Florent Guiotte
email: florent.guiotte@univ-rennes2.fr

Sébastien Lefèvre
email: sebastien.lefevre@irisa.fr

Thomas Corpetti
email: thomas.corpetti@univ-rennes2.fr

Rasterization strategies for airborne LiDAR classification using attribute profiles

Keywords: airborne LiDAR, land cover mapping, attribute profiles, multilevel image description

This paper evaluates rasterization strategies and the benefit of hierarchical representations, in particular attribute profiles, to classify urban scenes issued from multispectral LiDAR acquisitions. In recent years it has been found that rasterized LiDAR provides a reliable source of information on its own or for fusion with multispectral/hyperspectral imagery. However previous works using attribute profiles on LiDAR rely on elevation data only. Our approach focuses on several LiDAR features rasterized with multilevel description to produce precise land cover maps over urban areas. Our experimental results obtained with LiDAR data from university of Houston indicate good classification results for alternative rasters and even more when multilevel image descriptions are used.

I. INTRODUCTION

Airborne LiDAR systems are a common source of aquisition for elevation data. Such systems provide accurate 3D point clouds of the scanned scenery. LiDAR is very popular over urban areas where it brings a valuable complementary source of information when used with multispectral or hyperspectral optical data in order to achieve land cover or land use mapping.

LiDAR data are voluminous, irregularly distributed point clouds coming along with intensity features and acquisition meta-data. Due to this complexity, LiDAR data for land cover mapping are often simplified to a digital elevation model (DEM) used as additional information for fusion with multispectral or hyperspectral images. In this work, we focused on providing such maps with LiDAR data only using multilevel image description. Classification of several urban classes was derived based on features from LiDAR data such as intensities, elevation and number of echoes.

In the following we review simple yet effective rasterization strategies of LiDAR data, that are subsequently used for multilevel image description with attribute profiles (APs). By doing so, we are then able to efficiently derive a precise land cover map through supervised classification.

The authors acknowledge the support of Région Bretagne (CAMLOT doctoral project).

II. RELATED WORK

A. Classification of LiDAR data

Numerous methods have already been proposed in the past decade for LiDAR point cloud classification, coming from various scientific fields such as geosciences (flow, erosion, rock deformations, . . . ), computer graphics (3D reconstruction) or Earth observation (detection of trees, roads, buildings, . . . ).

Among efficient techniques, some directly exploit the 3D point cloud structure [START_REF] Brodu | 3d terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology[END_REF], [START_REF] Mallet | Relevance assessment of full-waveform lidar data for urban area classification[END_REF], [START_REF] Niemeyer | Contextual classification of lidar data and building object detection in urban areas[END_REF] while in many applications the point cloud is first binned into a 2D regular grid (rasterization process) on which computer vision approaches can be applied (see e.g. [START_REF] Lodha | Aerial LiDAR Data Classification Using Support Vector Machines (SVM)[END_REF]). Apart from some specific applications where LiDAR points are fused with other data (e.g. hyperspectral images [START_REF] Damodaran | Attribute Profiles on Derived Features for Urban Land Cover Classification[END_REF]- [START_REF] Khodadadzadeh | Fusion of Hyperspectral and LiDAR Remote Sensing Data Using Multiple Feature Learning[END_REF], [START_REF] Pedergnana | Classification of Remote Sensing Optical and LiDAR Data Using Extended Attribute Profiles[END_REF]), most techniques consist in computing features to describe the point clouds, before using such features to classify the scene under study. While first works have been focused on the characterization of single points (often through height and intensity) without including information related to their neighbours [START_REF] Lodha | Aerial LiDAR Data Classification Using Support Vector Machines (SVM)[END_REF], more advanced approaches have included spatial relationships using a set of spheres or cylinders (of variable radius) around each point to extract consistent geometric features [START_REF] Mallet | Relevance assessment of full-waveform lidar data for urban area classification[END_REF], [START_REF] Niemeyer | Contextual classification of lidar data and building object detection in urban areas[END_REF], [START_REF] Weinmann | Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers[END_REF]. In this context, multiscale local 3D features (main orientation, variability around each point, . . . ) have proven their efficiency to classify LiDAR scenes (see e.g. [START_REF] Brodu | 3d terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology[END_REF]). Even if it is very efficient, the sphere used to assess the neighbourhood of points is isotropic (no orientation is promoted) which is not optimal since the geometry of objects is not taken into account. Therefore other multi-scale approaches have been proposed on LiDAR DEM, such as the popular attribute profiles [START_REF] Dalla Mura | Morphological attribute profiles for the analysis of very high resolution images[END_REF] that produce a multiscale description of the pixel and its surrounding [START_REF] Ghamisi | Land-cover classification using both hyperspectral and LiDAR data[END_REF], [START_REF] Khodadadzadeh | Fusion of Hyperspectral and LiDAR Remote Sensing Data Using Multiple Feature Learning[END_REF], [START_REF] Pedergnana | Classification of Remote Sensing Optical and LiDAR Data Using Extended Attribute Profiles[END_REF] before proceeding to the classification. The main idea behind is to compute multi-scale spatial features by taking into account the geometry of the scene. In this work, we suggest to explore various information derived from the LiDAR point cloud within the framework of attribute profiles.

B. Attributes profiles

Introduced in 2010 for remotely-sensed images, morphological APs [START_REF] Dalla Mura | Morphological attribute profiles for the analysis of very high resolution images[END_REF] enable a multi-scale description of data driven by their spatial and spectral information. Efficient computation of APs is achieved through tree-based representation of the gray level sets with either a max or a min-tree. They have then been superseded by self-dual attribute profiles (SDAPs) [START_REF] Dalla Mura | Self-dual attribute profiles for the analysis of remote sensing images[END_REF] built from a unique multi-scale representation of an image through the tree of shapes. In this tree, all nodes represent nested connected regions of similar pixels, with leaves made of the local extrema and the root gathering all pixels of the image. Then, successive filterings of the image (or equivalently the tree representation) are performed according to some predefined characteristics computed for each node such as area, moment of inertia, or standard deviation of the connected components. The filtered images are finally stacked together to form description vectors called SDAPs. The concatenation of the SDAPs from different bands in a single vector are called extended self-dual attribute profiles (ESDAPs) [START_REF] Cavallaro | Extended self-dual attribute profiles for the classification of hyperspectral images[END_REF]. From the concept of SDAPs, the differential self-dual attribute profiles (DSDAPs) contains the same information but expresses the difference between successive levels of the SDAPs. Previous works combining APs and LiDAR data only focus on DEM (cf. Sec. II-A). Yet, in recent LiDAR acquisition systems, multi-spectral information can be extracted and the question of the description of other features than DEM is open.

In this study we aim to enhance existing LiDAR classification methods using SDAPs and their derivative to better describe several features extracted from LiDAR data. Validation is performed on the IEEE DFC 2018 public dataset.

III. RASTERIZATION STRATEGIES

In this section we provide the features we extracted from LiDAR data and the multi-scale filtering we have chosen for the spatial description.

A. LiDAR features

LiDAR systems are usually exploited to provide unstructured 3D point clouds used to derive a DEM. Though a DEM brings useful information, additional features issued from LiDAR can still be exploited, in particular:

1) The spectral intensity associated with the first echo in each spectral band.

2) The number of echoes in each spectral band. Some structures, especially in vegetated areas, do not fully backscatter the laser pulse and yield in multi-echoes signals.

3) The position of the last echo. For multi-echoes backscattered signals, this enables to localize the last element encountered. In some situations (especially with trees), this enables to localize the ground surface. 4) The associated intensities of last echo. Analogously to the paired information of first echo position and intensities we have used the last echo spectral intensities in addition to its position. The two latter types of information are usually less employed. Nevertheless, they can help the classification process since all wooded areas are removed, as illustrated by Fig. 1(e).

The rasterization process aims to provide in each cell some representative values of the aforementioned features. For the sake of simplicity, we have chosen to average the values of intensity, elevation and number of echoes contained in each cell. Furthermore, we fill potential empty cells (missing data) through linear interpolation.

