H Kabbara

J Ghanbaja

C Noël

T Belmonte
email: thierry.belmonte@univ-lorraine.fr

SYNTHESIS OF Cu@ZnO CORE-SHELL NANOPARTICLES BY SPARK DISCHARGES IN LIQUID NITROGEN

Keywords: number: 52.80.Wq Discharge in liquids and solids Spark discharges, submerged discharges, Core-shell structures, Nanostructures, Time-resolved optical emission spectroscopy 2

Core-shell nanoparticles are synthesized by nanosecond-pulsed discharges in liquid nitrogen using a two-step process. In a first step, copper nanoparticles (resp. zinc nanosheets) are synthesized by eroding pure copper (resp. zinc) electrodes. In a second step, copper (resp. zinc) electrodes are removed and replaced by zinc (resp. copper) electrodes in the liquid loaded with copper nanoparticles (resp. zinc nanosheets). After erosion and air oxidation, once nitrogen has evaporated, Cu@ZnO core-shell nanoparticles are obtained in both configurations. The shell is always ZnO, because of the unusual formation of zinc nanosheets instead of zinc nanoparticles. When Cu electrodes are used first, copper nanoparticles are wrapped in ZnO nanosheets. When Zn electrodes are used first, copper nanoparticles hit zinc nanosheets and get coated to form also Cu@ZnO. In this latter case, Cu2O@ZnO are sometimes encountered too but to a much lesser extent. Copper oxidation is then attributed to a failure in the zinc protective shell. Time-resolved optical emission spectroscopy measurements reveal that Cu lines and Zn lines are never emitted simultaneously, the element in the liquid remaining outside the discharge area. If zinc nanosheets are synthesized first, N II lines are exclusively observed during the first 200 ns of discharges with copper electrodes, revealing for this process the possibility of highly energetic processes.

INTRODUCTION

Synthesis of nanoparticles (NPs) by discharges in dielectric liquids is an efficient way to process nanostructures at high rate and very low cost [START_REF] Belmonte | Interaction of discharges with electrode surfaces in dielectric liquids: application to nanoparticle synthesis[END_REF]. ZnO and CuO are semiconductors that are extensively studied. They both present an appreciable photocatalytic activity and might be good candidates for photocatalysis and solar cells [START_REF] Soejima | Alkaline vapor oxidation synthesis and electrocatalytic activity toward glucose oxidation of CuO/ZnO composite nanoarrays[END_REF][START_REF] Sathishkumar | Synthesis of CuO-ZnO nanophotocatalyst for visible light assisted degradation of a textile dye in aqueous solution[END_REF][START_REF] Liu | Hierarchical CuO-ZnO "corn-like" architecture for photocatalytic hydrogen generation[END_REF]. They also offer perspectives in sensing applications [START_REF] Wang | CuO nanoparticle decorated ZnO nanorod sensor for low-temperature H2S detection[END_REF][START_REF] Kim | One-pot hydrothermal synthesis of CuO-ZnO composite hollow spheres for selective H2S detection[END_REF][START_REF] Qi | Humidity sensing properties of KCl doped Cu-Zn/CuO-ZnO nanoparticles[END_REF]. Their band gaps are ~3.3 eV, 1.2-1.9 eV and ~2.3 eV for ZnO (intrinsic n-type), CuO(intrinsic p-type) and Cu2O(intrinsic p-type) respectively.

On the contrary, the CuO-ZnO system has thus far received little attention. The mixed systems of zinc oxide and copper oxide as nanosize core-shell structures have only been reported by Tamanis et al. [START_REF] Tamanis | Synthesis of Core/Shell CuO-ZnO Nanoparticles and Their Second-Harmonic Generation Performance[END_REF] very recently. Furthermore, Cu@ZnO core-shell nanostructures have been acknowledged to be important catalysts for methanol production from synthesis gas at moderate pressures and temperatures [START_REF] Schur | Continuous coprecipitation of catalysts in a micromixer: Nanostructured Cu/ZnO composite for the synthesis of methanol[END_REF][START_REF] Kalidindi | Synthesis of Cu@ZnO coreshell nanocomposite through digestive ripening of Cu and Zn nanoparticles[END_REF].

If the synthesis of copper oxides and zinc oxide NPs by discharges in liquids have been amply investigated [START_REF] Burakov | Synthesis and Modification of Molecular Nanoparticles in Electrical Discharge Plasma in Liquids[END_REF][START_REF] Tarasenko | Properties of zinc-oxide nanoparticles synthesized by electrical-discharge technique in liquids[END_REF][START_REF] Loa | Shape-controlled synthesis of Cu-based nanofluid using submerged arc nanoparticle synthesis system (SANSS)[END_REF][START_REF] Yao | Formation of uniform CuO nanorods by spontaneous aggregation: Selective synthesis of CuO, Cu2O, and Cu nanoparticles by a solid-liquid phase arc discharge process[END_REF][START_REF] Ashkarran | Photocatalytic activity of ZnO nanoparticles prepared via submerged arc discharge method[END_REF][START_REF] Lo | Fabrication of copper oxide nanofluid using submerged arc nanoparticle synthesis system (SANSS)[END_REF], the same is not true for core-shell structures and alloys. Panuthai et al. [START_REF] Panuthai | Characterization of copperzinc nanoparticles synthesized via submerged arc discharge with successive reduction process[END_REF] eroded copper-zinc (Cu-Zn) alloy electrodes with different copper contents (90Cu/10Zn and 65Cu/35Zn) by an arc discharge submerged in glycol, ethanol and deionized water. They could produce a mixture of Cu, Cu-Zn, and zinc oxide nanoparticles with size distributions depending on the chosen liquid. Karahaliou et al. [START_REF] Karahaliou | CuO/Ta2O5 core/shell nanoparticles synthesized in immersed arc-discharge: production conditions and dielectric response[END_REF] synthesized CuO@Ta2O5 core-shell NPs by anodic arc-discharges in water. They run discharges between a graphite electrode and a U-shaped circular stainless steel holder filled with compressed copper and tantalum micrograins. According to these authors, in the specific arrangement used, mechanisms involved in the synthesis of CuO@Ta2O5 NPs are unknown.

In the present work, we study the possibility to grow core-shell nanoparticles in the Cu-Zn-O system by discharges in liquid nitrogen via a three-step process. Discharges are run first between two copper electrodes to produce Cu NPs. Next, new discharges are run between two zinc electrodes in the same liquid where Cu NPs, formed during the first step, are present.

Cu@Zn core-shell NPs are thus expected to be obtained, zinc coating then copper nanoparticles. To get Zn@Cu NPs, steps 1 and 2 should just be swapped, but it turns out that things are more complex in this case and that will be described hereinafter. The last step corresponds to the oxidation of the synthesized core-shell NPs, which occurs after evaporation of liquid nitrogen, which leads to air exposure and transformation of metals into oxides.

Besides thorough investigations of as-formed nanoparticles by different diagnostics, we resorted to time-resolved optical emission spectroscopy at the nanosecond scale to clarify the growth mechanisms of these nanostructures.

EXPERIMENTAL SET-UP

The experimental set-up was presented in detail in reference [START_REF] Hamdan | Impacts created on various materials by micro-discharges in heptane: Influence of the dissipated charge[END_REF]. Briefly, a pin-to-pin electrode configuration was used. Electrodes were copper or zinc wires (1 mm in diameter). A high DC voltage power supply (Technix SR15-R-1200-15 kV-80 mA) fed a solid-state switch (HTS-301-03-GSM) connected to one pin-electrode, the other electrode being grounded. The voltage rise time was 20 ns without ballast resistor.

Experiments were run in two steps. The first step corresponds to the synthesis of one specific type of nanoparticles, say copper, in liquid nitrogen, which is known to be inert in our conditions. The experimental conditions retained here are an applied voltage of +10 kV and an inter-electrode gap distance of 100 µm. After the dielectric liquid is enriched with nanoparticles (i.e. after about one hour of successive discharges ignited at 10 Hz), the second step starts. It consists in running discharges in the same liquid, now loaded in copper nanoparticles, but with two electrodes made of zinc to get a Cu@Zn core-shell structure. The duration of a single discharge pulse is 200 ns. The duration of the second step of NPs synthesis process is also about one hour. The volume of the liquid used in the reactor is 100 cm 3 .

Nanoparticles are collected by sedimentation on a silicon wafer located under the pinelectrodes. We resorted to scanning electron microscopy (SEM) -XL30S-FEG by Philips -for structural and chemical observations. An EDXS (Energy-Dispersive X-ray Spectrometer) was used for elemental analysis and a TLD detector (through the lens detector) was used for high resolution imaging. A Philips CM200 device and a JEOL ARM 200F Cold FEG device were used for TEM investigation in order to study the crystallinity and the chemical composition of NPs. Electron Energy Loss Spectroscopy EELS experiments were performed in diffraction mode. The spectrometer is set to an energy dispersion 0.05 eV/channel. Thecondenser aperture, spectrometer entrance, camera length were respectively 150 μ m, 2.5 mm and 4 cm leading to a collection half angle of 20 mrad and an energy resolution of 0.5 eV measured at full width at half maximum (FWHM) of zero loss peak. Energy-Filtered Transmission Electron Microscopy (EFTEM) were recorded in STEM mode.

Optical emission spectroscopy was performed with a 550 mm focal length monochromator (Jobin-Yvon TRIAX 550) equipped with a 100 g mm -1 grating for overall spectra in the (250-900 nm) visible range, lines being strongly broadened and high spectral resolution useless. It was coupled with a HORIBA Jobin-Yvon i-Spectrum Two iCCD detector. Each measurement is averaged over 50 spectra recorded with an exposure time of 50 ns. Although discharges in dielectric liquids are known to be stochastic, using a solid-state switch with a 20-ns rise time ensures a high level of reproducibility because breakdown necessarily occurs within a time window inferior to the exposure time (see reference [START_REF] Hamdan | Comparison of Aluminium Nanostructures Created by Discharges in Various Dielectric Liquids[END_REF] for further details).

However, because of the generation of high-frequency signals by discharge current oscillations (at about 1 MHz), ghost lines are sometimes observed. They are easily identified in time-resolved data, for they disappear from one spectrum to the other.

