dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , primer: 344aF and 64R-23S, sequences in Supplementary Table  S1 ). Samples showing no positive PCR signals were assumed to be free of contaminating DNA and were further processed. RNA was reverse-transcribed into cDNA (QuantiTect Rev. Transcription Kit, Qiagen, Hilden, Germany). Transcripts were amplified using specific primers, which were designed using the web tool Primer3v. 0. 4. 0 software (http://biotools. umassmed. edu/bioapps/ primer3_www. cgi; parameters: product size: optimum full length of gene, GC% 40-60%, annealing temperature: 60 • C optimum). To confirm the specificity, primers were searched  (Altschul et al. , 1990)  against NCBI NR and against the Ca. A. hamiconexum metagenome  (Probst et al. , 2014). The designed primer pairs (see Supplementary Table  S1 ) were used individually using cDNA as a template and tested for amplification success (denaturation time: 5 min 95 • C; 30 cycles: 45 s 94 • C, 45 s 60 • C, 130 s 72 • C; final elongation: 10 min 72 • C).
Hamus Protein Analysis, Structure Determination, and Phylogenetic Tree Reconstruction The trans-membrane region of the hamus protein was predicted by TMHMM v2. 0 (http://www. cbs. dtu. dk/services/TMHMM/). The protein characteristics were analyzed using GenScript's Peptide Property Calculator (https://www. genscript. com/ssl-bin/ site2/peptide_calculation. cgi) and by NetNGlyc 1. 0 (http://www. cbs. dtu. dk/services/NetNGlyc/). Secondary structure was predicted using PSIPRED (http:// bioinf. cs. ucl. ac. uk/psipred/), alignment of the sequences was performed using the multi-sequence alignment program MAFFT (http://mafft. cbrc. jp/alignment/server/). PSIPRED prediction was combined with a fold recognition search using pGenThreader  (Lobley et al. , 2009). Hamus protein region 5-81 was searched against the NCBI database (blastp,  Altschul et al.
As revealed by scanning electron microscopy, such particles were easily visible in the preparations (Figure  1A ), and many particles were located in close vicinity or even touched by the hami of highly actively dividing cells (Figure  1A ). Electron microscopic images of ultrathin-sectioned cells showed that most of the cells exhibited dark inclusions within their cells (Figure  1B ). Such dense areas were examined using energy dispersive X-ray spectroscopy and were identified containing most likely phosphor and sulfur (in some cells) (data not shown). This result was further confirmed using EFTEM (Figure  S4 ).
The Hamus Protein Exhibits a Prominent β-Sandwich Fold and Thus Structurally Resembles Typical Archaeal S-Layers The secondary structure prediction program PSIPRED identified about 56 beta strands connected by loops and flanked by helical segments in some cases (Figure  S7 ). The first 110 amino acids showed sequence homologies to S-layer proteins from methanogenic Archaea (Methanococcus voltae, Methanococcus maripaludis, Methanothermococcus okinawensis, and Methanocaldococcus vulcanius). None of these homologous S-layer proteins have yet been investigated structurally and they do not show any sequence identity to solved structures of S-layer proteins, from bacterial Clostridium difficile [Protein Data Bank (pdb) entry 3cvz,  (Fagan et al. , 2009) ], Clostridium thermocellum (pdb entry 4qvs) or archaeal S-layer proteins from Methanosarcina acetivorans (pdb entry 3u2h, 1l0q;  Arbing et al.
, 2012). Therefore, it was not possible to find a suitable template for reasonable homology modeling just by using a BLAST algorithm. Putative structural conservation patterns compared to the archaeal S-layer proteins were investigated by applying the multi-sequence alignment program MAFFT in combination with the topology prediction provided by PSIPRED (pdb entry 3u2h). A parallel overlap between sequence and topology conservation to the DUF1608 domain in the S-layer protein from M. acetivorans was identified, when only parts of the hamus protein sequence (Ala334-Asp665) were searched. The M. acetivorans Slayer protein MA0829 comprises 671 aa and, similar to the hamus protein, has an N-terminal signal peptide. In addition, it contains the tandem-duplicated DUF1608 domains exclusively found in methanogenic Euryarchaeota.
