Pascal Brault
email: pascal.brault@univ-orleans.fr

Christophe Coutanceau

Paul C Jennings

Tejs Vegge

Johannes Berndt

Amaël Caillard

Steve Baranton

Styven Lankiang

Molecular dynamics simulations of ternary PtxPdyAuz fuel cell nanocatalyst growth

Keywords: Molecular dynamics simulations, ternary nanocatalyst, PEMFC, fuel cell electrode, core-shell, alloy

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Introduction

Improving design and/or reducing noble metal content in electrocatalysts for fuel cell electrodes while maintaining and/or increasing proton exchange membrane fuel cell (PEMFC) performance in terms of durability and power density are crucial challenges for PEMFC mass market applications [START_REF] Chen | Platinum-based nanostructured materials: synthesis, properties, and applications[END_REF][START_REF] Brian | Materials for fuel-cell technologies[END_REF][START_REF] Gasteiger | Activity benchmarks and requirements for Pt, Pt-alloy, and non-Pt oxygen reduction catalysts for PEMFCs[END_REF][START_REF] Nørskov | Towards the computational design of solid catalysts[END_REF]. A possible way consists in combining noble metals (Pt, Pd, Au ...) [START_REF] Chen | Platinum-Alloy Cathode Catalyst Degradation in Proton Exchange Membrane Fuel Cells: Nanometer-Scale Compositional and Morphological Changes[END_REF][START_REF] Greeley | Alloys of platinum and early transition metals as oxygen reduction electrocatalysts[END_REF][START_REF] Wang | Advanced Platinum Alloy Electrocatalysts for the Oxygen Reduction Reaction[END_REF][START_REF] Wu | Icosahedral Platinum Alloy Nanocrystals with Enhanced Electrocatalytic Activities[END_REF][START_REF] Peng | Designer platinum nanoparticles: Control of shape, composition in alloy, nanostructure and electrocatalytic property[END_REF][START_REF] Guo | Noble metal nanomaterials: Controllable synthesis and application in fuel cells and analytical sensors[END_REF][START_REF] Debe | Electrocatalyst approaches and challenges for automotive fuel cells[END_REF] and non-noble metals for preparing binary and ternary nanocatalysts. While non-noble metals are interesting from a cost reduction point of view, they may lead to reduced stability compared to pure platinum due to their dissolution capability [START_REF] Xiong | Ni and Cu) electrocatalysts synthesized by an aqueous route for proton exchange membrane fuel cells[END_REF][START_REF] Xiong | Effect of Atomic Ordering on the Catalytic Activity of Carbon Supported PtM (M = Fe, Co, Ni, and Cu) Alloys for Oxygen Reduction in PEMFCs[END_REF][START_REF] Mania | Dealloyed binary PtM3 (M = Cu, Co, Ni) and ternary PtNi3M (M = Cu, Co, Fe, Cr) electrocatalysts for the oxygen reduction reaction: Performance in polymer electrolyte membrane fuel cells[END_REF][START_REF] Srivastava | Efficient Oxygen Reduction Fuel Cell Electrocatalysis on Voltammetrically Dealloyed Pt-Cu-Co Nanoparticles[END_REF][START_REF] Watanabe | Activity and Stability of Ordered and Disordered Co-Pt Alloys for Phosphoric Acid Fuel Cells[END_REF][START_REF] Yu | Size and composition distribution dynamics of alloy nanoparticle electrocatalysts probed by anomalous small angle X-ray scattering (ASAXS)[END_REF]. Pt x Pd 1-x . binary catalysts have been proposed as possible candidates for both PEMFC electrodes, the anode for the hydrogen oxidation reaction and the cathode for improved oxygen reduction reaction (ORR) compared with pure platinum [START_REF] Wu | High performance Core-shell PdPt@Pt/C catalysts via decorating PdPt alloy cores with Pt[END_REF][START_REF] Garcia | CO tolerance of PdPt/C and PdPtRu/C anodes for PEMFC[END_REF][START_REF] Cho | Pd-based PdPt (19: 1)/C electrocatalyst as an electrode in PEM fuel cell[END_REF][START_REF] Alcaide | Performance of carbonsupported PtPd as catalyst for hydrogen oxidation in the anodes of proton exchange membrane fuel Cell[END_REF][START_REF] Mougenot | High Performance Plasma Sputtered PdPt Fuel Cell Electrodes with Ultra Low Loading[END_REF]. At the same time, Au addition was proposed to improve durability of the nanocatalysts [START_REF] Hernández-Fernández | Influence of the Preparation Route of Bimetallic Pt-Au Nanoparticle Electrocatalysts for the Oxygen Reduction Reaction[END_REF][START_REF] Yancey | Electrochemical Synthesis and Electrocatalytic Properties of Au@Pt Dendrimer-Encapsulated Nanoparticles[END_REF][START_REF] Zhang | Stabilization of platinum oxygen-reduction electrocatalysts using gold clusters[END_REF][START_REF] Sasaki | Core-Protected Platinum Monolayer Shell High-Stability Electrocatalysts for Fuel-Cell Cathodes[END_REF][START_REF] Tripkovic | First principles investigation of the activity of thin film Pt, Pd and Au surface alloys for oxygen reduction[END_REF]. On the other hand, the atomic structure and morphology [START_REF] Alloyeau | Nanoalloys[END_REF] also play very important roles in the electrocatalytic efficiency for ORR.

