Gilberto P Pereira

Brian Jiménez-García

Riccardo Pellarin

Guillaume Launay

Sangwook Wu
email: s.wu@pharmcadd.com

Juliette Martin
email: juliette.martin@cnrs.fr

Paulo C T Souza
email: paulocts@gmail.com

Rational Prediction of PROTAC-compatible Protein-Protein Interfaces by Molecular Docking

Proteolysis targeting chimeras (PROTACS) are heterobifunctional ligands that mediate the interaction between a protein target and an E3 ligase, resulting in a ternary complex whose interaction with the ubiquitination machinery leads to target degradation. This technology is emerging as an exciting new avenue for therapeutic development, with several PROTACS currently undergoing clinical trials targeting cancer. Here, we describe a general and computationally efficient methodology combining restraint-based docking, energy-based rescoring, and a filter based on minimal solvent-accessible surface distance to produce .

Introduction

In nature, Protein-Protein Interactions (PPI) have evolved to drive the assembly of macromolecular machines, impacting nearly all cellular processes [START_REF] Ryan | Protein-protein interactions in human disease[END_REF][START_REF] Keskin | Predicting Protein-Protein Interactions from the Molecular to the Proteome Level[END_REF][START_REF] Kuzmanov | Protein-protein interaction networks: probing disease mechanisms using model systems[END_REF] . For example, PPIs are critical to the functioning of systems such as ATP synthase [START_REF] Schweppe | Mitochondrial protein interactome elucidated by chemical cross-linking mass spectrometry[END_REF] , ion channels [START_REF] Cerdan | An Ion-Permeable State of the Glycine Receptor Captured by Molecular Dynamics[END_REF] or the ubiquitination machinery [START_REF] Kleijnen | The hPLIC proteins may provide a link between the ubiquitination machinery and the proteasome[END_REF][START_REF] Liu | The mechanism of ubiquitination in the cullin-RING E3 ligase machinery: conformational control of substrate orientation[END_REF] . Among these, there is a clear distinction between obligate PPIs, i.e those in which the unbound monomers are unstable in vivo, and non-obligate PPIs [START_REF] Ozbabacan | Transient protein-protein interactions[END_REF][START_REF] Ghadie | Are transient protein-protein interactions more dispensable?[END_REF] . Recently weak, transient, and ligand-mediated PPIs have gained attention. In particular, the field of PPIs mediated by Proteolysis Targeting Chimeras (PROTACS) has attracted significant attention and funding [START_REF] Garber | The PROTAC gold rush[END_REF] . While the technology is more than 20 years old [START_REF] Sakamoto | Chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation[END_REF] , the base concept remains: a heterobifunctional PPI promoter composed of a ligand that binds to a protein target (warhead), a ligand that binds an E3 ligase (recruiter), and a flexible linker connecting the two ligands, is used to bring into close proximity a target and the ubiquitination enzyme [START_REF] Sakamoto | Chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation[END_REF][START_REF] Crew | Identification and Characterization of Von Hippel-Lindau-Recruiting Proteolysis Targeting Chimeras (PROTACs) of TANK-Binding Kinase 1[END_REF][START_REF] Békés | PROTAC targeted protein degraders: the past is prologue[END_REF] . In eukaryotes, the ubiquitin-proteasome system continuously degrades proteins [START_REF] Guharoy | Tripartite degrons confer diversity and specificity on regulated protein degradation in the ubiquitin-proteasome system[END_REF] to enforce quality control and to achieve the cell cycle. The PROTAC molecule hijacks the degradation pathway, promoting proximity-based protein ubiquitination. Therefore, the PROTAC facilitates the transfer of ubiquitin to a target protein, leading to poly-ubiquitination, and downstream degradation by the proteasome [START_REF] Békés | PROTAC targeted protein degraders: the past is prologue[END_REF][START_REF] Bondeson | Catalytic in vivo protein knockdown by smallmolecule PROTACs[END_REF] . Some advantages of PROTAC-mediated degradation approaches include the use of lower compound concentration, decreased toxicity risks and the ability to target non-druggable proteins [START_REF] Pettersson | PROteolysis TArgeting Chimeras (PROTACs)-past, present and future[END_REF] .

In the last years, PROTAC-based approaches were pursued to some success, targeting and promoting the degradation of several proteins such as bromodomain 4 (BRD4) 16,17, , kinases [START_REF] Jaime-Figueroa | synthesis and biological evaluation of Proteolysis Targeting Chimeras (PROTACs) as a BTK degraders with improved pharmacokinetic properties[END_REF][START_REF] Zeng | Exploring Targeted Degradation Strategy for Oncogenic KRASG12C[END_REF] , fusion proteins [START_REF] Lai | Modular PROTAC Design for the Degradation of Oncogenic BCR-ABL[END_REF][START_REF] He | PROTACs: great opportunities for academia and industry (an update from 2020 to 2021)[END_REF] , growth factors [START_REF] He | PROTACs: great opportunities for academia and industry (an update from 2020 to 2021)[END_REF][START_REF] Cheng | Discovery of Potent and Selective Epidermal Growth Factor Receptor (EGFR) Bifunctional Small-Molecule Degraders[END_REF] and other proteins involved in various cancers [START_REF] Gao | Phase 1/2 study of ARV-110, an androgen receptor (AR) PROTAC degrader, in metastatic castration-resistant prostate cancer (mCRPC)[END_REF][START_REF] Hamilton | Abstract PD13-08: First-in-human safety and activity of ARV-471, a novel PROTAC® estrogen receptor degrader, in ER+/HER2-locally advanced or metastatic breast cancer[END_REF] . Currently, at least 18 PROTACS are undergoing phase 1/2 clinical trials [START_REF] Chirnomas | Protein degraders enter the clinic -a new approach to cancer therapy[END_REF] . A compilation of bioactive PROTAC molecules, along with their description in terms of physicochemical properties was recently published and is expected to help campaigns of PROTAC drug design [START_REF] Weng | PROTAC-DB: an online database of PROTACs[END_REF] .

While more and more experimentally determined structures for E3 Ligase-PROTAC-Target systems are being deposited in the Protein Data Bank (PDB) [START_REF] Burley | RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences[END_REF] , the data is still sparse for PROTAC design approaches. Thus, computational methodologies are being explored to predict the structure of ternary complexes and, using machine learning methods, degradation rates [START_REF] Drummond | In Silico Modeling of PROTAC-Mediated Ternary Complexes: Validation and Application[END_REF][START_REF] Drummond | Improved Accuracy for Modeling PROTAC-Mediated Ternary Complex Formation and Targeted Protein Degradation via New In Silico Methodologies[END_REF][START_REF] Zaidman | PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes[END_REF][START_REF] Weng | Integrative Modeling of PROTAC-Mediated Ternary Complexes[END_REF][START_REF] Bai | Modeling the CRL4A ligase complex to predict target protein ubiquitination induced by cereblon-recruiting PROTACs[END_REF] . One of the most popular tools for PPI prediction is protein-protein docking, where the prediction depends on two critical steps: sampling of the conformational landscape of the protein-protein complex and scoring of the near-native poses obtained [START_REF] Śledź | Protein structure-based drug design: from docking to molecular dynamics[END_REF] . Most often, PPI docking protocols rely on rigid-body approximations, disregarding conformational flexibility or approximating it in a rough manner [START_REF] Chen | ZDOCK: an initial-stage protein-docking algorithm[END_REF][START_REF] Dominguez | HADDOCK: a protein-protein docking approach based on biochemical or biophysical information[END_REF][START_REF] Cheng | Electrostatics and desolvation for effective scoring of rigid-body protein-protein docking[END_REF][START_REF] Karaca | A multidomain flexible docking approach to deal with large conformational changes in the modeling of biomolecular complexes[END_REF][START_REF] Jiménez-García | LightDock: a new multi-scale approach to protein-protein docking[END_REF][START_REF] Soler | Extensive benchmark of rDock as a peptideprotein docking tool[END_REF][START_REF] Roel-Touris | LightDock goes informationdriven[END_REF] . In the case of PROTAC-mediated ternary complexes, molecular docking approaches may require careful set-up and additional post-processing steps.

Indeed, recent approaches employ molecular docking combined with several steps of structure refinement and explicit inclusion of PROTAC ligand [START_REF] Zaidman | PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes[END_REF][START_REF] Weng | Integrative Modeling of PROTAC-Mediated Ternary Complexes[END_REF] . For example, the study by Weng and co-workers introduced a workflow (PROTAC-Model) which combines a local docking grid searching in rotational space algorithm (FRODOCK) [START_REF] Ramírez-Aportela | FRODOCK 2.0: fast proteinprotein docking server[END_REF] with filtering steps, full PROTAC construction, ternary complex rescoring and a final optimization using RosettaDock [START_REF] Gray | Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations[END_REF][START_REF] Roy Burman | Efficient flexible backbone protein-protein docking for challenging targets[END_REF][START_REF] Roy Burman | Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI rounds 37-45[END_REF] . One alternative route was proposed by Dixon and co-workers [START_REF] Dixon | Predicting the Structural Basis of Targeted Protein Degradation by Integrating Molecular Dynamics Simulations with Structural Mass Spectrometry[END_REF] , where a combination of molecular modeling, SAXS, and HDX-MS experiments led to the determination of physiologically relevant ternary complexes. One of the main points from this study is that the free energy landscape of these systems is not characterized by a single low-energy conformation but instead by several, interconvertible, low-energy minima, highlighting the complexity and flexibility of PROTAC-mediated biomolecular systems [START_REF] Dixon | Predicting the Structural Basis of Targeted Protein Degradation by Integrating Molecular Dynamics Simulations with Structural Mass Spectrometry[END_REF] . Finally, given the immense success that AlphaFold2 (AF2) [START_REF] Evans | Protein Complex Prediction with AlphaFold-Multimer[END_REF] and other machine learning-based methods have shown in the last CASP14 and CASP15 editions [START_REF] Du | The trRosetta server for fast and accurate protein structure prediction[END_REF][START_REF] Zheng | Folding nonhomologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations[END_REF] , some researchers have pointed out that in the absence of experimental structures, future PROTAC-based drug design projects will use AF2 predicted structures [START_REF] Thornton | AlphaFold heralds a data-driven revolution in biology and medicine[END_REF] .

In this study, we present a general approach to generate ligase-target binding modes compatible with PROTAC-mediated ternary complexes determined by X-ray crystallography.