B. Attribute filtering

As previously indicated, we consider here the application of attribute profiles over different rasterized versions of LiDAR data. In this paper, for the sake of simplicity, we have chosen to filter all LiDAR features solely based on the area attribute whose values have been set in the urban context. More precisely, we consider three main scales (in other contexts, automatic approaches can be used [START_REF] Cavallaro | Automatic Attribute Profiles[END_REF]):

• small values (1 to 5 m 2 ) remove small-sized objects (e.g. power lines) and can be regarded as denoising filters; • moderate values (5 to 50 m 2 ) remove medium-sized objects (cars, trees, . . . ); • large values (more than 50 m 2 ) remove larger objects (e.g. buildings) and therefore enable to automatically derive a digital terrain model (DTM).

IV. EXPERIMENTS A. Dataset and setup 1) Dataset: Our method was tested on the multi-spectral LiDAR acquisition of the University of Houston issued from 2018 IEEE GRSS Data Fusion Contest dataset 1 . The associated ground truth map has a spatial resolution of 0.5m. roads in white, grass in green, trees in dark green, residential buildings in light grey, non-residential buildings in medium grey, cars in red and trains in purple.

2) Classes: We choose generic urban classes (roads, grass, trees, residential buildings, non-residential buildings, cars and trains) of the dataset to evaluate the overall accuracy.

3) Classifier: We choose random forest (RF) to classify our data since such method provides reliable results with respect both to accuracy and computational efficiency [START_REF] Liaw | Classification and regression by randomForest[END_REF]. 4) Feature computation: ESDAPs have been created by filtering each raster with area attributes. According to observations made in Sec. III-B, we have chosen thresholds Λ = {10, 200, 5000} to compute attributes. In practice, all features mentioned in previous sections are tested independently and combined together. These features are digital surface model (DSM) (denoted as D), intensities (denoted as I), intensities of last echo (denoted as I r ), number of backscattered echoes (denoted as N ) and position of the last echo (denoted as D r ) . For the sake of comparison, we also ran the classification only with initial rasters (without multiscale analysis through SDAPs) to evaluate the benefits of APs and variants. The ESDAPs can be formulated as: ESDAP = {SDAP (D), SDAP (I), ..., SDAP (N )} 5) Train and test data: Unlike most common multi-scale features that rely on spatial windows, attribute profiles rely on specific connectivity that prevents the use of random points to train and test a classifier. As a matter of fact, two pixels in various spatial areas of the image but with similar characteristics are likely to share common nodes, and hence common features. Therefore a random choice of train and test points is unfair. To cope with such a bias, one solution is to spatially split the dataset in two images of same size where the first one is used to train the classifier and the other to test it, and conversely. In practice, we split horizontally the dataset in order to maximize the class distribution in each sub-image.

6) Validation criteria:

From the training split, 10% of the points have been randomly selected to train and evaluate our approach. The process has been repeated 100 times and we provide averaged overall accuracy (OA) and Cohen's kappa coefficient (κ) for each experience.

B. Results

Quantitative evaluations are depicted in Tab. I. As expected and reported in previous works [START_REF] Damodaran | Attribute Profiles on Derived Features for Urban Land Cover Classification[END_REF], [START_REF] Ghamisi | Land-cover classification using both hyperspectral and LiDAR data[END_REF], [START_REF] Pedergnana | Classification of Remote Sensing Optical and LiDAR Data Using Extended Attribute Profiles[END_REF], SDAPs improves the classification when used with DEM D only (first line). This observation is almost valid for all other features. Performances of DSDAPs are slighlty higher than SDAPs except for DEM.

On this dataset, it is surprising to note that multi-spectral intensities (first echo I or last echo I r ) are performing better than DSM D (lines 5, 3, 1 respectively). This information is likely to be an interesting feature to separate urban elements.

It is also worth noting that regarding positions, the last echo D r feature is more useful than first echo D (lines 1, 4) when combined with hierarchical features, with a significant improvement (about ∼ 10%). The ability of the last echo to assess buildings can explain such an observation.

Finally, as expected, the combination of all features (last lines) enables to achieve the best accuracy. Though the difference between DSDAPs and SDAPs is limited, the improvement with respect to the baseline is important and this demonstrates the ability of APs-based features to properly classify LiDAR data in urban environments. We can assess an improvement of κ = 0.06 while comparing pixelwise and DSDAPs description both with all features. We also can assess an improvement of κ = 0.30 while comparing SDAPs on DSM only with SDAPs on all LiDAR features.

For the sake of illustration, we provide in Fig. 2 the classification obtained using all features with DDSDAPs, line 14 of Tab. I. Let us remind that in practice, the complete image has been horizontally split to avoid common features issued from APs between train and test data. Therefore, as some classes appear in few parts of the image, they have not been learned and hence, not been properly classified (for instance the grassy slope at the bottom right has been classified as a building). Despite this difficulty, both quantitative evaluations and qualitative maps are interesting. The prediction map is indeed consistent as the objects exhibit few noise and proper borders. Even if some irregularities on the edges of some objects (e.g. buildings) appear, they are mainly due to the irregularity of the initial point cloud sampling. V. CONCLUSION In this study, we have addressed the classification of multispectral LiDAR using rasterized features and attribute filtering. Results showed that combination of different rasterized strategies can improve classification with the sole use of LiDAR data. In addition, considering SDAPs to model multiscale spatial organization futher improved our results to the point it allowed us to produce a precise land cover map over urban area. Futhermore, the proposed method is fast (for instance, the map from Fig. 2 (1202x4768 pixels) required only 2 seconds for DSDAPs description and 50 seconds for RF classification, considering a laptop CPU (i7-7600U CPU @ 2.80GHz, 4 threads)) and can be used with any supervised classifier (see [START_REF] Merciol | Efficient and large-scale land cover classification using multiscale image analysis[END_REF]). Additional improvements can be designed for this method in the future. On the one hand, we can extract many more features from LiDAR such as point density, orientation within a cell, ratio between spectral bands and summarize the cell with other metrics than mean value such as standard deviation or quantiles. On the other hand, we can enhance SDAPs by filtering more attributes such as moment of inertia, by using more advanced APs-based methods such as the local feature-based attribute profiles (LFAPs) [START_REF] Pham | Local Feature-Based Attribute Profiles for Optical Remote Sensing Image Classification[END_REF] or even by creating LiDAR specific attributes to be included during the construction of the tree. Regarding classification of overlapping classes (e.g. buildings beneath trees), it could also be interesting to head for 3D classification. With this in mind, 3D ground-truth would be optimal for LiDAR data.

Fig. 1 :

 1 Fig. 1: Some rasters over residential area, for first echo (a-b-c-d) and last echo (e-f-g-h) with original rasters (a-e), SDAP filtering with Λ = 10 (b-f), Λ = 200 (c-g) and Λ = 5000 (d-h). One can observe the interest of the last echo (second line) since structures below vegetation are clearly highlighted.

Fig. 2 :

 2 Fig. 2: Classification of the scene using DSDAPs with all features {N, I, I r , D, D r }. The 7 classes are represented as follows: roads in white, grass in green, trees in dark green, residential buildings in light grey, non-residential buildings in medium grey, cars in red and trains in purple.

TABLE I :

 I Overall accuracy (OA) and Cohen's Kappa coefficient (κ) for each feature and combination of them using pixels values, SDAPs, DSDAPs (lines 1-4) and their extended versions (lines 5-14). The dimension of the feature vector (d) is also indicated (intensities (I, I r ) and number of echos N are taken in each of the three spectral bands).

