dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Most of the spectra generated by mass spectrometry proteomic experiments remain unidentified after their analysis by an identification software. One of the main reasons for this poor identification rate is that most of these spectra correspond to the fragmentation of peptides carrying modifications  (Bogdanow et al. 2016; Griss et al. 2016). In conventional approaches, identification methods try to pair imperfect experimental spectra to ideal reference spectracalled theoretical spectragenerated from a peptide database  (Noor et al. 2021). However, in order to avoid excessive computations, these methods limit the mass difference ∆m between any experimental spectrum and the theoretical spectra that will be tentatively paired to itusually, ∆m is limited to the mass spectrometer tolerance.
For each PSM, SpecGlob returns its best alignment under the form of a string we called StModified, that gives several indications on how to align the peptide to the spectrum. We then evaluate the behaviour of SpecGlob on several simulated spectra datasets of increasing complexity. Firstly, after targeting modifications on specific amino acids, we compare our results to those obtained with the state-of-the-art method MODPlus, the recent version of MODa  (Na et al. 2012; Na et al. 2019). MODPlus selects candidate peptides that share sequence tags with Se, and next SpecGlob: rapid and accurate alignment of mass spectra aligns the spectra with a dynamic programming approach using an input modification list (limited to the modification database Unimod  (Creasy and Cottrell 2004)  or to a user-defined list).
Secondly, we extend the dictionary of modifications taken into account, possibly involving several amino acids. To this end, we pairwise compare all theoretical spectra generated from the human proteome while excluding self-identifications. This allows us to simulate, in the reference database, the systematic absence of the peptide at the origin of any simulated experimental spectrum. Then, as in  (Lysiak et al. 2021) , we also introduce a classification of all StModified sequences that evaluates the level of difficulty of exactly retrieving the original amino acids sequence of a simulated experimental spectrum starting from its StModified sequence. Based on this classification, we investigate the strengths and weaknesses of SpecGlob, and conclude that SpecGlob behaves very well on all tested datasets.
Spectra datasets and protein database Ethical Compliance: All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All spectra datasets were generated from the human proteome, downloaded from Ensembl99, release  GrCh38 (Yates et al. 2020). While the generation of spectra in SpecOMS is independent from the role a peptide plays (experimental (Se) or theoretical (St)), this generation differs through SpecGlob. When a peptide plays the role of St, it is modeled by a spectrum only containing b-ions, which is enough to represent the amino acids (because y-ions are complementary to b-ions). When a peptide plays the role of Se, it is considered as coming from an unknown peptide.
Consequently, as one cannot differentiate the b-ions from the y-ions before any identification, both types of peaks are generated. Firstly, we generated two simulated sets of 50,000 theoretical spectra to compare SpecGlob to MODPlus. Tryptic unique peptides whose lengths ∈  [12, 25]  are transformed into simulated spectra carrying modifications on some amino acids as follows: in dataset DatasetND, asparagines were deamidated (N+0. 984016) and sodium adducts were added on each aspartic acid (D+21. 981943); in dataset DatasetSCT, serines were substituted by alanines (S-15. 99), cysteines were carbamidomethylated (C+57. 0214) and threonines were deleted (T-101. 0477). Secondly, in dataset AllvsAll, all human tryptic peptides whose lengths ∈ [7,30] were selected to play the role of experimental spectra.
By design, for each PSM the sum of all the mass offsets inserted in the alignment is equal to ∆m. Several alignments are illustrated Table  1. In order to validate SpecGlob, we interpreted two simulated spectra datasets carrying systematic modifications on two or three specific amino acids by SpecOMS and SpecGlob on the one hand, and by MODPlus on the other hand. Because spectra were simulated, we were able to compute the percentage of spectra for which each approach restores the exact sequence with its induced modifications (Table  2 ), a sine qua non condition to consider a posteriori identifications as correct. Our first remark concerns the execution time of the combination SpecOMS/SpecGlob, which is particularly fast compared to MODPlus. When the search space is limited to the most frequent known modifications (a parameter set in MODPlus), SpecOMS/SpecGlob is about 40 times faster, a ratio that increases to 90 when all the modifications from Unimod are allowed.