Beside experimental investigations, molecular simulations in conditions matching experiments are also of particular interest [START_REF] Nørskov | Towards the computational design of solid catalysts[END_REF][START_REF] Strasser | Nørskov High Throughput Experimental and Theoretical Predictive Screening of Materials -A Comparative Study of Search Strategies for New Fuel Cell Anode Catalysts[END_REF]. While density functional theory (DFT) simulations are able to predict both structure and composition of nanocatalysts leading to higher ORR efficiency, molecular dynamics (MD) simulations are able to predict their growth mode and the final possible structure when simulations are carried out with initial conditions consistent with experimental ones [START_REF] Xie | Efficient amorphous platinum catalyst cluster growth on porous carbon: A combined Molecular Dynamics and experimental study[END_REF][START_REF] Brault | Molecular dynamics simulations of supported metal nanocatalyst formation by plasma sputtering[END_REF]. This is of interest for comparing structure predictions and consistency with ORR efficiency and cyclic voltammetry results. The present work is devoted to MD simulations applied to the synthesis of PtPdAu ternary catalysts by both a wet chemical method at room temperature and a plasma sputtering co-deposition, based on the availability of recent detailed experimental [START_REF] Lankiang | Oxygen reduction reaction at binary and ternary nanocatalysts based on Pt, Pd, Au[END_REF] and DFT results [START_REF] Tripkovic | First principles investigation of the activity of thin film Pt, Pd and Au surface alloys for oxygen reduction[END_REF].

Initial conditions for MD simulations.

Molecular dynamics simulation is a simple way to calculate at every times the trajectory of an ensemble of atoms subjected to forces, by solving the Newton equations of motion [START_REF] Haile | Molecular Dynamics Simulations[END_REF][START_REF] Allen | Computer Simulations of Liquids[END_REF][START_REF] Frenkel | Understanding Molecular Simulations[END_REF][START_REF] Rapaport | The Art of Molecular Dynamics Simulation[END_REF][START_REF] Plimpton | Fast Parallel Algorithms for Short-Range Molecular Dynamics[END_REF].

Growth of ternary catalysts is a typical situation, where MD simulations can be called for [START_REF] Lankiang | Oxygen reduction reaction at binary and ternary nanocatalysts based on Pt, Pd, Au[END_REF]. The required input parameters are only a relevant choice of initial conditions, especially the velocities that should be selected from velocity distributions as close as possible to those of experimental context studied.

In the present study, Pt, Pd and Au atoms are deposited on a model porous carbon issued from microscopy analysis [START_REF] Pikunic | Structural Modeling of Porous Carbons: Constrained Reverse Monte Carlo Method[END_REF], and thus expected to closely match carbon morphology of a PEMFC active layer. The dimensions of the model porous carbon substrate are 6 × 6 × 6 nm 3 , containing 17,040 atoms, corresponding to a density of 1.57 g cm -3 and to a porosity of 48%.

A picture of this substrate is shown in Figure 1. [START_REF] Xie | Efficient amorphous platinum catalyst cluster growth on porous carbon: A combined Molecular Dynamics and experimental study[END_REF][START_REF] Brault | Molecular dynamics simulations of supported metal nanocatalyst formation by plasma sputtering[END_REF][START_REF] Zhou | Misfit-energy-increasing dislocations in vapor-deposited CoFe/NiFe multilayers[END_REF][START_REF] Foiles | Embedded-atom-method functions for the fcc metals Cu, Ag, Au, Ni, Pd, Pt, and their alloys[END_REF].

Such many-body potentials have been validated in conditions of sputtering deposition [START_REF] Zhou | Misfit-energy-increasing dislocations in vapor-deposited CoFe/NiFe multilayers[END_REF][START_REF] Johnson | Alloy models with the embedded-atom method[END_REF][START_REF] Zhou | Atomic scale structure of sputtered metal multilayers[END_REF]. The metal-carbon interactions are estimated using a 12-6 Lennard-Jones (LJ) potential [START_REF] Graves | Molecular dynamics for low temperature plasma-surface interaction studies[END_REF]. The simulations are carried out using LAMMPS software [START_REF] Plimpton | Fast Parallel Algorithms for Short-Range Molecular Dynamics[END_REF]45]. Previous experimental results [START_REF] Lankiang | Oxygen reduction reaction at binary and ternary nanocatalysts based on Pt, Pd, Au[END_REF] and DFT calculations [START_REF] Tripkovic | First principles investigation of the activity of thin film Pt, Pd and Au surface alloys for oxygen reduction[END_REF] have proven that Pt 50 Pd 25 Au 25