RESULTS AND DISCUSSION

On the specificity of Zn erosion

If eroding copper electrodes under the chosen conditions leads to the synthesis of NPs with spherical shapes (Fig. 1a), eroding zinc electrodes gives mainly 2-dimensional nanostructures (nanosheets) with few spread NPs (Fig. 1b). Typically, their thickness is about 4 nm, according to AFM measurements. Spherical NPs are made of CuO, since copper NPs are exposed to air before TEM analysis, once liquid nitrogen has fully evaporated. These primary NPs have sizes ranging typically between 2 and 10 nm. However, larger primary NPs, with diameters closer to 30-50 nm are also present, but to a lesser extent. Similarly, nanosheets are made of ZnO. Unreported diffraction patterns and micro-EDX analyses prove undoubtedly that both metals are oxidized as CuO and ZnO respectively.

The behaviour of zinc is uncommon for a metal eroded by spark discharges in liquids. This is likely due to the use of liquid nitrogen as liquid dielectric. Indeed, all processes of the same kind are usually operated in deionized water, leading to spherical or elongated nanoparticles [START_REF] Tarasenko | Properties of zinc-oxide nanoparticles synthesized by electrical-discharge technique in liquids[END_REF][START_REF] Ashkarran | Photocatalytic activity of ZnO nanoparticles prepared via submerged arc discharge method[END_REF]. We assume that the synthesis of zinc nanosheets proceeds by a kind of exfoliation mechanism where a few atomic layers are detached from zinc grains submitted to the plasma flux. This mechanism would be driven by the specific structure of the zinc wire used as electrode as soon as sufficiently large thermal gradients are at stake. However, till now, no clear explanation dealing with the synthesis mechanism of these nanosheets can be put forward. Ongoing new experiments will maybe give a better understanding. Nevertheless, the specific nanosheet structure of zinc produced by plasma erosion of the electrodes is a key feature in the present work to explain how core-shell structures are formed, as described below.

Starting with copper electrodes

When copper nanoparticles, like those depicted in Fig. 1a, are synthesized first, a large part of these particlesi.e. those floating in the vicinity of the inter-electrode gap each time the discharge is ignited -get coated by zinc during the second step of the process. They represent about 35 ± 5 percent of the objects collected on a TEM grid. These NPs are then exposed to air, once liquid nitrogen is evaporated, and get oxidized. In Fig. 2a, a TEM overview of the core-shell NPs is depicted. Spherical NPs (Fig. 2a), similar to those obtained for pure copper (Fig. 1a), are visible. When we zoom in, we observed that these NPs are coated by a shell, which is very clear in Fig. 2b. The diffraction pattern corresponding to the selected area (Figs. 2c and2d) indicates the presence of pure copper and zinc oxide ZnO. We readily infer that the zinc shell protects the copper core from oxidation, once the nanostructure is put in the air. Zinc is turned into zinc oxide, which passivates the nanostructure and preserves its metallic core. On the other hand, the metallic character of the core could be confirmed by Electron Energy Loss Spectroscopy (Fig. 3). In Fig. 3a, the near-edge structure of L23-Cu in the EELS spectrum is formally identical to metallic copper's (after data available in references [START_REF] Leapman | Study of the L23 edges in the 3d transition metals and their oxides by electron-energy-loss spectroscopy with comparisons to theory[END_REF][START_REF] Ewels | A Complete Overhaul of the Electron Energy-Loss Spectroscopy and X-Ray Absorption Spectroscopy Database: eelsdb[END_REF][START_REF] Hofer | New examples for near-edge fine structures in electron energy loss spectroscopy[END_REF]). On the other hand, the near-edge structure of L3-Zn in the EELS spectrum (Fig. 3b) exhibits the same P1 and P2 contributions -due the fact that the electron beam changes from parallel to perpendicular to c-axis -as in ZnO. Finally, running discharges between zinc electrodes in liquid nitrogen loaded with copper nanoparticles does produce Cu@ZnO core-shell nanostructures.

Starting with zinc electrodes

When zinc sheets, as those depicted in Fig. 1b, are synthesized first, they are hit by copper NPs synthesized during the second step of the process and exiting the discharge. Then, the general sheet-like aspect of zinc is kept, as it is visible in Fig. 4a. Two types of core-shell NPs are produced: Cu@ZnO (~80-90% of core-shell objects) and Cu2O@ZnO (~10-20%).

Copper NPs synthesized in the discharge hit the zinc sheets at high speed and stick to them and/or get wrapped by zinc. Two situations arise.

First, a copper NP is fully covered by a zinc sheet. High-resolution TEM images (Fig. 4b) clearly show the full coverage of copper by zinc. The diffraction pattern corresponding to the selected area (Figs. 4c and4d) indicates the presence of Cu and ZnO. The metallic character of the core was again confirmed by Electron Energy Loss Spectroscopy (Fig. 5). In Fig. 5a, the near-edge structure of L23-Cu in the EELS spectrum is formally identical to metallic copper's [START_REF] Leapman | Study of the L23 edges in the 3d transition metals and their oxides by electron-energy-loss spectroscopy with comparisons to theory[END_REF][START_REF] Ewels | A Complete Overhaul of the Electron Energy-Loss Spectroscopy and X-Ray Absorption Spectroscopy Database: eelsdb[END_REF][START_REF] Hofer | New examples for near-edge fine structures in electron energy loss spectroscopy[END_REF]. The near-edge structure of L3-Zn in the EELS spectrum (Fig. 5b) exhibits the same features as those of zinc oxide. Finally, EDXS line scanning profiles along Cu@ZnO core-shell nanoparticles are provided as supplemental material 1.

Second, images made by Energy-Filtered Transmission Electron Microscopy (EFTEM) of a large Cu2O@ZnO NP (Figs. 6a to 6d) stress the possible presence of defects in the structure of the shell. The circled area (Fig. 6a) shows nanopores in the zinc shell. The diffraction pattern (Figs. 7b and7c) corresponding to this core-shell object (Fig. 7a) indicates the presence of Cu2O and ZnO. This time, copper is partly oxidized under the form of cuprous oxide. The fact that it is not fully oxidized in air, contrary to Cu nanoparticles that are transformed into CuO when no zinc is present -see Fig. 1a -is presumably due to the imperfect coverage by the ZnO shell that limits oxygen penetration into the NP core.

Protection provided by the zinc shell is patently less efficient as in the former case. Indeed, when a large nanoparticle is observed, the zinc shell is found to be non-uniform. Its thickness varies, as might be expected, from the side where the copper NP hits the zinc sheet to the opposite side, where the contact with zinc is very limited, if not non-existent. There, the zinc shell is not continuous and exhibits open pores (see the circled region in Fig. 7a). When oxidation occurs, we presume that copper gets oxidized till ZnO seals the pores thanks to its molar volume (14.52 cm 3 mol -1 ) which is one and a half time as big as zinc (9.16 cm 3 mol -1 ). Thus, Cu2O is formed instead of CuO. A schematic of the synthesis mechanisms for the synthesis of core-shell NPs is proposed in Fig. 8. Syntheses are reproducible with a satisfactory level, leading to very similar features as far as we could evaluate it by TEM analyses. Core-shell nanostructures did not exhibit any ageing effects after exposure to air and moisture for several weeks.

Time-resolved optical emission spectroscopy

Time-resolved optical emission spectroscopy was used to better understand how these coreshell NPs were synthesized. In Fig. 9a, time-resolved optical emission spectra of discharges between zinc electrodes, immersed in liquid nitrogen loaded in copper, are depicted. Copper loading is achieved by running ~18,000 discharges (30 min at 10 Hz) with the copper electrodes and by replacing them by zinc electrodes, as done in the two-step synthesis process of Cu@Zn core-shell NPs. We observe an intense background emission followed by the emergence of zinc emission lines when the medium cools down and becomes optically thin.

The intensity of the background emission is not given in Fig. 10a, but from raw data, we know it is the highest intensity observed during the process (in our condition, it reaches 55,000 counts at the wavelength at which the grating is blazed, i.e. 500 nm, after 150 ns, versus a tenth of this value for the zinc transition at 481.05 nm after 500 ns). The lack of copper lines, even though copper nanoparticles are present in liquid nitrogen, is likely due to the hindrance of an inward flux from the liquid phase by the huge pressure exerted from the core of the spark discharge outwards. The presence of intense background emission (mainly due to electron-ion recombination after [START_REF] Hamdan | Comparison of Aluminium Nanostructures Created by Discharges in Various Dielectric Liquids[END_REF]) at the beginning of the process masks most the lines that pop up in the spectrum after 200-250 ns typically. In Fig. 9b, time-resolved optical emission spectra of discharges between copper electrodes immersed in liquid nitrogen loaded in zinc (~18,000 discharges) are depicted. As explained previously, because of the very limited background emission, lines appear immediately. The most surprising result is the presence of N II lines (i.e. emitted by excited N + states) at short times, from 0 to 250 ns, without any simultaneous emission of N I or N2 I lines. The most intense atomic lines of nitrogen are known to be in the NIR spectral region [START_REF] Northway | Atomic nitrogen spectra in the argon inductively coupled plasma (ICP)[END_REF]. Data recorded from 650 nm and 800 nm show only a very weak broad line (between 100 and 300 ns) centred at ~745 nm, which is more likely due to the N II transition expected at 745.095 nm. New experiments would be necessary to investigate possible N I transitions beyond 800 nm.. Here again, we notice that no line of the metal which liquid nitrogen is loaded with, i.e. zinc in this case, is detected. The same argument, i.e. the expansion of the plasma that would limit the influx of zinc, can be put forward to explain this behaviour. Copper lines are observed after 200 ns. A detailed analysis of all transitions observed is given as supplemental material 2.

N II lines are observed in highly-energetic discharges like lightening events or high current pulsed arcs [START_REF] Orville | A high-speed time-resolved spectrscopic study of the lightning return stroke: Part II. A quantitative analysis[END_REF][START_REF] Martins | Characterization of a high current pulsed arc using optical emission spectroscopy[END_REF]. Indeed, producing N +* excited states requires at least 24.296 eV [START_REF] Locht | The dissociative ionization of nitrogen[END_REF], which is the energy necessary for dissociative ionization of N2 by electron collision. However, this value is probably overestimated in media where pressure can reach several thousand bars.

Indeed, the lowering of the ionization energy can be significant, leading to much lower values. As neither N I nor N2 I transitions are observed, we infer that these states are weakly populated and/or trapped in this optically thick medium. This observation might also result from a low concentration of one metal element with respect to the other.

