dataesr/scientific-paragraphs-categorization huggingface dataset:
Crystals were added to the saturated immersion oil for 10 min to 1 day or 0. 1 ml DMSO stock solution was added to the growing drop for 10 min to 1 h before flash freezing and data collection. Crystals soaked with a DMSO stock solution were cryoprotected for data collection by brief immersion in oil. X-ray diffraction data were collected in-house with an X-Ray generator (RU-200, Rigaku, Tokyo, Japan) and the Image Plate Detector mar300 (MarResearch, Norderstedt, Germany) or at the European Synchrotron Radiation Facility in Grenoble, France, on beamlines BM30, ID14-1, ID14-2, ID14-3, ID14-4 and ID29. Data were integrated and processed using MOSFLM and SCALA of the CCP4 suite. The crystals belong to the space group P4 1 2 1 2 (a ¼ b ¼ 57 Å and c ¼ 87 Å) with one monomer in the asymmetric unit.
The structures were solved by molecular replacement using PDB entry 2BIT as the search model. Bound ligands were manually identified and fitted into F o -F c electron density using Coot. The structures were refined by rounds of rebuilding in Coot and refinement using Refmac of the CCP4 suite. Data collection and refinement statistics for crystal structures are presented in Supplementary Tables  4 to 11. Crystallization and structure determination of ligand-CypA. Crystals of apo CypA suitable for ligand soaking were obtained by means of hanging drop vapour diffusion by mixing 1 ml of CypA 20 mg ml À 1 in 50 mM Tris pH 7. 8, 100 mM NaCl and 1 ml of Hepes 100 mM pH 7. 5, 20% PEG 10k (w/v), 5% ethanol at 20 °C, equilibrated over 500 ml of the same solution. Crystals formed in about one night. A 0.
1 ml DMSO stock solution of compound 22 was added to the growing drop for 10 min before flash freezing and data collection. X-ray diffraction data were collected in-house with the X-Ray generator RU-200 and the Image Plate Detector mar300. Data were integrated and processed using MOSFLM and SCALA of the CCP4 suite. The crystals belong to the space group P2 1 2 1 2 1 (a ¼ 40. 7 Å, b ¼ 52. 3 Å and c ¼ 86. 9 Å) with one monomer in the asymmetric unit. The structures were solved by molecular replacement using PDB entry 1CWA as the search model. Bound ligand was manually identified and fitted into F o -F c electron density using Coot. The structures were refined by rounds of rebuilding in Coot and refinement using Refmac of the CCP4 suite. Data collection and refinement statistics for crystal structure are presented in Supplementary Table  8.
The results were normalized to the GAPDH gene. Each data point represents the average of at least three replicates in cell culture. HCV RNA level reductions after treatment were assessed by comparing the level of HCV RNA in compound-treated cells with that of control cells treated with 1% DMSO. Assessment of anti-HIV activity. Compounds diluted in DMSO (0. 4 ml) was added to 40 ml of cell growth medium (RPMI 1640, 10% fetal bovine serum, 1% penicillin/streptomycin, 1% L-glutamine and 1% Hepes) in each well of 384-well assay plates (10 concentrations) in quadruplicate. One-millilitre aliquots of 2 Â 10 6 MT-4 cells were pre-infected for 1 and 3 h, respectively, at 37 °C with 25 ml of either cell growth medium or a fresh 1:250 dilution of an HIV-IIIb concentrated ABI stock (0. 004 multiplicity of infection).
Infected and uninfected cells were diluted in cell growth medium and 35 ml of a suspension of 2,000 cells was added to each well of the assay plates, respectively. The assay plates were then incubated in a 37 °C incubator. After 5 days of incubation, 25 ml of twofold concentrated CellTiter-Glo Reagent (Promega Biosciences, Madison, Wisconsin) was added to each well of the assay plates. Cell lysis was carried out by incubating at room temperature for 2-3 min and then chemiluminescence was read using an Envision multimode plate reader (PerkinElmer, Waltham, Massachusetts). Data were converted into percentages of the untreated control and non-linear regression was performed to calculate EC 50 values. For compound cytotoxicity assessment, the protocol was identical except that uninfected cells were used.
After equilibration, the plasma sample was diluted with blank cell culture medium and the cell culture medium diluted with blank plasma. Stability with hepatic microsomal fractions. The compounds (3 mM) were incubated in duplicate for up to 1 h in the presence of human hepatic microsomal fractions (0. 5 mg protein ml À 1 final) at 37 °C. The addition of cofactor solution (NADPH generating system) initiated the reaction and aliquots were removed at 0, 5, 15, 30, 45 and 60 min after the start. The concentrations of the compounds in each sample were determined using specific LC-MS/MS assays. The half-life for the disappearance of each was determined by fitting the concentration-time data with a monophasic exponential model. A control compound (verapamil) was run in parallel to test the enzymatic integrity of the microsomal fractions.
