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Prion protein β2–α2 loop conformational landscapeEnrico Caldaruloa,b, Alessandro Barduccic, Kurt Wüthrichd,e, and Michele Parrinelloa,b,1

aDepartment of Chemistry and Applied Biosciences, Eidgenössische Technische Hochschule Zurich, CH-8093 Zurich, Switzerland; bInstitute of ComputationalSciences, Universita della Svizzera Italiana, CH-6900 Lugano, Switzerland; cCentre de Biochimie Structurale, INSERM, CNRS, Université de Montpellier,FR-34090 Montpellier, France; dInstitute of Molecular Biology and Biophysics, Eidgenössische Technische Hochschule Zurich, CH-8093 Zurich, Switzerland;and eDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037

Contributed by Michele Parrinello, July 13, 2017 (sent for review July 23, 2015; reviewed by Peter G. Bolhuis and Hermann M. Schätzl)

In transmissible spongiform encephalopathies (TSEs), which arelethal neurodegenerative diseases that affect humans and a widerange of other mammalian species, the normal “cellular” prionprotein (PrPC) is transformed into amyloid aggregates represent-ing the “scrapie form” of the protein (PrPSc). Continued researchon this system is of keen interest, since new information on thephysiological function of PrPC in healthy organisms is emerging,as well as new data on the mechanism of the transformation ofPrPC to PrPSc. In this paper we used two different approaches: acombination of the well-tempered ensemble (WTE) and paralleltempering (PT) schemes and metadynamics (MetaD) to character-ize the conformational free-energy surface of PrPC. The focus ofthe data analysis was on an 11-residue polypeptide segment inmouse PrPC(121–231) that includes the β2–α2 loop of residues167–170, for which a correlation between structure and suscepti-bility to prion disease has previously been described. This studyincludes wild-type mouse PrPC and a variant with the single-residue replacement Y169A. The resulting detailed conforma-tional landscapes complement in an integrative manner the avail-able experimental data on PrPC, providing quantitative insightsinto the nature of the structural transition-related function of theβ2–α2 loop.

prion | β2–α2 loop | metadynamics

Transmissible spongiform encephalopathies (TSEs) are lethalneurodegenerative prion diseases that affect humans, domes-tic animals such as cattle and sheep, and wild-living animals suchas deer and elk (1–3). In these pathologies, the normal “cellularform” of the prion protein (PrPC) is transformed into the patho-logic aggregated “scrapie form” (PrPSc). Information on boththe physiological function of PrPC in healthy organism and thePrPC to PrPSc transformation mechanism is continuously evolv-ing (4–7). Unraveling the molecular nature of these so far elu-sive events carries the promise to support future prevention andtreatment of TSE and understanding of other prion-like diseasemechanisms.

The mature cellular form of the mouse prion protein is com-posed of a flexibly disordered N-terminal 100-residue polypep-tide segment, a 110-residue globular domain (Fig. 1A), and ashort, flexibly disordered C-terminal peptide segment. The struc-ture of the globular domain is composed of three α-helicesand a short two-stranded antiparallel β-sheet (8). This foldhas been observed in all of the mammalian PrPCs studied sofar, with local variations primarily in the β2–α2 loop of thepolypeptide segment linking β2 with the helix α2 (Fig. 1A)(9–19). Mutations in the β2–α2 loop and in its immediatespatial environment have been shown to affect susceptibilityto spontaneous prion disease; i.e., transgenic mice expressingmouse PrP (mPrP) with the amino acid substitutions S170N andN174T developed spontaneous TSE (20), whereas mice express-ing mPrP[Y169G/S170N/N174T] presented with resistance toinfection (21).

Studies of the β2–α2 loop dynamic conformational equilib-ria by NMR revealed two limiting conformations, i.e., a 310-helical turn and a type I β-turn (Fig. 1B). In particular, the wild-type mPrP(121–231) and the variant mPrP[Y169A](121–231)

(Fig. 1C) (17), on which we shall focus here, were characterizedwith the β2–α2 loop in the 310-helical turn and type I β-turnconformations, respectively. Experimental studies have shownthat mPrP[Y169A](121–231) does not show evidence of confor-mational transitions (17, 22), whereas mPrP(121–231) exhibitsslowed conformational exchange on the NMR chemical shifttimescale.

The present molecular dynamics (MD) simulations comple-ment the NMR experiments in the study of the β2–α2 loopconformational behavior. In particular, MD can provide infor-mation about short-lived, lowly populated conformational statesand transition pathways that are challenging to probe experi-mentally. Since these conformational transitions take place onthe micro- to millisecond timescale (17, 22), which exceeds thecapability of commonly accessible computers (23), we appliedadvanced MetaD sampling techniques (24).