Concerning the ability of each approach to correctly interpret modified spectra, MODPlus performs extremely well on DatasetND, while SpecOMS/SpecGlob is better at discovering unexpected modifications on DatasetSCT (Table  2 ). This result is not surprising. Indeed, DatasetND has been designed to be an ideal spectra dataset for MODPlus, which selects candidate peptides that share sequence tags with Se, and next performs the search with an input modification list (limited to Unimod or to a user-defined list). In DatasetND, only two abundant modifications perfectly described in Unimod were simulated on peptides. By contrast, it should be noted that SpecOMS/SpecGlob processes data without any a priori. On the other hand, DatasetSCT contains three modifications among which the deletion of T, a modification unreferenced in Unimod.
The absence of amino acid deletions in Unimod explains 96% of the incorrect PSMs provided by MODPlus (spectra representing peptides with at least one T), while other misinterpretations are mainly due to the presence of several consecutive modifications close to each other that MODPlus tends to interpret as one. Hence, although MODPlus is more efficient than SpecOMS/SpecGlob when it comes to analyzing abundant and known modifications in samples (such as in DatasetND), SpecOMS/SpecGlob is better at quickly highlighting the large variety of modificationsa sample can carry (including unknown modifications). Moreover, it should also be noted that SpecOMS/SpecGlob obtain approximately the same correct identification rates on both datasets, which shows a certain stability in its behaviour. Secondly, since SpecGlob can quickly align PSMs without any a priori, we challenged it on its ability to identify multiple and complex modifications, possibly involving several amino acids.
For this, we processed a set of 455,404 PSMs returned by SpecOMS  (David et al. 2017 ) when all theoretical spectra generated from the human proteome were pairwise compared, while excluding selfidentifications (AllvsAll dataset). We believe that only a simulated spectra dataset such as AllvsAll allows an evaluation of the accuracy of the results (percentage of well-localized offsets) in such a complex situation. While about a quarter of the PSMs can be optimally aligned with only one modificationthat can involve one or several amino acids-, a large majority of PSMs yields an optimal alignment carrying strictly more than one modification. Once SpecGlob has aligned spectra and produced a StModified string, a post-processing algorithm can be invoked to interpret the values of offset masses as editing operations.
Discussion Because considering the alignments of all experimental spectra to all theoretical spectra remains unrealistic due to excessive computational load, OMS methods have developed different strategies to filter and align PSMs. MODPlus is representative of those methods that only consider known modifications during alignments. If this strategy is well adapted to the analysis of experimentally enriched samples, the number of correct interpretations drops as soon as the number of spectra carrying unknown modifications increases. It is an important drawback since it is known that reference modification databases are partial (preparation of samples can infinitely vary and generate unexpected modifications) and are, and will probably remain, unsuitable for a certain number of studies (studies of modifications induced by food processes, for example).
Other methods, such as SpecOMS or MSFragger  (Kong et al. 2017) , filter the most promising PSMs (pairs of spectra that already share a certain number of peaks) and next, interpret the resulting ∆m between Se and St as a single modification, which may lead to an incorrect suggestion of modification. Once a PSM references the correct peptide, SpecGlob is efficient at finding the best alignment (around 92% of modifications correctly located on DatasetSCT). The percentage of correct PSMs, which seems to be the weak point, could still certainly be improved. Firstly, note that one spectrum in DatasetSCT may carry many modifications (up to fifteen in this dataset). The number of mass offsets required for an optimal alignment is not defined or limited in advance in SpecGlob, but when this number is too high, performances rapidly decline.
For instance, selecting only spectra that carry less than four modifications is a good way to improve confidence (see Table  2 ). Secondly, we set SpecOMS so as to provide only one PSM per Se as input to SpecGlob. Delivering more than one PSM per Se and then selecting the 'best' St returned by SpecGlob (considering e. g. the score of the obtained alignment and/or the colour of the corresponding PSM) would also certainly increase the number of correct PSMs. Given the rapidity of SpecGlob, increasing the number of processed PSMs is perfectly conceivable. We also demonstrate, when processing AllvsAll dataset, that even in the case where the Se sequence is not completely restored, SpecGlob followed by its post-processing step, can partly restore it. More precisely, SpecGlob can produce subsequences of the amino acids that are present in Se, thus giving clues to peptide identificationand in turn to protein identificationwith an efficiency that depends on their length and composition.
SpecGlob: rapid and accurate alignment of mass spectra SpecOMS followed by SpecGlob on the one hand, and MODPlus on the other hand, processed two simulated spectra datasets carrying modifications. In DatasetND, simulated spectra correspond to peptides with amino acids N and D modified as follows: N+0. 98, D+21. 98 giving rise to 0 to 13 modifications per spectra. In DatasetSCT, simulated spectra correspond to peptides with amino acids S, C and T modified as follows: S-15. 99, C+57. 02, T-101. 05 giving a set of spectra carrying 0 to 15 modifications depending on their composition.