Results and discussion

and Pt 70 Pd 15 Au 15 lead to improved catalytic activity towards ORR and that gold could be responsible for improved catalyst stability. The choice for gold content below 25% was dictated by the necessity to obtain a good compromise between catalytic activity and stability, due to gold segregation to nanocatalyst surface [START_REF] Tripkovic | First principles investigation of the activity of thin film Pt, Pd and Au surface alloys for oxygen reduction[END_REF][START_REF] Lankiang | Oxygen reduction reaction at binary and ternary nanocatalysts based on Pt, Pd, Au[END_REF]. Similar trends in chemical ordering were revealed when more comprehensive searches were performed, utilizing genetic algorithms to elucidate stable structures of PtAu and PtPdAu nanoparticles, though these searches were performed at 0K in vacuum [START_REF] Logsdail | Structures and Stabilities of Platinum-Gold Nanoclusters[END_REF][START_REF] Wu | Theoretical studies of structures and energies of Pd, Au-Pd, and Au-Pd-Pt clusters[END_REF].

Metal deposition is the first dynamical step leading to the nanocatalyst structure and composition. Increasing the atom number results in the coalescence of small aggregates (Figure 2(c)). With a higher number of atoms deposited, the Au atoms always stay on the surface, meaning that in the course of cluster growth, segregation of Au always occurs. DFT-level calculations (see calculational details in Ref [START_REF] Lysgaard | A DFT-based genetic algorithm search for AuCu nanoalloy electrocatalysts for CO2 reduction[END_REF][START_REF] Smidstrup | Improved initial guess for minimum energy path calculations[END_REF]) performed on 1-2 nm icosahedral Pt nanoclusters show low activation energies for Au-surface segregation, which would be negligible under experimental deposition conditions. Inthese calculations, the Au atom was initially placed in the subsurface layer of the particle and the nudged elastic band pathway calculated for Au moving to the surface. This revealed negligible energy barriers for the particle reconstruction allowing for exposure of Au to the surface. Compared to this low activation barrier, there was significantly greater stabilization of the particle when Au occupied surface compared to subsurface positions. Nevertheless, some Au atoms are present inside big clusters (Figures 2(d,e,f)). This occurs for Au atoms far enough from the outer surface. This means that segregation is only possible when a small number of atom exchanges are involved. When the Au atom is too deeply inside the cluster core, it can't move towards the surface. Figures 2(c-f In addition, Table 2 gives nearest-neighbor distances (up to the 7 th ) issued from ternary nanocatalysts RDF compared to theoretical face centered cubic crystal nearest-neighbor distances from single Pt, Pd and Au. of the former catalyst compared to that of the latter one. It is also worth to note that the surface enrichment by Au of the ternary catalysts after ageing tests was observed [START_REF] Lankiang | Oxygen reduction reaction at binary and ternary nanocatalysts based on Pt, Pd, Au[END_REF],

confirming experimentally the propensity of gold atom to segregate towards the surface.

Simulation of plasma sputtering deposition and growth of Pd 25 Au 25 @Pt 50 core@shell ternary nanocatalysts

As it is necessary to enrich the nanocatalyst surface by Pt in order to increase the ORR activity, a possible way is to use alternate plasma sputtering deposition, firstly of the core element(s) and secondly the shell element(s) [START_REF] Brault | Molecular dynamics simulations of supported metal nanocatalyst formation by plasma sputtering[END_REF]. For comparison to the Pt 50 Pd 25 Au 25 ternary nanocatalyst grown in conditions matching chemical synthesis (but also plasma sputtering with high pressure-target to substrate distance product [START_REF] Brault | Molecular dynamics simulations of supported metal nanocatalyst formation by plasma sputtering[END_REF]), we are considering the growth of the Pd 25 Au 25 @Pt 50 nanocatalyst. (p) 8000, (q) 9000 (r) 9500 (s) 9600 (t) 10000.

In the AuPd core deposition step, the nanocatalyst is growing to form clusters. It should be noticed that initial clusters grow on the carbon pore entrance (Figure 5(k)), as a result of the Au and Pd diffusion. This diffusion is due to the mean high kinetic energies involved in the present sputtering simulations: around 3 eV for Pd and Au (see Section 2). When first 5000 atoms, randomly selected as being either Pd or Au, have been released to the carbon substrate, the deposition of 5000 Pt atoms starts and the Pd-Au deposition is stopped. Figure 5(e) shows a well-defined core shell PdAu@Pt nanocatalyst with a very low surface Au content (Figure 5(o)). These snapshots are taken close before the starting of a cluster coalescence mechanism.