An earlier computational study of this system was performedusing an umbrella sampling method supplemented by a cut-basedfree-energy profile analysis (25). The windows in the umbrellasampling procedure were generated from trajectories obtained ina simulation of the mutant Y169G when transitions occur in thetimescale of the simulation. The reaction coordinate was takento be the distance between the residues P165 and Q168.

Simulations of such systems are challenging and thoroughsampling is difficult. It is therefore of value to add calcula-tions with different methods, using a different force field. Here,we simulated in full atomistic detail the polypeptide chains ofresidues 121–231 of mPrP(121–231) [Protein Data Bank (PDB)code 2L39] and mPrP[Y169A](121–231) (PDB code 2L40) (17)solvated in explicit water, using the Amber99SB∗-ILDN (26)force field. To this effect we combined two different approaches:parallel tempering in its well-tempered ensemble version (PT-WTE) (27, 28) and metadynamics (MetaD) (24). At the heart ofthe MetaD there is the identification of suitable collective vari-ables (CVs), whose fluctuations are crucial for the transitionsbetween different conformational states. The role of MetaD isto amplify in a controlled way the fluctuations of these CVs,thus inducing rare conformational transitions within an afford-able computational time (29). The search for these CVs is made

Significance

β2–α2 loop conformational polymorphism in cellular prionproteins (PrPC) has a key role in the development of priondiseases. Using state of the art enhanced simulation meth-ods, we design its conformational landscape and calculate itsstatic and dynamic properties. We also show that in combina-tion with NMR data, these simulation methods can provide adetailed atomistic picture of the β2–α2 loop dynamics.

Author contributions: E.C., A.B., K.W., and M.P. designed research, performed research,contributed new reagents/analytic tools, analyzed data, and wrote the paper.

Reviewers: P.G.B., University of Amsterdam; and H.M.S., University of Calgary.

The authors declare no conflict of interest.1To whom correspondence should be addressed. Email: michele.parrinello@phys.chem.ethz.ch.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1712155114/-/DCSupplemental.

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Fig. 1. (A) Polypeptide backbone fold in the NMR structure of mPrP(121–231). The β2–α2 loop is highlighted in red and the rest of the polypeptidesegment 165–175 is shown transparent. (B) Polypeptide backbone structureof residues 165–175 with the β2–α2 loop highlighted in solid color. Thestructures are taken from the best representative NMR conformer depositedin the PDB (PDB codes 2L39 and 2L40). WT (red) contains a 310-helical turnconformation, whereas Y169A (blue) has a type I β-turn conformation.(C) Amino acid sequence alignment for the polypeptide segment 165–175 ofmPrP(121–231) and mPrP[Y169A](121–231), which includes the β2–α2 loop.Solid circles in Y169A indicate identical residues.

by performing a PT-WTE run. In these preliminary runs weobtained qualitative information on the different conformationalstates and could identify appropriate CVs. These provided theinput for a subsequent MetaD investigation. We were thus ableto quantitatively assess the population of the conformationalstates, the transition pathways, and the rate-limiting steps, findinga multiplicity of conformational states and possible transitions.

ResultsWe initially took advantage of the PT-WTE approach to investi-gate the conformational dynamics of the β2–α2 loop. At variancewith other enhanced sampling schemes, this replica-exchangealgorithm does not rely on the introduction of a bias potentialand thus it does not require any a priori knowledge of collec-tive variables. The enhancement of the potential energy fluctu-ations introduced by the use of WTE leads to an efficient dif-fusion in temperature space even with a reduced number ofreplicas. In our PT-WTE simulations of mPrP(121–231) andmPrP[Y169A](121–231) we used 16 replicas to span a temper-ature interval ranging from 300 K to 550 K. To avoid undesiredunfolding at the higher temperatures and since we are interestedonly in the conformational landscape of the β2–α2 loop at roomtemperature, we applied restraints on the secondary and tertiarystructural elements of the proteins that do not involve this loop(Table S1). This strategy ensured that the protein remains foldedat high temperature while the loop could sample the conforma-tional landscape.