	Description		Pixel			E•SDAPs			E•DSDAPs
	Evaluation metric	OA(%) κ(×100)	d	OA(%) κ(×100)	d OA(%)	κ(×100)	d
	1 D	64.79	46.52	1	72.67	58.80	4	69.46	54.77	4
	2 N	52.30	8.43	3	52.27	10.01 12	52.74	10.64 12
	3 Ir	67.82	51.06	3	70.42	54.35 12	72.54	57.84 12
	4 Dr	63.91	45.12	1	73.94	59.73	4	72.76	58.29	4
	5 I	68.36	52.18	3	72.00	57.30 12	73.28	59.25 12
	6 {D, Dr}	69.65	54.19	2	77.22	64.85	8	77.24	65.36	8
	7 {N, Dr}	68.64	52.56	4	78.88	67.44 16	78.45	67.05 16
	8 {I, D}	83.97	75.64	4	86.29	78.90 16	87.39	80.71 16
	9 {I, Dr}	82.26	72.84	4	86.97	79.86 16	87.93	81.46 16
	10 {N, I, D}	85.99	78.64	7	87.45	80.68 28	88.69	82.69 28
	11 {N, I, Dr}	84.80	76.70	7	88.29	81.91 28	88.99	83.09 28
	12 {I, D, Dr}	84.59	76.50	5	88.21	81.80 20	89.28	83.56 20
	13 {N, I, D, Dr}	85.87	78.46	8	88.92	82.90 32	90.04	84.74 32
	14 {N, I, Ir, D, Dr}	86.20	78.93 11	89.05	83.09	44	90.16	84.91	44

cf http://www.grss-ieee.org/community/technical-committees/data-fusion/

2018-ieee-grss-data-fusion-contest/

ACKNOWLEDGMENT

The authors thank the National Center for Airborne Laser Mapping and the Hyperspectral Image Analysis Laboratory in University of Houston for data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee.


Lucile Lecas

Lucie Hartmann

Lydia Caro

Sarah Mohamed-Bouteben

Claire Raingeval

Isabelle Krimm

Renaud Wagner

Vincent Dugas

Claire Demesmay

Lucie Hartman

Miniaturized weak affinity chromatography for ligand identification of nanodiscs-embedded G-protein coupled receptors

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

Ultra-miniaturized weak affinity chromatography for ligand identification of nanodiscs-solubilized G-protein coupled receptors

Integral membrane proteins represent 20-30% of the total proteins encoded in the human genome, including ion channels, transporters, receptors and enzymes. The G protein-coupled receptors (GPCRs) family is the most diversified family of membrane proteins and play a key role in nearly all physiological processes including vision, immune system, neurotransmission, cellular proliferation. As a consequence, GPCRs are involved in many diseases such as asthma, diabetes, Alzheimer disease and cancer, representing approximatively 30% of currently marketed therapeutic drug targets [START_REF] Hauser | Trends in GPCR Drug Discovery: New Agents, Targets and Indications[END_REF] . Still, 73% of the non-olfactory GPCRs remain untargeted 2 , explaining why the pharmaceutical industry places intensive efforts on GPCR drug discovery programs and is seeking for innovative and robust drug screening methodologies. Among these approaches, Structure-Based-Drug-Design (SBDD) applied to drugs targeting GPCRs appears highly promising but faces two main issues. Firstly, the overproduction of GPCRs leading to structure determination is challenging both in terms of quantities and stability. Because GPCRs are highly dynamics proteins that undergo a number of confor-mational changes upon ligand binding, this inherent conformational flexibility is actually a key issue for crystallographic structural studies. In the last decade, the implementation of GPCR-stabilization strategies, such as receptor mutagenesis, insertion of fusion proteins or use of antibody fragments 3 , induced a major breakthrough in GPCR structural biology, which in turn opened the route for SBDD [START_REF] Cooke | Structures of G Protein-Coupled Receptors Reveal New Opportunities for Drug Discovery[END_REF][START_REF] Andrews | Structure-Based and Fragment-Based GPCR Drug Discovery[END_REF] . The second issue concerns the screening of novel ligands, and in particular weak affinity ligands such as fragments for the development of new drugs. Fragments are small molecules, with restricted physicochemical properties known as the rule of three. This rule suggests that fragments have limited molecular weight (< 300 g/mol), high solubility (logP > 3) and few hydrogen bond donors (<3) and acceptors (<3) [START_REF] Jhoti | The "rule of Three" for Fragment-Based Drug Discovery: Where Are We Now?[END_REF] . Screening such small compounds is highly effective in terms of chemical space, and fragment libraries are usually composed of only one to several thousand molecules. The fragments hits that are identified typically display low to high M affinities and are then elaborated into active compounds through a second step based on structural approaches. Identifying such small molecules, the first step of the Fragment-Based Drug Design (FBDD) process, consists of screening fragments against a purified protein, using biophysical techniques, among which NMR, X-Ray crystallography and Plasmonic Surface Resonance (SPR) are the most extensively used [START_REF] Erlanson | Twenty Years on: The Impact of Fragments on Drug Discovery[END_REF] and other like weak affinity chromatography are currently under development [START_REF] Meiby | Fragment Screening by Weak Affinity Chromatography: Comparison with Established Techniques for Screening against HSP90[END_REF][START_REF] Duong-Thi | Weak Affinity Chromatography as a New Approach for Fragment Screening in Drug Discovery[END_REF][START_REF] Duong-Thi | High-Throughput Fragment Screening by Affinity LC-MS[END_REF][START_REF] Tsopelas | Advances with Weak Affinity Chromatography for Fragment Screening[END_REF] . Moreover while FBDD has been widely applied to soluble proteins [START_REF] Murray | Experiences in Fragment-Based Drug Discovery[END_REF] , very limited reports illustrate some examples of fragment screening against membrane proteins and GPCRs [START_REF] Igonet | Enabling STD-NMR Fragment Screening Using Stabilized Native GPCR: A Case Study of Adenosine Receptor[END_REF][START_REF] Chen | Fragment Screening of GPCRs Using Biophysical Methods: Identification of Ligands of the Adenosine A 2A Receptor with Novel Biological Activity[END_REF][START_REF] Congreve | Fragment Screening of Stabilized G-Protein-Coupled Receptors Using Biophysical Methods[END_REF][START_REF] Aristotelous | Discovery of Β2 Adrenergic Receptor Ligands Using Biosensor Fragment Screening of Tagged Wild-Type Receptor[END_REF][START_REF] Christopher | Biophysical Fragment Screening of the β 1 -Adrenergic Receptor: Identification of High Affinity Arylpiperazine Leads Using Structure-Based Drug Design[END_REF][START_REF] Navratilova | Screening for GPCR Ligands Using Surface Plasmon Resonance[END_REF][START_REF] Duong-Thi | Lipodisks Integrated with Weak Affinity Chromatography Enable Fragment Screening of Integral Membrane Proteins[END_REF] .

Undoubtedly, there is a major need for the identification and elaboration of novel GPCR ligands and in particular for techniques that enable the screening and identification of weak affinity fragments. Ideally, such techniques should accommodate the GPCR-specific requirements discussed above i.e., (i) minimize the sample size to fit the small amounts of purified GPCR that can be reasonably achieved, and (ii) be adapted to the dynamics and intrinsic conformational flexibility of the receptors in a membrane-like environment.

Among the variety of systems that have been developed over the years to investigate GPCRs in solution, lipid nanodiscs have particularly attracted attention for native GPCR interaction studies, most notably with techniques such as NMR and SPR [START_REF] Fredriksson | Nanodiscs for INPHARMA NMR Characterization of GPCRs: Ligand Binding to the Human A2A Adenosine Receptor[END_REF][START_REF] Bocquet | Real-Time Monitoring of Binding Events on a Thermostabilized Human A2A Receptor Embedded in a Lipid Bilayer by Surface Plasmon Resonance[END_REF][START_REF] Silvia | Surface Plasmon Resonance Applied to G Protein-Coupled Receptors[END_REF] . Nanodiscs are self-assembled soluble discoidal phospholipids bilayers encircled by an amphipathic protein called Membrane Scaffold Protein (MSP) [START_REF] Bayburt | Membrane Protein Assembly into Nanodiscs[END_REF] . They have been proved to give the membrane proteins a native-like environment associated with a high homogeneity in size and a high solubility and stability in aqueous solution [START_REF] Serebryany | Artificial Membrane-like Environments for in Vitro Studies of Purified G-Protein Coupled Receptors[END_REF] .

