dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene expression data of the GSE4843 data set (n ¼ 45 melanoma cell lines) were downloaded from GEO as series matrix file format that was already processed by authors of that respective study using the MAS 5. 0 algorithm. Expression values were log 2 transformed for further analysis such as heatmap cluster analysis and Pearson's correlation analysis. Gene set enrichment analysis. GSEA for transcription factor target gene sets negatively correlating with MITF expression levels in human melanoma cell line panels was performed using the BROAD javaGSEA standalone version (http://www. broadinstitute. org/gsea/downloads. jsp) and the curated C3 transcription factor target gene set collection (BROAD molecular signature database, MSigDbv4. 0, http://www. broadinstitute. org/gsea/msigdb/index. jsp).
We used the default setting of 1,000 permutations and the gene-set permutation mode. Gene probe identifiers were collapsed to symbols before the analysis. The probe '207233_s_at' representing MITF was used for Pearson's correlation analysis. For GSEA of siNT-versus siMITF-treated MZ7 melanoma cells, we used the mean of the log 2 fold changes of two biological replicates as metric for the pre-ranked gene list algorithm (GSEAPreranked) of the BROAD javaGSEA tool with 1,000 permutation and the canonical pathway (cp) subcollection of the C2 curated BROAD molecular signature gene-set collection. Gene expression analysis of human melanoma cohorts. Gene expression data (RNA-seq) of the TGCA SKCM cohort was accessed through the cBioportal for Cancer Genomics (http://www. cbioportal. org) using the R-based package CGDS-R and following the TCGA guidelines for the use of TCGA data.
We retrieved individual gene expression values for the genes of interest as RPKM (reads per kilobase of transcript per million mapped reads) normalized read counts. RPKM values o1 were set to 1, to avoid negative expression values on log 2 tranformation if necessary. All melanoma samples were ordered by increasing MITF expression values. The expression of the TNF response gene signature was determined by calculating the mean expression per sample from all genes of the TNF response signature. The moving average of the gene expression was calculated using a sample window size of n ¼ 20 and added to the chart. Log 2 -normalized gene expression data from the Lund melanoma cohort (GSE65904) for candidate genes was used to generate moving average plots as described above. Analysis of published 3HA-MITF ChIP-seq data.
The respective tracks including input and 3HA-MITF ChIP-seq results (GSE61967) were loaded into the UCSC genome browser. Significant 3HA-MITF binding peaks in the genomic region of the JUN locus were identified by statistical methods as previously described  35. Statistical tests. All statistical tests were performed with the R-computing platform. We specify within the manuscript or legends the name of the statistical test, whether it is based on parametric or non-parametric data, its direction (one-sided or two-sided) and paired or unpaired. If applicable, we also performed corrections for multiple comparison as indicated in the figure legends using the respective methods.
Acknowledgements We thank the  Department of Human Genetics  from the  University Hopital Bonn  ( Life & Brain Center ) and, in particular,  Stefanie Heilmann ,  Andrea Hofmann  and  Nadine Fricker  for technical processing of gene expression microarrays. We thank  Elmar Endl  and his team from the core FACS facility for cell sorting. We thank all members of the  Tu ¨ting lab and Ho ¨lzel lab  for critical discussions. We thank  David Fisher ,  Shigeki Shibahara ,  Jochen Uttikal  and  Eicke Latz  for providing plasmids. We thank  Dirk Schadendorf  and the  Universita ¨tsklinikum Essen UKE (Germany)  for providing human melanoma cell lines. This research was funded in part by the following grants:  BONFOR  grant  O-105. 0068  to  J. K. ,  BONFOR  grant  O-108. 0044  to A. G. and M. H. DFG CRC832 core support  M.
The KBDOCK Database The KBDOCK database has been described previously  [9, 26]. Briefly, KBDOCK combines information extracted from the Pfam protein domain classification  [27]  with coordinate data for structural DDIs from the Protein Data Bank (PDB)  [28]. The latest version of KBDOCK uses Pfam version 27. 0 and a snapshot of the PDB that we took in June 2013. In this study we only deal with hetero DDIs involving two distinct PDB chains. However, many of the DDIs extracted directly from PDB structures are redundant, either because a single crystal structure may contain several symmetry-mates, or because a given complex may have been solved several times under different crystallographic conditions, for example. Therefore, to achieve a robust classification and reliable statistics, KBDOCK eliminates redundant DDIs by applying the NRDB90 program  [29]  with a threshold of 99% sequence identity to the entire set of sequences built from the concatenation of the two interacting domain sequences in each DDI.