In a second step, a PSIPRED prediction combined with a fold recognition search (pGenThreader) was performed, which can be applied to individual protein sequences. Three hits were indicated with a p-score of 10 -8 and an overall coverage of more than 50%. Interestingly, none of the proposed proteins were associated with S-layer proteins: (1) a cytoplasmic response regulator of two-component system, which controls heparin and heparan sulfate acquisition and degradation in the human gut symbiont Bacteroides thetaiotaomicron (pdb entry 4a2l;  Lowe et al. , 2012) , (2) a xyloglucanase from C. thermocellum (pdb entry 2cn3,  Martinez-Fleites et al. , 2006) , and (3) a human DNAdamage binding protein (pdb entry 3ei3;  Scrima et al. , 2008). We have performed homology modeling based on the first three hits of pGenThreader (Figure  6 ).
In all three models the first 110 aa were removed. Two of the models (Figures  6A, C ) show a propeller-like assembly reminiscent of a WD40 repeat. Although not being suitable for homology modeling, also the lower ranking hits proposed by pGenTHREADER revealed a consistent picture with an always returning motif of the WD40 propeller domain, which is often seen in S-layer proteins  (Veith et al. , 2009; Klingl et al. , 2011)  and represents one of the most conserved domains for protein-protein interaction. In the model of the hamus protein based on the template of the DNA-damage binding protein (pdb entry 3ei3) this motif would cover the region from aa 230-600, while in the model based on the template of the xyloglucanase from C. thermocellum (pdb entry 2cn3) the WD40 like array would correspond to aa 440-800.
Acknowledgments Research on SM1-MSI was supported by the  German Research Foundation (Deutsche Forschungsgemeinschaft) , grant no. MO 1977/3-1  given to CM-E. AJP was supported by the  German National Academic Foundation (Studienstiftung des deutschen Volkes). We thank  Uwe-G. Maier  for allocation of the EM facility,  Marion Debus  and  Silvia Dobler  for technical assistance and  Reinhard Wirth  and  Robert Huber  for support and discussions.
Strategy for the Analysis of Proteome Variations as a Function of the Experimental Conditions-The general strategy for the production of the different data sets and their exploration is summarized in Fig. 2. The individual steps of this pipeline are described in the following sections. LC-MS/MS Acquisition-All digests or protein extracts were analyzed using a LTQ Velos Orbitrap equipped with an EASY-Spray nanoelectrospray ion source and coupled to an Easy nano-LC Proxeon 1000 system (all devices are from Thermo Fisher Scientific, San Jose, CA). Chromatographic separation of peptides was performed with the following parameters: Acclaim PepMap100 C18 precolumn (2 cm, 75 m i. d. , 3 m, 100 Å), Pepmap-RSLC Proxeon C18 column (50 cm, 75 m i. d. , 2 m, 100 Å), 300 nl/min flow, gradient rising from 95% solvent A (water, 0.
1% formic acid) to 35% solvent B (100% acetonitrile, 0. 1% formic acid) in 217 min followed by column regeneration for 23 min giving a total run time of 4 h. Peptides were analyzed in the orbitrap in full ion scan mode at a resolution of 30,000 (at m/z 400) and with a mass range of m/z 400 -1800. Fragments were obtained with a collision-induced dissociation (CID) activation with a collisional energy of 40%, an isolation width of 2 Da, and an activation Q of 0. 250 for 10 ms. MS/MS data were acquired in the linear ion trap in a data-dependent mode in which the 20 most intense precursor ions were fragmented, with a dynamic exclusion of 20 s, an exclusion list size of 500 and a repeat duration of 30 s. The maximum ion accumulation times were set to 100 ms for MS acquisition and 50 ms for MS/MS acquisition.