When the clusters have coalesced, the nanocatalyst morphology changes to that of a nanowire, and for further Pt deposition, the nanowire is continuously enriched by segregating Au atoms (Figure 5(p-r)). When continuing Pt deposition, the Au coated nanowire suddenly transforms into nanofilm morphology (Figure 5(h-i and r-s)), for which the outermost surface is coated by an almost complete Au monolayer. It should be noted that the film interface with carbon is also coated by an Au monolayer. This shows the strong propensity of Au to diffuse outside the nanocatalyst. A small quantity remains in the Pd phase, as the difference in surface energies is lower than for Pt. While the final nanofilm is almost completely covered by an Au atom monolayer, the efficiency of the catalyst for ORR will be affected. Though by controlling the sputtering process, suitable morphologies and surface compositions can be tuned to the desired one, which is relevant for the targeted catalysis efficiency.

For the nanowire phase, where Pt is still released to the surface, the structure evolved from crystalline to disordered, just before the transformation into nanofilm morphology. This can be seen on the side view of Figure 5(g, h); the corresponding RDF plot is displayed in Figure 6, where the structure change from crystalline one to disordered one is depicted by the broadening of 1 st , 3 rd and 5 th NN peaks and wiping out of 2 nd , 4 th and 6 th NN peaks. The nanofilm formed just after the transition is also disordered with the same effects on the RDF plots, i.e. broadening and wiping out of peaks. Further deposition of Pt atoms leads to complete crystallization of the nanofilm (Figure 5(i-j). This is supported by the total RDF plots (Figure 7) just after the nanowire to nanofilm transformation and at the end of the simulated deposition. One can further notice that the RDF of the non-crystallized film is similar to the RDF of a liquid. Indeed when checking nanocatalyst temperature increase above the thermostated substrate at 300K (Figure 8), temperature peaks correspond to phase transformation from cluster to nanowire, nanowire crystallization, nanowire to nanofilm transformation and nanofilm crystallization, followed by temperature relaxation into the substrate. This means that the energy delivered by Pt impact on the existing nanocatalyst is confined into the nanostructure and when it increases due to Pt deposition, it leads to various transformations.

The nanofilm morphology allows completely dissipating the energy into the substrate due to the highest nanocatalyst contact area with the carbon substrate. One should recall that the only ----just after nanofilm formation ____ final nanofilm way to dissipate energy of the cluster is through interaction with the thermostated substrate.

The maximum temperature rise is 350 K leading to a catalyst film temperature of 650 K.

Supported Pt nanoclusters are known to fuse below 650 K [START_REF] Sellin | Effects of Temperature and Atmosphere on Carbon-Supported Platinum Fuel Cell Catalysts[END_REF]. Transition to nanofilm in a fused state is expected to have higher wettability and thus the nanofilm can relax to a crystalline state when cooling down. An alternative to chemical synthesis is the plasma sputtering method that allows versatile deposition of nanocatalyst. MD simulations matching conditions of such a technique show possible tuning of the composition, structure and morphology of the cluster. High surface active site content is possible when considering core@shell structure growth. MD simulations show that morphology transformation, occurring during such a growth process, is driven by the kinetic energy transferred by Pt atoms on the already deposited PdAu core. This transferred kinetic energy is thus confined in the nanostructure for a while, allowing successive transformation from cluster to nanowire and nanofilm due to nanocalyst temperature increase. These transformations allow final thermal relaxation of the nanocatalyst.

Figure 1 .

 1 Figure 1. Snapshot of the model porous carbon substrate.

3. 1

 1 MD simulations for a chemical synthesis at room temperature of Pt 50 Pd 25 Au 25 and Pt 70 Pd 15 Au 15 .

Figures 2 and 3

 3 display growth steps of the Pt 50 Pd 25 Au 25 and Pt 70 Pd 15 Au 15 nanocatalysts, respectively, in conditions mimicking a room temperature synthesis.

Figure 2 .

 2 Figure 2. Snapshots of the Pt 50 Pd 25 Au 25 nanocatalyst growth on porous carbon. Total number of injected atoms is: side view (a) 1000 (b) 2000 (c) 3000 (d) 5000 (e) 7000 (f) 10000, top view (g) 1000 (h) 2000 (i) 3000 (j) 5000 (k) 7000 (l) 10000. • C, • Pt, • Pd, • Au.

Figure 3 .

 3 Figure 3. Snapshots of the Pt 70 Pd 15 Au 15 nanocatalyst growth on porous carbon. Total number of injected atoms is: side view (a) 1000 (b) 2000 (c) 3000 (d) 5000 (e) 7000 (f) 10000, top view (g) 1000 (h) 2000 (i) 3000 (j) 5000 (k) 7000 (l) 10000. • C, • Pt, • Pd, • Au.