Our aim is to produce a workflow that facilitates the initial stage of PROTAC in silico design, where the PROTAC molecule is still unknown or where structural information of the ternary complex is lacking. Our approach is tested on a manually curated dataset of 13 ternary complexes extracted from the PDB and relies on the predictions provided by LightDock [START_REF] Jiménez-García | LightDock: a new multi-scale approach to protein-protein docking[END_REF][START_REF] Roel-Touris | LightDock goes informationdriven[END_REF][START_REF] Roel-Touris | Integrative modeling of membrane-associated protein assemblies[END_REF] .

LightDock is combined with energy rescoring afforded by VoroMQA [START_REF] Olechnovič | VoroMQA: Assessment of protein structure quality using interatomic contact areas[END_REF] and a filtering step based on the minimal solvent-accessible surface distance (SASD) between anchor atoms of the ligands bound to the ligase and target proteins, computed using Jwalk [START_REF] Bullock | Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry[END_REF] . We show that restrained-LightDock simulations produce reasonable PPI structures, which are compatible with experimental structure of the ternary complex, for nearly all systems under scrutiny. We further compare the performance of our method with AlphaFold2-Multimer [START_REF] Evans | Protein Complex Prediction with AlphaFold-Multimer[END_REF][START_REF] Martin | When Alphafold2 predictions go wrong for protein-protein complexes, is there something to be learnt?[END_REF] , highlighting critical limitations in the machine-learning based method for this kind of application, and benchmark its computational efficiency against PROTAC-Model 31 highlighting that the present approach is able to efficiently generate reasonable starting ligase-target binding modes.

Methods

Dataset selection and curation

Thirteen PROTAC ternary complex crystal structures were extracted from the PDB [START_REF] Burley | RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences[END_REF] , selected based on three criteria: (1) that the crystal structure contained an E3 ligase, a ligand molecule, and a target; (2) that the crystal structure resolution was below 4 Å and (3) that the ligand was a PROTAC and not a molecular glue. The dataset is composed of three types of E3 ligases (9 VHL [START_REF] Gadd | Structural basis of PROTAC cooperative recognition for selective protein degradation[END_REF][START_REF] Farnaby | BAF Complex Vulnerabilities in Cancer Demonstrated via Structure-Based PROTAC Design[END_REF][START_REF] Chung | Structural Insights into PROTAC-Mediated Degradation of Bcl-xL[END_REF][START_REF] Yu | A selective WDR5 degrader inhibits acute myeloid leukemia in patient-derived mouse models[END_REF][START_REF] Dragovich | Antibody-Mediated Delivery of Chimeric BRD4 Degraders. Part 2: Improvement of In Vitro Antiproliferation Activity and In Vivo Antitumor Efficacy[END_REF][58][START_REF] Kraemer | Quaternary Complex of human WDR5 and pVHL:ElonginC:ElonginB bound to PROTAC Homer[END_REF] , 2 Cereblon 60-63 and 2 cIAP [START_REF] Schiemer | Snapshots and ensembles of BTK and cIAP1 protein degrader ternary complexes[END_REF] ), multiple different PROTAC molecules and a diverse set of protein targets, from kinases to bromodomains or proteins implicated in DNA repair (as WRD5). For the realistic docking case scenario, unbound monomers were selected to be different from the initial 13 crystal structures in terms of interface side-chain packing 54,64- 71 . All protein termini were capped using the psfgen tool in VMD [START_REF] Humphrey | visual molecular dynamics[END_REF] , residue names were converted to standard names, and hydrogens were removed prior to molecular docking experiments. A listing of the structures used in the redocking and realistic experiments is given on Tables S1 andS2.

Molecular Docking and refinement protocol

Docking experiments were carried out using version 0.9.2 of the LightDock framework [START_REF] Jiménez-García | LightDock: a new multi-scale approach to protein-protein docking[END_REF][START_REF] Roel-Touris | LightDock goes informationdriven[END_REF] .

LightDock is a multiscale framework for the 3D determination of binary macromolecular complexes based on the Glowworm Swarm Optimization algorithm [START_REF] Krishnanand | A Glowworm Swarm Optimization Based Multi-robot System for Signal Source Localization[END_REF] . The sampling in LightDock is driven by the swarm approach, which relies on the metaphor that, in nature, glowworms (which represent ligand, or protein target, poses) feel attracted to each other depending on the amount of emitted light (scoring, energetic value of a docking pose). In this way, the docking poses, which constitute the swarm of glowworms in LightDock, are optimized towards the energetically more favorable ones through the translational, rotational, and, if the option is selected, some degree of flexibility at the level of the protein backbone is achieved through the optimization of an anisotropic network model (ANM) [START_REF] Jiménez-García | LightDock: a new multi-scale approach to protein-protein docking[END_REF][START_REF] Roel-Touris | LightDock goes informationdriven[END_REF] . In principle, the ANM helps finding better docking poses since it "smoothes" the energetic docking search landscape. In this study, the ANM option was not explored for two reasons: (i) as the difference in RMSD between the individual monomers and the respective structures in the ternary complex crystal is small or non-existent (for the realistic docking or the redocking experiments, respectively), the ANM may actually just add noise and (ii), it would significantly increase the computational cost of each run because it would require that each docking simulation be run for at least another 50 steps to allow the optimization in ANM space to converge. Every swarm represents an independent docking simulation, and the number of swarms is automatically determined depending on the receptor surface nature, ensuring full sampling coverage of the protein complex surface. If contact residue-restraints are provided, swarms are filtered according to their compatibility with these restraints, focusing sampling to the region of interest described by the restraints [START_REF] Jiménez-García | LightDock: a new multi-scale approach to protein-protein docking[END_REF][START_REF] Roel-Touris | LightDock goes informationdriven[END_REF] . To define the contact residues for restraints, the user specifies two lists of residues, one for the ligand (target protein) and one for the receptor (E3 ligase).

Given a structure, a contact between two residues i and j is present if the distance between any heavy atom of i and any heavy atom of j (for the DFIRE scoring function) is below 3.9 Å.

For each pose, we count the number of "satisfied residues'' for the ligand list and the receptor list, where a satisfied residue in the ligand list is a residue which is in contact with any residue of the receptor list, and vice-versa. The number of satisfied residues for the ligand and the receptor are normalized by the total number of residues in the user lists, which leads to two fractions PL and PR, respectively. The fractions are combined with the docking score as follows:

εf = ε + ε(PR + PL) ( 1
)
where ε is the unbiased score and εf is the final score. The term involving the satisfied fractions acts as a "booster", promoting the poses which satisfy the most of the user-specified residues, as evaluated and benchmarked in different scenarios in [START_REF] Roel-Touris | LightDock goes informationdriven[END_REF] . The number of glowworms per swarm was set to 200 (which is the default in the protocol), and 50 docking iterations were carried out (since 50 steps is generally enough for convergence if ANM is not enabled) using the DFIRE scoring function [START_REF] Roel-Touris | LightDock goes informationdriven[END_REF][START_REF] Zhang | Docking prediction using biological information, ZDOCK sampling technique, and clustering guided by the DFIRE statistical energy function[END_REF][START_REF] Faraggi | Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction[END_REF] .

The docking experiments were carried out in two scenarios: (1) redocking (or bound) and ( 2) realistic (unbound) docking, starting from the monomers within crystal ternary complexes and from independent monomeric forms of the proteins found in the PDB, respectively.

Furthermore, in both redocking and realistic docking experiments, we explored the enforcing of contact residue restraints to help drive the docking towards the PPI. Contact residue restraints were defined by identifying residues in the ligand binding site whose C-alpha atoms were within a given distance (4, 6 or 8Å) from any heavy atom of the ligands bound either to the E3 ligase or the target protein, determined using MDAnalysis [START_REF] Michaud-Agrawal | MDAnalysis: a toolkit for the analysis of molecular dynamics simulations[END_REF][START_REF] Gowers | MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations[END_REF] . We excluded residues whose relative Solvent Accessible Surface Area (SASA) was below 25%, with SASA computed using naccess [START_REF] Hubbard | Naccess[END_REF] . From each docking experiment, the top 200 poses were extracted according to the LightDock predicted ranking. Lightdock, like many other state-of-the-art docking approaches, does not sample side-chains due to the massive computational cost associated with sampling the configurational space comprising all accessible rotameric states of all amino acid side chains. While it would be possible to include backbone flexibility in the simulations via the ANM, for the reasons described above (computational cost of doubling the number of simulation steps to ensure convergence, possibility that the ANM would only add noise) we did not pursue this avenue. As such, docking experiments were carried out in a rigid-body fashion.

To improve protocol accuracy, a rescoring step was pursued using the energy function from

VoroMQA [START_REF] Olechnovič | VoroMQA: Assessment of protein structure quality using interatomic contact areas[END_REF] , which was sufficient in the case of the redocking experiment, and then filtered according to either the Center-of-Geometry (COG) distance between the recruiter and warhead ligands or the existence and length of the SASD between small-molecule anchor atoms [START_REF] Bullock | Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry[END_REF] for the realistic docking experiment. The former was computed using MDAnalysis [START_REF] Michaud-Agrawal | MDAnalysis: a toolkit for the analysis of molecular dynamics simulations[END_REF][START_REF] Gowers | MDAnalysis: A Python package for the rapid analysis of molecular dynamics simulations[END_REF] while the latter was computed using Jwalk [START_REF] Bullock | Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry[END_REF] . The SASD filter was calibrated using the predicted PPIs, exploring several SASD ranges (Table S6) and the reference structures in the benchmark dataset with the aim of recovering as many acceptable solutions as possible while also encompassing at least 90% of the SASDs computed on the crystal structures (see Table S1). The rescoring step corresponds to a re-ranking of the LightDock-generated structures in ascending order of the calculated VoroMQA energies. The filtering step shifts all solutions which do not pass the criteria to the bottom of the ranking, maintaining the order obtained from the energy rescoring. This filtering allows one to discard predicted PPI structures where the either or both of the binding sites are occluded, effectively acting as an implicit "PROTACability filter".

Docking accuracy

After each experiment, the accuracy of the docking protocol was evaluated using the CAPRI standards [START_REF] Méndez | Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures[END_REF][START_REF] Lensink | Docking, scoring, and affinity prediction in CAPRI[END_REF] and the DockQ score [START_REF] Basu | DockQ: A Quality Measure for Protein-Protein Docking Models[END_REF] . These criteria employ three metrics between docking poses and crystallographic complexes, classifying docking solutions as incorrect, acceptable, medium or high quality. The three similarity measures are the fraction of conserved native contacts (Fnat), the interface root mean squared deviation (RMSD) and the ligand RMSD, where ligand refers to the target protein, which were computed in this study using C-alphas.

As PROTAC-mediated ternary complexes are more and more suspected to be flexible [START_REF] Dixon | Predicting the Structural Basis of Targeted Protein Degradation by Integrating Molecular Dynamics Simulations with Structural Mass Spectrometry[END_REF] , we consider that any solution with acceptable or higher quality is a reasonable solution. The reference structures used for both experiments were the PROTAC ternary complex crystals.