Acknowledgements This work was supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , the  Fonds Europeen de Developpement E ´conomique et Regional (FEDER) Number 48748 , the  Agence Nationale de Recherche sur le SIDA et les Hepatites Virales (ANRS)  and the  Fondation pour la Recherche Medicale (FRM).
Data availability. Data supporting the findings of this study are available within the article and its Supplementary Information files and from the corresponding authors upon reasonable request. PDB: the complex structures for compounds 5, 6, 9, 11 to 24, 26 to 29 with CypD K133I mutant enzyme are deposited under accession codes 3RCI, 3R59, 3RCF, 3RCG, 3R4G, 3R54, 3RD9, 3R49, 3R56, 3RCL, 3RDB, 3RCK, 3RDA, 3R57, 3RDC, 4J58, 4J5E, 4J5D, 4J5B, 4J59 and 4J5C, respectively. The complex structure for compound 22 with CypA is deposited under accession code 3RDD. Stereo views of cocrystal structures of the compounds with cyclophilin D are available at https://figshare. com/articles/Stereo_-views_of_cocrystal_structures_of_cyclophilin_inhibitors_with_cyclophilin_D/ 3490493.
This strategy has the great advantage to study the same set of proteins, both in MALDI-MSI and LC-MS/MS, after in situ extraction, and thus it reduces the risk of mistakes in protein identification. Thanks to this approach, these authors developed a publically available database, based on the identification of proteins extracted on matrix-coated tissue sections. Recently, Spraggins et al. [14]  published a proof-of-concept study based on MALDI-FTICR-MSI of intact proteins linked with proteomic data obtained by LC-MS/MS. The use of high mass accuracy allowed them to identify proteins and protein modifications. Nonetheless, to our knowledge, there is little information in the literature about the validation of the relative quantitation of proteins in MALDI-MSI. Different kinds of strategies at different tissue scales have been developed.
Subjects and Tissue Preparation Our study included eight healthy young men (age 25 years) selected from Saint-Etienne University. They all underwent standard medical examination, and performed a maximal exercise stress before their inclusion in the study. The study was approved by the local Ethical Committee (ClinicalTrials. gov Identifier: NCT00759304), and all subjects provided written informed consent for participation. Needle biopsies were taken under local anesthesia from the right vastus lateralis muscle, after overnight fasting. Biopsies were mounted with tissue freezing medium, cryofixed in isopentane (-160 • C) cooled on liquid nitrogen and stored at -80 • C. Serial cross-sections (10 µm thick, 10 mm 2 ) were performed using a cryostat (Microm, Francheville, France) at -20 • C.
MALDI Mass Spectrometry Imaging For MALDI-MSI, sections were collected on conductive indium-tin-oxide glass slides (Bruker Daltonics, Bremen, Germany). Muscle sections were subjected to washing steps using 70% and 95% Ethanol to deplete lipids, and were dried in a desiccator for 30 min. The matrix was applied using the ImagePrep station (Bruker Daltonics) according to the protocol detailed in Table  1. The matrix was 10 mg/mL sinapinic acid in water/acetonitrile 60:40 (v/v) with 0. 2% trifluoroacetic acid. The MALDI spectra were acquired on an Autoflex Speed MALDI-TOF/TOF mass spectrometer with a Smartbeam laser using FlexControl 3. 4 and FlexImaging 3. 0 software packages (Bruker Daltonics). For protein imaging, ions were detected in positive linear mode at a mass range of m/z 2000-20,000 with a sampling rate of 0.
LC-MS/MS Sample Preparation, Data Acquisition, Database Search and Protein Identification 'On Tissue', 'SA-coated', 'Post-MSI' and 'Muscle homogenate' samples were desalted using C18 Spin Tubes (Agilent Peptide Cleanup, Agilent Technologies, Wilmington, DE, USA), according to the manufacturer's instructions. Briefly, the resin was wet using 200 µL of 50% acetonitrile, the tubes were centrifuged at 1500 g for 1 min, and this step was repeated once. The resin was then equilibrated with 200 µL of 0. 5% trifluoroacetic acid in 5% acetonitrile, tubes were centrifuged at 1500 g for 1 min, and this step was repeated once. Samples were then loaded on the resin, and the resin was washed three times using 200 µL of 0. 5% trifluoroacetic acid in 5% acetonitrile. Samples were eluted using 20 µL of 70% acetonitrile with 0.
