dataesr/scientific-paragraphs-categorization huggingface dataset:
[9]  and Gozzi et al. [13]. MOE software (Chemical Computing Group, Montreal, Canada) package was used to perform this study  [21]. MOE has been proved to be an effective tool for pharmacophore construction  [22] , other alternative software in this field might be Catalyst, LigandScout, and Phase, which are also effective for pharmacophore modeling. The resulting test set contained 524 conformations of these 50 compounds. Inhibitors with IC 50 values of less than 10 µM were considered as moderate or highly active compounds. All others were designated as none active. This filter resulted in a number of 26 active and 24 non active compounds. The pharmacophore model was developed by aligning the structures of the five most active compounds (training set), which were very well aligned as it is clear in Figure  1  were the backbones of the compounds fits very well above each other due to structure similarity.
Pharmaceuticals 2017, 10, 8 4 of 13 MOE software (Chemical Computing Group, Montreal, Canada)package was used to perform this study  [21]. MOE has been proved to be an effective tool for pharmacophore construction  [22] , other alternative software in this field might be Catalyst, LigandScout, and Phase, which are also effective for pharmacophore modeling. The resulting test set contained 524 conformations of these 50 compounds. Inhibitors with IC50 values of less than 10 µ M were considered as moderate or highly active compounds. All others were designated as none active. This filter resulted in a number of 26 active and 24 non active compounds. The pharmacophore model was developed by aligning the structures of the five most active compounds (training set), which were very well aligned as it is clear in Figure  1  were the backbones of the compounds fits very well above each other due to structure similarity.
Only four compounds out of the 24 non active compounds were selected and hence identified as false positive, which represents 17% of the non-active compounds. The compounds that were selected as false positives were compounds 29, 31, 36, and 40. The nine features of this model as described were used to search in part of the ZINC database "All Now" (all purchasable in 2 weeks), with the aim of identifying an active CK2 inhibitor. However, by applying all the nine features of the developed pharmacophore model, it was not possible to identify any compound from the selected database. For that reason, the search was repeated using only eight out of nine features together with the excluding volume. The best result obtained was by omitting F2 (this feature is outside the main backbone of the indeno[1,2-b]indoles) and keeping all other features, which resulted in the selection of 57 compounds.
The 57 compounds selected belong to different chemical backbones such as naphtho[2,3-b]furanedione, quino[2,3-b] acridinetetrone, naphthacenedione, and pyrazoloquinazoline. From this set of compounds, only two pyrrolizine based compounds were excluded from further observations, due to their inappropriate structure for enzyme inhibitors. They are rather fragment-like compounds according to "the role of three" filters (MW ≤ 300, cLogP ≤ 3, number of H-bond donors ≤ 3, number of H-bond acceptors ≤ 3)  [23] , which can be misleading for the development of active and selective inhibitors. The structures of the 55 selected compounds are shown in Supplementary Table  S1. Surprisingly none of the selected compounds belonged to the indeno[1,2-b]indole group of compounds. As a control, the ZINC database was searched on the presence of indeno[1,2-b]indole like compounds and-to our surprise-it did not contain any.
Only four compounds out of the 24 non active compounds were selected and hence identified as false positive, which represents 17% of the non-active compounds. The compounds that were selected as false positives were compounds 29, 31, 36, and 40. The nine features of this model as described were used to search in part of the ZINC database "All Now" (all purchasable in 2 weeks), with the aim of identifying an active CK2 inhibitor. However, by applying all the nine features of the developed pharmacophore model, it was not possible to identify any compound from the selected database. For that reason, the search was repeated using only eight out of nine features together with the excluding volume. The best result obtained was by omitting F2 (this feature is outside the main backbone of the indeno In order to select the best candidates among the hit compounds, docking studies of the selected compounds against the ATP active site of the CK2 kinase were performed.
A conformational search for the 55 selected compounds was carried out by MOE and all the resulting 777 conformations were used for the docking study. The conformations of all structures were docked in human CK2 enzyme using the MOE. For this purpose, the 3D X-ray crystal structure of the catalytic subunit of protein kinase CK2 (PDB ID: 3C13, resolution 1. 95 Å  [24] ) was used. All conformations were sorted according to S score (energy-based scoring method implemented in MOE) to rank the best ligand in terms of orientation and binding to the active site, the top three ranking conformations with the minimum S score were selected after visual 2D inspection. Visual 2D inspection is a common additional tool in order to exclude "false positives due to assumptions and shortcomings in docking methods and scoring function"  [25] , where the active site is analyzed to determine protein-ligand interaction and show exactly which atoms have tether restraints  [21].
