dataesr/scientific-paragraphs-categorization huggingface dataset:
Many molecular processes within a cell are carried out by molecular machines built from a large number of proteins organized by their protein-protein interactions (PPIs). Exploring PPIs in their cellular context is critical to better understand the protein function. Förster resonance energy transfer measured by fluorescence lifetime imaging (FLIM-FRET) enables to monitor PPIs and to map their spatial organization in a living cell with high spatial and temporal specificity. But both the accurate measurement and the interpretation of multi-exponential FLIM-FRET data associated to mixtures of interacting and non-interacting proteins are difficult. Here we show that a simple diagram plot can find interesting visualization properties by clustering pixels with similar decay signatures. FLIM diagram plot can be used to provide valuable information about stoichiometry and binding mode in PPIs, even in the presence of large differences in protein expression levels of the different interacting partners.
This makes fluorescence lifetime approaches interesting for bioassays with application opportunities in biomedical and life science areas  (12)  FLIM-FRET can additionally reveal sub-populations in a mixture of protein complexes. A fluorescence decay sums photons emitted from the different species present in the excitation volume. The resulting fluorescence decay is multiexponential and it is theoretically possible to extract distinct lifetimes and amplitudes of the different species composing the mixture. FLIM methods have generally been limited to mixtures of two species with mono-exponential decays as lifetime imaging of biological samples hardly provides the data quality and photons counts required for higher resolution. Even limited to two species, reasonably accurate values can be obtained for the mean lifetime τ , but not necessarily for the lifetimes and amplitudes of the individual components  (13).
To obtain robust fitting parameters for bi-exponential decay, it is recommended to use global analysis with a prior knowledge that only a limited number of fluorescent molecule species whose lifetimes do not vary spatially are present in the sample  (14) (15) (16). Some efforts for alternative analysis  (17) , like phasor based analysis  (18, 19)  are also available, including attempts for denoising FLIM datasets and reducing bias and uncertainty in parameters estimations  (20, 21). In the particular context of PPIs, non-interacting species (donor only decay) or interacting proteins where donors and acceptors are too far apart to undergo FRET can be discriminated from interacting ones in bi-molecular interactions assuming for example spatial invariance of the donor lifetime components across the data set.
In addition, the fraction of decays for which a twoexponential model does not statistically improve the quality of fits as compared to a more parsimonious mono-exponential model is about 50%. In sharp contrast to α 1 , the lifetime value of the shortest-lived component τ 1 was almost independent on the nature of the protein labelled with the donor and was constant at about 2. 0 ns. Taken together, these data indicate that running similar measurements with the fluorescent donor labelling once the less expressed partner and once the most expressed one provides different FLIM parameters when donors and acceptors expression levels are very different. Visualizing and comparing the (α 1 , τ 1 ) distributions of both situations provide an easy analytic tool to prevent misinterpretation on the relative amount of interacting species in the sample.
As seen on Figure  3 , the resulting FLIM diagram resulted in three separable clusters of pixels with lifetimes shortened to about 2 ns, 1. 2 ns and 0. 5 ns, respectively. These three clusters were corresponding to the acceptor located at the major binding site and with the addition of one or two acceptors, respectively, on the additional sites. Interestingly, this pattern was very similar to that observed while exploring the interaction of PvdA with PvdI. This led to the conclusion that multiple PvdA are bound to one molecule of PvdI. This was confirmed by FLIM measurements performed on bacterial strains expressing PvdA-eGFP and PvdI-mCherry (eGFP was labelling the most expressed partner). Using this opposite labelling scheme, the data evidenced the absence of sknewness of τ 1 distribution with a central value of τ 1 ≈ 2.
Conclusions Multi-exponential decays resulting from FLIM data aiming at characterizing PPIs are difficult to interpret, even when limited to double-exponential decays. One reason comes from the high variability of lifetimes and amplitudes of the individual components resulting from the limited number of photons in the decays of individual pixels. The proposed FLIM diagram plot is a useful visual approach for a more straightforward interpretation of the lifetime data. In this approach, populations of pixels with similar decay signatures can be visualized. By plotting the lifetimes as a function of their amplitudes the density of points reveals central tendencies together with the distribution of the individual parameters. We showed in a few examples that it can be used to provide valuable information about stoichiometry and binding mode of PPIs, even in the presence of large differences in protein expression levels of the two interacting partners.