  ) also show that, even if a number of the Pd atoms segregate to the outer surface with Au atoms, when increasing the cluster size Pd and Pt atoms are mixed. Top views in Figures2(gl) indicate a low Pt surface ratio. Actually, at the final stage of metal deposition (Figures2(f,l)), the cluster skin has a composition close to Pt 10 Pd 45 Au 45 while the core has a composition of ca. Pt 60 Pd 20 Au 20 .

Figure 3

 3 Figure 3 displays snapshots illustrating the growth of Pt 70 Pd 15 Au 15 prepared by a room

Figure 4 .

 4 Figure 4. Normalized total RDF g(r) of final configuration corresponding to Figures 2(l) and 3(l).

Figure 5

 5 Figure 5 displays the evolution of the Pd 25 Au 25 @Pt 50 nanocatalyst growth on the previous

Figure 5 .

 5 Figure 5. Snapshots of the deposited core@shell ternary nanocatalyst Pd 25 Au 25 @Pt 50 . Total number of injected atoms is: side view (a) 1000 (b) 2000 (c) 3000 (d) 5000 (e) 7500 (f) 8000, (g) 9000 (h) 9500 (i) 9600 (j) 10000; top view (k) 1000 (l) 2000 (m) 3000 (n) 5000 (o) 7500 (p) 8000, (q) 9000 (r) 9500 (s) 9600 (t) 10000.

Figure 6 .

 6 Figure 6. Total RDF of the ordered and disordered Pd 25 Au 25 @Pt 50 nanowire corresponding to Figure 5 (g and h)

Figure 7 .

 7 Figure 7. Total RDF plot of the initial and final Pd 25 Au 25 @Pt 50 nanofilm corresponding to Figure 5(i and j).

Figure 7

 7 Figure 7 gives a clear account of the crystallization of the nanofilm for the further Pt

Figure 8 .

 8 Figure 8. Nanocatalyst temperature increase during the deposition. The various phase changes are indicated.

  Dynamics simulations are carried out for describing the growth of carbon supported Pt 50 Pd 25 Au 25 and Pt 70 Pd 15 Au 15 in conditions matching chemical synthesis or soft plasma sputtering deposition at high pressure. Comparison with DFT prediction and electrochemical analysis highlights the role of spontaneous upward diffusion of gold atoms. Moreover, measured ORR decreasing activity on chemically synthetized Pt x Pd y Au z above 25% atomic Au content is consistent with the surface Au content increase, and in turn with the lowering of the surface PdPt active site number predicted by MD simulations.

Table 1 .

 1 Table1gives the surface energies of the low index facets for Pt, Pd, Au compared to the experimental ones[START_REF] Foiles | Embedded-atom-method functions for the fcc metals Cu, Ag, Au, Ni, Pd, Pt, and their alloys[END_REF]. Although the EAM and average experimental surface energies of the low index faces of Pt, Pd, Au in Jm -2[START_REF] Zhou | Misfit-energy-increasing dislocations in vapor-deposited CoFe/NiFe multilayers[END_REF].

	calculated values from EAM differ from the average experimental values by ca. 40%, the
	order of the experimental values E s	Pt > E s	Pd > E s	Au is conserved, albeit a moderately enhanced
	propensity for structures with higher surface may be expected.
		Pt		Pd	Au
	(111)	1.44		1.22	0.79
	(100)	1.65		1.37	0.92
	(110)	1.75		1.49	0.98
	Experimental,
		2.49		2.00	1.5
	face averaged

Table 2 .

 2 NN

		distances (in Å) determined from RDF of figures 2(l) and 3(l), from theoretical
	fcc crystal Pt, Pd and Au. a 0 is the lattice parameter.
		1 st NN	2 nd NN	3 rd NN	4 th NN	5 th NN	6 th NN	7 th
	NN distance	2 1 a 0	a 0	2 3 a 0	2 a 0	2 5 a 0	3 a 0	2 7 a 0
	Pt	2.77	3.92	4.80	5.54	6.2	6.79	7.33
	Pd	2.75	3.89	4.76	5.50	6.15	6.74	7.28
	Au	2.88	4.08	5.00	5.77	6.45	7.06	7.63
	Simulated	2.72	3.92	4.82	5.57	6.27	-	7.37
	The RDF of both ternary catalysts are very similar. The peak positions are close to Pt and Pd

values, while Au values are slightly higher. One should notice that Au does not contribute significantly to the RDF except to the 1 st NN because Au atoms are mainly located at the