It then superposes and spatially clusters the remaining DDIs in order to identify a small number of DFBSs for each Pfam domain  [9]. Finally, the DDI instances involving each DFBS are filtered again, this time using a 60% sequence similarity threshold, in order to retain mostly distinct pairs of domains associated with any given DFBS. For example, the PDB contains 11 hetero DDIs for the Kunitz_legume domain (Pfam accession No. PF00197) which KBDOCK spatially clusters to identify 4 DFBSs on this domain. For each DFBS, the DDIs are filtered using a 60% sequence identity threshold, which gives a total of 5 non-redundant hetero DDIs corresponding in this case to 5 DFIs. More specifically, one DFBS of Kunitz_legume binds both Trypsin (PF00089) and Peptidase S8 (PF00082), another binds a different DFBS on Trypsin, the third binds Alpha amylase (PF00128), and the fourth binds Thioredoxin (PF00085).
Overall, when considering only hetero interactions, the above filtering and clustering procedures give a total of 4001 Pfam DFBSs located on 2153 different Pfam domains or families, and involved in a total of 5139 non redundant DDIs. As two or more non redundant DDIs can still correspond to the binding between the same two Pfam domains at the same binding sites, the 5139 non redundant hetero DDIs have been mapped to a total of 3084 distinct DFIs. The KBDOCK web server is available at http://kbdock. loria. fr. This provides an easy-to-use public interface to explore and analyse domain family interactions and their binding sites and to propose protein docking templates. In order to carry out the analyses presented here, several specialised KBDOCK queries were implemented in Prolog. A full dump of the database is also available from the above web site.
Structural Annotation of DFBSs We use the DSSP program  [21]  to annotate domain and DFBS residues with secondary structural information. DSSP defines eight types of SSE, namely, α-helix (H), 3/10-helix (G), π-helix (I), residue in isolated β-bridge (B), extended strand (E), hydrogen bonded turn (T), bend (S), and loop/irregular (L). However, because several of these types are broadly quite similar, and because only a few instances of turns and bends were found in the KBDOCK database, the eight DSSP types were grouped into three main SSE types which we denote here as α (H, G and I), β (B and E), and γ (T, S, and L). Given a binding site DFBS f,b involving Pfam domain f and binding site b, let D f,b represent the set of non redundant DDIs that involve this binding site. In fact, 596 of the 4001 DFBSs studied here belong to more than one DDI instance in our set of 5139 non-redundant DDIs.
Classifying and Analysing DFBSs The secondary structural features of DFBSs and domain surfaces were estimated after annotating each amino acid residue with the DSSP program  [21]. The eight original DSSP types were grouped into three secondary structure element (SSE) classes, denoted here as α, β and γ. The three SSE propensities were then calculated as described in the Methods section. We initially attempted to define SSE classes automatically using various clustering methods (hierarchical, expectation maximization, and density clustering). However, no well separated clusters ever appeared in the data and the calculated log-likelihood values were not significant. Furthermore, data visualization suggested a quasi-continuous distribution of the data over the three SSE classes. Consequently, we decided to analyse the overall distribution of the three SSE propensities over DFBSs and domain surfaces using ternary diagrams (http://mypage.
9% × 24. 9% = 10. 4% (the percentage product gets doubled because the pair can be either (α-rich, γ-rich) or (γ-rich, α-rich)). These random pair-wise probabilities were multiplied by the total number of DFIs in our dataset (3084) to obtain the numbers of expected DFIs for random binding (as shown in parentheses in Table  4 ). Thanks to our large dataset, all numbers are greater than 5, and we can use a χ 2 test to assess the difference between the two distributions. The result obtained indicates that the observed distribution of DFIs over the 15 possible SSE class pairs is significantly different from the one expected from random binding (p < 0. 001). This represents a strong confirmation, on a large dataset, of previous hypotheses that certain SSE classes of binding sites favorably or unfavorably interact with other SSE classes.