All MS/MS data were processed with an in-house Mascot search server (Matrix Science, Boston, MA; version 2. 4. 1). The mass tolerance was set to 7 ppm for precursor ions and 0. 5 Da for fragments. The following modifications were used in variable modifications: oxidation (M), phosphorylation (STY), acetylation (K, N-term), and deamidation (N, Q). Glutathionylation (C) was added for additional analyses of trypsin digests. The maximum number of missed cleavages by trypsin was limited to two for all proteases used. MS/MS data were searched against a C. albicans ORF database (# 6209) retrieved from the Candida Genome Database website  (31)  (http://www. candidagenome. org/). Quantitative Analysis in Label-free Experiments-Proteins in the various homogenates were digested in solution, in duplicate, overnight at 37 °C by sequencing grade trypsin (12.
5 g/ml; Promega, Madison, WI) in 20 l of 25 mmol/L NH 4 HCO 3. LC-MS/MS acquisition was performed with a 4-hour gradient. MS/MS data were processed with an in-house Mascot search server (Matrix Science, Boston, MA; version 2. 4. 1). Label-free quantification in between subject analysis was performed on raw data with Progenesis-LC software 4. 1 (Nonlinear Dynamics Ltd, Newcastle, U. K. ) using the following procedure: (1) chromatograms alignment, (2) peptide abundances normalization, (3) statistical analyses of features, and (4) peptides identification using the Mascot server. A decoy search was performed and the significance threshold was fixed to 0. 05. The resulting files were imported into Progenesis-LC software. Peptides with an ion score less than 15 were rejected. Conflicts for the identification of some peptides were resolved manually.
Proteins with similar normalized abundance variations (ANOVA p-value lower than 0. 05) were classified by the Auto-Class Bayesian clustering system (webserver Autoclass@IJM, http:// ytat2. ijm. univ-paris-diderot. fr/)  (32). Farnesol and mutation effects were investigated with a two-factor analysis. p-values associated were calculated with a dedicated R script. Gene ontology analyses were performed on all clusters obtained with the GO Slim mapper from the Candida Genome Database website. Functional enrichments for each GO slim term were calculated, relative to the whole identified proteome, using the hypergeometric distribution. Quantitative Analysis by TMT Labeling Experiments-Aliquots of 100 g of protein from each cell homogenate preparation were reduced with 10 mM Tris(2-carboxyethyl)phosphine hydrochloride (TCEP), alkylated with 18 mM iodoacetamide according to the TMT sixplex kit protocol (Thermo Fisher scientific, San Jose, CA) then digested in solution overnight at 37 °C by sequencing grade trypsin (12.
5 g/ml; Promega) in 20 l of 25 mmol/L triethyl ammonium bicarbonate (TEAB). Samples from the WT, WT-farnesol, mca1⌬/⌬, and mca1⌬/⌬-farnesol conditions were labeled with 126 (monoisotopic m/z ϭ 126. 12773), 127 (m/z ϭ 127. 13108 Da), 128 (128. 13444 Da), and 129 (m/z ϭ 129. 13779 Da) labels, respectively. The samples were mixed, desalted, and concentrated with a 50 mg C18 SPE column (Biotage, Uppsala, Sweden) and the peptides precipitated with acetone. The resulting peptide mixtures were analyzed in triplicate by LC-MS/MS with a 4-hour gradient. MS/MS were acquired with HCD fragmentation and the orbitrap analyzer set at a resolution of 7500 at m/z 400 in a top20 protocol. The maximum ion accumulation time was set to 100 ms for MS acquisition and 50 ms for MS/MS acquisition. Data were processed with Proteome Discoverer 1.
4 software (Thermo Fisher scientific, San Jose, CA) and the Mascot search engine with the trypsin digest parameters described above and the additional modifications carbamidomethylation (C) and TMTsixplex (K and N-term). The precursor mass tolerance threshold was set at 0. 7 ppm and 0. 02 Da for fragment tolerance. The percolator node was used and a 5% FDR was applied. Quantification results in triplicate were treated as replicates and were visualized with Proteome Discoverer 1. 4 to allow comparisons with the results obtained by label-free methods. Peptide Analysis by Native Peptidomic Techniques-Aliquots of 500 g of protein from cell homogenates from each condition were resuspended (in duplicate) in 1 ml of 50 mM NH 4 HCO 3 buffer. Sizeexclusion chromatography was performed with a 10 kDa cut-off Amicon membrane (Millipore).