Acknowledgments This research was funded by the  National Key R&D Program of China  ( 2021YFA1501401 ), the  National Science Foundation of China  ( 22172051 ,  21872053 , and  21573074 ), the  Science and Technology Commission of Shanghai Municipality  ( 19520711400 ), the  Research Funds of Happiness Flower ECNU  ( 2020ST2203 ), the  Open Project Program of Academician and Expert Workstation ,  Shanghai Curui Low-Carbon Energy Technology Co. , Ltd. , and the  JORISS program. K. Z. thanks  ENS de Lyon  for a temporary position as an invited professor in France.
Acknowledgements We thank the  Institut de France ,  Académie des Sciences for Financial support , the  China Scholarship Council  for a grant to  Mr. Xiaoping LIU , the  CNRS , the  ENSCM, and the "Direction du Numérique" of the Université de Pau et des Pays de l'Adour. Mésocentre de Calcul Intensif Aquitain & CINES  under allocation A005080045 made by  Grand Equipement National de Calcul Intensif (GENCI)  are acknowledged for computational facilities. We also thank  Angus Shepard  from  James Cook University (Townsville, Australia ) for the English corrections.
We also analyzed HSulf-2DSG by SEC-MALLS (Figure  2D ). As expected, HSulf-2DSG eluted significantly later than HSulf-2 WT. However, the detected peak was shouldered, thus suggesting the presence of two species. Furthermore, results obtained for the major species yielded a calculated unexpectedly high MW of 239 ± 30 kDa. In line with this, SAXS analysis confirmed a dramatic reduction in size of HSulf-2DSG compared with the WT, with a calculated D max of 24 nm and an MW of $240 kDa, which supported well our SEC-MALLS data (Figures  S1E-S1H ). To understand better the discrepancies between MALDI MS and SAXS/SEC-MALLS MW estimations, we analyzed HSulf-2DSG by mass photometry, another biophysical approach that allows accurate mass measurement of proteins in solution, in their native state. The results (Figure  2F ) confirmed the presence of two species in a 1:2 ratio, whose calculated masses matched that of a monomer (137 kDa) for the first species and that of a dimer (283 kDa) for the second and most abundant one.
Primary tumors, along with lymph nodes and lungs, were collected for further analysis. The results showed little effect of HSulf-2 WT expres- sion on the tumor size (Figure  6A ). Our data are therefore in disagreement with previous work, which reported either anti-oncogenic  (Peterson et al. , 2010)  or pro-oncogenic  (Zhu et al. , 2016)  effects of HSulf-2 WT expression in MDA-MB-231 cells using similar in vivo mouse models. However, it should be noted that a major difference between these three studies is the size of xenografted tumors achieved ($0. 04 cm 3 and 3-4 cm 3 respectively, in the studies mentioned above). Such conflicting data clearly exemplify the complexity of HSulf regulatory functions and possible bias that could result from the experimental design. In contrast, expression of the HSulf-2DSG variant significantly promoted tumor growth in comparison with both dsRed and HSulf-2 WT conditions.
In conclusion, we report here a most unexpected PTM of HSulf-2 by identifying the presence of a CS/DS chain on the enzyme. Our data highlight this GAG chain as a non-catalytic regulatory element of HSulf-2 activity, which may change per-spectives in the study of this highly intriguing enzyme and complex regulatory mechanism of HS biological functions. Finally, it is worth noting that such a structurally and functionally relevant feature as a GAG chain on HSulf-2 has remained overlooked for more than 20 years. Beyond the field of the Sulfs, our findings therefore strongly encourage reconsidering afresh the importance of PTMs in complex enzymatic systems.
SAXS analysis SAXS data were collected at the European Synchrotron Radiation Facility (Grenoble, France) on the BM29 beamline at BioSAXS. The standard energy as set to 12. 5 keV and a Pilatus 1M detector was used to record the scattering patterns. The sample-to-detector distance was set to 2. 867 m (q-range is 0. 025-6 nm-1). Samples were set in quartz glass capillary with an automated sample changer. The scattering curve of the buffer (before and after) solution was subtracted from the sample's SAXS curves. Scattering profiles were measured at several concentrations, from 0. 5 to 1. 5 mg/mL at room temperature. Data were processed using standard procedures with the ATSAS v2. 8. 3 suite of programs  (Petoukhov et al. , 2012). The ab initio determination of the molecular shape of the proteins was performed as previously described  (Perard et al.