The conformations of all structures were docked in human CK2 enzyme using the MOE. For this purpose, the 3D X-ray crystal structure of the catalytic subunit of protein kinase CK2 (PDB ID: 3C13, resolution 1. 95 Å  [24] ) was used. All conformations were sorted according to S score (energy-based scoring method implemented in MOE) to rank the best ligand in terms of orientation and binding to the active site, the top three ranking conformations with the minimum S score were selected after visual 2D inspection. Visual 2D inspection is a common additional tool in order to exclude "false positives due to assumptions and shortcomings in docking methods and scoring function"  [25] , where the active site is analyzed to determine protein-ligand interaction and show exactly which atoms have tether restraints  [21].
ZINC05765165 has interaction via hydrogen bonding with Asn118, as well as two indirect interactions with His160 and Asp120 via water molecules (Figure  3(c) ). From the docking poses of the best selected compounds, it is clear that that docking strongly proposes the interaction between some functional groups of the compounds and some amino acid residues from the enzyme, and this is in line with known ATP kinase inhibitors. It might indicate that the compounds fit well in the ATP binding site of the enzyme. Compound ZINC35643753 and compound ZINC44136135 are commercially available compounds as all compounds included in the ZINC database, both are anthracycline analogues. This group of compounds is among the most effective drugs used in chemotherapy for treatment of both According to MOE docking, ZINC35643753 has direct bonding to Asp175, beside indirect bonding with His160, Asp120, Trp176, Glu81 and Asn161 via water molecules, Figure  3a , while ZINC44136135 shows direct interaction with Asp195 and Asn118, and arene-arene interaction with Phe113, as well as two indirect bonding with Leu45 and Asn118, via water molecules (Figure  3b ).
ZINC05765165 has interaction via hydrogen bonding with Asn118, as well as two indirect interactions with His160 and Asp120 via water molecules (Figure  3c ). From the docking poses of the best selected compounds, it is clear that that docking strongly proposes the interaction between some functional groups of the compounds and some amino acid residues from the enzyme, and this is in line with known ATP kinase inhibitors. It might indicate that the compounds fit well in the ATP binding site of the enzyme. Compound ZINC35643753 and compound ZINC44136135 are commercially available compounds as all compounds included in the ZINC database, both are anthracycline analogues. This group of compounds is among the most effective drugs used in chemotherapy for treatment of both solid tumors and leukemia in adults and children.
Nevertheless those compounds suffer from several severe adverse effects, especially cardiotoxicity  [26]. From the three compounds identified by ZINC database mining only bikaverin (6,11-dihydroxy-3,8-dimethoxy-1-methylbenzo[b]xanthene -7,10,12-trione, Figure  5 ) was chosen for further experiments. Compound ZINC35643753 and compound ZINC44136135 belong to a group of drugs known to have severe toxicities as it is mention above, while bikaverin is a natural compound with different biological activities. The fact that bikaverin is a natural compound with antitumor activity encourage us to investigate whether this activity might be related to CK2 inhibitory activity  [27]. Pharmaceuticals 2017, 10, 8 8 of 13 solid tumors and leukemia in adults and children. Nevertheless those compounds suffer from several severe adverse effects, especially cardiotoxicity  [26].
From the three compounds identified by ZINC database mining only bikaverin (6,11-dihydroxy-3,8-dimethoxy-1-methylbenzo[b]xanthene -7,10,12-trione, Figure  5 ) was chosen for further experiments. Compound ZINC35643753 and compound ZINC44136135 belong to a group of drugs known to have severe toxicities as it is mention above, while bikaverin is a natural compound with different biological activities. The fact that bikaverin is a natural compound with antitumor activity encourage us to investigate whether this activity might be related to CK2 inhibitory activity  [27]. xanthene-7,10,12-trione), first isolated from the culture of Fusarium vasinfectum  [28]. Bikaverin was tested on inhibition with purified human protein kinase CK2 expressed in E. coli as described before  [29]. For this purpose eightconcentrations rangingfrom 0.