7, median 5), excluding an outlier DFBS having 142 distinct Pfam partners (belonging to the Immunoglobulin V-set domain PF07686). Statistical comparison of the distribution of DFBSs over the five SSE classes in each row with the global DFBS distribution (last row) reveals that this distribution is significantly different (χ 2 test, p < 0. 001) only when the number of partners is greater than 3. In this row, individual comparisons for each SSE class (z-test) yields a significant difference only for the α-rich (lower percentage, p < 0. 01) and β + γ (higher percentage, p < 0. 001) classes. This observation suggests that, to accommodate large numbers of partners, the binding sites tend to involve decreased α and increased β secondary structure. Finally, we used DSSP to calculate the solvent accessible surface (SAS) of each DFBS (this calculation assumes that each binding site contributes equally to the buried SAS at a DDI interface).
Distribution of DFBSs on Multi-Partner Pfam Domains KBDOCK allows to monitor multiple binding capacities at the levels of both individual DFBSs and Pfam domains. In particular, it is quite natural to think that Pfam domains having the highest number of Pfam partners also have the highest number of DFBSs. This led us to analyse the distribution of Pfam domains depending on the number of DFBSs per domain, as shown in Figure  4. The distribution of Pfam domains depending on their number of DFBSs per domain. In fact only 9. 5% (205/2153) of Pfam domains harbour four or more DFBSs, with a maximum of nine for Actin domain (PF00022). The final promiscuity of these domains then depends on the promiscuity of each harboured DFBS. More concretely, Table  7  lists the binding multiplicities of the nine Pfam domains having the highest numbers of DFBSs per domain (excluding the outlier case of Immunoglobulin V-set domain already mentioned above).
As might be expected, most of these domains such as Ras, PKinase, Ubiquitin, and C1-Set are central to essential cellular processes such as regulation, signaling, and the immune system, for example. It is interesting to see that the Actin domain currently has the highest number of DFBSs but not the highest number of Pfam partners, which is held by the Ras domain (56 versus 21). This is consistent with the promiscuity of individual DFBSs on these two domains, which is up to 49 for Ras, but only 10 for actin. Thus, promiscuity of DFBSs can strongly expand the promiscuity of Pfam domains, especially for domains that are part of hub proteins. Kinase domains, for example, are frequently observed in PPI network hubs, with some 400 hubs having some kind of kinase activity  [22]. It can be seen in Table  7  that the Pfam protein kinase domain (PF00069) harbours 8 DFBSs of which two can bind to 16 and 18 different Pfam partners, resulting in a total number of 39 different Pfam partners.
However, these earlier studies of PPIs have been somewhat limited by the relatively small numbers of hetero protein-protein complexes available, and by the problem of how to select a suitable sub-set of protein binding sites to work with. Because KBDOCK is built from a complete and recent (June 2013) snapshot of the PDB and the latest version of Pfam (v27. 0), our SSE-based classification of binding sites may be considered as the most comprehensive one to date. Indeed, we believe the work described here represents the largest systematic and non-redundant study of protein domain interactions to have been described to date. Furthermore, we believe that the very large coverage and lack of redundancy of our dataset make our classification quite reliable compared to earlier studies which used sub-sets of the PDB.
As well as supporting previous findings regarding the preference for γ-rich features in binding sites, our results also show that there seems to be nothing particularly special about the SSE composition of a DFBS when compared to the rest of its domain surface. From a global point of view however, our results also show that "extreme" SSE compositions are much more common in DFBSs than in whole domains. More than 25% DFBSs in our dataset completely lack α helices, and more than 50% completely lack β-strand residues. Furthermore, our results show that there exist certain non-random pairing preferences between the different SSE classes. In particular, interactions involving the same two SSE classes are more frequent than expected from random binding. Conversely, binding of an α-rich or α + γ DFBS to a β + γ DFBS is significantly disfavored.
Knowledge of these secondary structure pairing propensities could be useful for the prediction of unknown DDIs, especially if combined with other physical properties  [10]. Additionally, we find that just over 80% of DFBSs in our dataset interact with just one type of Pfam domain, and only around 3. 5% of them interact with more than three different Pfam domains. Our SSE propensity analysis indicates that DFBSs with three or more distinct Pfam partners have a lower probability of being classified as α-rich and a higher probability of being classified as β + γ compared to the overall frequency of these SSE classes. This contradicts somewhat the results of Keskin and Nussinov  [8]  that multi-partner interfaces preferentially consist of α helices, but their study performed in 2007 involved fewer DDIs than ours.