This protocol was used to simulate both mPrP(121–231) andmPrP[Y169A](121–231), using the NMR conformers as startingstructures. Due to the enhanced sampling, we could observe sig-nificant conformational heterogeneity in runs of about 100 ns.In Fig. 2 we report the root-mean-square deviation (rmsd) of

the polypeptide segment 165–175 with respect to the NMRconformers observed at 300 K for both systems. This analysishighlights that the β2–α2 loop in mPrP(121–231) (Fig. 2, Top)may transiently populate type I β-turn conformations similar tothose determined by NMR in mPrP[Y169A](121–231), whereasmPrP[Y169A](121–231) exhibits a more fluxional behaviorwith more frequent transitions and visits a larger number ofconformations.

While the PT-WTE runs were very informative (Fig. 2),there is a question regarding the extent to which the structuralrestraints might influence sampling. Furthermore, as in all PT-type calculations, the transition states could not be well exploredand it was not easy to infer the mechanisms leading to conforma-tional exchange. To get an improved quantitative estimate of theunderlying free-energy surfaces, we decided to perform extensivemultidimensional MetaD simulations.

An analysis of the PT-WTE run led us to consider four dif-ferent CVs for each protein, which enable us to establish ametric for comparison with the 310-helical turn and the typeI β-turn. The two competing conformations are defined by aset of hydrogen bonds and dihedral angle arrangements (Fig.S1). As reference values for the dihedral angles we used the

Fig. 2. Time evolution of the rmsd for the polypeptide segment 165–175during the PT-WTE simulation. (Top) rmsd for the polypeptide segment165–175 of mPrP(121–231) from the best representative NMR conformerof the ensemble mPrP(121–231) (310-helix, red circles) and the ensemblemPrP[Y169A](121–231) (β-turn, blue circles). (Bottom) rmsd for the polypep-tide segment 165–175 of the mPrP[Y169A](121–231) from the best repre-sentative NMR conformer of the ensembles mPrP(121–231) (310-helix, redcircles) and mPrP[Y169A](121–231) (β-turn, blue circles).

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best representative NMR structures of mPrP(121–231) andmPrP[Y169A](121–231).

In mPrP(121–231) the hydrogen bonds crucial for defining the310-helical turn conformation are HN 168 –O165, HN 169 –O166,and HO169 –Oγ D178 (sh310). In mPrP[Y169A](121–231) thethird one of these hydrogen bonds is of course not included. Asreferences for the 310-helical turn we chose the dihedral anglesψ V166, ψ D167, and ψ Y169 (sd310), where 310-helix standardvalues for the dihedral angles were chosen. To describe the ref-erence type I β-turn conformational state, we used the hydrogenbonds HN 170 –O167 and HN 167 –Oγ 170 (shβ ) and the dihedralangles ψ V166, ψ D167, and ψ A169 (sdβ ). The choice of theseCVs enabled us to reversibly sample the different conformationalstates of the β2–α2 loop and to achieve convergence (Fig. S2)without using any restraints.

With self-explanatory notation we label the four CVs as sh310 ,shβ , s

d310 , s

dβ . The outcome of the MetaD calculation is, within a

constant, the multidimensional free-energy surface (FES),

FES(sh310 , shβ , s

d310 , s

dβ ) = −kBT lnP(sh310 , s

hβ , s

d310 , s

dβ ), [1]

where T is the temperature, kB the Boltzmann constant, andP(sh310 , s

hβ , s

d310 , s

dβ ) is the joint probability distribution of the

four CVs. As discussed previously in the literature with appro-priate sets of CVs, minima in the FES correspond to stable con-formational states (29).

Using this set of CVs, we performed for both proteins long,well-tempered MetaD runs of duration 500 ns. The resultingFES confirmed and greatly enriched the picture provided by thePT-WTE simulations, indicating the presence of four main free-energy minima: two have a 310-helical turn character (h1 andh2), and two have a β-turn character (b, b′) (Fig. 3). The vari-ations between h1 and h2 arise from the different orientationof the carbonyl group of residue 169. In the h1 conformationalstate, it points toward the solvent while it is oriented toward theprotein core in the h2 conformational state (Fig. 3). The b and b′

conformational ensembles were analyzed with the STRIDE algo-rithm (30). We found that both are β-turn structures but the typeof turn and the residues involved in these secondary structuremotifs are different. In fact, while the b conformational ensem-ble is dominated by the type I β-turn, defined by the residuesD167 and S170, b′ is dominantly a type VIII β-turn, defined bythe residues V166 and Y/A169. We also used the d2D algorithm(31) that derives secondary structure populations from the calcu-lated chemical shifts (Figs. S3 and S4).

To represent the four-dimensional free-energy surface intwo dimensions, we examined the projections of FES(sh310 , s

hβ ,

sd310 , sdβ ) onto various 2D planes. Among all of the projections

tested, we found that the projection on the sd310sdβ plane (Fig.