, one full scan MS and the 10 major peaks in the full scan were selected for MS/MS). Full-enhanced-scan MS spectra were acquired with one microscan (m/z 400-1600). Dynamic exclusion was used with two repeat counts, 15 s repeat duration and 45 s exclusion duration. For MS/MS, isolation width for ion precursor was fixed at 2 m/z, single charged species were rejected; fragmentation used 37% normalized collision energy and a default activation of 0. 250. Thermo Proteome Discoverer v1. 3 (Thermo Fisher Scientific, Waltham, MA, USA) was used for raw data file processing, and MASCOT was used for database search (http://www. matrixscience. com). For protein identification, the Uniprot Taxonomy Human (01/10/2013, 84,843 sequences) protein database was used. The following parameters were considered for the searches: peptide mass tolerance was set to 500 ppm, fragment mass tolerance was set to 0.
Label-Free Protein Quantitation The spectra (Thermo raw files) acquired for 'Post-MSI' samples were loaded into the Progenesis QI software (version 1. 0, Nonlinear Dynamics, Newcastle, UK) and label-free quantitation was performed as described by Theron et al. [4]. Briefly, for each sample, the profile data of the MS scans and MS/MS spectra were converted to peak lists comprising m/z and abundance with Progenesis QI using a proprietary algorithm. One sample was automatically set as the reference, and the retention times of all other samples were automatically aligned to create maximal overlay of the two-dimensional feature maps. Features with only one charge, with retention time windows lower than 6 s or with retention time lower than 20 min and higher than 80 min were excluded from further analyses.
MALDI Data Analysis Spectra from each imaging sequence were baseline-subtracted (TopHat) and smoothed (0. 5 m/z Savitzky-Golay window, two cycles), and peaks were chosen (thresholds of 1. 5 S/N, total possible peaks at 1000, peak width 1. 0 Da) by a batch processing macro written in FlexAnalysis software (Version 4. 3, Bruker Daltonics). Spectra normalization, using the Total Ion Count, and peak picking were performed using Scils Lab software (version 2016b, Scil GmbH, Bremen, Germany).
Back-Correlation of MALDI and LC-MS Data Back-correlation was performed to link the peaks observed by MALDI-MSI with the identification and label-free quantitation of proteins, using a 0. 5% mass tolerance window. The correlation between quantitation of proteins by label-free LC-MS/MS (variable LF) and peak intensities of proteins observed by MALDI-MSI (variable MSI) was calculated using the formula: Correlation index (LF,MSI) = ∑(LF-LF) (MSI-MSI) ∑(LF-LF) 2 ∑(MSI-MSI).
Therefore, regardless of the treatment, 'On Tissue', 'SA-coated' and 'Post-MSI' extractions resulted in a similar number of proteins. These results demonstrated that a bottom-up approach is possible after in situ extraction, and that the matrix deposition and laser ablation have no major impact on the number of identified proteins. Beyond the number of proteins identified in each condition, we wanted to make sure that the common 42 identified proteins corresponded to the most abundant peptides. The peptide spectrum match (PSM) can be used to evaluate the relative abundance of a protein within a sample. For each protein, we calculated an abundance index (%PSMs) as the percentage of the sum of PSMs for each condition (i. e. , 'On Tissue', 'SA-coated', and 'Post-MSI') (Table  2  and Supplementary Data 1).
All proteins were classified according to their main cellular compartment, but some of them are described in the database as belonging to several cellular compartments (Supplementary Data 2). However, although the cellular repartition was qualitatively similar for proteins from 'On Tissue', 'SA-coated' and 'Post-MSI' extracts, there were quantitative differences for some individual proteins. For the 42 proteins identified in the three extraction procedures, we compared the evolution of the abundance index to evaluate the proportion of proteins within each procedure. These revealed three groups: the first one corresponded to the proteins showing a decreasing abundance index during the extraction steps; the second one corresponded to the proteins showing an increasing abundance index during the extraction steps; and the third one corresponded to the proteins showing different kinds of patterns (Table  2 , Figure  4 ).