Peptides and proteins under 10 kDa were isolated and concentrated with a Speedvac and resuspended in 0. 1% formic acid. Peptides were isolated and concentrated with five zip-tip C18 tips for each condition. Peptides were analyzed by LC-MS/MS with a 4-hour gradient. MS/MS data were processed with Proteome Discoverer 1. 4 software (Thermo Fisher Scientific) and the Mascot search engine with a "no enzyme" search parameter. The percolator node was used for FDR evaluation with a 5% filter in relaxed FDR and 1% in strict FDR. Peptides were flagged as identified when present in at least one condition with a Q-value under 0. 05. The Venny web resource (http://bioinfogp. cnb. csic. es/tools/venny/) was used to produce graphical outputs and visualize specific peptides from different conditions. Analysis of Peptide Occurrence by a Bottom-up Approach-Triplicate samples from the four conditions were reduced with 10 mM dithiothreitol, alkylated using 55 mM iodoacetamide and digested FIG.
2. Scheme of the study workflow. overnight at 37 °C by the GluC protease (12. 5 g/ml; Sigma Aldrich) in solution in 20 l of 20 mM sodium phosphate buffer, pH7. 8. The resulting peptides were analyzed by LC-MS/MS with a 4-hour gradient. MS/MS data were processed with Proteome Discoverer 1. 4 software (Thermo Fisher scientific, San Jose, CA) and the Mascot search engine with semi-GluC search parameter. The percolator node was used for false discovery rate (FDR) evaluation with a 5% filter in relaxed FDR and 1% in strict FDR. The Venny web resource was used to visualize peptides specific to the different conditions. Evaluation of Mca1p Cleavage Specificity-Peptides identified by these analyses (native peptidomics and bottom-up digestions), at all FDR, were considered and only those exclusively present in the WT-farnesol condition with a Q-value under 0.
albicans protein sequence database (# 6209). For substrate validation, label-free approaches with Progenesis-LC software 4. 1 (Nonlinear Dynamics Ltd, Newcastle, U. K. ) of GluC and native peptidomics data sets were performed in a between-subject way. Potential substrates of Mca1p found on an identification basis were validated by considering those over-represented in WT-farnesol condition relative to the other conditions. Anova p-values under 0. 05 calculated after label-free analysis of native peptidomics and GluC data sets were considered to select best potential substrates. A two factor analysis was performed to discriminate farnesol and/or mutation dependent or independent effects. GO analyzes were performed on the obtained list of potential substrates with the GO Slim Mapper tool of the Candida Genome Database website.
P-values for functional enrichments were calculated with R 3. 0. 1 software for each GO term. Data Availability-The complete sets of data are available as. raw files, Proteome Discoverer 1. 4. msf files and the Candida albicans protein sequence database provided as a multiple fasta in the Peptide Atlas repository under the identification number: PASS00561. The msf files can be visualized using a freely available java application Thermo-msf-parser 2. 0. 5 available at https://code. google. com/p/ thermo-msf-parser/.
Evaluation of the Mca1p Cleavage Specificity-We used the Proteome Discoverer 1. 4 software to determine changes to the peptide profile and abundance after GluC digestion, native peptidomic analyses, and trypsin digestion (Fig. 3 ; see supplemental Files S1, S2, and S4 for the complete data sets). The peptides found are represented in a Venn diagram according to the conditions (Fig. 3A ). More peptides could be identified after Tryptic and GluC digests than native peptidomic analysis. Comparisons of protein profiles by SDS-PAGE indicated that farnesol and/or mca1 disruption did not lead to any massive degradation of proteins (supplemental Fig. S1 ). We compared the cleavage site context for peptides found by native peptidomic analyses only in the WT-farnesol condition with those found only in the mca1⌬/⌬-farnesol condition.