4) provides the most informative representation of the four-dimensional FES.

The projection of the NMR conformers on the sd310 sdβ planes

indicates that our FES can not only capture the differencebetween the 310-helical turn and type I and type VIII β-turn con-formations, but also describe the heterogeneity of the 310-helicalturn ensemble (conformations h1 and h2) that is also observedin the experiments (17). We searched for b′-like conformationalstates among the prion proteins deposited in the PDB. In sev-eral cases, unbiased MD simulations starting from the depositedstructures evolved toward a b′-like conformation. Examples aremPrP(121–231)[Y169A/Y225A/Y226A] (PDB code 2L1K) andseveral human prion proteins (PDB codes 1QM1 and 1QM3)(10). Notably, we have noticed that in human prion proteins theV166M mutation destabilizes the HN 169 –O166 hydrogen bond,which is characteristic of the 310-helical turn conformation, pos-sibly due to different steric crowding with the valine and methio-nine sidechains, respectively.

Fig. 3. To provide a representation of the different manifolds of confor-mational states in the different minima, we draw superpositions of lowfree-energy structures extracted from the MetaD simulations. The β2–α2loop is shown in solid color whereas the rest of the polypeptide segment165–175 is transparent. (Left) The ensembles of conformers of mPrP(121–231); (Right) the ensembles of conformers of mPrP[Y169A](121–231). Wealso show explicitly the CO atoms of residue 169 for the ensembles h1 andh2. The sequence locations at the start and end of the polypeptide segment165–175 are indicated.

Coming back to the two variants studied here, we investigatedthe structural stability of the conformations corresponding to themain minima. We thus randomly selected conformers from thefour minima and ran 100-ns unbiased MD simulations. All of theminima appeared to be stable over this timescale. Furthermore,to test the stability of our results relative to the force field, werepeated this last calculation using the Charmm22∗ (32) forcefield. In contrast to the results presented here, we found that inthe WT protein, the b and b′ conformations can interconvert ona timescale of 100 ns (Figs. S5 and S6).

The populations of the different conformational states havebeen calculated from the four-dimensional FES. The WT pro-tein populates the 310-helical turn and the β-turn conformationalstates with a probability of 0.80 and 0.20, respectively, whereasthe mutant protein populates the 310-helical turn and theβ-turn conformational states with a probability of 0.65 and 0.35,respectively. We believe that, despite possible force-field inac-curacy, the FES provides a reasonable description of the sys-tems, especially considering the overall good correspondencebetween the NMR structures and the positions of the minima.We note some qualitative differences between mPrP(121–231)and mPrP[Y169A](121–231) in the conformational region h .In the WT protein the sidechain of Y169 contributes to the

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Fig. 4. FESs of mPrP(121–231) (Top) and mPrP[Y169A](121–231) (Bottom)as a function of sd310 and s

dβ . The black triangles are the NMR conform-

ers of mPrP(121–231) whereas the black circles are the NMR conformersof mPrP[Y169A](121–231). The yellow lines show the transition pathwaysbetween the h1 and h2 conformational states. The yellow circle shows theposition of the transition state between the h1 and h2 minima. The greenlines show the transition pathways between the h2 and the b conforma-tional states. The green circle represents the transition state between theh2 and b minima.

stabilization of the 310-helical turn, with the sidechain-to-sidechain D178 hydrogen bond and the stacking interaction withF175 (Fig. S7) (25, 33, 34). This extra stabilization is missingin mPrP[Y169A](121–231). Overall, the variant protein showsmuch greater flexibility.

The calculation of the solvent-accessible surface area (SASA)for the polypeptide segment 165–175 (Fig. 5) shows that in theβ-turn conformational state several hydrophobic residues aremore exposed to the solvent than in the 310-helical turn confor-mational state. Moreover, the partially polar residue Y169, whichis strictly conserved in mammals, has twice the exposure to thesolvent in the β-turn conformational state.

In prions, the exposure of hydrophobic residue is often asso-ciated to fibril formation (35, 36); moreover, the residue Y169seems to have a peculiar role in the formation of fibrils (37).This is also predicted by the Zyggregator algorithm (38), whichshows that the polypeptide segment 165–175 is more prone to

Fig. 5. SASA of the polypeptide segment 165–175 calculated using theg sas tool of GROMACS (39). The SASA of each residue is normalized tothe maximum allowed solvent accessibilities (40). The light-blue bands high-light the hydrophobic residues whereas the light-yellow band highlights thepartially polar Y169.

form fibrils in mPrP(121–231) than in mPrP[Y169A](121–231)(Fig. S8).