, 2018). Radius of gyration (Rg) and forward intensity at zero angle (I(0)) were determined with the programs PRIMUS  (Konarev et al. , 2003) , by using the Guinier approximation at low Q value, in a Q. Rg range <1. 5: lnIðQÞ = ln Ið0Þ À R 2 g Q 2 3 Porod volumes and Kratky plot were determined using the Guinier approximation and the PRIMUS programs. The pairwise distance distribution function P(r) were calculated by indirect Fourier transform with the program GNOM  (Svergun, 1992). The maximum dimension (Dmax) value was adjusted in order that the Rg R value obtained from GNOM agreed with that obtained from Guinier analysis.
Mass photometry analysis Mass Photometry experiments were acquired using a Refeyn OneMP (Refeyn Ltd. , Oxford, UK) MP system. AcquireMP and DiscoverMP softwares were used to record movies and analyze data respectively using standard settings. Microscope coverslips (high precision glass coverslips, Marienfeld) were cleaned following Refeyn Ltd. individual rinsing procedure. To keep the sample droplet in shape, reusable self-adhesive silicone culture wells (Grace Bio-Labs reusable CultureWell TM gaskets) were used. Contrast-to-mass calibration was achieved using a mix of proteins with molecular weight of 66, 146, 480 and 1048 kDa in 50 mM Tris, 300 mM NaCl, 5 mM MgCl 2 , 5 mM CaCl 2 , pH 7. 5. Samples drops were formed by adding 1 to 2mL of protein solution into 18 to 19 mL of buffer, to reach a drop volume of 20 mL and a final protein concentration of 40-60 nM.
MALDI-TOF MS analysis of HSulf-2DSG MS experiments were carried out on a MALDI Autoflex speed TOF/TOF MS instrument (Bruker Daltonics, Germany), equipped with a SmartBeam II TM laser pulsed at 1 kHz. The spectra were recorded in the positive linear mode (delay: 600 ns; ion source 1 (IS1) voltage: 19. 0 kV; ion source 2 (IS2) voltage: 16. 6 kV; lens voltage: 9. 5 kV). MALDI data acquisition was carried out in the mass range 5000-150000 Da, and 10000 shots were summed for each spectrum. Mass spectra were processed using FlexAnalysis software (version 3. 3. 80. 0, Bruker Daltonics). The instrument was calibrated using mono-and multi-charged ions of BSA (BSA Calibration Standard Kit, AB SCIEX, France). HSulf-2DSG was desalted as previously described  (Seffouh et al. , 2019b). MALDI-TOF MS analysis was achieved by mixing 1.
, 2017) , using NaCl Gradients calibrated with authentic standards (Iduron). HSulf-2 (2 mg) digestion with bovine hyaluronidase (Sigma/Aldrich, 2mg) was achieved after a 2 h incubation in 50 mM Tris pH 7. 5 at 37 C, prior to the endosulfatase assay, which was performed as above. Incubation of heparin with bovine hyaluronidase alone showed no effect on the disaccharide analysis (data not shown). Analysis of HSulf-2/heparin binding immuno-assay As reported before  (Seffouh et al. , 2019a) , microliter plates were first coated with 10 mg/mL streptavidin in TBS buffer, then incubated with 10 mg/mL biotinylated heparin, and saturated with 2% BSA. All the incubations were achieved for 1h at RT, in 50 mM Tris-Cl, 150 mM NaCl, pH 7. 5 (TBS) buffer. Next, the recombinant protein was added, then probed with H2.
FACS analysis Cells (1million for each condition) were washed with PBS, 1% BSA (the same buffer is used all along the experiment), and incubated with 5 mg of HSulf-2 enzymes (2 h at 4 C). After extensive washing, cells were incubated with H2. 3 primary antibody (dil. 1/500, 1 h at 4 C), washed again, then with secondary AlexA 488-conjugated antibody (Jackson ImmunoResearch Laboratories, dil. 1/500, 1 h at 4 C). FACS analysis of cell fluorescence was performed on a MACSQuant Analyzer (Miltenyi Biotec, excitation 485 nm, emission 535 nm) by calculating median over 25000 events. Data are represented as means +/À SD of three independent experiments.