Using the simple experimental strategy of labelling the two interacting partners successively with donor/acceptor and then with acceptor/donor, the graphical representation of the corresponding FLIM data are easy to interpret. Even in situations of large excess of one of the partners, it is relatively easy to discriminate complexes with one-to-one stoichiometry from those with unbalanced stoichiometries. Although helpful, several limitations are not improved by this visualization technique. In particular, the stoichiometry of the complexes cannot be precisely determined and the information remains mostly qualitative. Nevertheless, we believe that multidimensional visualization of FLIM data enhance the usefulness with which FRET experiments in cells can be interpreted for PPIs characterizations.
Fluorescence decays were processed using a commercial software package (SPCImage V2. 8, Becker & Hickl, Germany). A binning of 5x5 pixels was applied before processing the fluorescence decays. FLIM data were further analysed using a homemade ImageJ plugin and R scripts. Software and Hardware Availability A R package named flimDiagRam for Fluorescence Lifetime Imaging data analysis is available on https://github. com/jgodet/flimDiagRam. Most figures of this article can be reproduced independently using the code found on this repository.
ACKNOWLEDGEMENTS We acknowledge  Dr Ludovic Richert  for his valuable assistance on FLIM data acquisition and for the technical maintenance and development of the FLIM setup. We thank  Dr. Isabelle Schalk  at  University of Strasbourg  for providing bacteria strain constructions expressing fluorescent labelled proteins. This work was funded by grants from  Fondation pour la Recherche en Chimie  (https://icfrc. fr/). YM is grateful to the  Institut Universitaire de France (IUF)  for support and providing additional time to be dedicated to research.
, 2017). In the first MalDH sub-group, made up of mitochondrial and cytosolic clades  (Madern, 2002; Madern et al. , 2004 , Boucher et al. , 2014, all  enzymes are dimeric. In the second sub-group, primary sequences of MalDH display stronger identity with LDHs than with the mitochondrial and cytosolic clades of MalDHs. They have been identified as LDH-like MalDHs, and most of them are tetrameric  (Madern, 2002). LDH-like MalDHs are identified under accession numbering (01339) in the Conserved Domain Database (CDD)  (Marchler-Bauer et al. , 2011). LDH-like MalDHs are mainly found in Bacteria and Archaea, with an exception within the Alveolate  (Madern, 2002; Madern et al. , 2004 , Boucher et al. , 2014). The folding and association pathway has been analyzed for the LDHs and LDH-like MalDHs; it involves a series of sequential steps.
X-ray crystallography. The crystal structure of I. isl MalDH was determined in the apo form. Data collection and refinement statistics are shown in Table  S1. I. isl MalDH crystallizes in the space group P22121 with a tetramer in the asymmetric unit. The tetrameric assembly corresponds to the oligomeric state determined in solution (Fig. 1 ). In three monomers, the electron density of an equivalent region corresponding to the mobile loop (residues 98 to 105, normalized numbering) was weak not allowing the corresponding residues to be modeled. The final tetrameric structure of I. isl MalDH is shown in Fig. 3a. Subunit contacts that maintain the tetramer state occur through interfaces along the P, Q and R axes. As expected, the structure belongs to the NAD(P)-binding Rossmann-like Domain CATH superfamily (3.
40. 50. 720, http://www. cathdb. info (Dawson et al. , 2017). The in silico structural analysis made with the Superfamily server  (Wilson et al. , 2009)  using the I. isl MaDH amino-acid sequence predicts two domains: a NAD(P)-binding Rossmann-fold domain (residues 7 to 138) and a LDH C-terminal-like domain (residues 146 to 308). When the I. isl MalDH structure is compared to all PDB content using the DALI server (http://ekhidna2. biocenter. helsinki. fi/dali/ (Holm and Laakso, 2016) , the three highest scored structures correspond to two LDHs and one MalDH: Metallosphaera sedula MalDH (M. sed MalDH, Z=37. 6, PDB ID: 6IHD,  (Lee et al. , 2019) , Plasmodium vivax LDH (Z=36. 8, PDB ID: 5HRU,  (Choi and Ban, 2016)  and Lactobacillus casei LDH (Z=36. 2, PDB ID: 6J9T, http://dx. doi. org/10. 2210/pdb6J9T/pdb).