To gain insight into the transition mechanisms we tracedthe minimum free-energy pathways interconnecting the h1,h2, and b ensembles of conformers in the four-dimensionalFES. In Fig. 4, these pathways are projected onto the sd310sdβ plane. This analysis indicates that while for both proteins,the highest free-energy barrier corresponds to the transitionh2→ b, the heights are significantly affected by the Y169Areplacement. In particular, the barriers between h2→ b inmPrP[Y169A](121–231) are lower than in mPrP(121–231). Thereason for these differences can be traced to the larger sterichindrance of Y169 (25) movements when compared with A169.Moreover, we have seen that, when going from h2 to b, Y169rotates to point toward the solvent, while mPrP[Y169A](121–231) can also use pathways in which A169 points toward thehelix α3. Such pathways turn out to be energetically favored(Fig. S9).

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It is worth noting that in this representation the transitionstates do not correspond to the saddle points in the 2D FES,confirming that the conformational dynamics of the β2–α2 loopcannot be satisfactorily described with only these two CVs, letalone with a single one (25).

To turn our exploration of the FES into kinetic data, weassume that our investigation has indeed captured the natureof the transition state, so that Kramers’ theory (41) can be usedto quantify the kinetic effects of the Y169A amino acid replace-ment. The qualitative trends are quite clear. Following Kramers’kinetic transition rate (ki→j ) between two generic conformationsi and j, using Eq. 2,

ki→j ≈ κe−

∆G∗i→jkBT

2π, [2]

where ∆G∗i→j corresponds to the free-energy barrier betweenconformational states i and j, and κ is the prefactor in ensemble i.Under reasonable assumptions the prefactor can be calculated bymeans of the autocorrelation time of unbiased MD trajectories(κ = 1/τ corri ) (42, 43). We focused here on the h2→ b transitionthat corresponds to the highest free-energy barrier and thus is therate-limiting step in the structural interconversion of the β2–α2loop. The results in Table 1 show that the rates of conformationalexchange in mPrP[Y169A](121–231) can be up to three orders ofmagnitude faster than in mPrP(121–231).

DiscussionWe have taken advantage of two enhanced sampling schemesto obtain a comprehensive and detailed picture of the confor-mational dynamics of the β2–α2 loop in mPrP(121–231) andmPrP[Y169A](121–231). In particular, PT combined with theWTE approach provided an initial characterization of the struc-tural heterogeneity of the two proteins, suggesting the most rel-evant ensemble of conformers. This initial analysis allowed us todevise collective variables that can distinguish between the dif-ferent ensembles of conformers and allowed us to obtain insightinto the structural and kinetic properties of the β2–α2 loop andtheir dependence on the Y169A mutation.

A comparison with previous work (25) shows that overall weget the same picture, although ours is more nuanced and we havebeen able to clearly identify four different ensembles of conform-ers. When we project our data on the variable dr , we find forFES(dr ) results that are compatible with those of ref. 25, tak-ing into account the different force fields. However, Fig. 6 showsthat dr is not able to distinguish between the h1, h2 ensembles ofconformers and that b′ and b cannot be properly identified. Thisleads to a severe underestimation of the barriers. We would haveincurred the same underestimation if we had used the barriers inFES(dr ) rather than the four-dimensional ones.

Here, we compare the results of our computational investi-gation with previous NMR investigations of mPrPs. RegardingmPrP(121–231), we find, in agreement with NMR experiments,that the most populated conformational state of the β2–α2 loopis the 310-helical turn. Furthermore, we confirmed the existenceof a lowly populated type I β-turn conformation in mPrP(121–231), as was suggested by NMR chemical shift analysis ofmPrP[Y169A](121–231) (17, 22).

Table 1. Parameters used in Eq. 2 and resulting transition rates

Transitions κ, s−1 ∆G∗i→j, kJ·mol−1 ki→j, s−1

WT, h2→ b ≈109 38 35WT, b→ h2 ≈109 34 176Y169A, h2→ b ≈109 26 4,437Y169A, b→ h2 ≈109 18 111,848

Fig. 6. We plot our data, first in a one-dimensional representation, using dras the CV, similar to ref. 25 (Top). The CV dr is the distance between the Cαatoms of residues P165 and Q168. The one-dimensional FES does not resolvein a clear fashion all of the minima. Instead, if we supplement dr with SR =sdβ−s

d310√2

, which is the direction perpendicular to the diagonal (sd310 = sdβ ) of

the plane in Fig. 4, the different minima are better separated, both formPrP(121–231) (Middle) and for mPrP[Y169A](121–231) (Bottom). This CVSR is useful to distinguish between the h conformational states (SR . −0.7)and the b′ (−0.7 . SR . 0.7) and the b conformational states (SR & 0.7). Theblack vertical line at 0.82 nm represents the position of the transition stateidentified in ref. 25. It follows from this comparison that the estimation ofthe transition state on dr is less accurate.