The weak background emission observed in this situation suggests that the electron-ion recombination process is limited, which might be due either to a lower concentration of charged species or to an increase in the electron temperature as the cross-section of this process is favoured at low energy.

Since zinc NPs exhibit a specific sheet-like shape, we wondered whether this feature could play a role on the behaviour of discharges. Then, we used brass (63wt.%Cu-37wt.%Zn) electrodes instead of zinc electrodes during the first step of the process. In this case, and in order to keep the size of this paper reasonable, we will admit that spherical Cu and Zn primary NPs with size distributions below 10 nm are synthesized. The almost perfect overlap of spectra recorded when zinc or brass electrodes (Fig. 10) are used shows the non-influence of the shape of Zn NPs. It also shows that copper NPs do not interfere in the excitation process of N II lines.

In any situation (Zn@Cu or Cu@Zn), Cu and Zn lines are never observed simultaneously.

The metal-containing discharge made by erosion of the electrodes remains clear of metallic

NPs initially contained in the dielectric liquid. This means that the synthesis of core-shell NPs occurs in post-discharge. Two steps are then required: one to produce NPs in discharge and one to coat them in post-discharge. Until now, no information is available on the way the transition from one step to the other occurs. New experiments are needed to better understand this transition.

CONCLUSION

We showed that Cu@Zn core-shell NPs are synthesized by a two-step process, whichever step is processed first. Nevertheless, the zinc shell has not the same feature, which gives, after air oxidation, either Cu@ZnO or Cu2O@ZnO nanostructures. This possibility is mainly due to the formation of zinc nanosheets, an uncommon shape in this kind of process that usually produces spherical nanoparticles, like for copper. The formation mechanism of these 2D nanostructures is a major stake and it is still under investigation to be fully clarified. From time-resolved optical emission spectroscopy, we found out that copper and zinc never emit lines at the same time. No alloying is then possible in these conditions. That is why core-shell structures are found. When liquid nitrogen is loaded with Cu NPs, only zinc lines are emitted.

On the other hand, when liquid nitrogen is loaded with Zn nanosheets or NPs, N II lines are emitted during the first 200 ns before copper lines become visible. This process is observed here for the first time and remains unclear but it might be useful to better understand processes in dielectric liquids submitted to high voltages. Electron Energy Loss Spectroscopy near-edge fine structures for the Zn L23. Reference spectra for CuO, Cu2O, Cu are taken in [START_REF] Hamdan | Impacts created on various materials by micro-discharges in heptane: Influence of the dissipated charge[END_REF] whereas reference spectra for Zn and ZnO are taken in [START_REF] Hamdan | Comparison of Aluminium Nanostructures Created by Discharges in Various Dielectric Liquids[END_REF] and [START_REF] Leapman | Study of the L23 edges in the 3d transition metals and their oxides by electron-energy-loss spectroscopy with comparisons to theory[END_REF] respectively. [START_REF] Hamdan | Impacts created on various materials by micro-discharges in heptane: Influence of the dissipated charge[END_REF] whereas reference spectra for Zn and ZnO are taken in [START_REF] Hamdan | Comparison of Aluminium Nanostructures Created by Discharges in Various Dielectric Liquids[END_REF] and [START_REF] Leapman | Study of the L23 edges in the 3d transition metals and their oxides by electron-energy-loss spectroscopy with comparisons to theory[END_REF] respectively.

CAPTIONS

Figure 1 :

 1 Figure 1: a) Copper oxide (CuO) nanoparticles synthesized between two Cu electrodes

Figure 2 :

 2 Figure 2: a) TEM overview of Cu@ZnO core-shell NPs. b) High-resolution TEM image

Figure 3 :

 3 Figure 3: a) Electron Energy Loss Spectroscopy near-edge fine structures for the Cu L23. b)

Figure 4 :

 4 Figure 4: Starting with zinc electrodes. a) TEM overview of Cu@ZnO core-shell NPs. b)

Figure 5 :

 5 Figure 5: Starting with zinc electrodes. a) Electron energy loss spectroscopy near-edge fine

Figure 6 :

 6 Figure 6: Starting with zinc electrodes. Identification of weakly abundant Cu2O@ZnO NPs.

Figure 7 :

 7 Figure 7: a) TEM image showing Cu2O@ZnO NP. b) Corresponding diffraction pattern of

Figure 8 :

 8 Figure 8: Schematic showing the formation mechanisms of core-shell nanoparticles. a) Cu

Figure 9 :

 9 Figure 9: Time-resolved optical emission spectra of a) discharges between zinc electrodes

Figure 10 :Supplemental Material 2 :

 102 Figure 10: Time-resolved optical emission spectra recorded at 100 ns of discharges between

Figure 1 :

 1 Figure 1: a) Copper oxide (CuO) nanoparticles synthesized between two Cu electrodes

Figure 2 :

 2 Figure 2: Starting with copper electrodes. a) TEM overview of Cu@ZnO core-shell NPs. b)

Figure 3 :

 3 Figure 3: Starting with copper electrodes. a) Electron Energy Loss Spectroscopy near-edge

Figure 4 :

 4 Figure 4: Starting with zinc electrodes. a) TEM overview of Cu@ZnO core-shell NPs. b)

Figure 5 :

 5 Figure 5: Starting with zinc electrodes. a) Electron Energy Loss Spectroscopy near-edge fine

Figure 6 :

 6 Figure 6: Starting with zinc electrodes. Identification of weakly abundant Cu2O@ZnO NPs.

Figure 7 :

 7 Figure 7: a) TEM image showing Cu2O@ZnO NP. b) Corresponding diffraction pattern of

Figure 8 :

 8 Figure 8: Schematic showing the formation mechanisms of core-shell nanoparticles. a) Cu

Figure 9 :

 9 Figure 9: Time-resolved optical emission spectra of a) discharges between zinc electrodes

Figure 10 :

 10 Figure 10: Time-resolved optical emission spectra recorded at 100 ns of discharges between

SUPPLEMENTAL MATERIAL 1

Supplemental Material 1: EDXS line scanning profiles along two Cu@ZnO core-shell nanoparticles synthesized by starting with zinc electrodes. Supplemental Material 2: Optical emission spectrum recorded at 200 ns of discharges between copper electrodes immersed in liquid nitrogen loaded in zinc (18,000 discharges).

Only N II lines and Cu I lines are detected. No Zn I lines are found. Integration time: 50 ns.

The spectrum is normalized in intensity.


Tamir Dingjan

Émilie Gillon

Anne Imberty

Serge Pérez

Alexander Titz

Dr Paul A Ramsland
email: paul.ramsland@rmit.edu.au

Dr Elizabeth Yuriev
email: elizabeth.yuriev@monash.edu

Virtual screening against carbohydrate-binding proteins: evaluation and application to bacterial Burkholderia ambifaria lectin

Keywords: Burkholderia ambifaria, carbohydrate, fluorescence polarization, isothermal titration calorimetry, lectin, structure-based virtual screening, surface plasmon resonance, validation

Bacterial adhesion to human epithelia via lectins constitutes a therapeutic opportunity to prevent infection. To enable discovery of compounds able to inhibit bacterial lectins, a virtual screening protocol has been iteratively developed via 194 retrospective screening protocols against four bacterial lectins (BambL, BC2L-A, FimH and LecA) with known ligands. Specific attention was given to the rigorous evaluation of retrospective screening, including calculation of analytical errors for enrichment metrics. The developed virtual screening workflow was applied to BambL from the lung pathogen Burkholderia ambifaria, predicting 15 active compounds from virtual libraries of approximately 7 million compounds. Experimental validation using fluorescence polarization confirmed the inhibitory activity for two compounds, which were further characterized by titration calorimetry and surface plasmon resonance. This report demonstrates the utility of virtual screening protocols, integrating ligand-based pharmacophore filtering and structure-based constraints, in the search for bacterial lectin inhibitors.