In the following, the structural properties of I. isl MaDH are compared with homologs. These homologs were chosen on the basis of the fact that their biochemical properties have been established and their crystal structures also contain a tetramer in the asymmetric unit. Structures of an allosteric LDH from Thermus thermophilus solved in both inactive and active states (T. the LDH, accession codes 2V6M and 2V7P) and the Chlorobaculum tepidum MalDH (C. tep MalDH, accession code 1GUZ) obey the previous criteria and were chosen as reference models for canonical LDH (in the two states) and MalDH respectively. Because of its strong similarity with I. isl MalDH, the enzyme from the crenarchaeon M. sedula was also taken into account. As expected, the overall architecture of I. isl MaDH monomer is similar to all MalDHs and LDHs previously reported (Fig.
3b ). However, the superposition reveals a noticeable difference. The α1G/α2G helix of each monomer is straight instead of kinked as observed in structures of homologs (Fig. 3c ) with the exception of Haloarcula marismortui and Haloferax volcanii MalDHs structures (with respective accession codes 4JCO and 4BGU). In spite of its closest similarity with I. isl MalDH, M. sed MalDH has a kinked helix (not shown). At a first glance, the quaternary assembly of I. isl MalDH contrasts to what has been observed with canonical tetrameric MalDHs and LDHs. This can be clearly observed thanks to a PyMOL superposition of the respective tetramers (Fig. 3d ). The view through the bottom of the Q axis shows that AB-like dimer of T. the LDH and C. tep MalDH have almost the same arrangement. In contrast, when superimposed on other structures, the AB-like dimer of I.
Comparison of the I. isl MalDH quaternary structure with homologs. To go further in theses structural comparisons, other MalDH and LDH structures were superposed on I. isl MalDH using the program THESEUS  (Theobald and Wuttke, 2006). RMSD This quaternary structure corresponds to a new relative association of two active dimers leading to a more compact tetrameric assembly. Such a profound structural changes has rarely been described for closely related enzymes and mainly concerns those that display allosteric behavior  (Singh et al. , 2015; Barciszewski et al. , 2016; Jiao et al. , 2017). The I. isl MalDH peculiar quaternary geometry relies on contact reorganization between subunits. An analysis of the interfaces of I. isl MalDH, C. tep MalDH and T. the LDH was made using PISA qt-interface (from the CCP4 suite  (Winn et al.
Enzymatic assay and protein determination The activity of I. isl MalDH for reduction of oxaloacetate to malate was carried out at 70°C in 500 µL of 50 mM phosphate buffer pH 7. 0 containing 50 mM NaCl. Assays using pyruvate as substrate were done in 500 µL of 2-(N-morpholino) ethane sulfonic acid (MES) pH 6. 0 and supplemented with 50 mM NaCl. The reaction was monitored spectrophotometrically at 340 nm by following the oxidation of NADH (0. 5mM) on a Jasco 540. To record the enzymatic profile of I. isl MalDH, various substrate concentrations were tested. The data were analyzed using Michaelis-Menten or allosteric sigmoidal equations in GraphPad Prism version 7. 03. The protein concentration was estimated from the absorbance at 280 nm using a nanodrop Thermofischer. The server https://web. expasy.
org/protparam/ (Gasteiger et al. , 2005)  was used to compute a molecular weight of 33. 5 and a theoretical extinction coefficient of 21430 M -1 cm -1. 4. 5. Crystallization and structure determination I. isl MalDH was crystallized by vapor diffusion using the sitting drop method at 293 K (HTXLab, CRIMS©). The Tb-Xo4 crystallophore™  (Engilberge et al. , 2017 , Engilberge et al. , 2018)  was added to the 4 mg ml -1 protein solution at a 10 mM final concentration. The crystals were grown in 2 days by mixing 1 µL of the protein solution with 1 µL of 10% PEG 6000 and 100 mM Hepes at pH 7 reservoir solution. No supplementary cryoprotection was needed. The crystals were harvested directly to the drop and flash-frozen in liquid nitrogen. I. isl MalDH diffraction data were collected on the Proxima-1 beamline at the synchrotron radiation source SOLEIL (Gif-sur-Yvette, France).