AlphaFold-multimer structure predictions

Calculations using AlphaFold2-multimer [START_REF] Evans | Protein Complex Prediction with AlphaFold-Multimer[END_REF] were carried out using a local installation of version 2.1. All templates available on the PDB up to the 30 th of April 2018 were included. The AF2 confidence score was extracted to evaluate the confidence of the tool on the predicted proteinprotein complexes [START_REF] Evans | Protein Complex Prediction with AlphaFold-Multimer[END_REF] . The accuracy of AF2 predictions (Figure S6) was assessed using the same metrics as the predictions supplied by LightDock.

PROTAC-Model benchmark calculations

To compare the results and performances of our method with PROTAC-Model 41 , we selected the systems shared between the two studies (Table S5) and ran PROTAC-Model using the default settings for a run without Rosetta refinement. The E3-ligase linker anchor atom selected for FRODOCKs' local docking was the one present in the corresponding reference crystal structure. Calculations were carried out using 20 cores on a 40 CPU Intel Xeon Silver 4210R CPU machine running at 2.40 GHz.

Results and discussion

The docking workflow recovers PROTAC-mediated ternary complex-compatible PPIs

To evaluate the accuracy of our protocol, we first carried out redocking experiments on the benchmark dataset of 13 PROTAC-mediated complexes (Table S1). Here, we consider that the docking was successful if it generated at least one acceptable or higher quality solution within a given cohort (Top 1, 5, 10, 20, 50 or 100 ranked poses). From the information gathered in the literature, in particular the reports about ternary complex flexibility from Dixon and co-workers [START_REF] Dixon | Predicting the Structural Basis of Targeted Protein Degradation by Integrating Molecular Dynamics Simulations with Structural Mass Spectrometry[END_REF] , we hypothesized that accurate prediction of these interfaces would be a challenge for an unbiased docking algorithm. A way to help drive the docking towards the correct interface is through the introduction of contact residue restraints, which we pursued as detailed in the experimental section. This restraint selection approach ensures that the docking algorithm is directed towards the PPI while maintaining the overall protocol general and applicable to any ligase-target pair of interest. Importantly, these restraints correspond to an energetic bias which is added to the overall score per satisfied restraint. The redocking experiment is not realistic, as the side-chains are perfectly packed in the crystal, but represents the first test to assess protocol accuracy. Figure 1 shows, for the 6BOY complex (A), a visual representation of the residues used in the restraints for the CRBN ligase at each selection threshold (B). Table S3 contains an exhaustive description of the number of restraints per protein and system. As expected, unbiased docking simulations were not able to generate acceptable poses for the large majority of cases. For instance, we obtained an accuracy of 23% for the cohort of best 20 poses (Figure 1C, Top 20 light-blue bar). The reason behind the poor accuracy of the unbiased docking experiment is that the algorithm attempts to maximize the interaction surface as these are the most energetically favorable in the absence of restraints guiding the algorithm (Figure S2). On the other hand, the restrained docking calculations achieved satisfactory accuracy for the cohort of Top 20 poses (77%, 69% and 54% for calculations employing 4, 6 and 8 Å contact residue restraints, Figure 1C).

Furthermore, we observed significant improvements in accuracy once the VoroMQA energy rescoring was carried out (Figure 1D). In particular, the protocol employing 6 Å contact residue restraints achieved 92% accuracy for the Top 20 cohort.

Minimal solvent-accessible path is essential to recover acceptable Ligase-Target binding poses in realistic docking scenarios

The same protein pairs that were used in the benchmark described above, were adopted for a realistic docking experiment. In this case, the docking inputs correspond to crystallographic ligand-bound structures of the monomeric forms for the E3 ligase and target proteins.

Importantly, we did not enforce any prior knowledge on which linker composition and length might fit the ternary complex. As expected, there is a significant drop in accuracy when comparing the redocking with the realistic experiments (from 77% to 23% for the best performing setups at the Top 20 level, respectively), as illustrated by Figures 2C and2D. One possible reason may be that the sidechain packing in the redocking experiment is optimal and thus finding appropriate solutions is possible whereas in the realistic case, optimal side-chain orientations are not accessible. Nonetheless, we are able to prioritize more acceptable poses after VoroMQA energy rescoring [START_REF] Olechnovič | VoroMQA: Assessment of protein structure quality using interatomic contact areas[END_REF] , leading to an increase in accuracy, from 23% to 46% at the Top 20 level for the best performing protocol (restraints defined with a 6 Å cutoff). To further improve the accuracy of the realistic docking calculations, we decided to impose a filtering scheme to remove highly ranked but physically unreasonable poses, where the COG euclidean distance between ligands (Figure 2A) is too large and/or the ligand binding sites are occluded. To account for binding site occlusion, we filtered the predicted docking poses through the existence and length of the solvent-accessible surface distance between the two ligands (SASD, Figure 2B).

The selected SASD filter was an interval from 3 to 13.7 Å (Table S1). As shown in Figure 3A, the filters improved remarkably the performance of the workflow, with the accuracy rising from 46% to 77% (Top 20, 6 Å contact residue restraints). However, the enhancement in performance observed here is still a product of a dual effect: the docking algorithms' ability to produce meaningful solutions and the accuracy of the post-processing procedure, i.e. the energy-rescoring and SASD filtering. To ascertain the performance of the post-processing approach, we removed those systems for which no meaningful docking solutions were found for the best performing setup, which employs 6 Å contact residue restraints (Figure 3B), two VHL cases (PDB IDs 7JTP, 7KHH, Figure S4). In this analysis, we considered successes the cases where LightDock was able to generate at least one acceptable solution for that particular system, a classification which allows to decouple the docking engines' ability to produce meaningful solutions and the post-processing filters' ability to recover those solutions. Since the structural differences of the ligase-target systems between their ternary complex and monomeric states are small (see Table S2), and their interfaces in the ternary complex are shallow (and may be as small as two contact residues), we believe that even small perturbations to sidechain orientation may be sufficient to induce the algorithm into error. We found that after removal of those systems, the protocol exhibits an accuracy of 82% and 91%

for the Top 10 and 20 poses, respectively. Interestingly, if we only consider the most well studied ligases (CRBN and VHL), discarding the two systems employing inhibitor of apoptosis E3 ligases (cIAPs), the accuracy of the procedure rises to 89% and 100% at the Top 10 and Top 20 levels respectively. In conclusion, we achieved good accuracy if one considers that unbound monomer docking in the case of PROTACs is challenging and no structural refinement was performed. As examples, the highest ranked structures found after PROTACability filtering for three complexes are given in Figure 3C-E whereas Figure S3 illustrates the highest-ranking acceptable solutions for all systems here explored. comparing the 6BOY crystal structure (grey) to the best prediction (ranked first) obtained using our workflow (colored). D) Showcase comparing the 6HAX to crystal structure (grey) to the best prediction (ranked third) obtained using our workflow (colored). E) Showcase comparing the 6W8I crystal structure (grey) to the best prediction (ranked sixth) obtained using our workflow (colored).

Discussion

In this study, we propose a novel workflow for the generation of PROTAC-compatible PPI structures in the case that no known PROTAC molecule exists, no information of a possible linker is available, and the ligase-recruiter and target-warhead co-crystals are available. To this end, we manually curated a dataset of 13 PROTAC-containing ternary complexes from the PDB to serve as a benchmark dataset. Using this dataset for protocol benchmarking, we carried out both redocking and realistic docking experiments (see Results) where the accuracy of the docking was measured by evaluating the quality of the predicted structures using CAPRI and DockQ criteria [START_REF] Méndez | Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures[END_REF][START_REF] Lensink | Docking, scoring, and affinity prediction in CAPRI[END_REF][START_REF] Basu | DockQ: A Quality Measure for Protein-Protein Docking Models[END_REF] .

We found that both the inclusion of contact residue restraints and the rescoring using the energy function of VoroMQA 51 , were fundamental to increase the accuracy of the calculations in both redocking and realistic docking experiments. In the case of redocking, the best performing protocol (restraints defined with a 6 Å cutoff, rescored) achieved a striking accuracy of 85% and 92% for the 10 or 20 best ranked predicted PPIs, respectively. The unbiased docking experiments, in comparison, yielded poor accuracy. This was expected, as most docking scoring functions are developed to reproduce interfaces with large contact areas and PROTAC-mediated complexes exhibit typically small and transient PPIs, as illustrated in Figure S1. In the realistic docking experiments, the accuracy was much lower, even with restraints (below 50% for the Top 20). Since many high-ranking solutions presented occlusion of the ligand binding sites in either one or both of the proteins, we envisioned that an extra filtering step was needed, such that the predicted PPIs where it is impossible to thread a solvent-accessible path connecting the two ligands would be discarded. Based on this idea, we decided to use the minimal SASD [START_REF] Bullock | Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry[END_REF] as a filter and applied it to the VoroMQA-rescored predicted PPIs. The application of the filter improves the docking accuracy, which rises from 46% to 77% for the Top 20 cohort. Nonetheless, the approach was unable to produce acceptable solutions in two cases. If we consider only the remaining 11 cases, we state that the post-processing workflow (rescoring + SASD filtering) is highly accurate for both the Top acceptable solutions for the two cIAP systems comes at the cost of overall performance at the early stages (Top 5 to Top 20, Figure 3E). It is important to note that, recently, researchers have pointed out to the fact that the cIAP structures used in this work contain only one domain of the full cIAP protein and that the full length cIAP interacts with their substrates from different domains, which bind different parts of the substrate [START_REF] Hunkeler | Structures of BIRC6-client complexes provide a mechanism of SMAC-mediated release of caspases[END_REF] , thus making a proper prediction of these PPIs challenging.

Our protocol is general and applicable to any ligase-target protein pair, as it only requires the corresponding ligand-bound structures of the monomeric forms for each protein. Furthermore, it is computationally cheap as only three steps are necessary: (1) restrained docking using LightDock, (2) energy rescoring using VoroMQA and (3) filtering by SASD. As such, our protocol may be useful to accelerate PROTAC-based drug discovery campaigns where no known PROTAC is available while being computationally tractable, with an average run-time per system of 24.1 minutes using 20 cpu cores (481 minutes per system on a single core (Table S4), and simple to employ.

Nonetheless, there are some inherent limitations to our work. does not consider the ligand molecules when computing the minimal SASD, meaning that this path may run across the ligand molecular structures depending on the positions of the anchor atoms. A further potential improvement can come from the application of an in-house developed tool which does exclude from the path construction the voxels occupied by ligand atoms (Figure S5). As such, the proposed workflow is accurate but improvable, and future work in our group is aimed at increasing the quality of the docking-predicted PPIs.