Introduction

Target dependence of docking protocols and scoring functions is a known obstacle to out-of-thebox application of structure-based virtual screening. [START_REF] Yuriev | Trends, and New Ideas in Molecular Docking: 2012-2013 in Review[END_REF] Carbohydrate-binding proteins present a particularly challenging target type as they exhibit a range of unique ligand-binding features, such as formation of hydrogen-bonding networks. [START_REF] Agostino | Molecular Docking of Carbohydrate Ligands to Antibodies: Structural Validation against Crystal Structures[END_REF] Therefore, appropriate methodologies need to be tailored for these targets, which are involved in many disease processes. For example, in epithelial infection, adhesion to human tissues is commonly achieved via binding of cell surface carbohydrates by bacterial carbohydrate-binding proteins, termed lectins. [START_REF] Klemm | Bacterial Adhesins: Function and Structure[END_REF][START_REF] Kline | Bacterial Adhesins in Host-Microbe Interactions[END_REF] Interfering with this binding has been pursued as a new mode of antibacterial therapy, mainly due to the slow selective pressure exerted by anti-adhesive agents. [START_REF] Dickey | Different Drugs for Bad Bugs: Antivirulence Strategies in the Age of Antibiotic Resistance[END_REF][START_REF] Sattin | Glycoconjugates and Glycomimetics as Microbial Anti-Adhesives[END_REF] Inhibition of lectin-carbohydrate binding by glyco-derived compounds has met with promising clinical success in human urinary [START_REF] Kranjčec | D-Mannose Powder for Prophylaxis of Recurrent Urinary Tract Infections in Women: A Randomized Clinical Trial[END_REF][START_REF] Porru | Oral D-Mannose in Recurrent Urinary Tract Infections in Women: A Pilot Study[END_REF] and respiratory [START_REF] Von Bismarck | Successful Treatment of Pseudomonas Aeruginosa Respiratory Tract Infection with a Sugar Solution--a Case Report on a Lectin Based Therapeutic Principle[END_REF][START_REF] Hauber | Inhalation with Fucose and Galactose for Treatment of Pseudomonas Aeruginosa in Cystic Fibrosis Patients[END_REF] infections, albeit hampered by the weak affinity of lectins for carbohydrates (typically in the milli-to micromolar range). Approaches to design efficient lectin inhibitors have explored two main directions: presentation of carbohydrate epitopes on multivalent scaffolds (glycodendrimers, [START_REF] Touaibia | Mannosylated G(0) Dendrimers with Nanomolar Affinities to Escherichia Coli Fimh[END_REF] glycopeptides, [START_REF] Visini | Structural Insight into Multivalent Galactoside Binding to Pseudomonas Aeruginosa Lectin Leca[END_REF][START_REF] Caumes | Multivalent Thioglycopeptoids Via Photoclick Chemistry: Potent Affinities Towards Leca and Bc2l-a Lectins[END_REF] glycoclusters, [START_REF] Boukerb | Anti-Adhesive Properties of Glycoclusters against Pseudomonas Aeruginosa Lung Infection[END_REF][START_REF] Ligeour | Synthesis of Branched-Phosphodiester and Mannose-Centered Fucosylated Glycoclusters and Their Binding Studies with Burkholderia Ambifaria Lectin (Bambl)[END_REF] and glycofullerenes [START_REF] Buffet | Fucofullerenes as Tight Ligands of Rsl and Lecb, Two Bacterial Lectins[END_REF] ) and design of glycomimetics, compounds combining carbohydrate character with functional groups able to form specific binding interactions or provide avenues for further optimization. [START_REF] Cecioni | Glycomimetics Versus Multivalent Glycoconjugates for the Design of High Affinity Lectin Ligands[END_REF] Indeed, small glycomimetics with high affinity are more likely to lead to drug development, owing to their more favorable pharmacokinetic properties. [START_REF] Wagner | Novel Strategies for the Treatment of Pseudomonas Aeruginosa Infections[END_REF] Such high affinity glycomimetic inhibitors have been reported for multiple lectins, including FimH, [START_REF] Klein | Fimh Antagonists for the Oral Treatment of Urinary Tract Infections: From Design and Synthesis to in Vitro and in Vivo Evaluation[END_REF][START_REF] Cusumano | Treatment and Prevention of Urinary Tract Infection with Orally Active Fimh Inhibitors[END_REF] LecA, [START_REF] Rodrigue | Aromatic Thioglycoside Inhibitors against the Virulence Factor Leca from Pseudomonas Aeruginosa[END_REF][START_REF] Kadam | T-Shape" Interaction with Histidine Explains Binding of Aromatic Galactosides to Pseudomonas Aeruginosa Lectin Leca[END_REF][START_REF] Wagner | Covalent Lectin Inhibition and Application in Bacterial Biofilm Imaging[END_REF] LecB, [START_REF] Sommer | Orally Bioavailable Lecb Inhibitors Block Biofilm Formation of Pseudomonas Aeruginosa[END_REF][START_REF] Hauck | Discovery of Two Classes of Potent Glycomimetic Inhibitors of Pseudomonas Aeruginosa Lecb with Distinct Binding Modes[END_REF][START_REF] Sommer | Cinnamide Derivatives of D-Mannose as Inhibitors of the Bacterial Virulence Factor Lecb from Pseudomonas Aeruginosa[END_REF] and BambL. [START_REF] Richichi | Synthesis of a Selective Inhibitor of a Fucose Binding Bacterial Lectin from Burkholderia Ambifaria[END_REF] BambL (the Burkholderia ambifaria lectin) displays affinity for fucosylated human blood group determinants. [START_REF] Audfray | Fucose-Binding Lectin from Opportunistic Pathogen Burkholderia Ambifaria Binds to Both Plant and Human Oligosaccharidic Epitopes[END_REF] B. ambifaria, a bacterial member of the Burkholderia cepacia complex (BCC), was identified in clinical isolates from cystic fibrosis patients, [START_REF] Coenye | Nov., a Novel Member of the Burkholderia Cepacia Complex Including Biocontrol and Cystic Fibrosis-Related Isolates[END_REF] a disease in which lectin-mediated bacterial adhesion to fucosylated lung epithelia is suspected to play an important role. [START_REF] Glick | Activity of Fucosyltransferases and Altered Glycosylation in Cystic Fibrosis Airway Epithelial Cells[END_REF][START_REF] Venkatakrishnan | Host Mucin Glycosylation Plays a Role in Bacterial Adhesion in Lungs of Individuals with Cystic Fibrosis[END_REF] Compounds able to inhibit the BambL-saccharide interaction may have therapeutic potential as anti-adhesives. The present study applies our structural knowledge of the BambL binding site based on the protein's crystal structure [START_REF] Audfray | Fucose-Binding Lectin from Opportunistic Pathogen Burkholderia Ambifaria Binds to Both Plant and Human Oligosaccharidic Epitopes[END_REF] and on our previous simulations of the protein interacting with fucosylated oligosaccharides. [START_REF] Dingjan | Molecular Simulations of Carbohydrates with a Fucose-Binding Burkholderia Ambifaria Lectin Suggest Modulation by Surface Residues Outside the Fucose-Binding Pocket[END_REF] In the latter study, we have determined the atomic interactions made by fucosylated carbohydrates recognized by BambL. Each monomer of BambL has two independent but very similar binding sites that feature a network of hydrogen bonds within the fucose-binding pocket, complemented by interactions with solvent-exposed residues outside the binding cleft. Hydrogen-bonding interactions formed by the intramonomeric binding site Arg15, Glu26, and Trp79 were most significant for carbohydrate recognition, accompanied by hydrophobic stacking against Trp74 and hydrogen-bonding to Ala38 (Figure 1). One widely used tool for discovery of novel inhibitors is structure-based virtual screening, an in silico approach that rapidly assesses the potential for molecules to interact with a specific binding site using molecular docking techniques. [START_REF] Yuriev | Trends, and New Ideas in Molecular Docking: 2012-2013 in Review[END_REF][START_REF] Yuriev | Challenges and Advances in Computational Docking: 2009 in Review[END_REF][START_REF] Yuriev | Latest Developments in Molecular Docking: 2010-2011 in Review[END_REF] However, reports describing screening against carbohydrate-binding proteins often apply virtual screening methods as a "black box", with neither investigations of how well the method would be expected to work (efforts termed retrospective screening or validation), nor attempts to improve the screening method. As Grant and Woods [START_REF] Grant | Recent Advances in Employing Molecular Modelling to Determine the Specificity of Glycan-Binding Proteins[END_REF] pointed out, the docking methods used for carbohydrate virtual screening are typically evaluated without negative controls, precluding accurate measurement of the method's ability to discriminate between binders and non-binders.

In the present work, we set out to iteratively develop and validate a workflow for virtual screening against bacterial lectins. Several questions were asked to guide the development of the virtual screening workflow: to what degree is carbohydrate screening enrichment artificially enhanced by poor camouflage (mismatch in molecular properties between actives and decoys)?; can combinations of ligand-based and structure-based screening improve enrichment?; and finally, can an enriching screening method unearth novel lectin ligand scaffolds in a prospective search? Through extensive method development and evaluation, 194 screening protocols combining ligand-based and structure-based screening approaches have been tested using a panel of four bacterial lectins with multiple known ligands (BambL, BC2L-A, FimH and LecA). We have paid specific attention to the validation aspect, using property-matched carbohydrate decoys and calculating analytical errors attending enrichment metrics. Experimental validation of the screening approach was carried out via a prospective search for novel inhibitors against BambL.

Confirmatory bioassays using fluorescence polarization-based competitive binding, surface plasmon resonance (SPR), and isothermal titration calorimetry (ITC) identified two hit compounds showing lectin inhibition.

Methods

Virtual screening method development

Selection of actives and decoys

Target bacterial lectins were selected based on the availability of crystallographic structures, glycan array data, and known binding activity for greater than 10 actives. Four lectins were chosen: BambL, 28 BC2L-A from Burkholderia cenocepacia, [START_REF] Lameignere | Structural Basis for Mannose Recognition by a Lectin from Opportunistic Bacteria Burkholderia Cenocepacia[END_REF][START_REF] Sulak | A Tnf-Like Trimeric Lectin Domain from Burkholderia Cenocepacia with Specificity for Fucosylated Human Histo-Blood Group Antigens[END_REF] FimH from uropathogenic Escherichia coli, [START_REF] Hung | Structural Basis of Tropism of Escherichia Coli to the Bladder During Urinary Tract Infection[END_REF] and LecA from Pseudomonas aeruginosa. [START_REF] Cioci | Structural Basis of Calcium and Galactose Recognition by the Lectin Pa-Il of Pseudomonas Aeruginosa[END_REF][START_REF] Garber | On the Specificity of the D-Galactose-Binding Lectin (Pa-I) of Pseudomonas Aeruginosa and Its Strong Binding to Hydrophobic Derivatives of D-Galactose and Thiogalactose[END_REF] Known actives for these lectins were sourced from published literature 19-20, 22, 27, 41-53 and from glycan array data deposited in the Consortium for Functional Glycomics Gateway database, [START_REF]Functional Glycomics Gateway[END_REF] from which unique saccharide actives of tetrasaccharide size and smaller were selected. 3D structures of actives were manually constructed using Maestro. [START_REF]Schrödinger Release 2015-3: Maestro, version 10.3[END_REF] Complete sets of actives are shown in Supplementary Table S1.

Small molecule decoys were selected from the ZINC database [START_REF] Sterling | Zinc 15 -Ligand Discovery for Everyone[END_REF][START_REF] Irwin | A Free Tool to Discover Chemistry for Biology[END_REF][START_REF] Irwin | Zinc -a Free Database of Commercially Available Compounds for Virtual Screening[END_REF] using the Silico toolkit's molecular property-matching module. [START_REF]Silico -a Perl Molecular Modelling Toolkit[END_REF] Decoy selection was based on similarity to the actives in the following molecular properties: number of heavy atoms, number of hydrogen bond acceptors and donors, number of rotatable bonds, and LogD (predicted using QikProp 60 ). A total of 20 decoys were selected per active. The resulting 2D structures were elaborated to corresponding 3D structures using LigPrep 61 without enumeration of protomers, tautomers, or conformers.

Carbohydrate decoys were generated separately for each target lectin by combinatorial assembly.

For each target, component monosaccharides were chosen to avoid known binding epitopes, then assembled in a combinatorial fashion to form di-, tri-and tetrasaccharides using Instant JChem reactor modules. [START_REF]Instant Jchem, version 6.0[END_REF] For example, the target lectin LecA displays affinity for D-galactose epitopes; therefore, carbohydrate decoys were assembled from L-fucose, D-glucose, D-mannose, D-N- acetylgalactosamine, and D-N-acetylglucosamine monosaccharides. Assembly involved constructing all glycosidic linkages at all positions on all substrate saccharides, forming all possible oligosaccharides. The required number of decoys were randomly selected from each size category (i.e., di-, tri-, and tetrasaccharide sizes) to match the distribution of glycan actives from glycan arrays. A total of 40 decoys per active were chosen.