We also see a tendency for DFBSs having more than one Pfam interaction partner to have slightly smaller binding site surface areas than single-partner DFBSs. This confirms a previous observation by Keskin et al. [11]  on a larger dataset, who already commented that with a large interface, it would be more difficult to bind to various complementary sites. More than 50% of Pfam domains in our dataset harbour only one DFBS and 91% of these DFBSs are single-partner. Thus almost half of Pfam domains are monogamous in their physical relationships with other domains, and this finding could have considerable implications for the interpretation of large-scale PPI networks and for drug targeting. Conversely, the "promiscuous" DFBSs are mostly found in domains containing more than one DFBS. In particular the 9.
27 icroarray hybridizations were done for 48 samples (two rootstock 3 two phenological stages 3 two years 3 two treatments 3 three biological replicates) by the Plateforme Biopuces (Institut National des Sciences Appliquees, Toulouse, France) according to the manufacturer's instructions. Microarray data were analyzed using the R  28  and R/Bioconductor softwares. 29 Quality control was done using the ArrayQualityMetrics package,  30  and identified outliers were excluded from further analyses. Data were rma normalized  31  and differentially expressed genes between stressed and control samples as well as stressed/control differentials between 110R and 125AA rootstocks were identified using the Limma package. 32 Differentials with absolute fold changes above 1. 5 and P-values below 0. 05 were considered significant.
Significantly affected gene categories were identified using a Wilcoxon rank sum test in R and the MapMan Ontology. 33, 34 Differentials for the different categories were visualized using PageMan. 35 MapMan mappings for the Cribi 12X grapevine genome are based on closest homologs regarding to the Arabidopsis thaliana genome. Concerning results presented in the Tables and Supplemental Tables, each sequence corresponding to the ID indicated was manually blasted (blast n) against NCBI database. Microarray data have been deposited in the NCBI GEO database with accession number GSE66391.
ACKNOWLEDGEMENTS We would like to dedicate this paper in deepest recognition to the memory of  Dr. Anthony Peccoux , assistant professor of viticulture at the  College of Agriculture, Food and Natural Resources, University of Missouri , who passed away in November 2012. Anthony enthusiastically designed the field trial and collected data during season 2009 in the course of his PhD work. We thank  Pr. Hans Schultz  and  Pr. Manfred Stoll  ( Hochschule Geisenheim University ) for helpful discussions. The PhD work of  M. Berdeja  was supported by an  Erasmus Mundus grant. Part of this work was supported by the  Conseil Regional d9Aquitaine (France)  and the  land of Hesse (Germany).
The KD is flanked by a four-helix bundle (FHB, α 5 to α 8) with the α 5 inserted into a groove formed by α H and the loop α I-α 5 (Fig. 1 ). FHB domains are ubiquitous and have numerous and divergent functions from secreted bacteriocins to human cytoskeleton binding proteins. Noteworthy a FHB domain is present in MAST1 (pdb code 2M9X), a member of the microtubule-associated Ser/Thr-protein kinase family but with no function yet associated. The sequence conservation for the FHB residues is weak amongst the LegK family but secondary structure prediction suggests that LegK1 and LegK2 might each have an all α -helical domain C-terminal to the KD (Supplementary Fig. S2 ). It is noteworthy that a mutant of LegK1 deleted of the C-term domain was still active for phosphorylation  24. The position of the FHB in LegK4, i.
Both LegK4 and LegK4  were then further purified on an anion exchange column (HiTrap Q; 5 ml; GE Healthcare) equilibrated with 20 mM Tris, pH 8. 0 and 5% glycerol. The proteins were eluted with a NaCl gradient (0 to 1 M). A final size exclusion chromatography step (HiLoad 16/600 Superdex 200 pg column; GE Healthcare) was performed in 20 mM Tris, pH 8. 0, 150 mM NaCl, 5% Glycerol (V/V). Selenomethionine labelled LegK4 1-445 (SeLegK4 1-445 ) was produced following the protocol described in (13) and purified as LegK4. Crystallization, data collection and processing. LegK4 1-445 and SeLegK4 1-445 were concentrated to 8-10 mg/ml using an Amicon Ultra 10-kDa concentrator. The vapour diffusion sitting drop technique was used for crystallization with drops, consisting of 1 μ l protein and 1 μ l reservoir buffer equilibrated against 500 μ l of reservoir solution in 24-well plates (Intelli-plates, ARI) at 292K.