It is very encouraging that the results from the present cal-culations and earlier experimental studies by NMR in solution(17, 22) are mutually supportive and in interesting ways comple-mentary. Thus, regarding mPrP(121–231), the calculations show,in agreement with implications from combined analysis of theNMR chemical shifts of mPrP(121–231) and mPrP[Y169A](121–231), that the β2–α2 loop forms a 310-helical turn and that thereis an admixture of type I β-turn conformation. Similarly, forthe variant protein with Y169A, the calculations find confor-mations that coincide closely with the experimental structures.With regard to the thermodynamic equilibria and the exchangekinetics between the multiple locally different conformations, thecombined results from the two approaches expand the overallview of the system.

The calculations showed that the exchange rate between the310-helix turn and the type I β-turn conformations is orders ofmagnitude faster in the variant protein than in mPrP(121–231),which explains why a single set of narrow NMR signals of theβ2–α2 loop was observed over the temperature range from 5 ◦Cto 40 ◦C (17, 22).

It also appears that the model calculations overestimated thepopulations of the minor species in both proteins, i.e., the con-formations b and b′ in mPrP(121–231) and the h-type conforma-tions in mPrP[Y169A](121–231). The aforementioned combinedanalysis of the chemical shifts of WT mPrP(121–231) and vari-ants generated by single-amino acid exchanges indicated that thepopulations of the minor species should, in each case, be well

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below 5%. This type of quantitative inaccuracy is to be expectedfrom the presently available force fields. The conclusion thatthe two proteins show characteristically different behavior isnonetheless clearly established, and the present model calcula-tions provide an explanation why NMR does not manifest con-formational transitions for mPrP[Y169A](121–231). Combiningthe information from the two investigations thus provides us witha rather complete picture of the structure, thermodynamics, andconformational exchange kinetics of the β2–α2 loop in prionproteins.

Materials and MethodsMD simulations were performed using Gromacs 5.0.4 (39) with PLUMED 2.0plug-in (44) and the Amber99SB∗-ILDN force field (26). We also used theCharmm22∗ force field (32) to test the stability of the conformers found inthe MetaD run.

The initial conformation taken from the best representative structure ofthe 20 NMR conformers deposited in the PDB (PDB ID codes 2L39 and 2L40)(17) was solvated in a truncated dodecahedron box with periodic boundaryconditions with ∼9,000 TIP3P (45) water molecules.

To accelerate the sampling, we first used the PT-WTE (28) scheme. Sec-ond, taking advantage of the conformational heterogeneity found in thePT-WTE runs, we performed multiple-walkers (MWs) MetaD simulations (46),where the starting state of the β2–α2 loop was taken to consist of equalpopulations of the 310-helical turn conformation and the type I β-turnconformation.

Additional details of the MD parameters and on system equilibration areprovided in Simulation Details.

ACKNOWLEDGMENTS. We thank Dr. GiovanniMaria Piccini for the NudgedElastic Band calculation on the four-dimensional free-energy surfaces. Calcu-lations were supported by a grant from the Swiss National SupercomputingCenter under Projects s387 and s550. A.B. acknowledges the support of theFrench Agence Nationale de la Recherche, under Grant ANR-14-ACHN-0016.

1. Prusiner SB (1998) Prions. Proc Natl Acad Sci USA 95:13363–13383.2. Collinge J (2001) Prion diseases of humans and animals: Their causes and molecular

basis. Annu Rev Neurosci 24:519–550.3. Weissmann C, Enari M, Klöhn PC, Rossi D, Flechsig E (2002) Transmission of prions.

J Infect Dis 186:S157–S165.4. Aguzzi A, Calella AM (2009) Prions: Protein aggregation and infectious diseases.

Physiol Rev 89:1105–1152.5. Pinnock EC, et al. (2016) LRP/LR antibody mediated rescuing of amyloid–induced cyto-

toxicity is dependent on PrPC in Alzheimer’s disease. J Alzheimers Dis 49:645–657.6. Küffer A, et al. (2016) The prion protein is an agonistic ligand of the G protein-

coupled receptor Adgrg6. Nature 536:464–468.7. Aguzzi A, Baumann F, Bremer J (2008) The prion’s elusive reason for being. Annu Rev

Neurosci 31:439–477.8. Riek R, et al. (1996) NMR structure of the mouse prion protein domain PrP(121–231).