Other computational studies based on molecular docking have been pursued in recent years, aiming at the establishment of easy-to-use and accurate PROTAC drug design strategies. For example, the group behind PROsettaC [START_REF] Zaidman | PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes[END_REF] and the group of Tingjun Hou 31 both aimed at reproducing the crystallographic PPI of PROTAC-mediated ternary complexes. The workflow by Weng and co-workers [START_REF] Weng | Integrative Modeling of PROTAC-Mediated Ternary Complexes[END_REF] shown to be able to generate medium to high quality solutions for 12 out of the 14 PROTAC systems queried in a realistic docking scenario. However, it requires a priori knowledge of the PROTAC to use, which is critical for the filtering and retrieval of the protein-protein docking poses closest to the reference structure. We hypothesize that this is not the most optimal approach, since according to the data from Dixon and co-workers [START_REF] Dixon | Predicting the Structural Basis of Targeted Protein Degradation by Integrating Molecular Dynamics Simulations with Structural Mass Spectrometry[END_REF] , the crystal structure is one possible solution but it is not necessarily the only acceptable solution and trying to reproduce it may lead to discarding other biologically relevant conformations.

Nonetheless, we compared the computational cost and the accuracy of our method to PROTAC-Model by Weng et al [START_REF] Ramírez-Aportela | FRODOCK 2.0: fast proteinprotein docking server[END_REF] (Table S5) across the systems shared between the two studies without employing PROTAC-Models' Rosetta refinement, using 20 cores. From this comparison, we observe that while PROTAC-Model is superior to our approach at reproducing VHL-based ternary complex crystallographic structures, our method was (1) able to recover solutions for both cIAP systems and (2) performs slightly better than PROTAC-Model for the Cereblon systems studied. Furthermore, while our method took, on average, 18.7 minutes to generate the PPI predictions, PROTAC-Model takes, on average, over two hours (155.4 minutes), meaning that our approach is 8x more efficient that PROTAC-Model while using less information since our method does not require knowledge of the linker portion binding the two ends of the PROTAC.

Since DeepMinds' AlphaFold2 is now able to predict protein-protein complexes in the multimer variant, we also questioned whether AF2-multimer [START_REF] Evans | Protein Complex Prediction with AlphaFold-Multimer[END_REF] would be able to correctly predict the PROTAC-mediated PPI As shown in Figure S7, the AFs' accuracy according to DockQ and/or CAPRI was low. As an example, the predictions obtained for 6BOY when run through AF2-Multimer, which are in complete disagreement with the experimental structure. A possible avenue to improve on these predictions is the inclusion of ligands in the process, as proposed by Hekkelman and co-workers [START_REF] Hekkelman | AlphaFill: enriching AlphaFold models with ligands and cofactors[END_REF] . Thus, it appears that AF2multimer cannot predict PROTAC-mediated PPIs, highlighting the critical contribution of the restraints. For the moment, physical-based approaches combined, as described here, seem to be a better choice towards computational design of PROTACs-compatible PPIs.

Nonetheless, future work in our group will aim at improving the accuracy and capabilities of the PROTACability workflow by exploring different scoring functions (including free energy methods like the Molecular Mechanics-Poisson Boltzmann (or its Generalized-Born variant)

Surface Area method [START_REF] Weng | Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes[END_REF] ), protein flexibility and different filtering schemes.

Conclusion

In this study we have described a new LightDock-based protocol for PROTAC drug design.

Our workflow combines restraint-enabled LightDock simulations with energy-based rescoring from VoroMQA and SASD calculations from Jwalk to yield at least acceptable quality PPI complex structures, starting only from the ligand-bound monomeric form of the E3 ligase and the protein target. The protocol is generalizable to any E3 ligase/target pair, fast and accurate, achieving 92% and 77% accuracy for the top 20 cohort on the redocking and realistic docking experiments, respectively, when benchmarked against a manually curated and diverse dataset of PROTAC-mediated ternary complexes. In the case that no PROTAC molecule is known to bind the ligase/target pair of interest, our approach is able to provide initial configurations which reproduce the protein-protein binding interface and provide the minimal solvent accessible path connecting the ligand and target ligands, potentially accelerating PROTAC drug design campaigns by narrowing down the chemical space to be searched for linker selection. Compared to a state-of-the-art method, PROTAC-Model, our method is a compromise between accuracy and computational efficiency. While it is less accurate than PROTAC-Model at reproducing VHL-based ternary complex crystal PPIs, it is still able to generate near-native solutions, using a fraction of the computational time.

  Docking accuracy was measured by evaluating the presence of solutions with at least acceptable quality in the top 1, 5, 10, 20, 50 or 100 protein-protein docking poses. Acceptable solutions thus adhere to either (i) a DockQ score ≥ 0.23, or (ii) a fraction of native contact (Fnat) >=10% and either ligand RMSD ≤ 10 Å or interface RMSD ≤ 4 Å 81 . A pose is acceptable if it passes either the CAPRI or DockQ criteria.

Figure 1 -

 1 Figure 1 -A) Crystal structure of the PROTAC-mediated complex 6BOY. The ligase component cereblon (CRBN, orange) is bound to the bromodomain target BRD4 (blue), with the complex being stabilized by the PROTAC ligand dBET6 (green). B) The center-of-mass of the thalidomide-like CRBN ligand from 6BOY (green) is used to select surface-accessible residues to act as docking restraints (orange) at different cutoff radii. C) Redocking calculations performed either with or without contact residue restraints. D) Redocking calculations after rescoring with the VoroMQA energy function.

Figure 2 -

 2 Figure 2 -Realistic docking experiments carried out for the PROTAC benchmark dataset. A) Depiction of the COG-COG euclidean distance (grey) between the two ligands composing the dBet6 PROTAC (green), bound to the E3 ligase (orange) and a bromodomain target (blue) [PDB id: 6BOY]. B) Illustrative figure highlighting the minimal SASD path (grey) between the two ligands. C) Accuracy of realistic docking with contact residue restraints. D) Accuracy of realistic docking followed by VoroMQA rescoring. In the two bottom panels, the top scoring structures of each cohort (Top 1, 5, 10, 20, 50 or 100) were analyzed. Thus, T1 indicates that we analyzed the best scoring structure for all systems to see if its quality was at least acceptable according to either the CAPRI classification or the DockQ score.

Figure 3 -

 3 Figure 3 -Accuracy of the realistic docking followed by rescoring and SASD filtering. A) Docking accuracy before and after filtering the predicted poses using either the COG-COG euclidean distance between ligands or the SASD computed using Jwalk. B) Summary of the performance of the best protocol. The first bar corresponds to the best performing setup, employing both VoroMQA energy rescoring and SASD filtering (dark green). The success cases are those (11 out of 13) for which at least acceptable-scored solutions were found by the docking engine in the full top 200 predicted structures (light orange). The last bar (dark brown) highlights the performance of the protocol when considering the successful cases that contain either CRBN or VHL ligases (9 out of 11 in total). C) Showcase

  Having taken the decision to keep it computationally cheap and fast, there are no structural refinement steps. This is clearly fundamental when looking at the showcases in Figures3C, 3D and 3E. It appears that even when guided by restraints, the LightDock still tries to maximize the interface contact area, leading to a super-packed binding mode. One of the reasons for this behavior is that the training set of the DFIRE[START_REF] Zhang | Docking prediction using biological information, ZDOCK sampling technique, and clustering guided by the DFIRE statistical energy function[END_REF] scoring function was based on typical protein-protein complexes, which tend to have larger interface areas than those present in PROTAC-mediated ternary complexes (FigureS2). Possible avenues for improvement could pass by including protein flexibility by means of Normal Mode Analysis (NMA) in internal coordinates[START_REF] Diaz | Using normal mode analysis on protein structural models. How far can we go on our predictions?[END_REF] , the inclusion of the ligand molecules in the structures during docking, testing other docking scoring functions or refinement of the docked poses based on Molecular Dynamics simulations. Finally, Jwalk[START_REF] Bullock | Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry[END_REF]

Acknowledgements

Computer hours awarded to the GENCI project number 2022-A0120713456 on the Jean-Zay and Joliot-Curie Rome clusters of the French National Supercomputing Center are gratefully acknowledged. We gratefully acknowledge CC-IN2P3 (https://cc.in2p3.fr) for providing a significant amount of the computing resources and services needed for this work. The authors also acknowledge the support provided by the CNRS and PharmCADD.

Financial support

Brian Jiménez-García (BJG) is employed by Zymvol Biomodeling on a project which received funding from the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement No. 801342 (Tecniospring INDUSTRY) and the Government of Catalonia's Agency for Business Competitiveness (ACCIÓ). PCTS, JM, GPP, GL and RP are supported by the French National Center for Scientific Research (CNRS).

Further funding of GPP and PCTS came from a research collaboration with PharmCADD.

Data availability and code

A step-by-step tutorial, codes, scripts and an example using 6HAX (for realistic docking) are available on GitHub (https://github.com/GilbertoPPereira/PROTACability). All reference structures used in the dataset and results obtained in this work are available at Zenodo: https://zenodo.org/record/8136088.

Conflicts of Interest

All authors declare no existing conflict of interest.

Supporting Information

Additional details, including a computational efficiency benchmark for PROTACability. the datasets used for model benchmarking, data on the parametrization of the SASD filter, comparison of Protein-Protein Interface sizes, and a schematic workflow of the PROTACability protocol are given.


Raphaël Chattot
email: raphael.chattot@umontpellier.fr

Camille Roiron

Kavita Kumar

Vincent Martin

Carlos Augusto

Campos Roldan

Marta Mirolo

Isaac Martens

Luis Castanheira

Arnaud Viola

Rémi Bacabe

Carlos Augusto Campos Roldan

Sara Cavaliere

Pierre-Yves Blanchard

Laetitia Dubau

Frédéric Maillard

Jakub Drnec

Break-in Bad: on the Conditioning of Fuel Cell Nanoalloy Catalysts

Keywords: Nanocatalysts, Fuel Cell, Conditioning, Operando X-ray Diffraction, Online ICP-MS 2

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Nanocatalysts play a critical role in the performance, cost and lifetime of numerous fuel cell technologies. This is particularly true for the proton-exchange membrane fuel cell (PEMFC), where the combined highly corrosive environment and mechanism complexity of the cathodic oxygen reduction reaction (ORR) mostly imposes the use of noble, scarce and expensive platinum (Pt) group metal-based catalysts. Consequently, significant research effort at the fundamental level is dedicated to the design of cost-effective materials able to boost the widespread development of PEMFC in numerous industrial sectors. To date, numerous nanostructures of Pt alloyed with one or several early or late transition metal element(s) have shown tremendous improvements in catalytic properties for the ORR, even far above the commercial performance targets, when tested in thin-film-(rotating) disk electrodes configuration at the laboratory scale [START_REF] Mistry | Nanostructured Electrocatalysts with Tunable Activity and Selectivity[END_REF] .