Native LegK4 1-445 was co-crystallized with 1 mM Adenylyl imidodiphosphate (AMP-PNP) and 2 mM MgCl 2 , which were added to the protein 10-20 minutes prior to setting-up crystallization drops. Plate shaped crystals were obtained for LegK4  in complex with AMP-PNP, with a reservoir solution consisting of 10% (w/v) polyethylene glycol (PEG) 20000, 20% (w/v) PEG 550 mono-methyl ether, 30 mM MgCl 2 , 30 mM CaCl 2 and 100 mM Tris/Bicine pH 8. 3. Bip ramidal crystals for SeMet derivative LegK4 1-445 grew with a reservoir solution 12% (w/v) PEG 4000, 20% glycerol, 0. 1 M MOPS/HEPES pH 7. 5, 5 mM Hexaammine cobalt (III) chloride. Crystals were then flash-cooled, and stored in liquid nitrogen. Data for native LegK4  and Se-LegK4 1-445 were collected at 100K at ID23EH2 (European Synchrotron Radiation Facility) and Proxima 2a (SOLEIL) beamlines, respectively.
Structure solution and refinement. A high-redundancy data set was collected on a single Se-LegK4 1-445 crystal at the K absorption edge wavelength (0. 979 Å). The initial heavy-atom sites were located using AutoSol 46 from the Phenix program suite  47. Despite the limited resolution of 3. 7 Å, the initial phases had a very good figure of merit (FOM) of 0. 580, and an excellent experimental electron density map enabled us to manually build an initial model. The resulting model was then used for molecular replacement calculations using the monoclinic data set at 2. 49 Å resolution with Phaser 47 , in which two monomers were positioned with a translation function Z-score of 9. 7. The resulting electron density map was then subjected to the AutoBuild program, part of the Phenix program suite  47.
An initial model consisting of 526 residues in 12 chains and 269 water molecules was obtained. Both native and derivative models were completed with sessions of manual model building using Coot combined with model refinement using Phenix  47. Representative pictures of the electron density map are shown in Supplementary Fig. S1. Figures were generated with Pymol  48. The coordinates and structure factors of the structures of apo-LegK4 1-445 and AMP-PNP•LegK4 1-445 have been deposited in the protein data bank with PDB codes 5CLR and 5CKW, respectively. Small angle X-ray scattering. SAXS data were recorded on beamline Swing at SOLEIL Synchrotron (Gif sur Yvette, France) at a wavelength of 1. 003 Å on a 17 cm × 17 cm low-noise Aviex charge-coupled device detector positioned at a distance of 1800 mm from the sample, with the direct beam off-centered.
The useful Q-range was 0. 004-0. 61 Å -1 , where Q = 4π sinθ /λ is the scattering vector, and 2θ is the scattering angle. 50 μ l of LegK4 1-445 (10 mg. ml -1 ) in buffer S (50 mM Tris pH 8. 0, 200 mM NaCl, Glycerol 5% (v/v), 1 mM DTT) were injected into a size-exclusion column (Agilent BioSec-3) using an Agilent© HPLC system cooled at 15 °C and eluted directly into the SAXS flow through capillary cell  40. SAXS data were collected online throughout the whole elution time, with a frame duration of 2 s and a dead time between frames of 1 s. A first data set of 90 frames, collected before the void volume, were averaged to account for buffer scattering. A second data set was collected for the sample, from which the 10 frames corresponding to the top of the elution peak were averaged and used for data processing after baseline subtraction.
1% HCOOH in H 2 O/CH 3 CN (20/80)) was applied. MS data were acquired using Analyst QS 1. 1 software (Applied Biosystems). Screening for phosphorylated peptides was achieved with ProteinPilot 4. 0 software (Applied Biosystems) using the Paragon database search algorithm against a home-made database including the sequence of LegK4 and LegK4.
Acknowledgments The project was supported by a FINOVI foundation (www. finovi. org)project. We acknowledge the use of the  UMS3444/US8  platform protein science facility and thank  I. Zanella-Cléon  and  V. Gueguen-Chaignon  for their assistance. We thank  Proxima2 and Swing Beamline  staff from SOLEIL and ID23EH2 beamline staff from  ESRF. This work was performed within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon , within the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).