For stable transduction, puromycin selection was started 36 h post-infection (at the concentration of 2 mg/mL, Life Technologies) and was maintained thereafter. Proliferation assay MDA-MB-231 cells were seeded in flat bottom 96-well plates (Corning, Falcon, 10 000 cells/well) and incubated at 37 C in a 5% CO2-95% air atmosphere until confluence. Cells were tracked (one image every 4 h) using an automated live-cell imager with high-throughput capabilities and built-in data analysis (Essen IncuCyte Zoom live-cell microscopy system). Cell proliferation was monitored by analyzing the occupied area (% confluence) of cell images over time. Wound-healing migration assay MDA-MB231 cells were seeded in flat bottom 96-well plates (Corning, Falcon, 80 000 cells/well) and incubated until confluence. The cell monolayer was scratched using the Essen Bioscience WoundMaker to create scratch-wounds of a standardized width ($600 mm) and the wound-healing process was pursued in the presence of growth medium containing the cell proliferation inhibitor mitomycin (5 mg/mL).
Sections were counterstained with hematoxylin to visualize nucleus. Control sections were incubated with secondary antibody alone. Lungs were inflated using 4% PFA and embedded in paraffin. The metastatic burden was assessed by serial sectioning of the entire lungs, every 200 mm. Hematoxylin and eosin staining was performed on lung and lymph nodes sections (5 mm thick). Images were acquired using AxioScan Z1 (Zeiss) slide scanner and quantified using Fiji software.
ACKNOWLEDGMENTS The authors would like to thank the animal unit staff ( I. Jeannin ,  S. Bama ,  C. Magallon ,  N. Chaumontel , and  H. Pointu ) at the  Interdisciplinary Research Institute of Grenoble (IRIG)  for animal husbandry,  Rabia Sadir  for her help with mass photometry, and  Caroline Mas  of the ISBG biophysical platform. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) within the  Grenoble Partnership for Structural Biology. Platform access was supported by  FRISBI  ( ANR-10-INBS-05-02 ) and GRAL, a project of the  UniversiteGrenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). This work was also supported by the  CNRS  and the  GDR GAG  ( GDR 3739 ), the '' Investissements d'avenir'' program Glyco@Alps  ( ANR-15-IDEX-02 ), by grants from the  Agence Nationale de la Recherche  ( ANR-12-BSV8-0023 ,  ANR-17-CE11-0040 , and  ANR-19-CE13-0031 ) and  UniversiteGrenoble Alpes  ( UGA AGIR program ), the  Swedish Research Council  ( 2017-00955  to G.
Acknowledgements F. J. thanks all the students and coworkers who were involved in the research projects presented in this manuscript and whose names appear in the references. Financial support for this research from the  CNRS , the  Université de Reims Champagne-Ardenne , the  Université de Montpellier , the  Ecole Nationale Supérieure de Chimie de Montpellier , the  Région Grand Est (France) , the  Indo-French Center for the Promotion of Advanced Research (CEFIPRA) , the  Agence Nationale de la Recherche (France)  and the  Australian Research Council  is greatly acknowledged.
5 μs, an acquisition of 3. 28 s, a spectral window of 10,000 Hz, and 64 K data points zero-filled to 128 K before Fourier transformation with 0. 3 Hz line broadening. For 4AA, two-dimensional (2D)  1  H-1 H COSY homonuclear experiment (cosygpprqf sequence with water presaturated and gradients) was performed at 298 K with quadrature phase detection in dimensions using the QF detection mode in the indirect one. Increments (384) in the indirect dimension were obtained, 4 K data points were collected, and 64 transients were accumulated in the direct dimension. A square sine-bell function was applied in the two dimensions before Fourier transformation. 9] [40]  The measurements were made at four temperatures: 288, 298, 308, and 323 K. 2. 4. Molecular Simulation. All the MD simulations presented in this study have been performed in acidic solutions.
Quantum calculations have been used to calculate the partial charges of the CB7 molecule with the M062X method with the 6-31G(d,p) base. A cubic simulation box of 20 Å length was used and composed with two thousand water molecules, one 4AA, and one CB7 molecule. pH is taken into account using protonated 4AA (protonation takes place in the azo group). Cl -was added in order to maintain the neutrality of the solution. AMBER force field  41  was chosen for guest and host molecules, and the SPCE dehydration model was used for water. The pressure and temperature were fixed, respectively, to 1 atm and 300 K. A cut off radius of 8 Å was applied for short-range interactions. The MD simulations were performed with the LAMMPS package  42  first with an equilibration step in the NVT ensemble of 1 ns followed by an acquisition time in NPT of 20 ns.