Datasets were indexed and integrated using XDS  (Kabsch, 2010). Scaling, density modification and molecular replacement were done with the CPP4 suite using SCALA  (Evans, 2006) , DM  (Cowtan, 1999)  and PHASER  (McCoy et al. , 2007). The model used for molecular replacement was the structure of Haloferax volcanii MalDH (RCSB PDB accession code: 4BGU). The structure was refined with multiple cycles of manual building using COOT  (Emsley et al. , 2010 ) and of refinement using phenix. refine (PHENIX suite,  Afonine et al. , 2012)  and BUSTER (https://www. globalphasing. com/buster). The NCS option was not used to avoid symmetric bias. The TLSMD server (http://skuld. bmsc. washington. edu/~tlsmd/ (Painter and Merritt, 2005; Painter and Merritt, 2006)  was used to generate TLS groups. Model quality was validated with MolProbity (http://molprobity.
SAXS experiments SAXS experiments were performed at the European Synchrotron Radiation Facility (ESRF) on the beamLine BM29 "BioSAXS" using an X-ray wavelength of 1. 25 Å and a sample distance from the detector of 2. 85 m. I. isl MalDH data were collected at 0. 39, 1. 68, 4. 45, 6. 1 and 9. 31 mg ml -1 in the scattering vector range from 0. 003 to 0. 494 Å -1. Buffer solution scattering curves were recorded before and after each protein sample. To prevent radiation damage, each measurement has been made 10 times and then averaged. Sample scattering curves were obtained after subtraction of the averaged buffer signals using standard protocols with PRIMUS  (Konarev et al. , 2003). The chimera scattering curve, used for the rest of the analysis, was created from a low (0. 39 mg ml -1 ) and a high concentration (9.
Primary sequence alignment, structural superpositions and representations Primary sequences were aligned using MUSCLE  (Edgar, 2004). To perform homogeneous comparisons between PDB files, chains were renamed according to MalDH and LDH standards, as monomers A, B, C and D, respectively located at upper left, upper right, down right and down left positions. Structural superpositions of the I. isl MalDH crystal structure onto canonical MalDH and LDH structures were made using THESEUS  (Theobald and Wuttke, 2008; Theobald and Steindel, 2012)  The MalDH of Chlorobaculum tepidum (PDB ID: 1GUZ,  (Dalhus et al. , 2002) ), R-state (PDB ID: 2V6M) and T-state (PDB ID: 2V7P) of the LDH of Thermus thermophilus  (Coquelle et al. , 2007)  were selected. These model structures were chosen depending on their high resolution and tetrameric fold.
, numerous Archaeal genome sequences of species from the Crenarchaeota phylum were deposited in Data Bank. In this phylum, several species from the Thermoprotei class display a single gene sequence encoding putative MalDHs with stronger LDH sequence similarity compared to other LDH-like MalDHs, as it can be identified by Blast searches against Protein Data Bases. Consequently, MalDHs from the Thermoprotei class provide an appropriate reservoir of new MalDH/LDH homologs for investigating fundamentals of molecular evolution mechanisms within the whole super family. In the present work, biochemical and structural properties of the putative MalDH from Ignicoccus islandicus (I. isl MalDH) were determined, in order to shed light on the peculiar relationship between LDHs and MalDHs from Crenarchaeota.
Acknowledgements The authors acknowledge  Drs Céline Armanet-Brochier ,  Frank Gabel ,  Jacques Coves  and for helpful discussions and  Giuseppe Zaccai  for a critical reading of the manuscript. The authors also acknowledge funding by  Agence Nationale de la Recherche (ANR)  grant to DM (program AlloAnc  ANR-16-CE11-0011 ) and to  EG (program Ln23   ANR-13-BS07-0007-01 ). This work used the platforms of the Grenoble Instruct center (ISBG;  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology (PSB).
From the multiple search dates, the cadence of images taken per date, and multiple search methods-including visual inspection and use of the asteroid-hunting Catalina Sky Survey automated moving object detection software  19  -we estimated that our detection efficiency would be approximately 99% for satellites 10 cm and larger. Orbiting satellites would spend some fraction of their orbits in front of or behind the asteroid. The multiple dates and 5-h observing windows per date ensured that orbiting objects were detectable. We conducted our search for natural satellites using PolyCam (Fig. 1 ). Initially, when the spacecraft was at a range of 3100 km, the search was over an area of 60 km radius when our observations were sensitive to 0. 5 m or larger satellites. Later, as the spacecraft got closer, satellites as small as 8 cm would have been bright enough to be imaged within 18 km of Bennu.