Here, we propose a strategy that combines the preparation of nanodiscs-embedded GPCRs and the ultra-miniaturization of weak affinity chromatography for the detection of GPCRbound weak affinity fragments. In this approach, the nanodiscembedded target is immobilized on the chromatographic support via the biotinylated nanodiscs. The primary innovations concern (i) the biomimetic assembly that allows the immobilization of a fully label-free and purified integral membrane protein, (ii) the immobilization strategy on a streptavidin generic monolithic column that allows an immediate (a few minutes) and in-situ UV-monitored grafting without any protein waste, (iii) the reduction of the protein consumption at the µg level for one reusable affinity column.

The technique is exemplified using the adenosine A2A receptor (AA2AR), a prototypical class A GPCR receptor. AA2AR has been extensively studied during the past few decades making it an ideal model with a large repertoire of ligands [START_REF] De Lera Ruiz | Adenosine A 2A Receptor as a Drug Discovery Target[END_REF][START_REF] Müller | Recent Developments in Adenosine Receptor Ligands and Their Potential as Novel Drugs[END_REF] and 3D X-ray structures available in the presence of antagonists, agonists and in complex with a G-protein [START_REF] Carpenter | Human Adenosine A2A Receptor: Molecular Mechanism of Ligand Binding and Activation[END_REF] . Nevertheless, AA2AR remains a hot topic and the discovery of additional compounds could lead to numerous possibilities for therapeutics applications against inflammation, asthma, cardiovascular diseases, central nervous system diseases and cancer [START_REF] Pinna | Adenosine A2A Receptor Antagonists in Parkinson's Disease: Progress in Clinical Trials from the Newly Approved Istradefylline to Drugs in Early Development and Those Already Discontinued[END_REF][START_REF] Franco | Adenosine A2A Receptor Antagonists in Neurodegenerative Diseases: Huge Potential and Huge Challenges[END_REF][START_REF] Sek | Targeting Adenosine Receptor Signaling in Cancer Immunotherapy[END_REF][START_REF] Chen | Adenosine Receptors as Drug Targets -What Are the Challenges?[END_REF] .

The home-made affinity nano-columns are fully characterized by frontal chromatographic experiments. It is demonstrated that specific and unspecific ligand/receptor interactions can be distinguished, that binding constants from low to high µM range can be determined and that the binding pocket of uncharacterized ligands can be assessed with competition experiments using a reference compound. At last, the applicability of this method for FBDD is demonstrated with 6 fragments recently identified as ligands or non-ligands of AA2AR.

EXPERIMENTAL SECTION Biochemistry Methods

Production and purification of AA2AR

The wild-type human AA2AR receptor N-terminally fused to the flag and deca-histidine tags was recombinantly produced with the Pichia pastoris system as previously described [START_REF] Hartmann | Expression of Eukaryotic Membrane Proteins in Pichia Pastoris[END_REF] . AA2AR receptors were then prepared as follows. After cell lysis and whole membrane preparation [START_REF] Hartmann | Expression of Eukaryotic Membrane Proteins in Pichia Pastoris[END_REF] , membranes were diluted to ca. 2 mg.mL -1 in cold solubilization buffer (50 mM HEPES pH 7.4, 500 mM NaCl, 0.5 % β-DDM (w/v), 0.05 % CHS (w/v), 25 mM imidazole, 1 µM DPCPX, 0.3 mM EDTA) containing an antiprotease cocktail (Roche). The suspension was incubated for 30 min at room temperature, followed by centrifugation at 100,000 x g for 30 minutes. Solubilized proteins were then loaded on a 1 mL HisTrap HP column (GE Healthcare) at 1 mL.min -1 flow rate. After extensive washing with a 50 mM HEPES pH 7.4, 500 mM NaCl, 0.05 % β-DDM (w/v), 0.005 % CHS (w/v), 25 mM imidazole, 1 µM DPCPX buffer, the bound receptor was eluted with a linear gradient of 25 to 500 mM imidazole. Fractions eluting at around 150 mM imidazole were pooled, concentrated down to 400-500 μL (Vivaspin 6, 30k MWCO), and loaded onto a Superdex 200 increase 10/300 GL column (GE Healthcare). Proteins were separated at 0.3 mL.min -1 with a 50 mM HEPES pH 7.4, 150 mM NaCl, 0.02 % β-DDM (w/v), 0.002 % CHS (w/v) 1 µM DPCPX buffer. Fractions containing the purified AA2AR receptor in detergent were pooled and directly used for reconstitution in lipid nanodiscs.

Membrane scaffold protein purification and biotinylation

The membrane scaffold protein MSP1E3D1(-) was produced and purified as previously described [START_REF] Denisov | Directed Self-Assembly of Monodisperse Phospholipid Bilayer Nanodiscs with Controlled Size[END_REF] . MSP1E3D1(-) was then incubated in presence of a biotinylation reagent (EZ-LinkTM NHS-PEG4-Biotin, Thermo Scientific) at a 1:5 MSP:biotin molar ratio in a 20 mM HEPES pH 7.4, 100 mM NaCl, 0.5 mM EDTA buffer. After a one-hour incubation at room temperature, the reaction was stopped by adding 5 mM Tris-HCl pH 7.4. The resulting biotinylated-MSP1E3D1(-) was isolated from the free reagent in a final desalting step (HiTrap desalting, GE Healthcare) in a 20 mM Tris-HCl pH 7.4, 100 mM NaCl, 0.5 mM EDTA storage buffer, flash-frozen in liquid nitrogen and stored at -80 °C until use. The actual biotinylation was further confirmed in a standard Western-blot revealed with extravidin-HRP.

Nanodisc assembly and purification

The biotinylated-MSP1E3D1(-) was mixed at a 1:70 molar ratio with purified lipids (POPC/POPG; 3/2 molar ratio) previously dissolved at a 24 mM concentration in a 50 mM HEPES pH 7.4, 150 mM NaCl, 48 mM Na-cholate buffer, and incubated for 15 minutes on ice. The purified receptor was then added to the MSP:lipid mixture at 1:10 receptor:MSP1E3(-) molar ratio and further incubated for 60 minutes on ice. Selfassembly was initiated by detergent removal using BioBeads SM-2 (Biorad) (0.25 g of Biobeads per mL of reconstitution mixture) and allowed to proceed overnight at 4 °C on a tube rotator. The Biobeads were then removed by centrifugation and the recovered supernatant was directly loaded on a 1 mL HisTrap HP column (GE Healthcare) previously equilibrated in a 50 mM HEPES pH 7.4, 300 mM NaCl, 10 mM imidazole buffer. After extensive washing with the equilibration buffer, the AA2AR-containing discs were eluted with the same buffer containing 500 mM imidazole. The eluted fractions were then pooled and loaded on a Superdex 200 10/300 GL column (GE Healthcare) previously equilibrated in a 50 mM HEPES pH7.4, 150 mM NaCl buffer. Elution was carried out at 0.3 mL.min -1 and the fractions containing the pure AA2ARnanodiscs were finally aliquoted, flash-frozen in liquid nitrogen and stored at -80°C. The same procedure was followed for the preparation of empty nanodiscs, except that the MSP:lipids molar ratio was 1:130 instead of 1:70. In order to remove free lipids, empty nanodiscs were finally purified on a Superdex 200 10/300 GL column in the same conditions used for AA2AR-nanodiscs.

Electron microscopy analysis

The nanodisc samples were diluted to approximately 0.2 µg.mL -1 in purification buffer. Negative staining was performed using 2 % (w/v) uranyl acetate. Observation was carried out using a transmission electron microscope Philips CM 120 with a filament LaB6 (lanthanum hexaboride) at 120 kV. Images (Figure S1) were recorded at 45,000× magnification using a Gatan 794 CCD camera.

Competition ligand-binding assay

Competition ligand-binding assays were performed in triplicates using 5 nM of [3H]-ZM241385 as the tracer, and 10-10 to 10 -3 M of competitor molecules. AA2AR in lipid discs (1 μg/mL) were incubated in 96-well plates in binding buffer (50 mM Tris-HCl pH 7.4, 10 mM MgCl2, 1 mM EDTA). After 1 h incubation at room temperature, proteins were precipitated by supplementation with 0.1% gamma globulins and 25% PEG 6000 (Sigma-Aldrich) for 15 min. The reaction was stopped by rapid filtration through GF/B unifilters (Perkin Elmer) presaturated in 0.3% (v/v) PEI, followed by three successive washes with icecold buffer containing 50 mM Tris-HCl pH 7.4 and 8% (w/v) PEG 6000. Binding data were analyzed with the GraphPad Prism7 software (Figure S3). Inhibition constants (Ki) were calculated according to the Cheng-Prussof equation and were expressed as pKi.