Discussion In this study a pharmacophore model for ATP-competitive inhibitors of human protein kinase CK2 was developed on the basis of known inhibitors with an indeno  [1,2-b] indole scaffold. This model has been challenged against a set of compounds and was able to select most active compounds and excluded most nonactive ones, which indicate its validity. By using this model for database mining using the ZINC compound database, bikaverin ZINC05765165 was identified as a Bikaverin was tested on inhibition with purified human protein kinase CK2 expressed in E. coli as described before  [29]. For this purpose eightconcentrations rangingfrom 0. 001 µM to 100 µM of bikaverin were used in a capillary electrophoresis (CE)-based activity assay  [30]  in comparison the enzyme without inhibitor. The obtained dose-response curve is shown in Figure  6 , and was used to determine the IC 50 value of bikaverin, which turned out to be 1.
24 µM. The IC 50 value was determined in three independent biological replicates with basically similar results (1. 29, 1. 30 and 1. 12 µM) the mean value was determined. It is important to note that all attempts to achieve 100% inhibition were not successful. This was not due to limited solubility of the compound. As it is shown in Figure  6 , maximum activity with the highest concentration of bikaverin was 80%, and the relative IC 50 value was 0. 78 µM. Pharmaceuticals 2017, 10, 8 8 of 13 solid tumors and leukemia in adults and children. Nevertheless those compounds suffer from several severe adverse effects, especially cardiotoxicity  [26]. From the three compounds identified by ZINC database mining only bikaverin (6,11-dihydroxy-3,8-dimethoxy-1-methylbenzo[b]xanthene -7,10,12-trione, Figure  5 ) was chosen for further experiments.
Discussion In this study a pharmacophore model for ATP-competitive inhibitors of human protein kinase CK2 was developed on the basis of known inhibitors with an indeno[1,2-b]indole scaffold. This model has been challenged against a set of compounds and was able to select most active compounds and excluded most nonactive ones, which indicate its validity. By using this model for database mining using the ZINC compound database, bikaverin ZINC05765165 was identified as a.
Discussion In this study a pharmacophore model for ATP-competitive inhibitors of human protein kinase CK2 was developed on the basis of known inhibitors with an indeno  [1,2-b] indole scaffold. This model has been challenged against a set of compounds and was able to select most active compounds and excluded most nonactive ones, which indicate its validity. By using this model for database mining using the ZINC compound database, bikaverin ZINC05765165 was identified as a hit. By testing this natural compound with recombinant human CK2 it turned out to have an IC 50 value of 1. 24 µM. Bikaverin, also known as lycopersin  [31] , is a reddish pigment produced by different fungal species. Chemically it is a polyketide with a tetracyclic benzoxanthone structure. It has been reported to possess diverse biological activities e.
Also further studies to test other selected structures from the 55 compounds and modify them accordingly is planned with the hope of finding new highly active and selective inhibitor of CK2. Actually, the aim of this study was to primary in silico filter the database and try to introduce new backbones serving as possible new hits for human CK2 which was performed by discovering that bikaverin is an active CK2 inhibitor with inhibitory activity comparable to other natural inhibitors of the target enzyme such as emodin which has an IC 50 value of 0. 58 µM in our test system.
Pharmacophore Generation Molecular Operating Environment software package (MOE, Chemical Computing Group, Montreal, QC, Canada) was used to perform this study  [21]  running on an Intel Core, Duo based, 2. 26 GHz processor. The molecular structures of the inhibitors were built with MOE and energy was minimized in the MMFF94x force field as implemented in the software. In order to develop a reliable pharmacophore model to identify new CK2 inhibitors, a database of known indeno[1,2-b]indole inhibitors was created. The structures of the inhibitors together with their inhibitory activity were published by us as mentioned above. In total this data base contained 50 compounds (Tables  1 2 3 ). The pharmacophore model was developed by aligning the minimized structures of the five most active compounds in the database namely compounds 1-5 with IC 50 values of 25, 170, 360, 430, and 610 nM, respectively (training set).