We detected none at this sensitivity level. Source data are provided in Supplementary Table  The OSIRIS-REx lightcurves have a different shape than the ground-based lightcurves, with four peaks instead of three, due to the Approach phase data being taken at much lower solar phase angle (4 to 18 degrees) than any of the earlier telescopic data (60 to 71 degrees). Bennu's rotation rate has accelerated since 1999, but previous work could not distinguish between continuous spin-up due to YORP and a step change in rotation rate due to a change in moment of inertia  34. Here we confirm that the acceleration has continued to the present day and is most likely due to the YORP effect. We derive an acceleration of 3. 63 ± 0. 52 x 10-6 degrees day-2, with a rotation rate of 2011. 1697 ± 0. 0011 degrees day-1, and a period of 4.
The 11 and 12 September dates were chosen because Bennu's apparent position in J2000 celestial coordinates placed it in a part of the Milky Way that is relatively less dense in stars. From the beginning of the Approach phase to the time of the spacecraft's second asteroid approach maneuver (15 October), Bennu traversed a very dense part of the Milky Way as seen from the spacecraft. Although none of the dates were optimal, 11 and 12 September were the best available while also maximizing the region of space around Bennu that was searched for dust. Modeling of possible dust trails for Bennu. The model of ref. 6  was implemented to model potential dust trails from Bennu. We use an adapted version of the code originally developed by Jean-Baptiste Vincent  37. The Vincent version of the model uses numerical integration to track the position and velocity of a dust particle that is ejected from the surface of the parent body.
OCAMS data Linear fit for all OCAMS data Linear fit for OCAMS data < 8°F ig. 3 Phase function observations and models for Bennu. Over-plotted are the model fits to the new data: a Lommel-Seeliger model and a revised H,G 12 model. The ground-based measurements are published in  18. We originally converted the ground-based measurements to the v-band magnitude scale, plotted with 1-sigma photometric error bars. The 1-sigma photometric error bars for OCAMS data are less than or equal to the size of the data points. Inset, upper right: The OSIRIS-REx measurements at low phase angles (0 to 12 degrees). We plot this subset of the data to illustrate the small opposition effect present for asteroid Bennu. The small opposition effect is consistent with what we would expect for low-albedo, carbonaceous bodies.
The red line is the linear fit to observations made at less than 7. 5 degrees phase angle. The blue line is the linear fit to all OSIRIS-REx data points at greater than 7. 5 degrees phase angle. Source data are provided in Supplementary Tables  2  and 3  Centaur population. The goal of this method is to estimate the total mass of a possible coma from light measured using photometry of an annulus between two circular apertures. We also estimate the approximate time that dust would remain in the given annulus. Dividing these two quantities provides us with a mass loss rate. A median co-add of images from the Dust Search campaign was created, and then two photometry measurements of Bennu at radii of 20 and 30 arcseconds were measured. The IRAF phot package was used to determine instrumental magnitude of these apertures.
Each post-30 October search consisted of a 3 × 3 MapCam mosaic and a single PolyCam field centered on Bennu. The 30 and 31 October PolyCam fields were not used for satellite searching as Bennu was located near the edge or outside the small PolyCam field-of-view due to the greater navigational uncertainty after the third asteroid approach maneuver. Three different methods were utilized to search for satellites within the OCAMS images. The first involved manually blinking the 15 or 30 images taken per field per visit for moving objects. The second combined, or stack and shifted, all of the images taken during a visit on the motion of Bennu. The combined images minimized the signal of background stars while enhancing the brightness of objects moving at the rate of Bennu. The third search method used the moving object detection software of the Catalina Sky Survey (CSS) to automatically detect satellites  19.
The CSS software was used on 5 images at a time. Due to the large number (15-30) images taken per visit, up to 6 different combinations of the images were run through the CSS software enabling multiple opportunities for detection. The software approach was not used on 23-25 October due to the slow apparent motion of Bennu and any satellites relative to the background stars. It was also not applicable on 30-31 October and 10-11 November due to the short exposure times used and the low number of detectable background stars. The sensitivity and efficiency of the satellite search was improved by conducting a search for Earth Trojan Asteroids (ETAs) during the outbound cruise phase in February 2017  43, 44  By exercising the entire moving object detection process, lessons learned during the ETAs search resulted in changes to detection software, number and cadence of observations, exposure times, and the use of both PolyCam and MapCam.