dataesr/scientific-paragraphs-categorization huggingface dataset:
The coordinates and structure factors were deposited in the Protein Data Bank at RCSB under the references PDB identifiers: 4N1I, 4N2R, 4N4B, and 4N2Z for UmAbf62A, UmAbf62A in complex with arabinose, and PaAbf62A and PaAbf62A in complex with cellotriose, respectively. Soaking Experiments-C3 was obtained from Megazyme, arabinose from Sigma, and arabino-xylo-oligosaccharides (XA 3 XX) were prepared as described previously  (29). A PaAbf62A-cellotriose complex was obtained by soaking a PaAbf62A crystal for 35 min in the reservoir solution supplemented by C3 at saturating concentration. To form a UmAbf62A⅐XA 3 XX complex, a crystal of UmAbf62A was soaked for 2 h in solution containing 20% (w/v) PEG 3350, 0. 2 M sodium phosphate, 0. 1 M sodium acetate (pH 3. 0), 3 mM XA 3 XX, and 50 mM arabinose as cryoprotectant.
Other thermodynamic parameters were derived from the equation: ϪRT ln K a ϭ ⌬G ϭ ⌬H Ϫ T⌬S. Phylogenetic Clustering-A total of 106 sequences corresponding to individual GH62 modules were extracted from the CAZy database  (5). They were aligned using the Muscle version 3. 7 software  (30) , operating with default parameters. A matrix of maximum likelihood distances of the resulting aligned sequences was determined using Jones, Taylor, and Thornton  (JTT) distances  (31)  and subsequently subjected to hierarchical clustering method of Ward  (32). ␣ ϭ ␤ ϭ ␥ ϭ 90°␣ ϭ ␤ ϭ ␥ ϭ 90°␣ ϭ ␤ ϭ ␥ ϭ 90°␣ ϭ ␤ ϭ 90°; ␥ ϭ 117. 4°␣ ϭ ␤ ϭ 90°; ␥ ϭ 117. 3°N o. of molecule/AU 1 1 1 1 1 Matthews coefficient (Å 3 /Da) 1.
RESULTS Phylogenetic Analysis of GH62 Family-To better characterize and compare the two fungal arabinofuranosidases presented in this work, a phylogenetic analysis of the GH62 family was carried out using publically available bacterial and fungal sequences. Of the 106 sequences extracted from the CAZy database (12th September 2013), only 17 enzymes (including PaAbf62A and UmAbf62A) have been characterized (EC 3. 2. 1. 55). The bioinformatics analysis revealed two distinct subfamilies that did not correspond to taxonomical segregation (Fig. 1 ). PaAbf62A and UmAbf62A, which share 38% identity, belong to two distinct subfamilies termed GH62-1 and GH62-2, respectively. Subfamily GH62-1 includes 30 members that are clustered into two distinct subgroups, the larger one having three biochemically characterized members, whereas none of the five members of the smaller subgroup has been characterized at present.
3 ). Nevertheless, the coordination of the calcium ion in UmAbf62A and PaAbf62A structures is not identical because in the former the calcium is hepta-coordinated with a pentagonal bipyramidal ligation involving His 261 -N⑀2, Gln 198 -N⑀2, and five water molecules, whereas in PaAbf62A, the calcium ion is hexa-coordinated with a squared bipyramidal ligation involving His 285 -N⑀2 and five water molecules (Fig. 7 ). It is noteworthy that the structures of some GH43 members (e. g. the structure of BT2895 Bacteroides thetaiotaomicron, Protein Data Bank code 3KST) also contain a calcium ion that displays the same coordination as that observed in PaAbf62A. Moreover, the chelation of calcium by EDTA or Chelex resin had no apparent effect on enzyme activity (data not shown). Coupled to the fact that the calcium ion has no direct interaction with the active site, this observation is consistent with a structural rather than functional role.
Taken together, these data strongly implies that the GH62 family belongs to the same clan as the GH43 family (i. e. clan GH-F), which is consistent with the predictions made by Pons et al. (16). Furthermore, it is interesting to note that His 161 /His 285 in UmAbf62A/PaAbf62A, respectively, which coordinates the calcium ion and binds the arabinose in the Ϫ1 subsite, is also conserved in the GH43 arabinofuranosidases and arabinanases from Bacillus subtilis (PDB code 3C7E) and Cellvibrio japonicus (PDB code 3QED), respectively. Consistent with this observation, we suggest that this histidine plays an important functional role in arabinose hydrolysis within the clan GH-F. In this work, tentative identification of the positive subsites involved in PaAbf62A xylan binding have been performed using the PaAbf62A-C3 complex.
