogenicity in the L1 pentamer and VLP to be realized,

epitopes within loops and their scaffold delivery structure mustbe designed to have the same level of fluctuations as for loopsin the VLP. Thus, efforts to produce new vaccines with scaffoldsother than pure VLPs could benefit by taking into consideration

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H. Joshi et al. / Vacc

ow loop conformations and fluctuations behave in the newonstruct. However, caution must be taken when relating fluctu-tion intensity with immunogenicity as structural and dynamicaleatures may cause apparent deviation from the hypothesizeductuation–immunogenicity relationship. Key to unraveling thiselationship is the use of MD simulations wherein the amplitudend characteristics of fluctuations is readily available for analysis.pitopes are universal molecular aspects of immunogenicity.herefore, even though the results obtained here were based onPV16, it is suggested that the proposed hypothesis is a promisingirection for computer-aided vaccine design strategies across aroad spctrum of pathogens.

Based on the MD results, monomer–monomer interactionseduce fluctuations by restricting the motions of the FG and HIoops. The loop fluctuation analysis shows a correlation betweenow fluctuation and increased assembly size which, based onrior experimental results [20], we hypothesize to imply a “lowuctuation–high immunogenicity” correlation. The studies pre-ented here involved simulations under conditions similar to thosen immunogenicity experiments. The simulations were carried outor 10 ns, a period much shorter than the characteristic time of

any viral processes such as virion self-assembly. However, theain focus here is on fluctuation of loops containing critical epi-

opes that occur on picosecond timescale (lowest frequency of theower spectrum, Fig. 5). Therefore, a 10 ns simulation is sufficiento investigate the systematic differences in epitope fluctuations formmunogenicity studies.

Loop fluctuation appears to be one mechanism responsible forxperimentally observed differences in immunogenicity between1 pentamers and VLPs. However, other complementary measuresan be used for achieving a more reliable vaccine discovery strat-gy and for optimizing immune response and thermal stability.hese measures could be affected by external conditions suchs salinity, temperature. Thus, a more comprehensive computer-ided vaccine discovery strategy could involve several distinctypes of simulations. However, simulating the temporal behav-or of such modified vaccine candidates may not be practical

ith traditional MD methods. This is because, for example, pre-icting the stability of a vaccine nanoparticle requires extremely

ong MD simulations (i.e., to predict the rate of crossing barrierso VLP disassembly). New algorithimic development is there-ore necessary to enable efficienet prediction of long-time VLPehavior with all the details that a traditional MD can give.

multiscale approach that allows such simulations of largeacromolecular assemblies over physiologically long timescales

hundreds of nanoseconds) has been developed [42,43]. Theresent work provides the starting point for the future imple-entation of this multifaceted approach for efficient vaccine

iscovery.

cknowledgements

This project was supported in part by funds from the NSF CRCrogram, Indiana University METACyt project, and Indiana Uni-ersity Office of Vice President for Research. Computations werearried out through support of the Indiana University Informa-ion Technology Center, and the Advanced Computing Facility atrgonne National Laboratory.

Contributors: P.J.O. proposed the immunogenicity–fluctuation

ypothesis; D.R.B. posed the challenge of explaining

mmunogenicity–assembly size relationship; H.J. and S.C. car-ied out MD simulations; H.J., S.C. and A.S. analyzed the MDesults; H.J., S.C., D.R.B. and P.J.O. wrote the paper.

[

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