This alignment was obtained using MOE's flexible alignment and all conformations of the molecules were considered for the alignment  [21]. Due to the relatively identical backbones of the compounds, the selected compounds were very well aligned (Figure  1 ). A conformational search was performed for each of the 50 compounds using the MMFF94x force field within the MOE to generate a multi-conformer database (test set). The search was performed using the default settings of MOE, and a query was developed based on the alignment of the five most active compounds (training set). The Consensus query of MOE was used to create all necessary features of the hypothetical pharmacophore model, tolerance was set at 1 and threshold was set at 80%. In order to optimize this multi features model, each individual pharmacophore feature was removed and the test set was scanned using the remaining features.
Database Search ZINC Database is a free database of commercially available compounds for virtual screening. It contains over 35 million purchasable compounds in ready-to-dock, 3D formats. ZINC is provided by Department of Pharmaceutical Chemistry at the University of California, San Francisco  [20]. The compounds in the ZINC Database are organized in several categories namely, Standard: for delivery within 10 weeks, including in-stock and make on demand compounds; Clean: where stricter filtering rules have been applied, in stock: for immediate delivery; and Boutique: expensive selection of compounds not included in the former categories, and each of these categories is also redivided into Lead-like, Fragment-like, Drug-like and All. Since we are trying to find an active structure which can be directly purchasable, we chose in this work the "All Now" (all purchasable in 2 weeks) database which contains around 13 million compounds and was used to search for compounds that fit to most features of the developed pharmacophore.
Receptor Refinement Three dimensional 3D structure of the CK2 complex with emodin was obtained from the Protein Data Bank (PDB) using PDB ID: (3C13) having a resolution of 1. 95 Å. The structure was optimized by adding hydrogen atoms using the MOE software  [21]. Then water molecules were removed from the structure and 3D protonation was done to change the state into ionization level. In the second step, energy minimization was performed using defaults parameters, where the force field was MMFF94x.
Database Generation The selected compounds from the database were rebuilt with MOE building option implemented in the software. The compounds were optimized by adding hydrogen atoms using the option of MOE software. Energy of the compounds was minimized using the following parameters gradient: 0. 05, Force Field: MMFF94X, Chiral constraint and Current Geometry. The conformation methodology was used to develop low energy conformations for each compound, applying the LowModMD method with RMS gradient of 0. 05, all other parameters were used as default. All of the compounds and their conformations were saved in mdb database and later employed for docking studies.
Molecular Docking The docking of the selected compounds from the database (compounds 1-55) into the active site of CK2 enzyme (3C13) was achieved using MOE-Dock implemented on MOE. The docking parameters were set as Rescoring 1: London dG, Placement: triangle matcher, Retain 30, Refinement Force field, and Rescoring 2: London dG. Docking part of MOE can give correct conformation of the ligand to obtain minimum energy structure. The top conformation for each compound was selected based on the S score and visual inspection was carried out by Lgplot implemented in MOE. Compounds showing significant interaction with the residues of binding pocket of CK2 were picked as promising hits. Prior to dock, the initial ligand from the complex structure was extracted. For the scoring function, lower scores indicate more favorable poses.
Conclusions In this study we were able to valorize the library of indeno  [1,2-b] indoles designed for CK2 inhibition to determine the necessary ligand binding requirement for ATP binding CK2 inhibitors. We were also able to use the new model for searching in ZINC database. We discovered a potential inhibitor of CK2 and we experimentally confirmed its potency to inhibit CK2. Bikaverin can be regarded as a hit scaffold for designing CK2 inhibitors. It is also possible to use the pharmacophore model to further identify new inhibitors for the target enzyme from other databases, and modify the selected structures accordingly. Finally discovering that bikaverin is a CK2 inhibitor is an important finding and provides an important contribution toward finding active and selective CK2 inhibitors.
Nature of the metal complexes inside the solid In order to determine the relative amount of mono-and dinuclear species, magnetic and EPR measurements were performed for both solids {Mn}-Py@LUS-E15 (S1), {Mn}-TMS@LUS-E (S2). The magnetic properties of S1 and S2 are shown in Fig. 4  and S2 † in the form of the c M T vs. T plot (c M being the molar magnetic susceptibility per Mn II ion and T the absolute temperature). They are qualitatively similar to those of the dinuclear precursor [Mn 2 L 2 (Cl) 2 (m-Cl) 2 ]. 18  c M T remains constant from room temperature down to ca. 100 K and it abruptly decreases upon further cooling, suggesting the occurrence of weak antiferromagnetic interactions between the Mn II ions within the graed metal complex. The least-squares ts of the experimental data through the appropriate expression derived from the spin Hamiltonian for an antiferromagnetically coupled dinuclear manganese(II) complex (H ¼ ÀJS 1 S 2 ),  18  assuming the presence of a variable amount of mononuclear manganese(II) species (a), gave J ¼ À1.