____ Pt 50 Pd 25 Au 25 ----Pt 70 Pd 15 Au 15 surface

  , forming a monolayer. Moreover, the existence of the 2 nd NN is due to parallel atomic planes. Indeed, 2 nd NN always only lies in planes above and below the reference atom plane.There is no 2 nd NN atom in a single fcc atomic plane. So, well defined NN peaks and the presence of 2 nd NN demonstrate the crystalline state of the model ternary nanocatalysts from MD simulations. The very slight differences between the two RDF plots indicate that Pt 50 Pd 25 Au 25 is slightly less ordered than Pt 75 Pd 15 Au 15 . This is mainly due to the lower Pt content, which, as the predominant element, is driving the crystallinity of the sample. The simulation results presented above are in very good agreement with those obtained experimentally for the ORR activity of Pt 50 Pd 25 Au 25 and Pt 70 Pd 15 Au 15 catalysts prepared by a room temperature chemical method (water in oil microemulsion) [32]: the low Pt surface composition in Pt 50 Pd 25 Au 25 compared with Pt 70 Pd 15 Au 15 is responsible of the lower activity

Acknowledgments

The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) for the Fuel Cells and Hydrogen Joint Technology Initiative under grant agreement #325327 (SMARTCat project).


Vilius Kurauskas

Elodie Crublet

Pavel Macek

Rime Kerfah

Diego F Gauto

Jérôme Boisbouvier

Paul Schanda

Sensitive proton-detected solid-state NMR spectroscopy of large proteins with selective CH 3 labelling: application to the 50S ribosome subunit

Solid-state NMR spectroscopy allows the characterization of structure, interactions and dynamics of insoluble and/or very large proteins. Sensitivity and resolution are often major challenges for obtaining atomic-resolution information, in particular for very large protein complexes. Here we show that the use of deuterated, specifically CH 3 -labelled proteins result in significant sensitivity gains compared to previously employed CHD 2 labelling, while line widths only marginally increase. We apply this labelling strategy to a 468 kDa-large dodecameric aminopeptidase, TET2, and the 1.6 MDa-large 50S ribosome subunit of Thermus thermophilus.

 The primary argument for using the CHD2 isotopomer is related to the fact that there are no intra-methyl 1 H-1 H dipolar couplings in CHD2, and that these interactions are expected to lead to strong line broadening; this assumption was supported by pioneering experiments on stochastically labelled proteins at low (20 kHz) MAS frequency, 10 but not systematically investigated. Here we show that, in experiments performed at high MAS frequency (50-60 kHz) the line widths in CHD 2 and CH3 methyl are comparable, and that CH3 labelling results in improved sensitivity compared to CHD 2. The enhanced sensitivity opens possibilities to study even very large objects, as demonstrated with the 1.6 MDa-large 50S ribosome, where sensitivity is a great challenge.

Fig. 1. Proton-detected 2D methyl 1 H- 13 C spectra of CH3 (blue) and CHD2 (red) methyls of (a) isoleucines and (b) alanines in TET2, obtained at 14.1 T (600 MHz 1 H Larmor frequency). These spectra were obtained with the CP-based pulse sequence shown in ESI Figure S1a. Traces along the positions indicated with dashed lines and numbers are shown in (c)-(e). Dashed 1D traces were obtained at the indicated positions in the spectra, but different pulse sequences, either omitting 2 H decoupling, or using MQ evolution (see Figure S1). Line-widths (full-width at half maximum) are indicated for these selected peaks. (f) shows the ratio of line widths observed for CH3 and CHD2 correlations, merged for both Ala and Ile samples. (g) Intensity ratios for cross-peaks observed in the Ile spectrum, taking into account the protein amounts in CH3/CHD2 samples. For all these spectra, resolution enhancement was applied using a 70-degrees shifted sine-bell square function (90 degrees would correspond to a cosine, i.e. no resolution enhancement), see ESI.