Library preparation

Three different library compositions were constructed: all actives with ZINC-derived decoys, all actives with carbohydrate decoys, and carbohydrate actives (i.e., those derived from glycan arrays) with carbohydrate decoys. In each case, Phase [START_REF]Schrödinger Release 2015-3: Phase, version 4.4[END_REF] was used to prepare the library. Compound states were generated at pH 7.0 ± 2.0 using Epik. [START_REF]Schrödinger Release 2014-1: Protein Preparation Wizard; Epik, version 3.6[END_REF] Stereochemistry was determined from 3D geometry; unspecified stereocenters were sampled, retaining four stereoisomers. Rings were sampled with a single conformer retained. High-energy states were removed. For each compound, up to 100 conformers were kept, with a maximum of 10 conformers retained per rotatable bond.

Receptor structure preparation

Crystallographic structures used for structure-based screening are shown bolded in Supplementary Table S2. Each receptor structure was truncated to a single chain and prepared using the Protein Preparation Wizard implemented within Maestro. [START_REF]Schrödinger Release 2014-1: Protein Preparation Wizard; Epik, version 3.6[END_REF] Bond orders and hydrogen atoms were added, metal coordination bonds and disulfide bonds created, and water molecules removed. Resultant models were refined by a restrained minimization of hydrogen atoms only. Receptor grids were generated by centering the cubic docking box on the crystallographic bound ligand, with a docking box side length of 20 Å and a diameter midpoint box side length of 15 Å.

Binding pocket constraints

A single constraint was defined in each lectin binding site as a 1 Å radius sphere centered on the most deeply buried ligand atom in the crystallographic complex. In all cases, this corresponded to a saccharide hydroxyl oxygen atom. Matching features were set to SMARTS patterns for a single carbon, nitrogen, oxygen, or sulfur atom. [START_REF]Schrödinger Release 2015-3: Maestro, version 10.3[END_REF]

Pharmacophore hypothesis

A binding pharmacophore was assembled for each lectin by tallying hydrogen bonding and hydrophobic interactions occurring in crystallographic structures (Supplementary Table S3).

Hydrogen bonds were defined using the Baker-Hubbard method implemented in MDTraj. [START_REF] Mcgibbon | A Modern Open Library for the Analysis of Molecular Dynamics Trajectories[END_REF] Hydrophobic interactions were defined as pairs of aliphatic carbon atoms within a 5 Å maximum distance, where each carbon atom was bonded only to carbon or hydrogen atoms. Frequentlyinteracting ligand features were incorporated into the pharmacophore hypothesis using the Develop Hypothesis Model tool in Phase. [START_REF]Schrödinger Release 2015-3: Phase, version 4.4[END_REF] The Advanced Pharmacophore Screening tool was used to find pharmacophore matches prior to structure-based screening: database keys were used for pre-screening, with tolerance of inter-site matching distances set to 2.0 Å.

Interaction constraints

Using the interaction tallies from crystallographic structures, the four most frequently hydrogenbonding receptor atoms were used as interaction constraints (Supplementary Table S3). In addition, a single hydrophobic interaction constraint was defined as a 1 Å sphere centered on the crystallographic ligand atom most involved in hydrophobic interactions.

Interaction constraints were applied in multiple configurations: first, a hit was required to participate in simultaneous hydrogen bonding interaction with all four atoms, satisfying all four interaction constraints (FH method); second, replacement of the least dominant hydrogen bonding constraint with a hydrophobic constraint (FI method); third, using all hydrogen bonding and hydrophobic constraints while varying the number of required constraint matches from one to four (FI-1, FI-2, FI-3, and FI-4), such that hits would be accepted if able to simultaneously satisfy the required number of constraints. For BC2L-A, no hydrophobic interactions were detected in crystallographic complexes. For both BC2L-A and LecA, binding-site calcium ions are known to be significant for ligand binding; these were defined as an interaction constraint alongside hydrogen bonding interactions. FimH and LecA complexes feature a broad hydrophobic patch at the entrance to the mannose-binding/galactose-binding site, respectively; hydrophobic interaction constraints capturing these interactions took the form of a 3.6 Å spherical constraint positioned between multiple binding site residues (Supplementary Table S3). During screening, hydrophobic constraints were specified to require a bonded carbon and hydrogen atom within the constraint radius using the SMARTS pattern C[H].

Virtual screening

Docking calculations were conducted via the Virtual Screening Workflow available within

Maestro. [START_REF]Schrödinger Release 2015-3: Maestro, version 10.3[END_REF] Receptor grids were generated within the screening workflow. To investigate the effect of different active-decoy set combinations on screening enrichment, three library compositions were investigated for each lectin: all actives -ZINC-derived decoys, all actives -carbohydrate decoys, and carbohydrate actives -carbohydrate decoys (Figure 2A). Screening was performed on each input library using the HTVS algorithm. Additionally, each screen was also performed using the Glide HTVS algorithm followed by a re-screening step using the Glide SP algorithm; [START_REF] Friesner | Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy[END_REF][START_REF] Halgren | Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening[END_REF] in these, the top-ranked 10% of hits were re-docked using SP mode, with enhanced sampling enabled and retaining only the highest-scoring state of each compound. The following protocols, summarized in Figure 2B, were tested in this investigation. Default: structure-based screening conducted using entirely default settings. F: input library filtering using ligand pharmacophore matching as described above followed by structure-based screening using default settings. FP: input library filtering followed by structure-based screening using a binding pocket constraint. FH: input library filtering followed by structure-based screening using four hydrogen bonding and metal coordination interaction constraints. FI: input library filtering followed by structure-based screening with the least-dominant hydrogen bonding constraint replaced by a hydrophobic constraint, maintaining a total of four required constraints. Finally, this screen was repeated with all hydrogen bonding and hydrophobic constraints, varying the number of required constraints from one to four. In total, 194 screening protocols were tested in an iterative fashion (Supplementary Tables S4 andS5). Enrichment metrics were calculated using the Silico toolkit. [START_REF]Silico -a Perl Molecular Modelling Toolkit[END_REF] For both ROC AUC and ROC-based Enrichment Factor, analytical errors were calculated according to the method communicated by McGann et al. [START_REF] Mcgann | The Statistics of Virtual Screening and Lead Optimization[END_REF] Prospective virtual screening library preparation Two large molecular databases were screened prospectively: the eMolecules Screening Library (approximately 7 million compounds) and the Otava Ltd. Catalogue and Glycomimetic Collection (containing 246 compounds). Web-based substructure search tools were used to extract compounds containing a SMILES string CXC(C)C(O)CX, where X = O for the eMolecules Screening Library and O/N for the Otava Ltd. catalogue. The total number of input compounds collected from eMolecules and Otava databases was 11,705. The Generate Phase Database module of Phase [START_REF]Schrödinger Release 2015-3: Phase, version 4.4[END_REF] was used to prepare the virtual library compounds by adding hydrogens and generating conformers (up to a maximum of 100 conformers per compound). Tautomers were enumerated at pH 7.0 using Epik. [START_REF]Schrödinger Release 2014-1: Protein Preparation Wizard; Epik, version 3.6[END_REF] Stereochemistry was determined from 2D parities where present; where absent, a maximum of four stereoisomers were retained per compound. Rings of five and six members were sampled, retaining one ring conformation per compound. Virtual screening was performed using the FI method as described above: after applying the ligand-based pharmacophore filter, resulting matches were screened using the HTVS algorithm. The top-ranked 10% of hits were then re-screened using the SP algorithm with enhanced sampling enabled, retaining only the highest-scoring state of each compound. Finally, the top 10% of hits were manually inspected, removing undesirable compounds (simple monosaccharides, compounds of molecular weight higher than 600 g/mol, compounds containing free thiols, and commercially unavailable compounds).

Isothermal titration calorimetry

Recombinant BambL was produced and purified as previously described by Audfray et al. [START_REF] Audfray | Fucose-Binding Lectin from Opportunistic Pathogen Burkholderia Ambifaria Binds to Both Plant and Human Oligosaccharidic Epitopes[END_REF] The protein was dissolved in buffer (20 mM Tris/HCl pH 7.5, NaCl 100 mM with CaCl2 100 µM). The protein concentration was checked by measuring A280 using a theoretical molar extinction coefficient of 40,450 M -1 cm -1 . Isothermal titration calorimetry (ITC) was performed with an ITC200 microcalorimeter (MicroCal, Inc). The BambL solution (50 µM) was placed in the 200 µL sample cell at 25 °C. Ligands were dissolved in the same buffer to a concentration of 1 mM, and loaded in the injection syringe. Titration was performed using 20 injections of 1 µL volume of ligand solution, which were spaced at 120 second intervals and injected for a duration of 2 seconds each. Where binding isotherms were observed, data were fitted with MicroCal Origin 7 software, according to standard procedures, using a 1:1 binding site model. Fitted data yielded the stoichiometry (n), the association constant (Ka) and the enthalpy of binding (ΔH). Other thermodynamic parameters (i.e. changes in free energy ΔG, and entropy ΔS) were calculated from the equation ΔG = ΔH -TΔS = RTlnKa in which T is the absolute temperature and R = 8.314 J mol -1 K -1 . Three independent titrations were performed for each ligand tested.

Surface plasmon resonance

Surface plasmon resonance (SPR) experiments were performed on a Biacore X100 instrument (GE Healthcare) at 25°C in HBS (10 mM Hepes/NaOH, pH 7.4, 150 mM NaCl, 0.05% Tween 20 and 3 mM EDTA) at a flow rate of 30 μL min -1 . Streptavidin (100 μg mL -1 ) was immobilized on a research grade CM5 dextran chip using amine coupling (3029.6 resonance units of streptavidin were fixed in channel 1, 2939.5 resonance units in channel 2). Biotin capture was then conducted to affix biotinylated polyacrylamide (PAA) probes (Lectinity, 200 μg mL -1 ) bearing galactose or fucose to channels 1 (PAA-galactose, 375 resonance units) and 2 (solution containing 90% PAAgalactose and 10% PAA-fucose, total of 447.5 resonance units). Inhibition experiments were performed with the fucosylated channel 2, and plots represent the subtracted data (channel 2channel 1). Inhibition studies consisted of the injection (association 180 s, dissociation 180 s) of incubated (>30 min at room temperature) mixtures of BambL (0.8 µM) and various concentrations of inhibitor (2-fold cascade dilutions from 200 to 0.2 µM). For each inhibition assay, BambL was injected to observe the full adhesion of the lectin onto the sugar-coated surface (0% inhibition).