Frequency 13 C ¼ 125. 76 MHz,  29  Si ¼ 99. 36 MHz. EPR spectra were recorded on a Brucker Elexsys e500 X-band (9. 4 GHz) spectrometer with a 4102ST cavity (modulation: 2 G, power: 4 mW). The simulated spectra were calculated using the Easyspin toolbox from Matlab. Variable-temperature magnetic susceptibility measurements were made using a Quantum Design MPMS-XL-5 SQUID susceptometer, using an applied eld of 1000 G.
Acknowledgements J. C. thanks the  French "Ministère de l'Enseignement, de la Recherche et de la Technologie" (MERT)  for a PhD fellowship, and the  C-MIRA program of Rhône-Alpes region in France  for nancial support to the collaboration between the  Universitat de València (Spain)  and the  Ecole Normale Supérieure de Lyon (France). The Spanish work was supported by the  MICINN (Spain)  (Project  CTQ2010-15364 ) and the  Generalitat Valenciana (Spain)  (Projects  PROMETEO/2009/108  and  ISIC/2012/002 ).
Enzyme assays NodB CD activity was assayed by incubating CO-II or CO-V with 0. 35 mM CD in 20 mM MOPS pH 7. 2 containing 10 mM DTT and 1 mM MnSO 4 at 30 C. At regular time intervals, the amount of released amino groups was quantified by using 3-methyl-2benzothiazolinone hydrazone hydrochloride reagent (MBTH)  [20]. For the determination of kinetic parameters, measurements of initial rates were made at several substrate concentrations ranging from 0. 2 to 4. 5 mM. K M and k cat were determined by non-linear regression using the Grafit program (Erithacus Software).
Acknowledgement The authors are grateful to  M. Lenon ,  M. Touvrey-Loiodice  and  P. Chaud  for skillful technical assistance. The authors acknowledge support from  ICMG FR 2607 ,  LabEx ARCANE  ( ANR-11-LABX-0003-01 ),  PolyNat Carnot Institute ,  French Agence Nationale de la Recherche  ( ANR-10-CD2I-0008 ,  BioCOS 2010-2014 ), and  Bill & Melinda Gates Foundation  ( ENSA 2012-2017 ).
Geometry and computational grid The resolution of a computational fluid dynamics problem requires several steps: the first being to define the geometry and the computational domain. For this, the numerical model of the cyclist's body was obtained by scanning a world-class cyclist sitting on a bicycle in timetrial position without helmet on. Secondly, the three timetrial helmets were scanned using a 3D scanner (ARTEC®). Lastly, the helmets were fitted to the cyclist's head, using CAD software (Figure  1 ). The three-dimensional grid was generated using the ANSYS Workbench Meshing® software and consists of a non-structured mesh composed of approximately 1. 6 × 10 6 tetrahedral and hexahedral elements.
FUnDing This work was supported by the  Ligue nationale contre le Cancer , the  Agence nationale pour la recherche  ( AO 2005 Emergence et Maturation de projet de biotechnologie à fort potentiel de valorization and "Investissements d'avenir programs , Grant Agreement LabExMAbImprove:  ANR-10-LABX-53 "), the  Institut national du Cancer , the  Association Cancer  and the  Fondation Langlois.
We used universal primers that specifically amplified the v5-6 region (ca 295 bp length) of the bacterial 16 S rRNA gene (BACTB-F: GGATTAGATACCCTGGTAGT; and BACTB-R: CACGACACGAGCTGACG)  61. To discriminate samples after sequencing, we labelled both forward and reverse primers at the 5′ end with a combination of two different 8 bp tags. The PCR products were purified, using the QIAquick PCR purification Kit (Qiagen GmbH, Hilden, Germany), and then pooled. Amplicons were then sequenced with an Illumina MiSeq platform, using the 2 × 250 bp protocol (Fasteris SA, Plan-les-Ouates, Switzerland). We included PCR blank controls in the sequenced multiplex to detect potential reagent contaminants. We analysed the sequence reads following Taberlet and colleagues  62  , with some adjustments, using OBITools package  63.