These results indicated that there was a lower abundance of cytosolic proteins and a higher abundance of structural proteins within the extracts after matrix deposition and laser ablation. Beside their subcellular location, other factors (such as their physicochemical properties) might also be important for the extractability of proteins from the matrix layer. Protein identifications were compared to the databases MSiMass List  [24]  and MaTisse  [13]  (Table  2 ). MSiMass List is a public database implemented by users who assign identities to the peaks observed in their own experiment. MaTisse is a publically available database which compiles the identifications obtained in top-down and bottom-up approaches after in situ protein extraction. Most of our proteins were already described in a MALDI imaging experiment (Table  2 ).
Among the 72 proteins identified in our experiment, only six were not described in these databases: Isoform 3 of kinesin-like protein KIF 15 (KIF15); Isoform 2 of aldo-keto reductase family 1 member B15 (ALDOC); Isoform 2 of DNA repair protein complementing XP-C cells (XPC); Isoform 3 of kinase D-interacting substrate of 220 kDa (KIDINS220); Olfactory receptor 2T35 (OR2T35); Phosphoglycerate kinase (PGK1). This result was independent of the extraction procedure as Isoform 2 of Aldo-keto reductase family 1 member B15 and Isoform 2 of DNA repair protein complementing XP-C cells were identified only on the 'On tissue' section, Isoform 3 of Kinesin-like protein KIF15 only on the 'SA-coated' section, and Olfactory receptor 2T35 in the three procedures.
Back-Correlation of the m/z of the Off-Tissue Identified Proteins to Those of the Proteins Detected by MALDI-MSI The second milestone of the MSI~LC-MS/MS-LF workflow is the back-correlation of the proteins identified by LC-MS/MS to those detected by MALDI-MSI. Based on the idea from Maier et   Protein identifications were compared to the databases MSiMass List  [24]  and MaTisse [13] (Table  2 ). MSiMass List is a public database implemented by users who assign identities to the peaks observed in their own experiment. MaTisse is a publically available database which compiles the identifications obtained in top-down and bottom-up approaches after in situ protein extraction. Most of our proteins were already described in a MALDI imaging experiment (Table  2 ). Among the 72 proteins identified in our experiment, only six were not described in these databases: Isoform 3 of kinesin-like protein KIF 15 (KIF15); Isoform 2 of aldo-keto reductase family 1 member B15 (ALDOC); Isoform 2 of DNA repair protein complementing XP-C cells (XPC); Isoform 3 of kinase D-interacting substrate of 220 kDa (KIDINS220); Olfactory receptor 2T35 (OR2T35); Phosphoglycerate kinase (PGK1).
5% (Figure  5 ; Supplementary Data al. [13] , that any MSI protein biomarker must be contained in the MALDI matrix layer to be detectable by the mass spectrometer, back-correlation relies on matching m/z identified by LC-MS/MS to m/z detected by MALDI-MSI. MALDI spectra were recorded from 2000 to 20,000 m/z, and a mean spectrum was calculated for each MALDI-MSI sequence. Mean spectra had a similar global pattern, and showed peaks with higher intensity at low mass range (Figure  5 ). We succeeded in linking protein identification to nine peaks observed by MALDI-MSI, using a mass tolerance interval set at 0. 5% (Figure  5    The label-free strategy was then applied to compare quantitation of 'Post-MSI' proteins extracted in situ and their MALDI-MSI peak intensities. The analysis resulted in the quantitation of 35 proteins from the 57 identified in 'Post-MSI' (Supplementary Data 4), and gave a relative quantitation for all proteins linked to a peak observed by MALDI-MSI.
Mapping the main cellular compartment for each identified protein suggested an apparent homogeneity of the three extraction procedures: the statistical tests (Khi square test, Supplementary Data 2) indicated that none of the extraction procedures changed the qualitative distribution of the proteins between the different cellular compartments. Moreover, the distribution is consistent with that observed by Maier et al. [13]  after in situ extraction and the bottom-up strategy. In addition, the repartition of proteins according to their cellular compartment is consistent with the characterization of the human vastus lateralis muscle by a global proteomic study  [23]. This study succeeded in linking protein identification to nine peaks observed by MALDI-MSI. In terms of the ratio between detected ions and ions linked with identification, this is consistent with what was achieved on peptide back-correlation by Minerva et al.