A thin-film-(rotating) disk electrode (thin-film on RDE) is the simplified, idealized version of half a fuel cell. It allows rapid screening of new catalysts activity and stability from only few quantities of material at the early stage of development, prior to further larger-scale production and subsequent more extensive in situ evaluation in complete fuel cell. Especially, such characterization in liquid electrolyte prevents from the rather complex membrane electrode assembly (MEA) manufacturing, which requires specific laboratory equipment and expertise [START_REF] Schmidt | The Oxygen Reduction Reaction on a Pt/Carbon Fuel Cell Catalyst in the Presence of Chloride Anions[END_REF][START_REF] Takahashi | Examination of the Activity and Durability of PEMFC Catalysts in Liquid Electrolytes[END_REF][START_REF] Shinozaki | Oxygen Reduction Reaction Measurements on Platinum Electrocatalysts Utilizing Rotating Disk Electrode Technique: II. Influence of Ink Formulation, Catalyst Layer Uniformity and Thickness[END_REF][START_REF] Garsany | Experimental Methods for Quantifying the Activity of Platinum Electrocatalysts for the Oxygen Reduction Reaction[END_REF] . This technique provides a truly intrinsic property of the catalyst, that is the kinetic current density for the ORR at a given electrode potential. This property is usually corrected from oxygen mass transport limitation and ohmic drop losses 'artifacts', that are more dependent on the electrochemical cell design and configuration than the catalyst itself. This should in principle favor general agreement between research groups when assessing the catalyst ORR performance. In that frame, numerous seminal works have evidenced the extreme sensitivity of the kinetic current density for the ORR to the experimental conditions [START_REF] Shinozaki | Oxygen Reduction Reaction Measurements on Platinum Electrocatalysts Utilizing Rotating Disk Electrode Technique: II. Influence of Ink Formulation, Catalyst Layer Uniformity and Thickness[END_REF][START_REF] Shinozaki | Oxygen Reduction Reaction Measurements on Platinum Electrocatalysts Utilizing Rotating Disk Electrode Technique[END_REF][START_REF] Kocha | Best Practices and Testing Protocols for Benchmarking ORR Activities of Fuel Cell Electrocatalysts Using Rotating Disk Electrode[END_REF][START_REF] Garsany | Impact of Film Drying Procedures on RDE Characterization of Pt/VC Electrocatalysts[END_REF][START_REF] Martens | A Comparison of Rotating Disc Electrode, Floating Electrode Technique and Membrane Electrode Assembly Measurements for Catalyst Testing[END_REF] , and established the 'best practices' regarding the thin-film electrode preparation, glassware cleanliness, purity of the chemicals or catalytic ink composition and formulation toward more reliable and reproducible results. However, there is still no clear consensus in the RDE literature regarding the testing protocol for the very first step of nanocatalyst electrochemical characterization that is electrochemical conditioning (also referred as activation or break-in), whereas it is gaining considerable attention at the device level [START_REF] Kabir | Elucidating the Dynamic Nature of Fuel Cell Electrodes as a Function of Conditioning: An Ex Situ Material Characterization and in Situ Electrochemical Diagnostic Study[END_REF][START_REF] Kim | Development of Optimal Conditioning Method to Improve Economic Efficiency of Polymer Electrolyte Membrane (PEM) Fuel Cells[END_REF][START_REF] Gatalo | Importance of Chemical Activation and the Effect of Low Operation Voltage on the Performance of Pt-Alloy Fuel Cell Electrocatalysts[END_REF] . The tacitly accepted common approach with RDE consists in repetitive electrode potential cycling until the cyclic voltammograms (CVs) become stable. Shinozaki et al. [START_REF] Shinozaki | Oxygen Reduction Reaction Measurements on Platinum Electrocatalysts Utilizing Rotating Disk Electrode Technique[END_REF] conducted one of the rare systematic studies on the impact of conditioning protocol, notably regarding the effects of potential scan rate and upper potential limit on the specific activity of polycrystalline bulk electrode (poly-Pt) and Pt/C catalyst. These authors suggest that since scan rates in the range 100-500 mVs -1 produce similar outcomes over 50 cycles, 500 mVs -1 has the advantage of requiring less time. Complementarily, it was revealed that upper potential limit of only 1.0 V vs. the Reversible Hydrogen Electrode (RHE) resulted in incomplete conditioning of both poly-Pt and Pt/C, while reaching 1.2 or even 1.4 V vs. RHE was found necessary for complete contaminant removal from the surface and maximization of the electrochemically active surface area (ECSA).

Nonetheless, by taking into consideration the issue of carbon corrosion in Pt/C catalyst at such high potential, 1.2 V vs. RHE as upper limit was found to represent a satisfactory compromise between catalyst surface cleanliness and structural integrity. Now, the question of how these findings transpose to more complex PtM/C nanostructures (where M is an early or late transition metal) currently investigated by the RDE literature is still largely open. Clearly, reaching satisfactory surface cleanliness while preventing the dissolution of non-noble elements (Ni, Co, Cu, etc.) is the main challenge in this case. Especially, in the context where the conditioning step aims at reaching a defined and stable initial surface state, should the dissolution of highly unstable surface and subsurface M species (even at the detriment of the catalyst activity) be part of the conditioning step in RDE experiments? These considerations are of primary importance when considering the distinction with conditioning procedures used in realistic PEMFC devices, that range from hours to days to perform [START_REF] Yuan | A Review of Accelerated Conditioning for a Polymer Electrolyte Membrane Fuel Cell[END_REF] . In fact, reaching complete conditioning in PEMFC means reaching stable (and if possible maximum) cell performance, but contrary to the RDE configuration, this performance depends not only on the catalyst surface state but also largely on other parameters of the membrane electrode assembly (MEA), such as membrane and ionomer hydration that impact proton conductivity. In other words, to pass PEMFC conditioning, reaching stable catalyst surface state and intrinsic activity is necessary, while reaching maximum intrinsic activity is not sufficient. Especially, numerous works reported on the collateral damages from PEMFC conditioning to the catalyst layers [START_REF] Kabir | Elucidating the Dynamic Nature of Fuel Cell Electrodes as a Function of Conditioning: An Ex Situ Material Characterization and in Situ Electrochemical Diagnostic Study[END_REF][START_REF] Christmann | Activation Mechanisms in the Catalyst Coated Membrane of PEM Fuel Cells[END_REF][START_REF] Kocha | Advances in Rapid and Effective Breakin/Conditioning/Recovery of Automotive PEMFC Stacks[END_REF] . Among others (temperature, gas atmosphere etc.), possible distinction between the RDE and PEMFC conceptions of this very first step of activation would likely contribute to the extreme discrepancies observed between PtM/C catalysts performance screened by the RDE and their practical performance in PEMFC [START_REF] Stephens | Toward Sustainable Fuel Cells[END_REF][START_REF] Ly | Integrating Nanostructured Pt-Based Electrocatalysts in Proton Exchange Membrane Fuel Cells[END_REF] . Note that in view of addressing the main shortcomings of the RDE setup (unrealistic liquid electrolyte, low current density due to low reactant concentration etc.), the literature is now dedicating raising attention to the Gas Diffusion electrode (GDE) setup [START_REF] Ehelebe | Benchmarking Fuel Cell Electrocatalysts Using Gas Diffusion Electrodes: Inter-Lab Comparison and Best Practices[END_REF][START_REF] Alinejad | Testing Fuel Cell Catalysts under More Realistic Reaction Conditions: Accelerated Stress Tests in a Gas Diffusion Electrode Setup[END_REF][START_REF] Inaba | Benchmarking High Surface Area Electrocatalysts in a Gas Diffusion Electrode: Measurement of Oxygen Reduction Activities under Realistic Conditions[END_REF][START_REF] Ehelebe | Evaluating Electrocatalysts at Relevant Currents in a Half-Cell: The Impact of Pt Loading on Oxygen Reduction Reaction[END_REF][START_REF] Ehelebe | Platinum Dissolution in Realistic Fuel Cell Catalyst Layers[END_REF][START_REF] Pinaud | Key Considerations for High Current Fuel Cell Catalyst Testing in an Electrochemical Half-Cell[END_REF] . Because the current 'best practices' in this case, notably regarding electrocatalyst break-in, are based on RDE-like electrode cycling protocol [START_REF] Ehelebe | Benchmarking Fuel Cell Electrocatalysts Using Gas Diffusion Electrodes: Inter-Lab Comparison and Best Practices[END_REF] , the above-mentioned questioning extends to the GDE setup.