Native Peptidomic and GluC Digest Experiments to Identify Potential Mca1p Targets-GluC-directed bottom-up experiments identified 68 peptides (from 54 proteins) among a total of 8549 identified peptides (from 1261 proteins) as potential substrates of Mca1p. Native peptidomic analyses identified nine peptides (from eight proteins) among 416 (from 137 proteins) as potential substrates (supplemental Table  S1 ). Substrate candidates were validated by a label-free approach on GluC digests and native peptidomics experiments (entire data sets in supplemental Files S6 and S7 respectively) with Progenesis-LC software. Peptides candidates (77 candidates) quantified (28 candidates) with higher abundances in WT-  as identified by GluC and native peptidomic approaches. The peptides identified were present only in farnesol treated WT samples, with K or R extremities; they were found using a semi-gluC database search (the complete data set is provided as supplemental File S4 and the label-free quantitation as supplemental File S6) and a no-enzyme database search for native peptidomic experiment (the complete dataset is provided as supplemental File S2 and the label-free quantitation as supplemental File S7) farnesol (23 candidates) than in the three other conditions and with an Anova p-value under 0.
Expression of some Mca1p partners predicted by the String 9. 0 database web resource Examples of differential protein expression analysis by the TMT-labeling approach. Proteins expressed differently in different samples were identified by TMT-labeling experiments. All identified proteins and peptides are listed in supplemental File S3. 126, 127, 128 and 129 labeling represents respectively WT, WT-farnesol, mca1⌬/⌬ and mca1⌬/⌬-farnesol conditions (http://string-db. org/) was increased by farnesol and was higher in the Mca1p-deleted than WT strains (Fig. 5 ).
Farnesol Induces Cdr1p Overexpression and its Glutathionylation in a Mca1p -Independent Way-Label-free analysis of tryptic digests and TMT-labeling experiments showed that farnesol increased the abundance of Cdr1p, an ABC multidrug transporter, in a Mca1p-independent way (Fig. 6A ). Cdr1p has been described as exporting glutathione from farnesol-treated cells, so we searched for signatures of this activity in our data. A database search of tryptic digests for the post-translational modification glutathionylation (C) revealed no overall difference (Q-value threshold at 0. 05) in the glutathionylated proteomes in the different conditions (Fig. 6C ). We found that Cdr1p itself was glutathionylated on cysteine 712 (Fig. 6B ) in the WT and the mca1⌬/⌬ extracts prepared after farnesol treatment.
Acknowledgments-We thank  Dr Veronique Albanese ,  IJM, Paris , for the generous gift of the anti-Ssb1 antibody. Camille Garcia  was the recipient of a  grant-in-aid  from the  CNRS. We thank  Alex Edelman & Associates  for the edition of the English text. □ S This article contains supplemental Figs. S1 and  S2 , Tables  S1  to S4, and Files  S1  to S9. ¶ To  whom correspondence should be addressed:  Department of Proteomics, Institut Jacques Monod, CNRS Univ Paris Diderot , 15  Rue Helene Brion , Paris 75013, France. Tel. :  33-01-57278029 ; E-mail: camadro. jean-michel@ijm. univ-paris-diderot. fr.
INTRODUCTION More than 100,000 structures are currently available in the Protein Data Bank (PDB)  (Berman et al. 2000) ; however, RNA-containing structures take up <6% of these depositions, including RNA structures complexed with other molecules. Although protein related structures constitute >90% of the structure database, <1/1000th of the proteins with known sequences have experimental structures available  (Moult 2008). Given the vast number of noncoding RNA molecules being discovered in cells and viruses, it is likely that a very small part of the RNA conformational space has been structurally characterized. RNA structure determination efforts still have a long way to go, and computational modeling could play a major role in providing structural insights for various biological problem explorations.