Scintillation Proximity Assay (SPA)

The SPA assay was adapted from Hansen et al. [START_REF] Hansen | Functional Stability of the Human Kappa Opioid Receptor Reconstituted in Nanodiscs Revealed by a Time-Resolved Scintillation Proximity Assay[END_REF] . Briefly, 100 ng of AA2AR-nanodiscs, 100 ng of biotinylated-AA2AR purified in detergent, 60 ng of empty nanodiscs or 10 µg of membrane preparation samples were incubated in triplicates in 96well plates (ProxiPlate-96, Perkin Elmer) in presence of 10 µL of streptavidin-coated yttrium silicate SPA beads (Perkin Elmer) and 25 nM of [3H]-ZM241385 in 100 µL final volume of binding buffer (50 mM Tris-HCl pH 7.4, 10 mM MgCl2, 1 mM EDTA). Non-specific binding is determined in parallel in presence of 10 µM ZM241385. The sealed plate is then placed in a TopCount scintillation counter and measured every hour until 48 hours (Figure S4). Before the last measurement, proteins are denatured by adding 5 µL of 20% SDS to each well.

Chromatographic methods

Instruments

Nano-LC experiments (frontal weak affinity chromatography experiments) were carried out with a capillary electrophoresis Agilent HP3D CE system (Agilent Technologies, Waldbronn, Germany) equipped with external pressure nitrogen supply allowing to work up to 1.2 MPa. System control and data acquisition were carried out using the Chemstation software (Agilent). The experiments were conducted using the CE system in the so-called "short-end" injection mode. The system was exclusively operated in the pressurization mode by applying external pressure (no voltage applied). The inlet of the capillary column is simply immersed in the solution to be infused and the external pressure forces the liquid to flow inside the capillary column. The detection was achieved "on-column" (in an empty section of the 10-cm capillary located just after the monolith), with a diode array detector operated in a multiwavelength mode. The effective length was 8.5 cm. Experiments were conducted under controlled room temperature (25°C).

Capillary monolith synthesis

Fused-silica capillaries with UV transparent coating (TSH, 75µm i.d.) were purchased from Polymicro Technologies (Molex). Poly(GMA-co-EDMA) monoliths were synthesized as described in a previous work [START_REF] Bruchet | Improved Chromatographic Performances of Glycidyl Methacrylate Anion-Exchange Monolith for Fast Nano-Ion Exchange Chromatography[END_REF][START_REF] Faye | In Situ Characterization of Antibody Grafting on Porous Monolithic Supports[END_REF] .

Streptavidin immobilization protocol

Streptavidin columns were prepared using the Schiff base method as previously described [START_REF] Lecas | Monolith Weak Affinity Chromatography for Μg-Protein-Ligand Interaction Study[END_REF] (Streptavidin from streptomyces avidinii, affinity purified, ≥ 13 U.mg -1 of protein, Sigma Aldrich)

RESULTS AND DISCUSSION

Preparation of nanodiscs-embedded AA2AR

In this study, the wild-type human AA2AR receptor N-terminally fused to a Flag and a deca-histidine tags was produced with a yeast Pichia pastoris recombinant clone overexpressing the corresponding DNA construct [START_REF] Hartmann | Expression of Eukaryotic Membrane Proteins in Pichia Pastoris[END_REF][START_REF] André | Enhancing Functional Production of G Protein-Coupled Receptors in Pichia Pastoris to Levels Required for Structural Studies via a Single Expression Screen[END_REF] . After extraction from the yeast membranes and purification in presence of a DDM/CHS detergent mixture, AA2AR was further assembled in lipid nanodiscs similarly to previously described conditions [START_REF] Bocquet | Real-Time Monitoring of Binding Events on a Thermostabilized Human A2A Receptor Embedded in a Lipid Bilayer by Surface Plasmon Resonance[END_REF] . For the purpose of the present affinity chromatography strategy, nanodisc particles have to be firmly immobilized on the chromatographic support. Therefore, the MSP employed here (i.e. MSP1E3D1) was in vitro biotinylated using a NHS-PEG4-Biotin reagent. While the resulting AA2AR nanodisc brings the receptor a stable membrane-mimicking environment compatible with an extensive use in dynamic conditions [START_REF] Hansen | Functional Stability of the Human Kappa Opioid Receptor Reconstituted in Nanodiscs Revealed by a Time-Resolved Scintillation Proximity Assay[END_REF] , it also allows a strong immobilization on streptavidin-coated supports via the biotin residues grafted on the MSP belt. Thanks to the presence of the PEG spacer arm that further maintains the assembly at distance from the support, the overall particle design thus ensures a full accessibility of the extramembrane domains of the receptor to the solvent and to any potential ligands it may contains.

The homogeneity of the nanodisc preparation was assessed by size exclusion chromatography and by negative staining electron microscopy (Figure S1), and the functionality of AA2AR in these nanodiscs was determined by ligand-binding assays performed in two different formats. In a first filtration-based competition binding assay, the pharmacology profiles of one agonist, adenosine, and three antagonist compounds, ZM241385, CGS15943 and xanthine amine congener (XAC) (Figure S2 for compounds structures) were evaluated (Figure S3). The pKi values obtained were relatively close to previously published data [START_REF] Fredholm | International Union of Pharmacology. XXV. Nomenclature and Classification of Adenosine Receptors[END_REF] , indicating that the receptor was correctly folded in the nanodisc preparation. In a second format relying on the SPA technology (Scintillation Proximity Assay), radio-ligand binding was followed over time in order to evaluate the stability of AA2AR nanodiscs at room temperature. This assay revealed a consistent binding ability over time, with still about 50% of [3H]-ZM241385 bound after more than 40 hours of measurement (Figure S4). Overall, the quality and stability of the AA2AR nanodisc samples fulfilled the requirements of the planned WAC studies.

AA2AR affinity columns: preparation and evaluation with known ligands

One of the main objectives here was to drastically reduce the quantity of GPCR used for the characterization of GPCRligand interactions. Therefore, we used an in-house developed miniaturized affinity chromatographic column (75 µm internal diameter and 8.5 cm length i.e. less than 0.5 µL in volume). With their unique characteristics, these nano-columns have an internal diameter about 60 times smaller than commercially available affinity columns (4.6 mm i.d), and 7 times smaller than the smallest in-house microcolumns previously described in the literature for affinity studies (500 µm i.d) [START_REF] Duong-Thi | Weak Affinity Chromatography for Evaluation of Stereoisomers in Early Drug Discovery[END_REF] , which corresponds to a 3600-fold or a 50-fold reduction of the column volume, respectively. At this level of miniaturization, an insitu synthesized poly(GMA-co-EDMA) monolith chromatographic support was preferred to conventional packed particulate ones. The epoxy surface groups of the monolith were then hydrolyzed into diols that were subsequently oxidized into aldehyde capable of reacting with streptavidin through Schiff base reaction [START_REF] Lecas | Monolith Weak Affinity Chromatography for Μg-Protein-Ligand Interaction Study[END_REF] . The resulting streptavidin-coated columns were finally used to immobilize the biotinylated nanodiscs containing AA2AR. The immobilization was performed dynamically, by simply flowing through the column a few microliters of a dilute solution of nanodisc-embedded AA2AR (µM range). The immobilization step was monitored using in-situ UV detection (Figure S5), in order to stop the sample flow once saturation is reached (no excess of protein consumed) and to quantify the total amount of nanodiscs captured. The typical immobilization step duration is quite short, viz about 10 minutes. For a set of 10 columns, around 14 pmol of protein were immobilized per column (14.2 ± 2.7 pmol, n= 10), which correspond to about 0.6 µg of AA2AR-containing nanodiscs. These miniaturized and reusable affinity columns thus lead to a considerable reduction of protein consumption (less than 1 µg per column.