In this contribution, the effects of electrochemical conditioning on PtNi nanocatalyst structure, chemical composition and performance for the ORR are investigated using operando high energy X-ray diffraction in both liquid electrolyte and solid electrolyte X-ray transparent PEMFC, on-line inductively coupled plasma mass spectrometry (ICP-MS) and rotating disk electrode (RDE) techniques, respectively. The sequential effects of two conditioning protocols are investigated on a material's library composed of a commercial Pt/C catalyst (20 wt. %, Tanaka Kikinzoku Kogyo, TKK), and two in-house catalysts that are faceted PtNi-rich/C (high Ni content) nanoparticles and PtNi-poor/C (low Ni content) nanosponges. Our results show how the conclusions previously obtained on poly-Pt and Pt/C surfaces extend to PtNi/C materials, and identify the conditioning step as a major source of discrepancy between catalyst performance and stability screened by the RDE and possibly observed in realistic PEMFC device. They also provide simple guidelines to be implemented in RDE testing to solve this issue. this sample features a large fraction of faceted nanoparticles mixing cubic and octahedral shapes, and will be referred in the following as 'Faceted PtNi/C'. An important note for the following is that this sample has been thoroughly washed with toluene, ethanol and deionized water but not acid washed after the synthesis (see Experimental Methods). The third catalyst investigated is an original PtNi/C material we introduced and described in previous contributions [START_REF] Chattot | Beyond Strain and Ligand Effects: Microstrain-Induced Enhancement of the Oxygen Reduction Reaction Kinetics on Various PtNi/C Nanostructures[END_REF][START_REF] Chattot | Surface Distortion as a Unifying Concept and Descriptor in Oxygen Reduction Reaction Electrocatalysis[END_REF][START_REF] Chattot | Disclosing Pt-Bimetallic Alloy Nanoparticle Surface Lattice Distortion with Electrochemical Probes[END_REF] , referred here as 'Sponges PtNi/C'. Compared with the Faceted PtNi/C sample, this catalyst is composed of large (ca. 50-60 nm) nanoporous multi-grained superstructures, themselves composed of ca. 5 nm PtNi nanocrystallites (Figure 1.c). Also, the Sponges PtNi/C sample features a lower content of 16 Ni at.%, notably because of the acidic treatment performed at the last step of the synthesis (22 h in 1 M H2SO4, see Experimental Methods). Overall, the PtNi/C catalysts investigated represent two different strategies toward enhanced ORR kinetics largely discussed in previous contributions [START_REF] Chattot | Surface Distortion as a Unifying Concept and Descriptor in Oxygen Reduction Reaction Electrocatalysis[END_REF][START_REF] Chattot | Building Practical Descriptors for Defect Engineering of Electrocatalytic Materials[END_REF] . Briefly, Faceted PtNi/C material is based on either the presence of alloyed Ni in surface or near surface regions of the catalyst to tune its surface electronic structure [START_REF] Stamenkovic | Changing the Activity of Electrocatalysts for Oxygen Reduction by Tuning the Surface Electronic Structure[END_REF] , while the Sponges PtNi/C material relies on the use of Ni as a sacrificial element (dealloying) for the implementation of nanoporosity and surface distortion with desirable effects on ORR kinetics [START_REF] Chattot | Beyond Strain and Ligand Effects: Microstrain-Induced Enhancement of the Oxygen Reduction Reaction Kinetics on Various PtNi/C Nanostructures[END_REF][START_REF] Chattot | Surface Distortion as a Unifying Concept and Descriptor in Oxygen Reduction Reaction Electrocatalysis[END_REF][START_REF] Dubau | Defects Do Catalysis: CO Monolayer Oxidation and Oxygen Reduction Reaction on Hollow PtNi/C Nanoparticles[END_REF] . Importantly, the catalytic site coordination is tuned at the global scale by faceting in the case of Faceted PtNi/C 30 , while it is tuned locally for the Sponges PtNi/C by the presence of structural defects [START_REF] Calle-Vallejo | Finding Optimal Surface Sites on Heterogeneous Catalysts by Counting Nearest Neighbors[END_REF] . between 0.05 V vs. RHE and 1.0 V vs. RHE. Then, three support CVs in O2-free 0.1 M HClO4 were recorded between 0.05 V vs. RHE and 1.0 V vs. RHE at 20 mV s -1 . Step 1 ends with three 'ORR' measurement cycles in quasi-stationary state, i.e. between 0.05 V vs. RHE and 1.0 V vs. RHE at 5 mV s -1 . Importantly, because operando WAXS and online ICP-MS measurements were not technically possible on rotating electrode with our setups, the 'ORR' were simulated in N2-saturated electrolyte and stationary thin-film electrode, while O2 and electrode rotation (1600 r.p.m.) were introduced in the RDE tests. This compromise in WAXS and online ICP-MS experiments still allows quantifying the catalyst structural stability at electrode potential relevant for the ORR after a given conditioning step, but the possible influence of oxygen present in solution is not captured (but was found insignificant for Pt(111) surface within such electrode potential range [START_REF] Drnec | Pt Oxide and Oxygen Reduction at Pt(111) Studied by Surface X-Ray Diffraction[END_REF] ).

Step 2 (Figure 2 Step 1+2 was investigated using a three-electrode electrochemical flow cell coupled to an ICP-MS presented in a previous contribution [START_REF] Abbou | Manipulating the Corrosion Resistance of SnO2Aerogels through Doping for Efficient and Durable Oxygen Evolution Reaction Electrocatalysis in Acidic Media[END_REF] .

More details are provided in the Experimental Methods section of the Supporting Information

The impact of the different conditioning steps on the catalysts surface can be first attested by the conventional approach, i.e. by analyzing the shape and the stability of the support CVs in Figure 3.a-c.

Clearly, and for both Pt and PtNi materials, the conditioning procedure composed of 50 potential cycles at 100 mV s -1 between 0.05 V vs. RHE and 1.0 V vs. RHE (Step 1) allows reaching highly reproducible CVs in the same potential range at lower sweep rate, as attested by the superimposition of the three consecutives cycles (cyan, dark cyan and blue traces in Figure 3.a-c). Interestingly, however, performing additional 50 potential cycles at 500 mV s -1 between 0.05 V vs. RHE and 1.23 V vs. RHE (Step 2) also leads to stable and superimposed CVs (green, orange and red traces in Figure 3.a-c) but slightly different that the ones obtained after Step 1. In the case of the reference Pt/C TKK in Figure 3.a, one can observe a significant increase of the electrical charges associated with adsorption and desorption of both under-potentially deposited hydrogen (referred as HUPD in the following) and surface oxides. This is in agreement with the results from Shinozaki et al. on Pt surfaces [START_REF] Shinozaki | Oxygen Reduction Reaction Measurements on Platinum Electrocatalysts Utilizing Rotating Disk Electrode Technique[END_REF] , confirming that the upper potential limit of 1.23 V vs. RHE leads to a better removal of surface contaminants compared to 1.0 V vs. RHE, possibly accompanied with surface reconstruction, as pointed out by studies on single crystals [START_REF] Ruge | Structural Reorganization of Pt(111) Electrodes by Electrochemical Oxidation and Reduction[END_REF][START_REF] Jacobse | Correlation of Surface Site Formation to Nanoisland Growth in the Electrochemical Roughening of Pt(111)[END_REF] . The question of the nature of such electroinactive species adsorbed on the catalyst surface (referred to as 'crap on the electrode' by A. Bard [START_REF] Bard | Effect of Adsorption of Electroinactive Species[END_REF] ) is still largely open, but both mineral or organic residues from nanocatalyst synthesis media (oleylamine, oleic acid, ethylene glycol etc.) are probable candidates here, and inherent to practical nanocatalysts.

Whereas the changes in the HUPD amplitude preserve its shape, this is not the case at higher potentials. In fact, one can also observe a ca. 30 mV decrease on the onset of surface oxide formation, associated with a pronounced tailing in their subsequent reduction to a potential as low as 0.4 V vs. RHE. This suggests that the catalytic sites ultimately 'cleaned' by the further conditioning of Step 2 are in average more oxophilic than the free sites after Step 1.

In the case of PtNi materials, the observed trends depend on the catalyst structure. As for Pt/C TKK, the surface of Faceted PtNi/C (Figure 3 high potential observed after Step 1+2 suggests better surface cleanliness compared to after Step 1. Also, the shape of the HUPD slightly evolves, with the development of a peak at potential higher than 0.30 V vs. RHE. This feature has been previously associated to an electrochemical signature of surface lattice distortion [START_REF] Chattot | Disclosing Pt-Bimetallic Alloy Nanoparticle Surface Lattice Distortion with Electrochemical Probes[END_REF] , which are implemented by design for this electrocatalyst. According to the CVs experiments, the necessity in surpassing 1.0 V vs. RHE during conditioning for complete contaminants removal on Pt/C seems to also apply for PtNi/C materials. However, whether the changes observed in the CVs of PtNi Operando WAXS and online ICP-MS allow to see beyond the previously discussed support CVs, and to directly address the questions of material adsorption trends, structural and chemical integrity in the ORR potential range during simulated quasi steady-state conditions. Rietveld refinement of the WAXS patterns was used to reveal the electrochemical strain dynamics (variations of the lattice constant) during three consecutive cycles of electrode polarization at 5 mV s -1 after the different conditioning steps (Figure 3.df). As discussed in details in dedicated previous contributions [START_REF] Chattot | Electrochemical Strain Dynamics in Noble Metal Nanocatalysts[END_REF][START_REF] Martens | Probing the Dynamics of Platinum Surface Oxides in Fuel Cell Catalyst Layers Using in Situ X-Ray Diffraction[END_REF] , the enhanced surface-to-volume ratio of nanoparticles allows adsorption processes to be monitored by 'bulk' X-ray diffraction technique. Briefly, the lattice constant of Pt nanoparticles was found minimum (maximum compressive strain) at the potential of zero total charge (pztc), where the surface is (rather) free of adsorbates. Excursion of the electrode potential below (or above) the pztc resulted in a homogeneous increase of the lattice constant over the nanoparticle volume, associated with cations (or anions) adsorption processes. Note that the double-layer charging was found to also contribute as a background in the observed lattice expansion. In the present case of PtNi alloys, the absolute values of the lattice constant also inform on the fraction of Ni being alloyed with Pt according to the Vegards' law. In this context, Figure 3.d-f unambiguously reveals the dramatic effects of the two conditioning steps regarding catalysts surface structural stabilization and/or integrity in the potential range of the ORR. In fact, the lattice strain dynamics is found not reproducible for any of the catalysts after Step 1, whereas Step 1+2 leads to highly reproducible trends for all the catalysts. This further confirms that even stable CVs between 0.05 and 1.0 V vs. RHE at 20 mV s -1 (far from the steady state) obtained after Step 1 do not necessarily ensure structural stability at 5 mV s -1 in the same potential range.

Looking first at the reference Pt/C TKK in Figure 3.d, the lattice constant tends to decrease from one cycle to the next (cyan, dark cyan and blue traces), mostly in the so-called double-layer potential region (from 0.30 to 0.60 V vs. RHE). In agreement with the support CVs, this suggests the progressive removal of the residual contaminants or surface oxides still adsorbed on the surface. Structural data acquired after Step 1+2 (green, orange and red traces), on the contrary, show excellent reproducibility over three cycles.

Compared to after

Step 1, the lattice strain dynamics after Step 1+2 shows a sharp hysteresis above 0.20 V vs. RHE, confirming the above-mentioned hypothesis of highly oxophilic sites (the ones more prone to contamination) to be finally electrochemically active at this stage. These sites are found to be reduced only at potential as low as 0.20 V vs. RHE, ultimately supporting the idea that some features in the ' HUPD ' are in fact associated to oxides reduction and/or oxidation [START_REF] Markovic | Surface Science Studies of Model Fuel Cell Electrocatalysts[END_REF][START_REF] Marichev | Reversibility of Platinum Voltammograms in Aqueous Electrolytes and Ionic Product of Water[END_REF][START_REF] Van Der Niet | Water Dissociation on Well-Defined Platinum Surfaces: The Electrochemical Perspective[END_REF][START_REF] Janik | On the Presence of Surface Bound Hydroxyl Species on Polycrystalline Pt Electrodes in the "Hydrogen Potential Region[END_REF][START_REF] Garcia-Araez | Analysis of Temperature Effects on Hydrogen and OH Adsorption on Pt(1 1 1), Pt(1 0 0) and Pt(1 1 0) by Means of Gibbs Thermodynamics[END_REF] . Now looking at the potential-resolved online ICP-MS data of Pt/C TKK measured in similar conditions in Figure 3.g, and in agreement with pioneer work using online ICP-MS [START_REF] Topalov | Towards a Comprehensive Understanding of Platinum Dissolution in Acidic Media[END_REF] , Pt mostly dissolves during the cathodic sweep of the cycling voltammogram (descending potential). This cathodic dissolution is attributed to the reduction of sub-surface oxygen species formed at high electrode potential according to the placeexchange mechanism [START_REF] You | In-Situ x-Ray Reflectivity Study of Incipient Oxidation of Pt(111) Surface in Electrolyte Solutions[END_REF] . However, the two sequential conditioning steps do not seem to impact the Pt dissolution trends in the potential region of the ORR in the case of Pt/C TKK (except the unexplained, peculiar case of the third cycle after Step 1).