Acknowledgments-The equipment used for the crystallization experiments and x-ray crystallography as well as isothermal titration calorimetry is part of the  Integrated Screening Platform of Toulouse  (PICT, AuthorIBiSA), we are grateful to  Dr. V. Gervais  for helpful assistance in the ITC experiment. We thank the  European Synchrotron Radiation Facility (ESRF) at Grenoble (France)  in particular the beamline ID29 staff and French synchrotron (SOLEIL) at  Saint-Aubin (France)  and the beamline Proxima1 staff for their assistance.
* This work was supported in part by grants from the  region Midi-Pyrénées  (to B. S. ) and the  National Institute of Agricultural Research (INRA)  CEPIA research department. □ S This article contains supplemental Fig. S1. The atomic coordinates and structure factors (codes  4N4B ,  4N2Z ,  4N1I , and4N2R) have been deposited in the Protein Data Bank (http://wwpdb. org/). 1 Both authors contributed equally to this work. 2Supported by  Région Midi-Pyrénées and European funds (FEDER).
The abbreviations used are: GH, glycoside hydrolase; PaAbf62A, Podospora anserina GH62 ␣-L-arabinofuranosidase A; UmAbf62A, Ustilago maydis GH62 ␣-L-arabinofuranosidase A; pNP-Ara, para-nitrophenyl-␣-L-arabinofuranoside; C3-C6, cello-oligosaccharides containing 3, 4, 5, or 6 glucosyl subunits; 2-HEC, 2-hydroxyethylcellulose; ITC, isothermal titration calorimetry; PDB, Protein Data Bank; X6, xylohexaose; XA 3  XX, an AXOS:arabino-xylo-oligosaccharide named according to the nomenclature used in Ref. 42.
Confocal microscopy Transfected cells on glass bottom dishes (Iwaki, Asahi Techno Glass) coated with poly-ornithine were incubated on the microscope stage at 37uC, 5% CO 2 of a LSM 510 (Zeiss) with a 63x Plan Apochromatic oil immersion objective (NA 1. 4). YFP-tagged protein was excited using an Argon ion laser (488 nm) and dsRedXP-tagged protein was excited using a green-neon laser (543 nm). Emitted light was detected through a 505-550 nm band-pass filter (YFP) and a 560 nm long pass filter through a dichroic mirror (Red fluorescence). Data was analysed using CellTracker v. 6 software (http://dbkgroup. org/celltracker).
Immunocytochemistry Cells were seeded on glass bottom dishes (Iwaki, Asahi Techno Glass) and fixed with 4% paraformaldehyde for 15 minutes and blocked with 1% BSA, 0. 1% Triton X-100 in PBS for 20 min. Cells were then incubated with SIRT1 antibody (1:200; Abcam Ab13749) in blocking buffer for 1 hour. After 3 PBS washes, cells were incubated with anti-rabbit IgG FITC conjugate (1:500; Invitrogen cat# A11008) for 30 minutes under light protection. After 3 PBS washes, cells were imaged using a LSM 710 confocal microscope (Zeiss). AQM Advance 6. 0 software (Kinetic Imaging) was used for image analysis.
miR-34a targets SIRT1 degradation upon etoposide treatment The functional role of miR-34a induction was confirmed by looking at one of the miR-34a downstream target involved in cell survival: SIRT1  [26]. We measured SIRT1 mRNA and protein levels upon etoposide treatment to determine miR-34a activity. SIRT1 mRNA level decreased concomitantly to the increase of miR-34a upon etoposide treatment (Fig. 4A ). We further examined SIRT1 protein levels by western blot and immunocytochemistry (ICC) and observed a decrease of protein levels upon etoposide exposure with no detectable levels after 24 hours of etoposide (Fig. 4B, C ). Taken together, our data suggest that etoposide-induced miR-34a via p53 activation, correlates with the down-regulation of SIRT1 expression. We next explored SIRT1 pro-survival function in MB cells by using a SIRT1 inhibitor: nicotinamide.