The chip was regenerated by injection of 1 M fucose in running buffer for 80 s. Binding was measured as resonance units over time after blank subtraction, and data were then evaluated by using the Biacore X100 evaluation software, version 2.0. For IC50 evaluation, the response was taken as the amount of lectin bound to the sugar-coated surface at equilibrium in the presence of a defined concentration of inhibitor. Inhibition curves were obtained by plotting the percentage of inhibition against the inhibitor concentration.

Competitive binding using fluorescence polarization

Assays were conducted as previously established for LecB [START_REF] Hauck | Discovery of Two Classes of Potent Glycomimetic Inhibitors of Pseudomonas Aeruginosa Lecb with Distinct Binding Modes[END_REF][START_REF] Sommer | The Virulence Factor Lecb Varies in Clinical Isolates: Consequences for Ligand Binding and Drug Discovery[END_REF] and other bacterial lectins. [START_REF] Beshr | Development of a Competitive Binding Assay for the Burkholderia Cenocepacia Lectin Bc2l-a and Structure Activity Relationship of Natural and Synthetic Inhibitors[END_REF][START_REF] Joachim | Development and Optimization of a Competitive Binding Assay for the Galactophilic Low Affinity Lectin Leca from Pseudomonas Aeruginosa[END_REF][START_REF] Beshr | Photorhabdus Luminescens Lectin a (Plla) -a New Probe for Detecting Α-Galactoside-Terminating Glycoconjugates[END_REF] A 60 μL solution containing lectin (225 nM for LecB, 150 nM for BambL) and reporter ligand FITC-fucoside [START_REF] Hauck | Discovery of Two Classes of Potent Glycomimetic Inhibitors of Pseudomonas Aeruginosa Lecb with Distinct Binding Modes[END_REF] (1.5 nm for LecB, 75 nM for BambL) in TBS buffer was added to 30 µL serial dilutions of testing compounds in TBS (in duplicates for LecB, single serial dilution for BambL) in black 96-well Costar plates (Fisher Scientific, France, cat. no.: 06-443-2). Concentration ranges were selected for each compound to cover the range over which inhibitory activity was visible.

For LecB, α-methyl fucoside, α-methyl mannoside and L-Fucα1-6[D-GlcNAcβ1-4]D-GlcNAcβ1- OMe were prepared in 5-fold dilutions from 33.3 mM to 400 nM, and 2-deoxy-2-fluoro-L-fucose was prepared using 2-fold dilutions from 25 mM to 0.195 mM. For BambL, concentration ranges were selected for each compound using 5-fold dilutions for: α-methyl fucoside (3.33

Results

Virtual screening optimization for bacterial lectins

Selection of target lectins, actives, and decoys

The BambL, BC2L-A, FimH, and LecA lectins were chosen for virtual screening optimization.

For each lectin, decoy compounds were selected from the ZINC database to approximate the molecular properties of known actives as described in the Methods. The molecular properties of ZINC decoys proved a poor match for the molecular properties of active compounds (shown for BambL in Table 1, for all four lectins in Supplementary Figure S1). A second set of decoy compounds was therefore assembled, which exhibited closer property-matching, except for the number of rotatable bonds. Both decoy sets were used for retrospective screening to compare the effect of decoy property-matching on retrospective enrichment.

Virtual screening method development

The docking software Glide predicts ligand placements within protein binding sites. When docking calculations are performed in a high-throughput manner, Glide may be used to predict binding activity for large collections of molecules. Glide has demonstrated appreciable docking accuracy for carbohydrate-binding proteins in general 2, 72-73 and for bacterial lectins specifically. [START_REF] Topin | Deciphering the Glycan Preference of Bacterial Lectins by Glycan Array and Molecular Docking with Validation by Microcalorimetry and Crystallography[END_REF][START_REF] Kadam | Structure-Based Optimization of the Terminal Tripeptide in Glycopeptide Dendrimer Inhibitors of Pseudomonas Aeruginosa Biofilms Targeting Leca[END_REF] A virtual screening workflow was developed using Glide 66-67 over a total of 194 retrospective virtual screens spanning all target and active/decoy sets. The multiple methods explored during the iterative workflow development are summarized in Figure 2. The left panel (Figure 2A)

indicates the three compositions of active and decoy sets trialled for each lectin (note that carbohydrate actives were not combined with ZINC decoys). The various screening protocols performed are summarized in the right panel (Figure 2B).

Iterative development of the virtual screening method explored combinations of pharmacophore filters, binding site constraints and protein-ligand interaction constraints over a total of 194 screening experiments. Filters were applied in a ligand-based manner prior to structure-based screening, while constraints were applied during structure-based screening to enforce the placement of ligand atoms either within the general binding site, or in positions able to form selected interactions with the lectin. Development of a screening method began with an entirely 'default' approach, to which pharmacophore filters were subsequently added ('F'). Binding pocket constraints were applied ('FP'), and then exchanged for hydrogen-bonding interaction constraints ('FH') and for general protein-ligand interaction constraints ('FI'). The above screening methods were each conducted using two scoring protocols implemented within Glide: High-Throughput Virtual Screening (HTVS) alone, and in conjunction with Standard Precision scoring (HTVS-SP).

Overall enrichment was measured using linear ROC AUC (Supplementary Table S4), which expresses the probability that an active compound will be ranked higher than a decoy (henceforth referred to as "AUC"). An AUC of 0.5 represents essentially random ranking of actives, while an AUC of 1.0 corresponds to perfect ranking of all actives above all decoys. AUC accords the same weight to rankings of actives throughout the hitlist, making the metric equally sensitive to actives present at the start and end of the hitlist. Since the early portion of the hitlist is of greater interest for prospective virtual screening, a metric more sensitive to early predictive ability was also employed. Specifically, to measure early enrichment, the ROC-based enrichment factor EF (Supplementary Table S5) was used, which describes the fraction of actives found once a fixed, chosen fraction of inactives have been found (here, set to 10%). By selecting inactive fractions in the early regions of the ROC curve, a method's ability to rank actives above decoys in the early portions of the hitlist can be measured. Both scoring protocols HTVS and HTVS-SP achieved very similar enrichments in most experiments (Supplementary Tables S4 andS5). Below quoted results

were determined using the HTVS-SP protocol. Analytical errors were calculated for both AUC and EF, and were taken into account in the comparisons between screening protocols. Where necessary, error values are quoted.

BambL

When property-matched ZINC-derived decoys were spiked with all actives (Figure 3 Early enrichment was also significantly higher when using fewer required constraint matches (EF:

9.33 [FI-1, FI-2]; Figure 3D). Carbohydrate decoys, with either all or carbohydrate actives (yellow and blue bars in Figure 3, respectively) yielded similar enrichment results for most protocols.

However, when using the optimal protocols FI-1 or FI-2, the early enrichment was lower with carbohydrate decoys, compared to ZINC-derived ones (Figure 3D). This finding demonstrated a more discriminating character of carbohydrate decoys, which would result in a more robust method validation via retrospective screening.

BC2L-A

Overall enrichment for BC2L-A was not significantly altered by the use of a pharmacophore filter (F) or binding pocket constraints (FP) (Supplementary Table S4). Early enrichment measures likewise remained within range of analytical error between experiments (Supplementary Table S5).

In screening of all actives against ZINC decoys, the introduction of four simultanously applied hydrogen-bonding constraints drastically reduced enrichment (AUC: 0.58 ± 0.07 [default] → 0.12

[FH]). Relaxing the number of required constraints to one was able to restore the overall enrichment (AUC: 0.65 ± 0.07 [FI-1]). Screening all actives against carbohydrate decoys performed similarly to screening against ZINC decoys. However, screening using only carbohydrate actives, with any number of interaction constraints (FI-1, FI-2, FI-3, and FI-4), returned no active compounds (Supplementary Tables S4 andS5). For this target lectin, enrichment relied strongly on the presence and ranking of non-carbohydrate actives.

FimH

Both overall and early enrichment against ZINC decoys was significantly improved by the use of a pharmacophore filter (AUC: 0. Supplementary Tables S4 andS5). Use of a binding pocket constraint (FP) and hydrogen-bonding constraints (FH) maintained the boost to early enrichment while decreasing overall enrichment.

Introduction of a hydrophobic constraint severely reduced both overall and early enrichment (AUC: 0.18 [FI]; EF: 1.76 [FI]). Relaxing the number of required constraint matches rescued the enrichment. Specifically, a single required constraint matched the highest overall and early enrichment to within the analytical error (AUC: 0.81 [FI-1]; EF: 7.65 [FI-1]). Carbohydrate decoys reduced overall enrichment using this protocol, more so for input containing only carbohydrate actives (AUC: 0.67). Early enrichment dropped from 7.65 to 5.00 and 2.22, for all and carbohydrate actives, respectively (Supplementary Table S5). Again, similar to BambL, these decreases demonstrated the greater discriminating power of carbohydrate decoys, compared to non-carbohydrate ones.

LecA

In general, screening performance for LecA was poorer than for other target lectins (Supplementary Tables S4 andS5). Using ZINC decoys, default screening settings discriminated against actives, performing worse than random selection (AUC: 0.49 [default]). Use of a pharmacophore filter improved overall enrichment slightly (AUC: 0.67 [F]), while the addition of any style of constraints returned both overall and early enrichment to random (AUC: 0.49 [FP]) or null (FH, FI) values. In particular, using any number of interaction constraints returned no active compounds. Screening using carbohydrate decoys performed similarly to ZINC decoys in all respects, yielding at best only random selection of actives.

In summary, retrospective screening enrichment at three out of four bacterial lectins benefited from the use of pharmacophore filters and application of only one or two interaction constraints, with greater numbers of constraints proving detrimental to enrichment. Significantly, the use of carbohydrate decoys was demonstrated to be most appropriate in order to evaluate a virtual screening methodology for application to carbohydrate-binding targets.