Regarding the PtNi/C materials, the structural stabilization is accompanied with important transformations.

In the case of Faceted PtNi/C in Figure 3.e, the lattice strain dynamics after Step 1 shows interesting features. First, the reversibility in lattice increase at high potential seems to be dominated by charges accumulation in the double-layer without pronounced hysteresis, suggesting the absence of significant oxidation of the surface at this stage. Second, as the 'ORR' cycles proceed after Step 1, the lattice parameter tends to contract at each subsequent cycle below 0.40 V vs. RHE and to expand above 0.60 V vs. RHE.

While the contraction of the lattice at low potential is somehow similar to the observation on pure Pt/C TKK during contaminant removal, the expansion of the lattice at higher potential is unique to the Ni-rich Faceted PtNi/C material, and could likely be attributed to Ni leaching from the surface. In fact, stable structural data obtained after Step 1+2 confirms Ni leaching occurs in the process of catalyst structural stabilization, as the lattice constant shift from 3.8095 Å (fresh) to ca. Overall, the WAXS data further confirm and extend the necessity of 'harsh' conditioning protocol from Pt/C to PtNi/C materials, in order to reach at least temporary stable structural state in the ORR potential range. However, both WAXS and ICP-MS data indicate the conditioning of PtNi/C materials does not only relates to surface contaminants removal, but also largely alters surface chemical composition. Not surprisingly, Ni is leached during the catalyst's stabilization process, and not only the vanishing of the alloying effects leads to more oxophilic surfaces and Pt dissolution rates, but also impacts the ORR performance, as shown below. S2 and Table S3). Clearly, the trends displayed on the Tafel plots (Figure 4.a-c) are catalyst-dependent. In fact, reaching complete conditioning results in no significant change of the kinetic current density for the ORR for the reference Pt/C TKK, in a massive loss of performance for the Faceted PtNi/C material and to nearly no change for the Sponges PtNi/C. As quantitatively shown in Figure 4.d, from one step to the other, the ORR specific activity of Pt/C TKK, Faceted PtNi/C and Sponges PtNi/C decreases by 10 %, 71 % and 25%, respectively. In terms of intrinsic activity enhancement factors values of PtNi materials compared to reference Pt/C TKK at similar state of conditioning (which is a convenient performance metric used between research laboratories), Faceted PtNi/C drops from 14.4 to 4.6 and Sponges PtNi/C from 10.7 to 8.9.

It appears thus clear that the question of whether the ORR activity from RDE is measured on a stable surface state or not is a complete game changer for PtM materials (M being a 3d transition metal), and must reach primary attention in the RDE literature. The danger in reporting ORR activity from highly unstable surface state is likely an overestimation of the catalyst performance to be found in the practical device after proper conditioning (due to transition metal leaching), but not only. In fact, performing consecutive ORR polarization curves at a given activation state allows attesting the relevance of a single point measurement.

In the cases of pure Pt, the standard deviations associated with the six ORR activity measurements across the different states of conditioning is below 6 %, while they reach 15% and 40 % in case of acid-treated and untreated PtNi samples, respectively. We finally propose a comparison of the two conditioning steps investigated here in RDE configuration and the structural degradation they produce with a fast conditioning protocol in single cell PEMFC device.

Similar PtNi materials (Faceted PtNi/C and Sponges PtNi/C) were synthetized in larger batch quantities and integrated into 5 cm 2 membrane-electrode assemblies (MEAs) with Nafion 115 membrane, targeting 0.4 mgPt cm -2 at the cathode. The MEAs were mounted inside a X-ray transparent PEM single cell [START_REF] Martens | X-Ray Transparent Proton-Exchange Membrane Fuel Cell Design for in Situ Wide and Small Angle Scattering Tomography[END_REF] , allowing the acquisition of WAXS patterns of the cathode in operando conditions. The cell was operated at 353 K and 100 % relative humidity, and the anode and cathode sides were fed with 104 sccm H2 and 250 sccm O2, respectively, with 0.5 bar backpressure. The conditioning protocol consisted in 5 repetitions of a potential cycle composed of 2 min at open circuit voltage (OCP), followed by 3 min at cell voltage of 0.85 V and 10 min at 0.65 V (Figure S7). Figure 5 shows a summary of the lattice expansion measured operando after different steps of conditioning in liquid and PEMFC cells for both Faceted PtNi/C and Sponges PtNi/C catalysts compared to their pristine powders measured ex situ. The lattice constant expansions measured in PEMFC at 353 K were corrected from the effect of thermal expansion (ca. 0.006 % compared to 298 K) [START_REF] Arblaster | Crystallographic Properties of Platinum[END_REF] .

Strikingly, in the case of the Faceted PtNi/C catalyst (Figure 5.a), a lattice expansion above 0.3 % compared to the pristine catalyst powder is observed in MEA at the very beginning of test, even before the conditioning procedure is performed. This value of lattice expansion exceeds the ones reached after any of the conditioning protocol investigated in liquid cell. This suggests the MEA manufacturing process (ink formulation, sonication, hot pressing etc.) causes more degradation (equivalent to 3 Ni at.% loss) than the potential cycling performed in liquid electrolyte. In fact, traces of a pure Ni phase are visible on the WAXS patterns of the pristine powder of this catalyst and still visible during all the measurements performed in liquid environment, while pure Ni is not detected anymore in the cathode catalyst layer of the MEA (see All these results point toward the idea that, beyond the differences in nature of the electrolyte and structure of the catalyst layer, the viability of the conclusions obtained from model RDE experiments to practical PEMFC device regarding PtM catalyst activity for the ORR may be in great part accounted to the conditioning step. Protection of the most fragile catalysts by the application of 'gentle' electrochemical protocols may lead to tremendous ORR activity in RDE, which is still a highly valuable approach in fundamental science. But because PtM nanocatalyst stability in the harsh environment of PEMFC cathode is a well-establish concern of primary importance, such practices in RDE are unproductive, and discredit this technique as a powerful screening method. For RDE results to possibly guide the application device development, we emphasize RDE conditioning protocol must ensure that the investigations are conducted on a realistic catalyst surface state, even if apparently unrealistic potential cycling up to 1.23 V vs. RHE may be required to achieve it. Note the apparent unrealistic upper potential limit can be seen as an approach to compensate for the unrealistic room temperature of the RDE compared to > 60 °C PEMFC, and the crucial significance of the temperature on the non-noble metal dissolution rate [START_REF] Đukić | Understanding the Crucial Significance of the Temperature and Potential Window on the Stability of Carbon Supported Pt-Alloy Nanoparticles as Oxygen Reduction Reaction Electrocatalysts[END_REF][START_REF] Dubau | Unveiling the Crucial Role of Temperature on the Stability of Oxygen Reduction Reaction Electrocatalysts[END_REF] . Thus, we propose the following steps to be implemented before any activity and/or stability measurements in RDE:

• The pre-treatment of the catalyst in 1 M aqueous H2SO4. The temperature and nature of the electrolyte can be adapted to the chemical nature of the PEM and the operating temperature of the device [START_REF] Gatalo | Importance of Chemical Activation and the Effect of Low Operation Voltage on the Performance of Pt-Alloy Fuel Cell Electrocatalysts[END_REF][START_REF] Han | Record Activity and Stability of Dealloyed Bimetallic Catalysts for Proton Exchange Membrane Fuel Cells[END_REF] .

• An electrochemical conditioning protocol composed of 50 potential cycles at 500 mV s -1 between 0.05 V vs. RHE and 1.23 V vs. RHE in O2-free electrolyte.

Complementary to the recent contribution from Gatalo et al. [START_REF] Gatalo | Importance of Chemical Activation and the Effect of Low Operation Voltage on the Performance of Pt-Alloy Fuel Cell Electrocatalysts[END_REF] on the importance of the chemical activation of commercial PtCu/C and PtNi/C catalysts on fuel cell performance, our results suggest that catalyst candidates possibly disactivating during this conditioning protocol in RDE would likely disactivate in PEMFC at the first start-up of the device. Moreover, and in relation with the PEMFC device application, ex situ 'pre-conditioning' of the catalyst (such as the proposed systematic acidic treatment after the synthesis) has been pointed by recent works as a promising approach in limiting the contamination of the PEM by transition metal leaching during the subsequent in situ conditioning [START_REF] Gatalo | Comparison of Pt-Cu/C with Benchmark Pt-Co/C: Metal Dissolution and Their Surface Interactions[END_REF][START_REF] Moriau | Resolving the Nanoparticles' Structure-Property Relationships at the Atomic Level: A Study of Pt-Based Electrocatalysts[END_REF][START_REF] Gatalo | CO-Assisted Ex-Situ Chemical Activation of Pt-Cu/C Oxygen Reduction Reaction Electrocatalyst[END_REF] . While finding an appropriate way to efficiently pre-condition a PtM-rich catalyst for improved M metal retention during PEMFC operation is still an ongoing challenge, we argue that the appropriate choice of electrocatalytic materials could be an attractive alternative. As shown here with the surface-distorted Sponges PtNi/C catalyst, emerging strategies in electrocatalysts design allowing reaching reasonably high ORR activity without the use of metastable particle shape nor chemical composition [START_REF] Chattot | Beyond Strain and Ligand Effects: Microstrain-Induced Enhancement of the Oxygen Reduction Reaction Kinetics on Various PtNi/C Nanostructures[END_REF][START_REF] Chattot | Surface Distortion as a Unifying Concept and Descriptor in Oxygen Reduction Reaction Electrocatalysis[END_REF][START_REF] Dubau | Defects Do Catalysis: CO Monolayer Oxidation and Oxygen Reduction Reaction on Hollow PtNi/C Nanoparticles[END_REF] should receive more attention.