In order to assess the functionality of the column-immobilized AA2AR nanodiscs, frontal affinity chromatography (FAC) was used to study protein-ligand interactions. FAC is a powerful chromatographic tool for the measurement of protein-ligand binding constants and is particularly suitable for the analysis of weak interactions. With this approach, solutions of increasing ligand concentration were continuously infused into the affinity column until their breakthrough, due to saturation of the binding sites. The on-line UV signal monitoring (or ion current signal monitoring if a mass spectrometer is used as detector) gives a typical sigmoid-like profile, with front and plateau at the breakthrough time (tplateau). Binding interaction between the ligand and the immobilized protein is characterized by an increase in breakthrough time with decreasing ligand concentration. If there is no specific interaction, the breakthrough time remains constant regardless of the ligand concentration [START_REF] Ohlson | Fragment Screening for Drug Leads by Weak Affinity Chromatography (WAC-MS)[END_REF] . Measuring the breakthrough times for several ligand concentrations permits to characterize the binding affinity, the amount of active proteins and to reveal potential non-specific interactions. Indeed, for each ligand concentration [L], the total amount of ligand captured in the column, q, can be experimentally determined using Equation ( 1):

𝑞 = (𝑡 𝑝𝑙𝑎𝑡𝑒𝑎𝑢 -𝑡 0 ) × 𝐹 × [𝐿] (Eq.1)
with F being the flow rate and t0 the column dead time.

The total amount of ligand captured is the sum of what is specifically interacting within each protein binding sites and what is non-specifically interacting with the column surface and/or, in our case, with the nanodiscs particles, as shown in Equation ( 2):

𝑞 = ∑ [ 𝐵 𝑎𝑐𝑡,𝑖 × [𝐿] 𝐾 𝑑,𝑖 + [𝐿] ] + 𝐾 𝑛𝑠 × [𝐿] (Eq.2)
specific non-specific with Bact,i being the amount of each protein active binding site i, Kd,i the dissociation constants of the ligand interacting with each of them and Kns the constant related to non-specific interactions.

If only non-specific interactions occur, Equation 2 becomes a linear equation and the amount of ligand captured varies proportionally to the applied ligand concentration. Otherwise, if non-specific interactions are negligible in comparison with specific ones, the type of interaction can be characterized by plotting a double reciprocal graph 1/q versus 1/[L]. Thus, a one-site interaction leads to a linear fit whereas a multi-site interaction leads to a non-linear fit. From there, the dissociation constants Kd,i and the numbers of active sites Bact,i can be determined by linear or non-linear regression [START_REF] Matsuda | Analysis of Multi-Site Drug-Protein Interactions by High-Performance Affinity Chromatography: Binding by Glimepiride to Normal or Glycated Human Serum Albumin[END_REF] .

In order to detect and quantify potential non-specific binding of ligands, the experiments carried out on AA2AR-nanodiscs grafted columns were also simultaneously performed on empty-nanodiscs grafted columns. Regardless of the ligand concentration, the normalized breakthrough time tplateau/t0 is equal to 1 if no non-specific interactions occur, or constant and higher than 1 otherwise. This provides a simple and convenient negative control.

FAC experiments were performed with three well-characterized compounds, two antagonists, theophylline and caffeine, and one agonist, adenosine (see Figure S2 for compounds structures). The breakthrough curves obtained for theophylline are represented in Figure 1. In the absence of AA2AR, the nor-malized breakthrough time was not modified upon theophylline concentration and was equal to 1.3 thus revealing low non-specific interactions (Figure 1A). In addition, as expected for non-specific interactions, the amount of theophylline captured on empty nanodiscs column varied proportionally to its concentration (Figure 1B). In the presence of AA2AR, the normalized breakthrough time increased as the theophylline concentration was decreased, as expected for specific interactions (Figure 1C). At the highest ligand concentrations, where nonspecific interactions predominate, the normalized breakthrough time tends to 1.3, corresponding to the normalized breakthrough time value measured on empty nanodiscs and characteristic of non-specific interactions. The 1/q versus 1/[L] plot was linear and thus typical of one-site interaction (Figure 1D). Only a small deviation occurred for the highest concentrations (lowest 1/[L] range) due to non-specific interactions that contribute more significantly to the overall amount captured. Overall, non-specific binding is considered low as it accounts for less than 30 % of total binding for theophylline concentrations lower than 25 µM. Assuming that the total amount of ligand retained in the column corresponds to the sum of what is specifically interacting with a unique protein binding site, and of what is non-specifically interacting with the column surface and/or the nanodiscs particles, the calculated Kd value for theophylline was 32 ± 11 µM. This Kd value is consistent with values reported in the literature (25 µM [START_REF] De Lera Ruiz | Adenosine A 2A Receptor as a Drug Discovery Target[END_REF] ). The amount of active sites Bact was 7.2 ± 1.3 pmol (correlation coefficient 0.9997). Therefore, based on the known total protein content of the column (Btot as equal to 11.3 pmol), the activity rate for AA2AR (Bact/Btot) was 63 ± 11 %. For caffeine, similar breakthrough curves were observed in the presence of AA2AR (Figure S6). A Kd of 41 ± 15 µM was calculated, in good agreement with the literature (23.4 µM 25 ). In addition, Bact was 11.6 ± 2.3 pmol (correlation coefficient 0.9999), indicating that the AA2AR activity rate was 104 ± 21 % with low non-specific interactions. Such high specific activities show that the grafting process is protein-friendly as it preserves the functionality of the nanodiscs-embedded AA2AR.

Concerning the binding of adenosine in the presence of AA2AR (Figure 2), the normalized breakthrough time increased with decreased adenosine concentrations, as expected for specific interactions. However, a strong deviation from linearity was observed on the double reciprocal plot 1/q versus 1/[L], suggesting a multi-site interaction. As the adenosine has previously been reported to specifically bind streptavidin, a two-site model was used and turned out as the best-fit line.

The calculated Kd values were 0.32 µM for the first site and 447 µM for the second (correlation coefficient 0.9998). These values are in good agreement with the reported values for the binding of the adenosine to AA2AR (0.7 µM 25 ) or streptavidin (100-200 µM [START_REF] Bing | Specific Interactions between Adenosine and Streptavidin/Avidin[END_REF] ). These results were further confirmed using FAC experiments carried out on control columns with either non-complexed streptavidin or immobilized empty nanodiscs (Figures S7 andS8). The amounts of active binding sites were also determined using the two-site model. The calculated values were 2.3 pmol for AA2AR resulting in an activity rate of approximatively 20 %, and 43 pmol for the streptavidin which is in good agreement with the expected amount of grafted streptavidin on the monolith 37 .

Competition experiments

In order to take the proof of concept a step further, ligandbinding experiments in a competition format were investigated. Competition experiments can be particularly valuable in the context of fragment screening to assess the binding site of fragments using a well-characterized competitor, or to serve as a control experiment for non-specific interactions. In the case of FAC experiments, the strategy involves a constant infusion of the competitor into the column, first alone then with the monitored ligand. The chromatograms are analyzed by measuring the change in the breakthrough time of the monitored ligand in the absence and in the presence of the competitor: competition for the same binding site is expected to selectively reduce the breakthrough time.

Here, a competition experiment was achieved between theophylline and the high-affinity AA2AR antagonist ZM241385 (ZM). When 10 µM ZM was added in the mobile phase with theophylline, the breakthrough time shrank to a value close to the one obtained with theophylline alone on empty nanodiscs (Figure 3). This competition experiment confirmed that the interaction between theophylline and AA2AR nanodiscs, observed using the miniaturized FAC system, corresponds to a specific interaction in the orthosteric pocket of the protein.

Figure 3. FAC analysis of the competition between theophylline and ZM241385 for the interaction with immobilized AA2AR nanodiscs. Frontal affinity breakthrough curves (Xaxis in reduced retention time t/t0) of a 12.5µM theophylline solution on (i) a control column with empty nanodiscs; (ii) on the AA2AR-nanodiscs column without competitor; (iii) on the AA2AR-nanodiscs column in presence of 10 µM ZM241385.