Nature 382:180–182.9. James TL, et al. (1997) Solution structure of a 142-residue recombinant prion protein

corresponding to the infectious fragment of the scrapie isoform. Proc Natl Acad SciUSA 94:10086–10091.

10. Zahn R, et al. (2000) NMR solution structure of the human prion protein. Proc NatlAcad Sci USA 97:145–150.

11. Garcı́a FL, Zahn R, Riek R, Wüthrich K (2000) NMR structure of the bovine prion pro-tein. Proc Natl Acad Sci USA 97:8334–8339.

12. Lysek DA, et al. (2005) Prion protein NMR structures of cats, dogs, pigs, and sheep.Proc Natl Acad Sci USA 102:640–645.

13. Christen B, Pérez DR, Hornemann S, Wüthrich K (2008) NMR structure of the bankvole prion protein at 20 C contains a structured loop of residues 165–171. J Mol Biol383:306–312.

14. Christen B, Hornemann S, Damberger FF, Wüthrich K (2009) Prion protein NMR struc-ture from tammar wallaby (Macropus eugenii) shows that the 2–2 loop is modulatedby long-range sequence effects. J Mol Biol 389:833–845.

15. Pérez DR, Damberger FF, Wüthrich K (2010) Horse prion protein NMR structure andcomparisons with related variants of the mouse prion protein. J Mol Biol 400:121–128.

16. Wen Y, et al. (2010) Unique structural characteristics of the rabbit prion protein. J BiolChem 285:31682–31693.

17. Damberger FF, Christen B, Pérez DR, Hornemann S, Wüthrich K (2011) Cellular prionprotein conformation and function. Proc Natl Acad Sci USA 108:17308–17313.

18. Gossert AD, Bonjour S, Lysek DA, Fiorito F, Wüthrich K (2005) Prion protein NMRstructures of elk and of mouse/elk hybrids. Proc Natl Acad Sci USA 102:646–650.

19. Christen B, Hornemann S, Damberger FF, Wüthrich K (2012) Prion protein mPrP[F175A](121–231): Structure and stability in solution. J Mol Biol 423:496–502.

20. Sigurdson CJ, et al. (2009) De novo generation of a transmissible spongiformencephalopathy by mouse transgenesis. Proc Natl Acad Sci USA 106:304–309.

21. Kurt TD, et al. (2014) Prion transmission prevented by modifying the 2–2 loop struc-ture of host PrPC. J Neurosci 34:1022–1027.

22. Christen B, Damberger FF, Pérez DR, Hornemann S, Wüthrich K (2013) Structural plas-ticity of the cellular prion protein and implications in health and disease. Proc NatlAcad Sci USA 110:8549–8554.

23. Shaw DE, et al. (2008) Anton, a special-purpose machine for molecular dynamics sim-ulation. Commun ACM 51:91–97.

24. Barducci A, Bonomi M, Parrinello M (2011) Metadynamics. Wiley Interdiscip Rev Com-put Mol Sci 1:826–843.

25. Huang D, Caflisch A (2015) Evolutionary conserved Tyr169 stabilizes the 2–2 loop ofthe prion protein. J Am Chem Soc 137:2948–2957.

26. Lindorff-Larsen K, et al. (2010) Improved side-chain torsion potentials for the Amberff99SB protein force field. Proteins 78:1950–1958.

27. Earl DJ, Deem MW (2005) Parallel tempering: Theory, applications, and new perspec-tives. Phys Chem Chem Phys 7:3910–3916.

28. Bonomi M, Parrinello M (2010) Enhanced sampling in the well-tempered ensemble.Phys Rev Lett 104:190601.

29. Valsson O, Tiwary P, Parrinello M (2016) Enhancing important fluctuations: Rare eventsand metadynamics from a conceptual viewpoint. Annu Rev Phys Chem 67:159–184.

30. Heinig M, Frishman D (2004) STRIDE: A web server for secondary structure assignmentfrom known atomic coordinates of proteins. Nucleic Acids Res 32:W500–W502.

31. Camilloni C, De Simone A, Vranken WF, Vendruscolo M (2012) Determination of sec-ondary structure populations in disordered states of proteins using nuclear magneticresonance chemical shifts. Biochemistry 51:2224–2231.