Experimental validation of predicted compounds against BambL

Experimental activity of the 15 selected compounds towards BambL was first evaluated using fluorescence polarization assays developed for this purpose. The fluorescence polarization assay method, originally developed by Hauck et al. [START_REF] Hauck | Discovery of Two Classes of Potent Glycomimetic Inhibitors of Pseudomonas Aeruginosa Lecb with Distinct Binding Modes[END_REF] for studying binding to LecB (another fucosebinding bacterial lectin) and other lectins, [START_REF] Beshr | Development of a Competitive Binding Assay for the Burkholderia Cenocepacia Lectin Bc2l-a and Structure Activity Relationship of Natural and Synthetic Inhibitors[END_REF][START_REF] Joachim | Development and Optimization of a Competitive Binding Assay for the Galactophilic Low Affinity Lectin Leca from Pseudomonas Aeruginosa[END_REF][START_REF] Beshr | Photorhabdus Luminescens Lectin a (Plla) -a New Probe for Detecting Α-Galactoside-Terminating Glycoconjugates[END_REF] has been applied to BambL. The assay measures displacement of a fluorescein-labeled reporter ligand FITC-fucose (N-(fluorescein-5-yl)-N'-(α-Lfucopyranosyl ethylen)-thiocarbamide) from the lectin binding site by the inhibitor under consideration. Here, this assay was used to evaluate the manually-selected virtual screening hits for inhibition of BambL and LecB.

Fucα1-OMe and Manα1-OMe (Figure 5) were also evaluated by fluorescence polarization, for comparison. Two screening hits showed ability to bind to BambL (Figure 5). Compounds 1 and 2 are entirely composed of carbohydrate rings, and both contain a fucose saccharide. Compound 1

showed lower inhibition of BambL-probe binding (Figure 6; IC50 19.9 μM), compared to Fucα1-OMe (IC50 2.4 μM). Inhibition by compound 2 was similar (Figure 6; IC50 1.4 μM) to Fucα1-OMe.

Since compound 2 contains an unmodified fucose saccharide, similar inhibitory activity was expected. Inhibition of LecB-FITC-fucose binding by compound 2 was intermediate between Fucα1-OMe and Manα1-OMe, while compound 1 was less active than either control saccharide (IC50 >> 1 mM). Isothermal titration calorimetry confirmed direct binding of both compounds to BambL in a onesite fitting model (Table 2, Figure 7), revealing essentially identical interactions with both the intra-and inter-monomeric binding sites. Both compounds exhibited dissociation constants in the micromolar range (1: KD of 18.8 ± 2.3 μM; 2: KD of 1.0 ± 0.1 μM), with lectin binding characterized by a greater enthalpic, rather than entropic component of binding. Despite the large difference in molecular size, the magnitude of the entropic component of binding is similar between 1 and 2.

BambL inhibition data from these two solution-based assay methods (fluorescence polarization and ITC) display excellent agreement. In surface plasmon resonance assays (Figure 8), BambL inhibition by virtual screening hits was less detectable (1: IC50 of 166 μM; 2: IC50 of 10.7 mM). The inhibition efficiency (IC50) of these hit compounds was lower than apparent affinity in solution assays. This could be explained by the strong avidity of BambL for multivalent surfaces, which is difficult to compete with when using monovalent ligands. Discussion 767778 The majority of virtual screening studies against carbohydrate-binding proteins do not evaluate the applied method nor use negative controls/decoys to establish retrospective enrichment. [START_REF] Park | Discovery and Biological Evaluation of Novel Α-Glucosidase Inhibitors with in Vivo Antidiabetic Effect[END_REF][START_REF] Park | Discovery of Novel Α-Glucosidase Inhibitors Based on the Virtual Screening with the Homology-Modeled Protein Structure[END_REF][START_REF] Hakamata | Virtual Ligand Screening of Alpha-Glucosidase: Identification of a Novel Potent Noncarbohydrate Mimetic Inhibitor[END_REF][START_REF] Bai | Identification of New Classes of Ricin Toxin Inhibitors by Virtual Screening[END_REF][START_REF] Barra | Discovery of New E-Selectin Inhibitors by Virtual Screening, Fluorescence Binding Assays, and Std Nmr Experiments[END_REF][START_REF] Kalas | Structure-Based Discovery of Glycomimetic Fmlh Ligands as Inhibitors of Bacterial Adhesion During Urinary Tract Infection[END_REF] Validation is critical for the improvement of screening methods against these targets, and allows users to judge the transferability of reported successes. Where retrospective screening has been performed, enrichment indicators used include the raw number of hits returned, [START_REF] Westerfors | Structure-Based Discovery of a New Affinity Ligand to Pancreatic Alpha-Amylase[END_REF] the ratio of retrieved actives to total actives, [START_REF] Ananthula | Strategies for Generating Less Toxic P-Selectin Inhibitors: Pharmacophore Modeling, Virtual Screening and Counter Pharmacophore Screening to Remove Toxic Hits[END_REF][START_REF] Huang | Novel Dual-Site-Binding Neuraminidase Inhibitor from Virtual Screening by Pharmacophore and Molecular Dynamics Methods[END_REF] and the widely-used 1% Enrichment

Factor [START_REF] Al-Asri | From Carbohydrates to Drug-Like Fragments: Rational Development of Novel Alpha-Amylase Inhibitors[END_REF] and ROC AUC. In this work, a virtual screening protocol for bacterial lectins was iteratively developed and evaluated using the ROC Enrichment Factor and ROC AUC metrics, with calculation of standard errors. The influence of decoy types on retrospective virtual screening enrichment was also examined. The virtual screening workflow was applied prospectively to BambL for identification of novel inhibitors; two of the identified hits consistently showed lectin inhibition in biophysical assays.

Decoy selection in this study was guided by two related considerations: avoiding known epitopes in decoys while matching decoys to actives in physical property space. Specifically, carbohydrate decoys were built from monosaccharides, excluding those with known specificity for a given lectin, as shown by experimental glycan array data. In that sense, these decoys are confirmed non-binders.

Non-carbohydrate decoys were drawn from ZINC, and are assumed to be non-binders based on the absence of carbohydrate epitopes in their structures. This type of assumption is a known caveat in virtual screening studies. [START_REF] Irwin | Community Benchmarks for Virtual Screening[END_REF] With respect to the second consideration, many have noted that retrospective screening enrichement depends on the resemblance between actives and decoys. [START_REF] Verdonk | Virtual Screening Using Protein-Ligand Docking: Avoiding Artificial Enrichment[END_REF][START_REF] Huang | Benchmarking Sets for Molecular Docking[END_REF] Our results are consistent with this observation, showing that enrichment in retrospective screens against bacterial lectins was influenced by the nature of the decoy compounds. Enrichment obtained using all actives may be artificially inflated by poor physical property-matching (either between ZINC-derived decoys and carbohydrate actives, or between carbohydrate decoys and noncarbohydrate actives). In contrast, the poorer enrichment obtained using carbohydrate decoys with carbohydrate actives in these screens provides a more genuine measure of the expected enrichment in a prospective virtual screening against carbohydrate-binding proteins.

Iterative development of a virtual screening workflow explored combinations of ligand-based and structure-based screening techniques. As noted above, the use of binding pocket constraints and interaction-based constraints have not been previously applied to carbohydrate-binding proteins.

In general, pharmacophore-based filtering improved enrichment compared to the entirely default screening setup. Subsequent addition of a binding pocket constraint also provided a small increase to enrichment. Interaction-derived constraints were initially applied in full force (i.e., requiring simultaneous formation of four binding interactions), causing a decrease in both early and overall enrichments. However, once the number of simultaneously-required constraints was reduced, enrichment measures increased again, affording the greatest enrichment for BambL and FimH.

These retrospective screening experiments demonstrate that a combination of ligand-based and structure-based techniques enhance enrichment against against carbohydrate-binding proteins.

In our previous efforts, [START_REF] Dingjan | Molecular Simulations of Carbohydrates with a Fucose-Binding Burkholderia Ambifaria Lectin Suggest Modulation by Surface Residues Outside the Fucose-Binding Pocket[END_REF] we have determined the atomic interactions made by fucosylated carbohydrates recognized by BambL. BambL-carbohydrate binding involved a network of hydrogen bonds within the fucose-binding pocket as well as interactions with satellite solventexposed residues outside of the binding cleft. The interacting residues identified in our earlier work were now used to define interaction constraints for prospective virtual screening. For BambL, the two-match interaction constraint screening method (FI-2) was genuinely able to prioritize binders over non-binders. Application of the FI-2 screening workflow to BambL identified 15 compounds with predicted binding affinity for BambL. Lectin inhibition was confirmed for two of these using fluorescence polarization, SPR, and ITC assays. Toxoplasma gondii TgMIC1, [START_REF] Allman | Potent Fluoro-Oligosaccharide Probes of Adhesion in Toxoplasmosis[END_REF] Mycobacterium tuberculosis UDB-galactopyranose [START_REF] N'go | Tetrafluorination of Sugars as Strategy for Enhancing Protein-Carbohydrate Affinity: Application to Udp-Galp Mutase Inhibition[END_REF] ). Since fluorine substitution can give rise to lectin specificity, we tested the binding of compound 1 to a second fucose-binding bacterial lectin, LecB from P. aeruginosa.

The fucomimetic compound 1 displayed a difference in inhibitory activity between lectins BambL and LecB. In fluorescence polarization assays (Figure 6), compound 1 inhibited BambL-probe binding with an IC50 of 19.9 μM, while barely inhibiting LecB-probe binding (IC50 >> 1 mM), an observation that confirms previous experiments with related 1,2-dideoxy-2-fluoro-L-fucose and LecB. [START_REF] Beshr | Development of a Competitive Binding Assay for the Burkholderia Cenocepacia Lectin Bc2l-a and Structure Activity Relationship of Natural and Synthetic Inhibitors[END_REF] A structural rationale for this observation can be suggested. In the LecB-fucose complex, [START_REF] Loris | Structural Basis of Carbohydrate Recognition by the Lectin Lecb from Pseudomonas Aeruginosa[END_REF][START_REF] Mitchell | Structural Basis for Oligosaccharide-Mediated Adhesion of Pseudomonas Aeruginosa in the Lungs of Cystic Fibrosis Patients[END_REF][START_REF] Marotte | X-Ray Structures and Thermodynamics of the Interaction of Pa-Iil from Pseudomonas Aeruginosa with Disaccharide Derivatives[END_REF] the fucose 2-position hydroxyl oxygen directly coordinates one of the two binding site calcium ions and forms a hydrogen bond to Asp99. Thus, the substitution of hydroxyl by fluorine is not favorable for binding. The BambL-fucose complex features a hydrogen bond donated by the backbone amide nitrogen of Asp38 to the fucose 2-position hydroxyl. [START_REF] Audfray | Fucose-Binding Lectin from Opportunistic Pathogen Burkholderia Ambifaria Binds to Both Plant and Human Oligosaccharidic Epitopes[END_REF] Therefore, in this case, fluorine can act as a weak hydrogen bond acceptor, making the substitution more acceptable. Compound 1 is therefore an excellent hit compound for the development of a specific inhibitor for BambL and related lectins.