We note that additional mutations were identified in each resequenced isolate (Figure  3 -source data 1). Repeated mutations to the same loci in independently evolved replicates strongly suggested that the mutations were causal. However, to conclusively demonstrate causality we introduced several of these mutations into the ancestral strains M. extorquens AM1 and PA1 (hereafter referred to as 'AM1' and 'PA1'). We focused here upon AM1 and PA1, as these two strains incurred mutations to all four loci described above and are genetically tractable. Since the secY mutation was not identified in PA1 nor the clcA mutation in AM1, we also constructed hybrid allele exchange vectors to move these mutations into the hosts in which they were not observed. We then measured the fitness of the reconstructed mutants against the natural isolate, DM4.
Acknowledgements The authors acknowledge financial support from the  National Institutes of Health  ( F32 GM106629  to JKM), the  Fundação de Amparo à Pesquisa do Estado de São Paulo , and the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior  (fellowships to AACN). The authors thank  Y Trotsenko  for providing M. extorquens  DM17 ,  C Miller  for advice on testing ClcA directionality,  DD Nayak  for outlining several of the early concepts of this project,  NF Delaney  for providing six Methylobacterium isolates, and members of the  Marx Laboratory  for offering helpful comments on the manuscript. Genome sequencing at the  University of Idaho IBEST Genomics Resources Core  was supported by grants from the  National Center for Research Resources  ( P20RR016448 ) and the  National Institute of General Medical Sciences  ( P20GM103397 ) from the  National Institutes of Health.
Acknowledgements This work was supported by the  UK Engineering and Physical Sciences Research Council  (Grant Nos. EP/G004250/1  and  EP/I021884/1 ). M. E. and P. C. D. are supported by  Marie   Curie Career Integration Grant  Nos. PCIG12-GA-2012-334056  and  PCIG12-GA-2012-333990  within the  Seventh Framework Programme  of the  European Union.
Funding statement. This work was supported by an  Institut Pasteur  grant and by the  French government 's  Investissement d'Avenir Program, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant n°ANR-10-LABX-62-IBEID). D. L. was supported by a grant from the  AXA Research Fund. Presentation during meetings. This work has been presented in part at the 53 rd Interscience.
Patients and methods In France, patients are referred to accredited cancer genetics clinics throughout the country when genetic susceptibility to renal cell tumours is suspected. Not all patients present with a personal or family history of renal cell tumour. Indeed, some have non-renal manifestations suggestive of a syndrome in which renal cell tumour risk is increased, and others are healthy relatives of individuals with an established genetic predisposition. The genes most commonly analysed in hereditary RCC are VHL (associated with von Hippel-Lindau disease), FH (Hereditary Leiomyomatosis with RCC), FLCN (BHD), SDHB (PGL4 syndrome), and MET (Hereditary Papillary RCC)  [9]. On November 15, 2013, we explored two overlapping sources, the PREDIR National Hereditary Kidney Cancer Centre database located at Hôpital Bicêtre near Paris, and the Molecular Genetics Laboratory at the Edouard Herriot University Hospital in Lyon.
The PREDIR centre strives to collect clinical and molecular data on all French carriers of a mutation predisposing to renal cell tumours, whereas the Lyon laboratory is the main provider of FLCN germline analysis in France. We retrieved clinical data for all patients with a diagnosis of molecularly-proven BHD, and selected those with a history of renal cell tumour at the time of genetic testing. All patients had signed an informed consent form before genetic testing, as legally required. Cases reported in two previous papers are included in this study  [10, 11]. Follow-up data for included patients are up-to-date as of 29 September 2014, the day the final version of this manuscript was submitted. Mutations were identified using both Sanger sequencing of exons and their flanking regions and multiplex ligation probe-dependent amplification (MLPA) on genomic DNA extracted from blood.
We expect the proportion in our study to be representative of the true renal cell tumour risk associated with the syndrome, as French index cases are offered FLCN genetic analysis because they present with all types of clinical manifestations suggestive of BHD. An exhaustive description of extrarenal manifestations is beyond the scope of this paper. Nevertheless, 67% and 58% of our cases also had pulmonary and dermatological manifestations respectively, in addition to RCC. These figures represent the minimal prevalence as pulmonary assessment by CT scan and examination by a dermatologist knowledgeable in the syndrome was not systematic. In our series, four patients had metastatic, albeit indolent RCC. Comprehensive family data was collected for all patients in this series, and we have no knowledge of other relatives (deceased or alive) with metastatic RCC who were not tested for FLCN mutations, and who were therefore not included in this study.