In order to investigate the properties of the different methyl isotopomers in ssNMR, we compared four samples of the enzyme TET2 from Pyrococcus horikoshii, a dodecameric (12x39 kDa) aminopeptidase involved in the cellular protein quality control, with different methyl labelling schemes. (Details about sample preparation can be found in the ESI.) With 353 residues per monomer and a total oligomer mass of 468 kDa, TET2 is among the largest proteins studied so far by ssNMR, making it thus a considerable challenge in terms of resolution and sensitivity. In the samples we incorporated either CH3 or CHD2 methyl groups on isoleucine residues or on alanines, respectively, in an otherwise deuterated background. The different lengths and flexibility of Ala and Ile side chains generally lead to different dynamics, reflecting the range of methyl cases encountered in proteins. Figure 1a,b shows comparisons of 1 H- 13 C correlation spectra of these four samples, obtained with cross-polarization transfer steps at a MAS frequency of 57 kHz. High-resolution spectra are obtained for this very large protein assembly for both isotopomers and both residue types. The spectra are similar to solution-state spectra of TET, showing that structure and dynamics in our solid-state preparations are conserved in the solid state, and allowing us to transfer previously known assignments from solution-state NMR 11 (see Supporting Information Figure S2). In CHD2 groups, the observed line widths are of the order of 30 -45 Hz ( 1 H) and 20-30 Hz ( 13 C), respectively (Figure 1c-e). These values are in good agreement with previously reported line widths obtained in fast-MAS solid-state NMR experiments on smaller model proteins with CHD 2 labelling 9,12,13 . For the CH3 isotopomer, the line widths along the 13 C dimension are similar to the ones observed with CHD 2 groups (20-30 Hz), suggesting that the intra-methyl heteronuclear 1 H-13 C dipolar coupling is not the dominant factor for line width under the chosen conditions (heteronuclear decoupling, fast MAS). This is expected, given that fast-MAS, the high deuteration level of the surroundings, and the 3-fold reduced dipolar 1 H- 13 C coupling that results from the rapid methyl rotation, make decoupling very efficient. Remarkably, the 1 H line widths in the CH3 sample are only slightly larger than the ones in CHD 2 samples (Figure 1e,f). The fact that reducing the number of methyl proton spins from three (CH 3) to one (CHD2) has only a modest effect on line widths suggests that the intra-methyl dipolar couplings are not determining the 1 H line widths. Sample inhomogeneity, dipolar inter-actions to remote protons as well as dynamics may be important contributions to line widths, such that intra-methyl 1 H-1 H dipolar interactions do not dominate. Of note, given that the line widths observed here are similar to microcrystalline samples, 9,12 our conclusions hold also for those systems. In more heterogeneous systems (fibrils, membrane proteins) the differences in line widths between the two isotopomers are expected to be similar or smaller than here, because line widths due to sample heterogeneity is independent of the labelling scheme. Importantly, in terms of sensitivity the CH 3 labelling scheme outcompetes CHD2 labelling, on average by a factor of about 1.9 (Figure 1g). This is due to the proton spin multiplicity (expected gain of 3), reduced by the somewhat faster relaxation of CH 3 methyls (see below). The experiments shown in Figure 1a,b were recorded with 2 H decoupling during chemical-shift evolution periods, which requires a probehead able to tune to 2 H frequency. Such equipment is not standard in contemporary MAS probes. As expected, while omission of 2 H decoupling has virtually no effect on line widths and sensitivity in CH 3-labelled methyls (Figure 1c), due to the absence of 1 JCD couplings, in CHD2 groups the undecoupled 13 C line widths are dramatically increased due to the scalar coupling (~20 Hz) multiplet structure. 12 Therefore, if 2 H decoupling is impossible with the available hardware, the CH3 isotopomer is clearly favoured in terms of resolution and sensitivity. The effects of 2 H decoupling on 1 H line widths are negligible, as the 2 JHD coupling is small (~1-3 Hz). 12 We have also investigated two different types of 13 C evolution, single-quantum (SQ), and multiple-quantum (MQ) evolution. In solution-state NMR, in the case of slow overall molecular tumbling, MQ coherence life times are significantly longer than SQ life times 14 . In contrast, Figure 1d establishes that MQ lines in our ssNMR experiments are almost two-fold larger than their SQ counterparts. This can be understood by considering that due to the absence of overall tumbling, cross-correlated relaxation is small (reducing the potential benefit of MQ evolution), and dephasing due to remote protons outweighs the potential gains from crosscorrelated relaxation. We also investigated the relative sensitivity obtained with INEPT-transfer and CP-transfer. INEPTbased transfer is favoured by large-scale dynamics and extensive deuteration, because both factors increase coherence life times and thus INEPT transfer efficiency. We find indeed that for the more dynamic Ile residues INEPT outcompetes CPtransfer, while for Ala we find similar sensitivity (Figure S4). Line widths are related to the life times of spin coherences, which are determined by spin relaxation (i.e., dynamics) and dephasing that is due to incompletely averaged dipolar interactions; the latter contribution depends on the MAS frequency. To further gain insight into the properties of the different labellings, we performed systematic analyses of apparent relaxation rate constants. These measurements, shown in Figure 2, corroborate the trends shown in Figure 1: (i) 1 H R2' relaxation rates are similar in CH3 and CHD2 samples, with values of ~100 s -1 and ~80 s -1 , respectively, at high MAS frequency (55-57 kHz). The expected line widths from these numbers (R 2'/π , ~25-30 Hz) are smaller than the experimentally observed values (~35-45 Hz), showing that part of the line width originates from sample and/or magneticfield heterogeneity. (ii) 13 C R2' of ~20-40 s -1 are found at high MAS frequencies (~55 kHz). The line widths predicted from these numbers (6-12 Hz) are significantly smaller than the experimentally observed line widths in this study and crystalline ubiquitin before 12 (~18-25 Hz), showing that heterogeneity limits the resolution of 13 C lines. (iii) Omission of 2 H decoupling leads to significantly shorter 13 C T2', in CHD2 (but not CH3), in agreement with the larger line widths (Figure 1c) (iv) MQ coherences decay faster than their SQ counterpart, again in line with data shown in Figure 1d. (v) Furthermore, proton R 1 rate constants are ~15% higher in CH 3 compared to CHD2. This allows higher repetition rate of experiments, and thus slightly increased sensitivity for CH3 samples (see Figure S4). (vi) We also measured 13 C R1 and 13 C R1ρ rate constants, and find that they are not MAS-frequency dependent. This finding establishes that coherent dephasing (i.e. contribution to the decay that is not due to dynamics, and which would be MAS dependent) is negligible, allowing quantitative interpretation of methyl dynamics. While 13 C R1 has been used for this purpose before, 13 the possibility to quantitatively interpret R 1ρ enables the study of slow (ns-ms) motion. 15 Fig. 2. MAS-frequency dependence of various spin relaxation parameters in Ile-labelled (circles) and Ala-labelled (triangles) TET2, with CH3 (blue) and CHD2 (red) isotopomers. Bulk methyl relaxation rate constants were obtained from a series of 1D spectra, using pulse sequences shown in ESI Figure S1.