Conclusion

Through iterative development and retrospective evaluation, a virtual screening workflow for bacterial lectins was developed. For two target systems, BambL and FimH, molecular properties of decoy compounds were found to exert influence upon retrospective enrichment. This finding showed that enrichment in virtual screening against carbohydrate-binding proteins could be artificially enhanced by poor decoy camouflage. In addition, the presence of non-carbohydrate actives was also conducive to over-estimated enrichment. Combined ligand-based and structurebased screening techniques were demonstrated to improve retrospective enrichment via the use of interaction-based constraints. For BambL, enforcing two interaction constraint matches provided the greatest overall enrichment in retrospective screening. A prospective virtual screen for compounds able to bind to BambL yielded two inhibitory molecules. Compound 1 (2-deoxy-2fluoro-L-fucose) displayed slightly reduced affinity for BambL compared to L-fucose, but multiple orders-of-magnitude reduction in affinity for LecB. The fucose 2-position is therefore suggested as an opportunity for designing selectivity for BambL in preference to other fucose-binding lectins.

Figure 1 .

 1 Figure 1. BambL-fucose crystallographic complex showing binding interactions. A single monomer of the protein is shown in white, carbohydrate shown in yellow with coloring by atom type. Hydrogen bonds shown as green dashes. Hydrophobic interactions shown as brown dashes. Non-polar hydrogen atoms omitted for clarity.

Figure 2 .

 2 Figure 2. Schematic depicting (A) composition of active and decoy sets and (B) retrospective screening protocols evaluated.

  mM to 213 nM), L-Fucα1-6[D-GlcNAcβ1-4]D-GlcNAcβ1-OMe (3.33 mM to 42.6 nM), and 2-deoxy-2-fluoro-L-fucose (16.6 mM to 213 nM). After addition of the reagents, the plates were sealed with film and shaken for 4 hours in darkness at room temperature. Fluorescence polarization was measured on a BMG LABTECH CLARIOstar multi-mode microplate reader (BMG LABTECH GmbH) with excitation filters at 482-16 nm and emission filters at 530-40 nm. The data were analyzed with Graphpad Prism 6 and fitted according to the four-parameter variable slope model.

  Figure 3A). Addition of a ligand pharmacophore filter (F) and a binding pocket constraint (FP) enhanced early enrichment (EF: 5.33 → 8.67 [F], 8.00 [FP]; Figure3B), but not overall enrichment (AUC: 0.86 [F], 0.80[FP]). Enrichment was decreased by the use of four hydrogen-bonding (FH) and interaction (FI) constraints, both early (EF: 6.67 [FH, FI]; Figure3B) and overall (AUC: 0.67 [FH, FI]; Figure3A). Inspection of the ROC profiles for these experiments (data not shown) revealed truncated curves, indicating that interaction constraints were overly restrictive. Methods relaxing the number of required interaction constraints were therefore evaluated (FI-1, FI-2, FI-3, FI-4). By reducing the number of required constraint matches from the initial four to one or two, overall enrichment was substantially improved (AUC: 0.93 [FI-1], 0.92 [FI-2]; Figure3C).

Figure 3 .

 3 Figure 3. Overall and early enrichment represented by ROC curve metrics for BambL HTVS-SP screening. (A) Overall enrichment for default, F, FP, FH, and FI protocols. (B) Early enrichment for default, F, FP, FH, and FI protocols. (C) Overall enrichment for FI-1, FI-2, FI-3, and FI-4 protocols. (D) Early enrichment for FI-1, FI-2, FI-3, and FI-4 protocols. Green: ZINC-derived decoys with all actives; yellow: carbohydrate decoys with all actives; blue: carbohydrate decoys with carbohydrate actives only. Error bars show the analytically calculated standard deviation.

Figure 4 .

 4 Figure 4. Workflow of the virtual screening protocol used for prospective screening at BambL.

Figure 5 .

 5 Figure 5. Fucose monosaccharide, mannose monosaccharide, and virtual screening hit compounds showing inhibitory activity towards BambL.

Figure 6 .

 6 Figure 6. Fluorescence polarization evaluation of competitive inhibition of BambL and LecB by virtual screening hit compounds. All replicate measurement values are shown (single measurement for BambL, duplicate measurement for LecB). (A) Titration of FITC-fucose with BambL reveals specific binding at probe compound concentrations of 10 nM (triangles, EC50 120 μM), 50 nM (squares, EC50 195 μM), and 90 nM (circles, EC50 193 μM). (B) Compared to Fucα1-OMe (squares, IC50 2.4 μM), hit compounds inhibit the BambL-probe association with similar affinity (2, circles, IC50 1.4 μM) or poorer affinity (1, triangles, IC50 19.9 μM). (C) Inhibition of the LecB-probe association by 2 (squares, IC50 5.5 μM) is intermediate in affinity between Fucα1-OMe (circles, IC50 1.1 μM) and Manα1-OMe (triangles, IC50 230 μM). 1 displays poorer affinity for LecB, with IC50 values greater than 1 mM (data not shown).

Figure 7 .

 7 Figure 7. Thermograms in the upper panels were obtained by titration of BambL (50 µM) with virtual screening hit compounds (1, left; 2, right) at 1 mM. Binding isotherms in the middle panels show the heat released as a function of total ligand concentration (shown as a molar ratio to the protein concentration). The solid lines indicate the best least-squares regression fit line to the experimental data, using a one-site binding model. Each titration was conducted in triplicate, with each replicate fitted individually (shown as black triangles, blue squares, and grey circles). Error bars show the variation in integrated heat peak area encountered during peak-shape analysis. Residuals in the bottom panels indicate the quality of the regression fit for the correspondinglycoloured replicate.

Figure 8 .

 8 Figure 8. Normalized inhibition by virtual screening hit compounds of BambL-fucosylated polyacrylamide association measured by SPR. Fucα1-OMe showed the greatest inhibitory ability (circles, IC50 24.1 μM), followed by 1 (squares, IC50 166 μM) and 2 (triangles, IC50 10.7 mM).

Compound 2

 2 is a component of naturally occurring N-glycans such as Nod factors (signalling molecules produced by symbiotic bacteria at legume roots).[START_REF] Geurts | Rhizobium Nod Factor Perception and Signalling[END_REF] N-glycans carrying the fucosylated chitobiose epitope, present in compound 2, display binding affinity toward BambL in glycan array data from the Consortium for Functional Glycomics. In solution-based assays (fluorescence polarization and ITC), compound 1 (2-deoxy-2-fluoro-L-fucose) displays excellent affinity for BambL in the same range as natural fucose. Substitution of carbohydrate hydroxyl groups by fluorine has been demonstrated in some cases to enhance affinity for protein receptors (e.g.,

Table 1 .

 1 Average molecular properties for BambL actives and decoys.

		Heavy
		atom		Rotatable	H-bond	H-bond
	Compound set	count	LogD	bonds	acceptor	donor
	Actives	31.7	-4.2	5.8	12.8	8.3
	Actives (Carbohydrate only)	32.5	-4.6	6.1	13.4	8.8
	ZINC	25.8	-1.2	5.6	6.9	3.8
	Carbohydrate decoys	37.6	-6.2	8.2	16.0	11.0

Table 2 .

 2 Titration microcalorimetry data for binding to BambL. Experiments were performed in triplicate; values shown are mean ± SD.

	Ligand	n	KD (μM)	ΔG (kJ•mol -1 ) ΔH (kJ•mol -1 ) TΔS (kJ•mol -1 )
	Fucα1-OMe 1.90 ± 0.01	1.5 ± 0.2	-33.2 ± 0.3	-41.9 ± 0.5	-8.7 ± 0.7
	1	1.92 ± 0.09 18.8 ± 2.3	-27.0 ± 0.3	-31.3 ± 1.6	-4.2 ± 1.9
	2	1.81 ± 0.12	1.0 ± 0.1	-34.2 ± 0.3	-38.2 ± 0.4	-4.0 ± 0.8

Acknowledgments

The authors acknowledge excellent technical assistance from Dirk Hauck (HIPS). TD acknowledges support received from the Monash University Postgraduate Publications Award. AI and SP acknowledge support from the French ANR through Glyco@Alps (ANR-15-IDEX-02) and Labex ARCANE (ANR-11-LABX-003). AT acknowledges funding from the Helmholtz Association of German Research Centers (grant no. VH-NG-934) and the Deutsche Forschungsgemeinschaft (grant no. Ti756/2-1). PR acknowledges an RMIT University Vice Chancellor's Senior Research Fellowship and the Victorian State Government Operational Infrastructure Support grant to the Burnet Institute.

Prospective screening against BambL

After the developed virtual screening method demonstrated consistent enrichment at BambL, a prospective screen using the same method was conducted in search of novel inhibitory chemistry.

The workflow shown in Figure 4 was used to select compounds, from the eMolecules Screening Compounds library and OTAVA Ltd. catalogue, with potential to inhibit the BambL protein. The workflow corresponds to the FI-2 screening method developed above, featuring a ligand-based pharmacophore filter to rapidly remove compounds lacking the atomic features required for binding, followed by a structure-based docking screen with binding interaction constraints to remove compounds unable to complement the BambL binding site architecture (see Supplementary Table S3 for constraint details). Manual selection according to the criteria outlined above identified 15 candidate compounds for experimental evaluation against BambL (Supplementary Figure S2).

FOR TABLE OF CONTENTS USE ONLY

Virtual screening against carbohydrate-binding proteins: evaluation and application to bacterial Burkholderia ambifaria lectin Tamir Dingjan 1 , Émilie Gillon 2 , Anne Imberty 2 , Serge Pérez 3 , Alexander Titz 4,5,6 , Paul A.