Acknowledgments: We thank  Philippe Courpron  for constant support, and  Manuel Chapelle  ( Bruker ) for expert advice. This work was supported by grants from the  European Commission MyoAge  ( EC Fp7 CT-223756 ),  Caisse d'Epargne Rhône-Alpes (CERA) , and  Fonds Européens de Développement Régional (FEDER). LT was supported by a postdoctoral fellowship from  Région Auvergne  and  FEDER  (n •  35380 T2a 2011 Prenusa ). Author Contributions: L. T. , D. B. and C. C. conceived and designed the experiment. J. -C. B. , F. R. and L. F. performed clinical research. L. T. , D. C. , C. C. -G. performed experiments. L. T. , D. B. , C. C. analyzed the data and wrote the manuscript. E. P. -G. , T. A. , D. R. , M. H. contributed reagents and materials. C. C. and D. B. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
The endogenous peptides and small proteins present in chicken sperm were identified in the context of the characterization of a fertility-diagnostic method based on the use of ICM-MS (Intact Cell Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry). The interpretation and description of these data can be found in a research article, "Intact cell MALDI-TOF MS on sperm: a molecular test for male fertility diagnosis"  (Soler et al. , 2016)  [1], and raw data derived from this analysis have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PRIDE: PXD002768. Here, we describe the inventory of all the molecular species identified, along with their biochemical features and functional analysis. This peptide/protein catalogue can be further employed as reference for other studies and reveal that the use of proteomics allows for a global evaluation of sperm cells functions.
Data This dataset consists of a compendium of peptidoforms and small proteoforms extracted from chicken ejaculated sperm, that were pre-fractionated using gel filtration or reverse phase chromatography and identified through a Top-Down mass spectrometry analysis. Hence, this set includes data regarding the identity of chicken sperm intact peptides and proteins as well as some structural biologically relevant information like N-terminal amino acids or post-translational modifications. See Figs. 1  and 2  and Supplementary Table  S1.
Experimental design and sample collection This dataset was produced with the objective of identifying by Top-Down mass spectrometry the peptides and small proteins contained in chicken sperm cells  [1]. Sperm cells (200 μL) protein extraction was performed by sonication in 400 mL of 6 M Urea 50 mM Tris-HCl pH 8. 8 buffer containing protease inhibitor cocktail (Roche, Switzerland). Samples were centrifuged (45 min at 13,000 rpm and 4 °C) and supernatants containing the extracted proteins were kept for further analysis. The protein content was determined using a Bradford assay (BioRad, Marnes-la-Coquette, France).
Protein/peptide fractionation strategies, Top-Down mass spectrometry identification protocol and results One mg of the extracted peptides/proteins were subjected to fractionation through chromatographic separation on an UltiMate 3000 RSLC system controlled by Chromeleon version 6. 80 SR13 software (Thermo Scientific Dionex, Sunnyvale, USA) using reversed phase (RP) and gel filtration (GF) chromatography as described elsewhere  [1]. A total of 35 and 65 fractions were obtained after RP and GF chromatography, respectively. All fractions obtained after fractionation were then analyzed by online micro-liquid chromatography tandem mass spectrometry (mLC-MS/MS) on a dual linear ion trap Fourier Transform Mass Spectrometer (FT-MS) LTQ Orbitrap Velos (Thermo Fisher Scientific, Darmstadt, Germany) coupled to an Ultimate s 3000 RSLC Ultra High Pressure Liquid Chromatographer (Thermo Scientific Dionex, Sunnyvale, USA) controlled by Chromeleon Software (version 6.
8 SR11; Thermo Scientific Dionex, Sunnyvale, USA). Proteo/peptidoform identification and structural  characterization were performed using ProSight PC software v 3. 0 SP1 (Thermo Fisher Scientific, Darmstadt, Germany). The detailed procedure followed for this analysis is described elsewhere  [1]. Raw data derived from Top-Down analysis have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PRIDE: PXD002768. In total, 1038 intact or fragment protein masses were detected by Top-Down mass spectrometry (Supplementary data Table  S1 ). A total of 447 biomolecules were detected in RP-derived fractions and 591 biomolecules in GF-derived fractions. From all the identified biomolecules, 65 were identified using both fractionation methods, while the rest was identified uniquely after RP or GF chromatography separation.
Functional analysis In order to identify which cell compartments/functions/pathways were mainly represented by the data set, a systems biology analysis was performed. In brief, the UniProtKB accession numbers from all m/z masses that were confidently identified through Top-Down analysis were recovered and listed. From these, the official human gene symbols (HuGO Gene Nomenclature Committee) were retrieved from Gallus gallus annotated proteins, whereas uncharacterized proteins were mapped to the corresponding Homo sapiens orthologs by identifying the reciprocal-best-BLAST hits. When manual annotation was needed (i. e. for deleted entries), this was performed by BLASTp (E value reÀ 04), together with Gene Ontology (GO) functional annotation. Because far more human genes are annotated and more information in databases is available for humans than for chicken, the human background was employed when possible.