Having thus established CH3 labelling provides enhanced sensitivity compared to CHD 2, with similar resolution, we turned to a very challenging application, the 50S ribosome particle from Thermus thermophilus. Ribosomes are among the most complex biological machineries, composed of RNAs and proteins, with a total molecular mass of 1.6 MDa (for the 50S subunit). Unlike TET2, where the 12-fold symmetry improves sensitivity and reduces spectral complexity, ribosomes are not symmetric. Sensitivity and resolution are thus a great challenge. Earlier studies by solution-state NMR have observed only highly mobile isotope-labelled proteins attached to ribosomes 16 , or flexible nascent polypeptides exiting the ribo-some, 17,18 while less flexible proteins are invisible due to unfavourable relaxation properties. Recently, ssNMR spectra have been reported for tightly ribosome-bound proteins, which were isotope labelled and bound to unlabelled 50S and 30S subunits, respectively. 19,20 This selective labelling of a single protein overcomes the severe resonance overlap problem but requires reconstitution of ribosomes from labelled/unlabelled subunits, which is not generally possible. We investigate here the possibility to study uniformly labelled ribosome (50S subunit, 1.6 MDa), obtained from bacterial cells grown in uniformly deuter-ated, Ala-CH3-labeled media, and sedimentation into a 1.3 mm ssNMR rotor 21 . The HSQC-type 1 H- 13 C correlation spectrum obtained in a total experimental time of 14 hours is shown in Figure 3 (black). As expected, the large number of non-equivalent Ala methyl sites -337 in total -results in significant resonance overlap. Nonetheless, for the resolved cross-peaks the line widths are similar to TET2 (~25-30 Hz for 13 C and 40-50 Hz for 1 H). Signal-to-noise ratios of these cross-peaks are of the order of 3 to 8. Given the small amount of sample (1.2 nmoles) these results are encouraging, and demonstrate that methyl labelling is a route to gain residue-specific information of very large complexes. We also performed an additional experiment, using a shorter 13 C evolution time and larger number of scans to favour sensitivity and confirm that the detected cross peaks are indeed reproducible (red spectrum in Figure 3). Resonance assignment and the spectral complexity are of course challenging. Solutions may involve unlabelling of protein components that can be reconstituted, or chemically methyl-labelled amino acid with low abundance, such as methylated cysteines 22 or lysines. The data shown here -to our knowledge the first reported ssNMR spectra of uniformly methyl-labelled ribosomes -establish that in terms of sensitivity and line widths, studies of ribosomes are feasible. In conclusion, we have shown here that CH3-methyl-labelling allows for high-resolution proton-detected ssNMR at high MAS frequencies. In terms of sensitivity, CH3 labelling significantly outcompetes CHD2 labelling. We believe that CH3 methyl labelling will be very useful for further ssNMR studies of structure, dynamics and interactions, in particular for large systems, as exemplified here.

Fig. 3 .

 3 Fig.3. Alanine methyl ssNMR spectra of Thermus thermophilus ribosome, obtained with 1.5 mg of deuterated, Ala-13 CH3 labelled material. (a) 2D spectra obtained with INEPT-transfer (black, pulse sequence FigureS1E), obtained in a total time of 14 hours. The spectrum in red was obtained in 9 hours, using CP-transfers (sequence FigureS1A) with increased number of scans at the expense of resolution (see ESI for details). (b) 1D traces of the black spectrum along the 1 H and13 C dimensions were extracted at the positions indicated with dashed lines, and the colours of the traces correspond to the colours of the arrows.

  This work was supported by the European Research Council (ERC-StG-2012-311318 and ERC-StG-2010-260887) and used the platforms of the Grenoble Instruct Center (ISBG; UMS 3518 CNRS-CEA-UJF-EMBL) with support from FRISBI (ANR-10-INSB-05-02) and GRAL (ANR-10-LABX-49-01) within the Grenoble Partnership for Structural Biology (PSB).