Identification of weak affinity ligands

Finally, the technique was tested for its capacity to identify weak affinity ligands such as fragments, and the possibility to discriminate weak affinity binders from non-binders, a particularly prominent issue with membrane proteins due to the presence of compounds used to maintain them in solution. The FAC strategy hitherto used to validate AA2AR-ligand interactions, and shared by others [START_REF] Duong-Thi | Lipodisks Integrated with Weak Affinity Chromatography Enable Fragment Screening of Integral Membrane Proteins[END_REF][START_REF] Conrad | Evaluation of Indole-Based Probes for High-Throughput Screening of Drug Binding to Human Serum Albumin: Analysis by High-Performance Affinity Chromatography[END_REF] , relies on the comparison of the normalized breakthrough times measured on columns grafted with protein over control columns (columns with empty-nanodiscs here). However, using control columns may lead to an overestimation of non-specific interactions due to the absence of the targeted protein, thus resulting in potential false negative signals as was previously suggested [START_REF] Duong-Thi | Lipodisks Integrated with Weak Affinity Chromatography Enable Fragment Screening of Integral Membrane Proteins[END_REF] . Since the normalized breakthrough time measured at high ligand concentrations in the presence of AA2AR is close to the one measured on the control column (Figure 1) an alternative approach relies on the comparison between the normalized breakthrough times for two extreme fragment concentrations on a single column containing the immobilized protein. Nonetheless, since the measures are realized on the same column, this strategy further avoids uncertainties associated with column variability due to the grafting efficacy. In order to determine what those extreme concentrations should be, normalized breakthrough time differences were simulated for several Kd values and various fragment concentrations. For the lowest affinities, 10 and 1000 µM showed the most significant discrepancy (Figure S9). With this set-up, and a minimal difference in normalized breakthrough time fixed at 10 %, Kd values as high as 250 µM are detectable. This approach was applied to six fragments selected from a published NMR screening carried out on AA2AR in detergent micelles [START_REF] Igonet | Enabling STD-NMR Fragment Screening Using Stabilized Native GPCR: A Case Study of Adenosine Receptor[END_REF] (see Figure S2 for compounds structures). Four of them were shown to bind AA2AR using STD-NMR experiments, while two were shown not to. As illustrated in Figure 4, significant differences between normalized breakthrough times measured at low and high concentrations were observed for fragments F534, F489, F462 and F359, whereas fragments F040 and F141 displayed identical normalized breakthrough times regardless of the concentration. Consequently, F534, F489, F462 and F359 were identified as ligands of AA2AR while F040 and F141 were identified as non-ligands. These results are in perfect agreement with the previously reported STD-NMR data [START_REF] Igonet | Enabling STD-NMR Fragment Screening Using Stabilized Native GPCR: A Case Study of Adenosine Receptor[END_REF] . Because the higher the normalized breakthrough time discrepancy, the higher the affinity for the target, these FAC experiments also allow a ranking of the ligands according to their increased affinities, starting from F359 than F462 and F489 and F534.

CONCLUSION

Here we have shown for the first time that lipid-nanodiscs systems and ultra-miniaturized affinity chromatography can be combined to identify specific small molecule ligands that bind to an integral membrane protein. The approach was exemplified using the AA2AR GPCR. Specific and unspecific ligand/receptor interactions were distinguished using empty nanodiscs as a negative control. Competition experiments can be performed to identify the binding pocket of uncharacterized ligands using a reference compound. Weak affinity ligands with Kd smaller than 250 µM should be detected. At last, the applicability of this method was demonstrated with 6 fragments recently identified as ligands or non-ligands of AA2AR.

Here, experiments were carried out at two extreme concentrations on a unique affinity column.

Besides being a suitable membrane-mimicking environment for AA2AR, the use of nanodiscs also offers multiple advantages with regards to the grafting procedure. AA2AR is not itself tethered to the stationary phase but through the biotinylated MSP that enables a homogenous oriented immobilization, and the presence of the spacer arm ensures a full accessibility of the binding pocket to small molecules. AA2AR remains full native, as evidenced by the high specific activities obtained. The strong and rapid biotin-streptavidin interaction associated with the UV-monitoring of the grafting step permits the use of the exact amount of grafted protein and a very rapid grafting step. The reduction of both the grafting duration and protein consumption represent two tremendous assets for proteins that are difficult to produce and that present a limited stability. A column contains only 0.6 µg of AA2AR on average (14 pmol). Streptavidin columns can be prepared in advance, stored for several months (4°C) before being grafted with the target of interest.

The method reported here is applicable to any membrane protein reconstituted in biotinylated nanodiscs and offers a real potential for FBDD on membrane proteins. Its coupling with mass spectrometry via a nanospray interface, should allow a rapid screening of a library (for a 30 min total analysis time, and considering mixtures of 10-20 compounds, 500 to 1000 compounds should be screened in one day, with a minimal protein consumption and with reduced stability issues).

Figure 1 .

 1 Figure 1. Frontal Affinity Chromatography analysis of the interaction between theophylline and immobilized nanodiscs columns (column dimensions: l=8.5 cm i.d.75 µm; applied pressure 12 MPa (0.14 cm.s -1 )) -AA2AR upper panels: empty nanodiscs. (A) Frontal affinity breakthrough curves (X-axis in reduced retention time t/t0) on an empty-nanodiscs column. (B) Plot of captured quantities (q) versus ligand concentrations [L] and on an empty-nanodiscs column accounting for non-specific interactions. +AA2AR lower panels: AA2AR-nanodiscs. (C) Frontal affinity breakthrough curves (X-axis in reduced retention time t/t0) on an AA2AR-nanodiscs column. (D) Corresponding double reciprocal plot 1/q versus 1/[L] accounting for a one-site model.

Figure 2 .

 2 Figure 2. FAC analysis of the interaction between adenosine and immobilized AA2AR nanodiscs. (A) Frontal affinity breakthrough curves with adenosine at various concentrations Xaxis in reduced retention time t/t0; (B) double reciprocal plot 1/q versus 1/[L] accounting for a two-site model.

Figure 4 .

 4 Figure 4. FAC analysis of fragments interaction with immobilized AA2AR nanodiscs. Frontal affinity breakthrough curves obtained on an AA2AR-nanodiscs column for several fragments prepared in a 67 mM phosphate pH 7.4 buffer.

ACKNOWLEDGMENT

The authors are grateful to the Imaging Center of the IGBMC (Illkirch) for the electron microscopy data.

Supporting Information

The Supporting Information is available free of charge on the ACS Publications website.

Supplementary methods, results and figures (PDF)

AUTHOR INFORMATION

Corresponding Author * Tel.: +33437423552 E-mail address: claire.demesmay@univ-lyon1.fr


Fabien Petit-Cancelier

Benjamin François

Xavier Pannecoucke

Samuel Couve-Bonnaire

Tatiana Besset
email: tatiana.besset@insa-rouen.fr

S -(Diethyl phosphonodifluoromethyl)Benzenesulfonothioate: A New Reagent for the Synthesis of SCF 2 PO(OEt) 2 -containing Molecules