32. Piana S, Lindorff-Larsen K, Shaw DE (2011) How robust are protein folding simulationswith respect to force field parameterization? Biophys J 100:L47–L49.

33. Gorfe AA, Caflisch A (2007) Ser170 controls the conformational multiplicity of theloop 166–175 in prion proteins: Implication for conversion and species barrier. FASEBJ 21:3279–3287.

34. Rossetti G, Giachin G, Legname G, Carloni P (2010) Structural facets of disease-linkedhuman prion protein mutants: A molecular dynamic study. Proteins 78:3270–3280.

35. Corsaro A, et al. (2011) High hydrophobic amino acid exposure is responsible ofthe neurotoxic effects induced by E200K or D202N disease-related mutations of thehuman prion protein. Int J Biochem Cell Biol 43:372–382.

36. Aguzzi A, Falsig J (2012) Prion propagation, toxicity and degradation. Nat Neurosci15:936–939.

37. Kurt TD, Jiang L, Bett C, Eisenberg D, Sigurdson CJ (2014) A proposed mechanism forthe promotion of prion conversion involving a strictly conserved tyrosine residue inthe 2–2 loop of PrPC. J Biol Chem 289:10660–10667.

38. Tartaglia GG, Vendruscolo M (2008) The Zyggregator method for predicting proteinaggregation propensities. Chem Soc Rev 37:1395–1401.

39. Abraham MJ, et al. (2015) GROMACS: High performance molecular simulationsthrough multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25.

40. Tien MZ, Meyer AG, Sydykova DK, Spielman SJ, Wilke CO (2013) Maximum allowedsolvent accessibilites of residues in proteins. PLoS One 8:e80635.

41. Kramers HA (1940) Brownian motion in a field of force and the diffusion model ofchemical reactions. Physica 7:284–304.

42. Kubelka J, Hofrichter J, Eaton WA (2004) The protein folding ‘speed limit’. Curr OpinStruct Biol 14:76–88.

43. Socci N, Onuchic JN, Wolynes PG (1996) Diffusive dynamics of the reaction coordinatefor protein folding funnels. J Chem Phys 104:5860–5868.

44. Tribello GA, Bonomi M, Branduardi D, Camilloni C, Bussi G (2014) PLUMED 2: Newfeathers for an old bird. Comput Phys Commun 185:604–613.

45. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparisonof simple potential functions for simulating liquid water. J Chem Phys 79:926–935.

46. Raiteri P, Laio A, Gervasio FL, Micheletti C, Parrinello M (2006) Efficient reconstructionof complex free energy landscapes by multiple walkers metadynamics. J Phys Chem B110:3533–3539.

47. Hess B (2008) P-LINCS: A parallel linear constraint solver for molecular simulation.J Chem Theor Comput 4:116–122.

48. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N·log(N) method forEwald sums in large systems. J Chem Phys 98:10089–10092.

49. Søndergaard CR, Olsson MH, Rostkowski M, Jensen JH (2011) Improved treatmentof ligands and coupling effects in empirical calculation and rationalization of pKavalues. J Chem Theor Comput 7:2284–2295.

50. Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescal-ing. J Chem Phys 126:014101.

51. Prakash MK, Barducci A, Parrinello M (2011) Replica temperatures for uniformexchange and efficient roundtrip times in explicit solvent parallel tempering simu-lations. J Chem Theor Comput 7:2025–2027.

52. Barducci A, Bussi G, Parrinello M (2008) Well-tempered metadynamics: A smoothlyconverging and tunable free-energy method. Phys Rev Lett 100:020603.

53. Palazzesi F, Barducci A, Tollinger M, Parrinello M (2013) The allosteric communicationpathways in KIX domain of CBP. Proc Natl Acad Sci USA 110:14237–14242.

54. Barducci A, Bonomi M, Prakash MK, Parrinello M (2013) Free-energy landscape of pro-tein oligomerization from atomistic simulations. Proc Natl Acad Sci USA 110:E4708–E4713.

55. Han B, Liu Y, Ginzinger SW, Wishart DS (2011) SHIFTX2: Significantly improved proteinchemical shift prediction. J Biomol NMR 50:43–57.

56. Shen Y, Bax A (2010) SPARTA+: A modest improvement in empirical NMR chemicalshift prediction by means of an artificial neural network. J Biomol NMR 48:13–22.

57. Bonomi M, Barducci A, Parrinello M (2009) Reconstructing the equilibrium Boltzmanndistribution from well-tempered metadynamics. J Comput Chem 30:1615–1621.

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