Acknowledgments This project was funded by the  Ligue nationale contre le Cancer (Comités de l'Indre et du Cher) , the  Myrovlytis Trust (BHD Foundation ) and the  Fondation Gustave Roussy. Kamal Achkar ,  Pascaline Berthet ,  Olivier Binet ,  Virginie Bubien ,  Nacera Brahimi ,  Olivier Caron ,  Bertrand Collin ,  Marie-Agnès Collonge-Rame ,  Isabelle Coupier ,  Albert David ,  Benedicte Demuynck ,  Yves Denoux ,  Catherine Dugast ,  Bernard Escudier ,  Thierry Flam ,  Jean-Marie Ferrière ,  Jean Friedel ,  Brigitte Gilbert-Dussardier ,  Sophie Lejeune-Dumoulin ,  Michel Longy ,  Gwenael Nadeau ,  Nicolas Poulhalon ,  Olivier Rixe ,  Stanislas Ropert ,  Olivier Rouvière ,  Jean-Michel Salé ,  Ayhan Ulusakarya ,  Laurent Yonneau ,  Hélène Zattara  are contributing collaborators. They are part of the  French National Cancer Institute «Inherited predisposition to Kidney Cancer» network  and should be acknowledged as such in the author list.
Study subjects Since 2009, the Sebitoli Chimpanzee Project monitors daily the extreme north community of chimpanzees (Pan troglodytes schweinfurthii) of the NP. We adhered to the research protocols defined by the Administration of Kibale National Park and all research was approved by the Museum national d'histoire naturelle. Data of this study refers to the period from the 5 th to 24 th February 2013, where a camera trap was placed at the border of their home range during the period of maize crop maturity. At this date, 72 chimpanzees were individually recognised out of an estimated number of 80 individuals. Twenty-six of them present hands, feet or limb deformities or scars resulting from being trapped in poaching snares. Eight are missing entire segment(s) of limb(s). Since 2009 ad libitum daily observations records and GPS waypoints of contacts with chimpanzees were used to determine the home range of the Sebitoli community by the method of Minimum Convex Polygon  [51] , estimated to be 25 km 2.
Data analysis The data set allowed only basic non-parametrical statistical approach because many observations did not enable reliable identification of individual chimpanzees. While the video-trap only captured the chimpanzees using the same access to the field and in some occasion do not record the full party, we compared the median of the party size during crop raiding and during feeding acitivities in the forest, this last one calculated under the chimpanzee habituation between February 3 rd 2009 and February 9 th 2013. It corresponds to the maximum number of chimpanzees observed during a feeding bout (N = 1 417; 61 577 minutes, mean = 43 minutes, SD = 65 min). A feeding bout was considered to start when at least a chimpanzee consumed a wild food item and ended when all the party members stopped eating.
Acknowledgments Data have been collected in the context of a scientific agreement signed between  National Museum of Natural History, Uganda Wildlife Authority and Makerere University  ( SJ445-12 ). We are grateful to  Uganda Wildlife Authority and Uganda National Council for Science and Technology  for authorization to conduct research and to the  Museum national d'histoire naturelle and Projet pour la Conservation des Grands Singes for logistical assistance  to the project. We thank  Victoria Whitehead  for her invaluable work for 'anglicizing' this manuscript. Special thanks to the staff of the  Sebitoli Chimpanzee Project  and especially to Ugandan assistants for their fieldwork.
Wound healing assay Cells were seeded at 40000 cells/cm 2 , treated with DZNep (1 mM) for 5 days. At day 4, a straight scratch was made with a 200 ml pipette tip and the wound was photographed under the microscope. After 24 h, cells were stained with crystal violet 0. 1% for 10 minutes and photographed under the microscope. The area of the remaining scratch was calculated using Image J software (http://rsb. info. nih. gov/ij/).
The InterPro database (http://www. ebi. ac. uk/ interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro.
as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48. 0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.
INTRODUCTION InterPro was originally launched in beta in October 1999, with a full version 1. 0 release in March the following year. From an initial core of four source databases (Pfam (1), PRINTS  (2) , PROSITE  (3)  and ProDom (4)), InterPro has expanded, so that it now integrates signatures from seven additional repositories: CATH-Gene3D  (5) , HAMAP  (6) , PANTHER  (7) , PIRSF  (8) , SMART  (9) , SUPERFAMILY  (10)  and TIGRFAMs  (11). Each source database has its own distinct biological focus and/or method of signature production. The aim of InterPro is to combine their individual strengths to provide a single resource, through which scientists can access comprehensive information about protein families, domains and functional sites. The InterPro database does not generate diagnostic models itself, but rather, groups one or more related member database signatures, and provides additional overarching functional annotations, including Gene Ontology (GO)  (12)  terms wherever possible.