CONCLUSIONS

In summary, using a combination of operando X-ray diffraction, online ICP-MS and RDE techniques, we have investigated in which extend Pt/C and PtNi/C fuel cell catalysts structures have to evolve in order to reach at least a temporary initial equilibrium (to be referred as beginning of life state) in the conditions of both simulated (RDE) and practical PEMFC cathode. In agreement with former studies on Pt/C materials, our results showed that rather aggressive conditioning protocol is needed to achieve complete cleaning and stabilization of Pt surface. However, this becomes problematic for PtNi surfaces, where surface stabilization is highly entangled with early stage degradation. Our results also show significant degradations occur for PtNi catalyst between their pristine powder and (conditioned) MEA forms. This means that, to guide the development of commercial PEMFC catalysts, RDE conditioning protocol has to be adapted to be relevant for the application. The combination of acidic pre-treatment with sufficiently harsh potential cycling protocol in RDE is shown to provide a satisfactory mimicking of the catalyst initial state to be found in PEMFC. However, for the two PtNi/C catalyst investigated, this translates into an ORR activity loss of either 25 % or 66 % compared to their peak value, depending on their initial structures and chemistry. From such considerations, the study of non-noble metal-poor catalysts in which the activity does not rely on alloying effects (but rather surface distortion structural effect for example) should receive more attention.

Finally, to the intuitive objection: 'PtM catalysts may rarely see potentials as high as 1.23 V vs. RHE in the PEMFC' one could argue 'the surface state of PtM catalyst conditioned below 1.0 V vs. RHE in RDE may hardly see the PEMFC'.

TEXTFigure 1 :

 1 Figure 1: TEM images and chemical composition of (a) Pt/C TKK reference catalyst, (b) Faceted PtNi/C catalyst and (c) Sponges PtNi/C catalyst. The global Pt weight fraction was estimated from ICP-MS and the chemical composition of the nanoparticles estimated from the lattice constant measured by Rietveld refinement of initial WAXS patterns and Vegard's law.

Figure 1

 1 Figure 1 shows transmission electron microscopy (TEM) images of the three catalysts investigated in this

Figure 2 :

 2 Figure 2: Experimental protocol for operando WAXS, online ICP-MS and RDE investigations. For the operando WAXS and online ICP-MS, the simulated 'ORR' steps were investigated under O2-free 0.1 M HClO4 on stationary electrode, while the RDE was performed in O2-saturated 0.1 M HClO4 with an electrode rotation speed of 1600 r.p.m. Step 1 (a) and Step 2 (b) were performed sequentially on the same electrode for operando WAXS and online ICP-MS, while O2 was introduced only after Step 1 or after Step 1+2 on separated electrodes for RDE measurements. For all experiments, the electrodes loadings were 20 µgPt cm -2 .

Figure 2 1 (

 21 Figure 2 depicts the experimental protocol followed in this study for operando WAXS, online ICP-MS and

  .b) is similar to Step 1, except the conditioning part consisted in 50 potential cycles at 500 mV s -1 in O2-free 0.1 M HClO4 between 0.05 V vs. RHE and 1.23 V vs. RHE (directly inspired from the recommendations of Shinozaki et al. for Pt surfaces 6 ). Step 1 and Step 2 were performed sequentially on the same electrode for operando WAXS and online ICP-MS, while O2 was introduced for ORR measurements only after Step 1 or after Step 1+2 on separated electrodes during RDE tests. CO-stripping measurements were performed at last, after ORR measurements. Overall, this protocol ensures strictly similar electrode history between ORR activity measurements and operando characterization. The operando WAXS patterns were measured during simulated ORR polarization cycles on thin-films electrodes loaded with 20 µgPt cm -2 (as for online ICP-MS and RDE experiments) of the different catalysts with a temporal resolution of ca. 350 ms. The measurements were performed in grazing incidence configuration in a three-electrode electrochemical flow cell (see Figure S2). The quantification of Pt and Ni dissolution in acidic media at room temperature during Step 1 or

  .b) appears also cleaner and more oxophilic after Step 1+2 compared to after Step 1. Clearly, the electrical currents associated to formation and reduction of surface oxides that were mostly disabled in this electrode potential range after Step 1, are visible after Step 1+2. Interestingly, these changes are highly asymmetrical (similar oxidative current but increased reduction current), suggesting a possible transition from a faradaic oxidation of residual contamination after Step 1 toward a 'reversible' Pt oxidation after Step 1+2. Finally, the acid-leached Sponges PtNi-poor/C show only minor changes in the CVs after Step 1 and after Step 1+2 (Figure 3.c). Still, the plateau in oxidative current at

  materials are caused only by contamination removal and/or changes in Pt:Ni surface composition and reconstruction remains unknown at this stage. Importantly, the apparent stability (reproducibility) of the support CVs recorded at 20 mV s -1 (for example here after Step 1) is no evidence for complete conditioning.

Figure 3 :

 3 Figure 3: Three first consecutive cycles of a)-c) support CVs measured at 20 mV s -1 with RDE; d)-f) lattice strain dynamics measured with operando WAXS; g)-i) Pt dissolution and j)-l) Ni dissolution measured with online ICP-MS after Step 1 (cyan, dark cyan and blue traces) and Step 1+2 (green, orange and red traces) for Pt/C TKK, Faceted

  3.8102 Å (after Step 1) to 3.8160 Å (after Step 1+2). If these fine shifts in lattice parameter are well-captured by our experimental setup, they only correspond to overall minor changes in Pt:Ni composition. According to the Vegards' law, the Faceted PtNi/C alloy sample experiences a loss of 0.7 % and 5.6 % of its initial Ni content after Step 1 and Step 1+2, respectively. Ni leaching is also detected by ICP-MS and integrations of the dissolution signals measured during the two Steps of the experimental protocol Figure S3-S6, and indicates that ca. 4 % and 7 % of the initial Ni content was lost after Step 1 and Step 1+2, respectively. However, the XRD method is concerning only Ni alloyed with Pt, while ICP-MS also detects the dissolution of the pure Ni phase present in this sample. The reproducible structural state dynamics of Faceted PtNi/C reached after Step 1+2 over the potential range of the ORR shows the presence of quasi-irreversible Pt oxidation (hysteresis) and a more pronounced lattice expansion in the HUPD region, both suggesting a decrease of the alloying effects regarding weakened adsorption of hydrogen and oxide species. Online ICP-MS measurements of Pt dissolution in Figure 3.h confirm the dramatic changes in surface state, as the increased Pt oxidation revealed by WAXS data translates in a 2-fold increase of Pt dissolution rate. The ECSA-normalized Pt dissolution rate from the Faceted PtNi/C is 5-fold higher than from Pt/C TKK, suggesting high chemical instability (see ECSA values in Table S1). This instability can be at least partially explained by the Ni dissolution data in Figure 3.j-k, as the significant Ni dissolution from Faceted PtNi/C is likely to promote concomitant Pt dissolution 46 . Finally, performing the second Step of conditioning only resulted in a mild decrease of the Ni dissolution rate. Note that the Ni dissolution peak around E = 0.4 V vs. RHE in Figure 3.k has been previously ascribed to the leaching of underpotentially-deposited Ni 47 . The trends observed on the Sponges PtNi/C are qualitatively close to the one observed for Faceted PtNi/C, except regarding Ni dissolution. Because the Sponges PtNi/C were previously dealloyed by acid washing post-synthesis, the lattice strain dynamics recorded after Step 1 is quite reproducible along three cycles (Figure 3.f). However, using a 1.23 V vs. RHE upper potential limit during the conditioning protocol still triggers further surface cleaning accompanied with Ni dissolution. The changes in lattice parameter are from 3.8610 Å (fresh) to ca. 3.8626 Å (after Step 1) to 3.8731 Å (after Step 1+2). This translates to a loss of 2.6 % and 20 % of their initial Ni content after Step 1 and Step 1+2, respectively. These values are significantly higher than those observed for Ni-richer Faceted PtNi/C, suggesting the incapacity of small crystallites to retain even a low initial Ni content. The stable structural state reached by the Sponges PtNi/C shows strain dynamics qualitatively closer to Pt/C TKK than Faceted PtNi/C, except the amplitude of the oxidation irreversibility is much lower. Finally, Pt dissolution from the Sponges PtNi/C in Figure 3.l is almost identical to the Pt/C TKK. Most importantly, the Ni dissolution from the Ni-poor Sponges PtNi/C (Figure 3.l) in the potential range of the ORR is found nearly 200-fold below the one of Ni-rich Faceted PtNi/C.

Figure 4 :

 4 Figure 4: Impact of the conditioning steps and measurement cycle number on the estimated ORR kinetic current density with RDE. Tafel plot for the ORR for (a) Pt/C TKK; (b) Faceted PtNi/C and (c) Sponges PtNi/C. (d) ORR kinetic current density at 0.95 V vs. RHE. In (d), the numbers above the bars indicate the corresponding enhancement factors in ORR activity compared to Pt/C TKK in similar conditions (step and cycle). The kinetic current densities are corrected from the ohmic losses and O2 mass transport limitation in solution. The error bars correspond to the standard deviations.

Figure 4

 4 Figure 4 shows the impact of the different conditioning protocols (Step 1, or Step 1+2) also with the number

Figure

  Figure S1). After PEMFC conditioning, the Faceted PtNi/C catalyst features a lattice expansion of 0.45 %,

Figure 5 :

 5 Figure 5: Lattice expansion measured operando after different steps of conditioning in liquid and PEMFC cells compared to the raw catalyst powders for a) Faceted PtNi/C and b) Sponges PtNi/C. The lattice constant expansions measured in PEMFC operando were corrected from the effect of thermal expansion (ca. 0.006 % at 353 K compared to 298 K).

ACKNOWLEDGEMENTS

We would like to thank ESRF for the provision of the beamtime at ID31 beamline as well as Helena Isern and Florian Russello for the help with the beamline preparation. R.C gratefully acknowledge financial support from the French National Research Agency through the HOLYCAT project (grant number n• ANR-22-CE05-0007). C.R., K.K., V.M., L.C., A.V, F.M. and L.D. gratefully acknowledge financial support from the French National Research Agency through the BRIDGE project (grant number n•ANR-19-ENER-0008-01). SC acknowledges IUF for financial support.

AUTHOR INFORMATION Corresponding Author

Raphaël Chattot, ICGM, Univ. Montpellier, CNRS, ENSCM, 34095 Montpellier Cedex 5, France. https://orcid.org/0000-0001-6169-530X ; email : raphael.chattot@umontpellier.fr

ASSOCIATED CONTENT

This information is available free of charge on the ACS Publications website Supporting Information: Details and methods about the electrocatalysts used, their synthesis, the electrochemical measurements, synchrotron wide-angle X-ray scattering measurements, Rietveld refinement, ICP analysis, TEM imaging, MEA fabrication and PEMFC tests.

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