2012). We now report a second round focusing on the prediction of large RNA structures, a lariat-capping ribozyme (formerly named GIR1), an adenosylcobalamin-binding riboswitch, and a T-box-tRNA complex  (Peselis and Serganov 2012; Zhang and Ferre-D'Amare 2013; Meyer et al. 2014). No closely homologous structures existed in structure databases at the time of the experiment, except for the Azoarcus group I intron  (Adams et al. 2004 ) as a potential template for the catalytic core of the GIR1 ribozyme, templates for the tRNA, and crystallographic structure of a segment of a T-box RNA without the tRNA  (Wang et al. 2010; Grigg et al. 2013). This round of prediction focuses on (i) the automatic assessment of de novo prediction of large RNA structures, especially structure topology, (ii) the evaluation of the contribution of simple and fast experimental data in structure prediction, such as chemical probing data, and (iii) the identification of bottlenecks in modeling 3D interactions.
THE THREE RNA PUZZLES Problem 5: the lariat-capping ribozyme The lariat-capping ribozyme represents an individual family of ribozymes that has evolved specific architectural features from a group I intron ancestor  (Meyer et al. 2014). The LC ribozyme catalyzes a distinct reaction involving formation of a 3-nt 2 ′ ,5 ′ lariat. The 188-nt long sequence is the following: 5 ′ -GGUUGGGUUGGGAAGUAUCAUGGCUAAUCA CCAUGAUGCAAUCGGGUUGAACACUUAAUU GGGUUAAAACGGUGGGGGACGAUCCCGUAAC AUCCGUCCUAACGGCGACAGACUGCACGGC CCUGCCUCUUAGGUGUGUUCAAUGAACAGU CGUUCCGAAAGGAAGCAUCCGGUAUCCCAAG ACAAUC-3 ′ The crystal structure was resolved to 2. 45 Å resolution. Two crystallographic models became available after modeling, with PDB ID's 4P95 and 4P9R. Problem 6: the adenosylcobalamin riboswitch An adenosylcobalamin riboswitch was crystallized  (Peselis and Serganov 2012).
Additional chemical-mapping data The Das group provided chemical-mapping data on the three puzzles to all the modelers. One-dimensional chemical-mapping data and mutate-and-map (M 2 ) data were acquired, quantitated, and normalized as described in  Kladwang et al. (2014)  and  Seetin et al. (2014) , respectively. Three probes were used: 1M7 (a SHAPE reagent, 1-methyl-7-nitroisatoic anhydride, which acylates 2 ′ -hydroxyls of flexible nucleotides); DMS (dimethyl sulfate, reacting with exposed N1/ N3 of adenosine/cytosine; and CMCT (1-cyclohexyl(2morpholinoethyl) carbodiimide metho-p-toluene sulfonate, reacting with exposed N1/N3 of guanosine/uracil)  ([Mortimer and Weeks 2007; Cordero et al. 2012a ] and references therein). Data were released to modelers on the RNA Mapping Database in standardized formats  (Rocca-Serra et al.
2011; Cordero et al. 2012b ) in accession codes RNAPZ5_ STD_0000, RNAPZ5_1M7_0002, RNAPZ5_DMS_0002; RNAPZ6_STD_0001, RNAPZ6_1M7_0002; RNAPZ10_ STD_0001, RNAPZ10_STD_0002. Each group was given the possibility to use those data and each group describes below at which stage and how these solution data were used during the modeling process.
Adamiak group The RNA 3D structure from the Adamiak group was predicted using automated method RNAComposer  (Popenda et al. 2012 ) in its batch mode. RNAComposer server (http ://rnacomposer. cs. put. poznan. pl) uses sequence and secondary structure topology information in dot-bracket notation. Secondary structure (using the RNAStructure software) (Reuter and Mathews 2010) was adjusted using experimental data given for that RNA sequence by the Das group. Additional information about potential pseudoknots or tertiary contacts was obtained from manual analysis of the mutateand-map data provided by the Das laboratory (RNA Mapping Database). For Problem 5, two interactions were found: (i) 28UC29 with 93GA94 and (ii) 111GACUG115 with 148CAGUC152. Both were introduced as squared brackets into extended dot-bracket notation input:  (((((.