Member database integrations In more detail, InterPro entries are typically constructed as follows: Signature generation: Protein family databases from the InterPro consortium identify groups of homologous protein sequences, based on sequence similarity and function. They use these sets of sequences to construct representative signatures that are used to iteratively search large sequence databases, such as the UniProt Knowledgebase (UniPro-tKB)  (13) , until no more proteins can be classified into the group. Upon member database release, these signatures are passed to InterPro for integration. Signature integration: At InterPro, the matches between the latest version of UniProtKB and the new signatures (and all other signatures) are determined and manually inspected by curators to ensure that they are accurate.
Aberrant signatures, both old and new, that generate false positive matches are identified and reported back to the member databases. The new signatures passing quality control are added to InterPro. Each InterPro entry is annotated with a name, a descriptive abstract and GO terms. Hierarchical relationships are identified between the entries, tracing those entries that represent smaller, functionally specific subfamilies of larger families, or specific subclasses of broader classes of domain (see Figure  1 ). Semi-automatic procedures create and maintain links to a range of other databases, including the protein interaction database IntAct (  14 ), the ENZYME  (15) , MetaCyc  (16) , UniPathway (17) and KEGG  (18)  databases, and the 3D structure database PDB  (19). InterPro signature matches to UniProtKB and to the UniParc protein sequence archive are regularly calculated using the InterProScan software package  (20).
This information is made available to the public via XML files and the database's Web interfaces, which users can search with either a protein sequence or a protein identifier. The In-terPro matches are also used to aid UniProtKB curators in their annotation of Swiss-Prot proteins, and are utilized by the automated systems that add annotation to UniProtKB/TrEMBL. Figure  1  summarizes the InterPro matches for UniPro-tKB entry Q3JCG5, a methionine-tRNA ligase from Nitrosococcus oceani, highlighting the value of the signature-integration process. According to a number of hierarchically-related InterPro entries, the protein can be classified into the methionine-tRNA ligase family (specifically, the type 1 subclass of this family), which is part of the wider family of methionyl/leucyl tRNA synthetases.
InterPro also predicts the protein to have a Rossmann fold, the two halves of which (shown in InterPro as two domains) are linked by a zinc-binding connective peptide domain. Two further domains are predicted--an anti-codon binding domain and a C-terminal domain. InterPro identifies these domains as subclasses of broader classes of domain, as indicated in the domain hierarchy annotation (for example, the C-terminal domain is predicted to be a specialized type of nucleic acid binding domain). Finally, towards the N-terminus of the protein, InterPro identifies a conserved site that is specific to class I aminoacyl-tRNA synthetases. The data used to generate the summary were drawn from 16 individual signatures from seven member databases, illustrated in Figure  2. By integrating signatures that represent the same biological entity into 11 entries, InterPro reduces redundancy.
By tracing the relationships between the entries, InterPro helps rationalize the protein match data and aids interpretation and summarization of the results. It is worth noting that the average number of member signatures per InterPro entry is 1. 4, the highest number of signatures in an entry being eight. The average number of signatures per entry is diminishing over time, as the member databases have the large protein families adequately represented and are now generating new signatures that cover (smaller) areas of protein space that are rarely represented in other databases. Nevertheless, there remain cases where new protein families will still be represented by multiple databases; for example, a publication describing a novel structure is likely to lead to that protein family being represented in CATH-Gene3D, Pfam and/or SUPERFAMILY.
Growth and coverage of the database New database entries continue to be integrated into Inter-Pro, and there have been 14 public releases since the last update (release 34. 0 in 2012 (  21 )), with an additional 5081 signatures being integrated into 3993 new entries. The latest release (version 48. 0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, and provides matches to 83. 5% of the sequences in UniProtKB release 2014 07 (see Table  1 ), an increase of 3. 9% compared with release 34. 0. These increases are set against a constant background of flux in InterPro's member databases. The number of entries provided by each resource since 2007 (when InterPro began collating these data) is shown in Figure  3 , along with the growth in InterPro entries over the same time period.