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The synthesis of organofluorine molecules is still nowadays a compelling challenge due to the importance of the fluorinated compounds [1] in pharmaceutical and agrochemical industries. [2] Thanks to the properties of the fluorine atom and the fluorinated groups, [3] features of the fluorine-containing molecules might be tuned at will. In particular, to further meet the demand of original fluorinated groups for academia and industrial applications, several research groups investigated the design and the incorporation of SCF2R moieties (R = H, F, FG, FG = functional group) onto molecules. [4] In particular, a strong interest was shown towards the SCF2PO(OEt)2 residue (Hansch-Leo parameter of  = 0.76). [5] Key reports generally dealt with the construction of this fluorinated group [6] or its direct introduction using an electrophilic reagent on various classes of compounds. [5,7] Nevertheless, despite these major advances, some synthetic limitations remain. To overcome them and since no radical SCF2PO(OEt)2-source existed, we thought that the design of a new reagent would be relevant, offering new chemical spaces for the functionalization of other classes of molecules with this fluorinated building block. Taking benefit from our in-home expertise, [5,7] we envisioned at first to use ArSSCF2PO(OEt)2 derivatives as the SCF2PO(OEt)2 radical source. Unfortunately, all attempts with different classes of compounds only led to the preferential incorporation of the ArS moiety instead of the desired fluorinated group. We reasoned that to reach the targeted goal, one solution relied on the "dessymetrization" of the S-S bond from the reagent in order to favor the incorporation of the SCF2PO(OEt)2 moiety. In the course of our investigations and inspired by the key advances made by several research groups, [4b] we anticipated that the reactivity of a ArSO2SCF2PO(OEt)2 reagent would be tailor-made as it should favor the targeted transfer of the SCF2PO(OEt)2 over the ArSO2 group. In this context, the design and the synthesis of the reagent II was achieved by mixing our in-home reagent I and sodium para-toluenesulfinate in acetic acid for 16 h at room temperature. Pleasingly, under these reaction conditions, the reagent II was obtained in 81% yield and its synthesis was easily scaled up to about 1g with a similar yield (3.25 mmol, 89%). Note that under acidic conditions, the reagent II is quite stable while its decomposition was observed in the presence of various bases in dichloromethane as a solvent. [8] Scheme 1. Synthesis of the new SCF2PO(OEt)2 reagent II. [a] Reaction performed on 1.13 mmol scale. [b] Reaction performed on 3.25 mmol scale. Mes = mesityl

The reactivity of the reagent II was evaluated for the synthesis of diethyl phosphonodifluoromethylated thioesters (Scheme 2). Indeed, the direct introduction of the SCF2PO(OEt)2 moiety was smoothly achieved when combining NaN3 and PIFA in a green solvent (EtOAc) at room temperature. A series of aromatic aldehydes (1a-1u) were engaged in our standard conditions. It turned out that electron rich substrates (1b-1g) were efficiently functionalized and the reaction was scaled up to 1 mmol scale in case of 1b leading to the corresponding product in a slightly lower yield (65% vs 79%). The transformation was tolerant to various functional groups such as alcohol (2c), halogens (2i-2k and 2o) and nitrile (2q). In case of electron poor aromatic aldehydes (compounds 1h, 1l, 1p-1q), a slight modification of the reaction conditions was necessary to ensure the synthesis of the corresponding diethyl phosphonodifluoromethylated thioesters in satisfactory yields (up to 61% yield). The substitution pattern on the aromatic ring did not have a strong impact on the outcome of the reaction as meta and ortho substituted aromatic aldehydes with electron rich and poor substituents (1m-1s) were converted into the expected products (2m-2s) in moderate to high yields. This allowed us to functionalize compounds of interest such as the Bn-protected vanillin (1t) and syringaldehyde (1u), offering an access to the corresponding diethyl phosphonodifluoromethylated thioesters in 66% and 68% yields, respectively. Heteroaromatic aldehydes such as pyrrole-2-carboxaldehyde 1v, 2-thiophenecarboxaldehyde 1w and benzo [b]thiophene-2-carboxaldehyde 1x were also smoothly converted into the corresponding fluorinated products 2v-2x in low to moderate yields. When an aliphatic aldehyde was engaged, 2y was obtained in 40% yield due to purification issues. Finally, due to the importance of fluoroalkylthio moieties in bioactive molecules, [2a] the synthetic value of the methodology was further proven by the late-stage functionalization of a complex molecule namely the cholesterol derivative 1z. Scheme 2. Synthesis of diethyl phosphonodifluoromethylated thioesters from aldehydes using the reagent II. Reaction performed on 0.2 mmol scale: aldehyde 1 (0.2 mmol), reagent II (0.3 mmol), NaN3 (2 equiv.), PIFA (2 equiv.) in EtOAc at 22 °C for 3 h under argon. Isolated yields were given. [a] Reaction on 1 mmol scale. [b] 6 h instead of 3 h. [c] The reaction was carried out using 0.4 mmol of aldehyde 1, 0.2 mmol of reagent II, NaN3

(2 equiv.), PIFA (2 equiv.) in CH2Cl2 at 22 °C for 16 h under argon. [d] The product was isolated in the presence of an inseparable impurity. [e] Reaction carried out at 27 °C.

Control experiments were conducted to get more insights into the reaction mechanism. The addition of 2,2,6,6tetramethylpiperidine 1-oxyl (TEMPO) or 2,6-di-tert-butyl-4-methylphenol (BHT) as radical inhibitors completely inhibited the reaction, whatever the added quantity. [8] In case of 2 equivalents of TEMPO, the corresponding TEMPO adduct was detected by GC-HRMS. Note that when PIFA and TEMPO were mixed, no degradation of the PIFA was observed. Therefore, based on these observations and literature data, [9] the following tentative mechanism was proposed (Scheme 3): after generation of the radical azide from sodium azide in the presence of PIFA, the corresponding acyl radical was obtained. The latter reacted with the reagent II to afford the expected product 2. Scheme 3. Plausible mechanism.

The reagent II was not only efficient for the functionalization of aldehyde derivatives, and other classes of compounds were evaluated. Indeed, when thiol 3 or disulfide 4 derivatives were engaged with the reagent II in the presence of LiBr, [10] the synthesis of difficult-to-synthesize unsymmetrical disulfides was successfully achieved (12 examples, up to 91% yields, Scheme 4). This transition metal-free approach was efficient offering a panel of para substituted aromatic diethyl phosphonodifluoromethylthiolated compounds bearing electron-donating groups (5b and 5c, in 72% and 52% yields, respectively) as well as halogens (5d and 5e) and a CF3 moiety (5f), the reaction being more efficient for electron rich systems. The substitution pattern on the aromatic ring did have a strong effect on the outcome of the reaction as demonstrated when comparing 5c, 5g and 5h. Note that a thiophene derivative was a reluctant substrate under these reaction conditions providing the expected compounds in only 22% yield. Pleasingly, the functionalization of aliphatic thiol derivatives was achieved not only with the benzylic derivative 5j but also with unactivated aliphatic derivatives (5k and 5l). This showcased the added-value of this approach compared to the existing methods, [7] and demonstrated further its synthetic utility.

Scheme 4. Synthesis of unsymmetrical fluorinated disulfides 5. Reaction conditions: RSH 3 (0.20 mmol), reagent II (0.2 mmol), LiBr (1 equiv.) in HFIP at 40 °C for 6 h under argon. Isolated yields were given. [a] Reaction performed using (ArS)2 4 instead of 3. [b] The product was isolated in the presence of an inseparable impurity.

Aiming at demonstrating further the synthetic potential of the reagent II, the difunctionalization of unactivated alkenes was studied. [START_REF] Zhu | the introduction of the SCF2H and SCH2F groups[END_REF] After intensive investigations, the diethyl phosphonodifluoro-methylthiolation sulfonylation of 6 was achieved leading to the corresponding product 7, although in a low yield (21%) despite all our efforts (Scheme 5). Nevertheless, the transformation was highly atom-economical and regioselective as 7 was obtained as a single regioisomer. [8] Scheme 5. Difunctionalization of the 4-phenyl-butene.

In summary, the synthesis of an original SCF2PO(OEt)2 reagent was achieved and its reactivity was investigated. Indeed, the direct diethyl phosphonodifluoromethylthiolation of aldehydes provided an access to unprecedented thioester derivatives. Preliminary mechanistic studies indicated a radical pathway. Moreover, the access to unsymmetrical SCF2PO(OEt)2-containing (hetero)aromatic, benzylic and non-activated aliphatic disulfides was achieved. With the reagent II, the portfolio of SCF2PO(OEt)2containing compounds was significantly extended, opening further the chemical space of SCF2FG-containing molecules and offering new possibilities for potential applications.

Acknowledgements

This work was partially supported by INSA Rouen, Rouen University, CNRS, EFRD, Labex SynOrg (ANR-11-LABX-0029), Région Normandie (Crunch Network) and Innovation Chimie Carnot (I2C). F. P.-C., B.F., T. B. thanks the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 758710). F. P.-C. thanks the Region Normandy for a doctoral fellowship. Keywords: Fluorine; sulfur; diethyl phosphonodifluoromethylthiolation reagent, radical pathway; synthetic methodologies