dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein Identification and Quantification Mass spectrometry was analyzed as described before  (Millan-Oropeza et al. , 2017). Protein identification was performed with X!TandemPipeline C++ 0. 2. 24  (Langella et al. , 2017)  using X!Tandem algorithm 2  (version Alanine 2017. 02. 01) and the Streptomyces coelicolor A3(2) database obtained from UniProt 3  (17. 10. 2018). Protein cleavage sites were defined for trypsin, with a maximum of 1 missed cleavage site. Carboxyamidomethylation of cysteine residues and oxidation of methionine residues were set to "fixed" and "potential" modifications, respectively. Precursor mass tolerance and fragment mass tolerance were set to 10 ppm. Data filtering was achieved according to a peptide E-value <0. 01, protein log (E-value) < -4 and to a minimum of two identified peptides per protein.
Peptide and protein False Discovery Rates (FDR) were estimated at 0. 03 and 0. 17%, respectively. MS1 peaks were detected and aligned using MassChroQ 2. 2. 12  (Valot et al. , 2011). Relative quantification of protein abundances was performed using three complementary methods: spectral counting (SC) defined as the number of MS2 spectra assigned to a protein  (Liu et al. , 2004) , extracted ion chromatograms (XIC) defined as the sum of MS1 intensities of all peptides associated with a protein and peak counting (PC) defined as the number of MS1 chromatogram peaks (peptides) attributed to each protein. XIC method is suitable for detecting subtle differences in protein abundance based on specific peptide data while SC allows only to detect larger abundance variations including that of presence/absence, taking advantage of both specific and shared peptides in our study  (Blein-Nicolas et al.
, 2012; Blein-Nicolas and Zivy, 2016). PC is a complementary method that quantifies and compares the number of detected peptides for a given protein in the samples. Data post-processing and statistical analysis were performed by using the R package 4  MCQR 0. 4. 3. Different bioinformatic pipelines were applied as indicated. For SC it involved: i) removal of proteins having <5 spectra in all samples, and ii) removal of proteins with <1. 5 variation between strains. For XIC it included: i) removal of peptides with high retention time variation >20 s and peak width >200 s, ii) normalization of peptide intensities based on a reference sample, iii) removal of shared peptides, iv) removal of peptides with >5% of missing values in the whole experiment, v) peptides correlated to a reference peptide with a coefficient of correlation (r 2 < 0.
Heatmaps were constructed using hierarchical clustering based on Euclidean distances. MS data are available via ProteomeXchange  (Perez-Riverol et al. , 2019)  with the identifier PXD012672. In order to annotate the functions of proteins with detected abundance changes (adjusted p value <0. 01), we used BioCyc database 5  and the Gene Ontology Resource 6  along with literature searches. The bioinformatic tools used for proteomics analysis (X!TandemPipeline C++, MassChroQ, MCQR) are open and free resources available in the following repository: https://forgemia. inra. fr/pappso.
Proteomes of S. coelicolor A3(2) cpkO and cpkN Mutants Are Strongly Altered Growth conditions and timing optimal for CPK production in S. coelicolor A3(2) occurred in cultures of medium 79NG at 27 h (Figure  1B ). Based on the identified peptides (Supplementary Table  S4 ), a total of 2,899 proteins (36% of the theoretical proteome) were identified in the studied strains M145, cpkO (P193) and cpkN (P196) (Supplementary Table  S5 ). The distribution of protein identifications in the strains (Supplementary Table  S6 ) is shown in Figure  2A. During data post-processing one sample (M145 A) was excluded from data analysis because of its dubious LC-MS/MS results, observed in principal component analysis and in peptide retention time variation (not shown). The data treatment resulted in 1862 valid proteins quantified by XIC (extracted ion chromatograms), SC (spectral counting) and/or PC (peak counting) complementary methods.
ACKNOWLEDGMENTS We are thankful to  P. Dyson  for providing the cosmid  11B05. 1. g04. Proteomics analyses were performed on the PAPPSO platform (http://pappso. inra. fr),which is supported by  INRAE  (http://www. inrae. fr), the  Ile-de-France regional council  (https://www. iledefrance. fr/education-recherche), IBiSA  (https: //www. ibisa. net),and  CNRS  (http://www. cnrs. fr).
Acknowledgements We thank  The Cambridge Crystallographic Data Centre , the  Engineering and Physical Sciences Research Council (UK)  and  The University of Edinburgh  for studentship funding to MGR. This work has made use of the resources provided by the  Edinburgh Compute and Data Facility (ECDF)  (http:// www. ecdf. ed. ac. uk/). This work was also supported by the  University of Bordeaux  through the  PhD grant for ET  and the  CNRS.
Results To understand the effects of the physical parameters (e. g. , those involved in the image formation process) for recovering free-energy profiles with cryo-BIFE, we designed several control systems where the projections are generated synthetically following the ideas of Ref. 43. The first system consists of conformations of the Hsp90 chaperone representing a low-dimensional (1D-2D) conformational space. The analysis is then extended to more realistic ensembles from MD simulations. Lastly, we apply cryo-BIFE to experimental cryo-EM data. To this end, we chose raw images of TMEM16F, a membrane channel and lipid scramblase  44  available at the EMPIAR databank  45. Free-energy profile recovery over controlled datasets. Hsp90 chaperone. Hsp90 (a heat shock protein) is a chaperone involved in the folding process of several kinases, transcription factors, and steroid hormone receptors  46.
Real cryo-EM data: TMEM16F ion channel. TMEM16F is a membrane channel and lipid scramblase that is activated by calcium binding. In Ref. 44  , cryo-EM experiments using different Ca +2 conditions and mem- brane/detergent compositions were performed to resolve TMEM16F's Ca +2 bound and unbound states. The cryo-EM particles under different conditions are available at the EMPIAR  45. In this work, we focus on the EMPIAR dataset with around 1. 2 million particles that was used to generate the Ca +2 -bound state in digitonin (EMPIAR code 10278). Since around 13% of these particles are used to generate the final reconstruction (all other particles are classified out), we wanted to investigate (1) if there could be a small population of the Ca +2 -unbound state in this set, and (2) if a free-energy profile from the Ca +2 -bound to the Ca +2 -unbound states can be extracted.
Starting from the PDB structures (Fig. 6A ), steered MD simulations were used, which included a lipid membrane and explicit solvent (see the Methods), to generate a path connecting both states. The C α -RMSD of the nodes for both states is shown in Fig. 6B. We randomly selected around 15,000 particles from the entire set, not only those used for the final reconstruction. In Fig. 6C , the free energy along the path using the same cryo-BIFE setup as for the previous systems is shown. It was observed that both the Ca +2 -bound and the Ca +2 -unbound states correspond to metastable basins of the system. Because the cryo-EM data set was prepared with Ca +2 , it is expected that the Ca +2 -bound state corresponds to the lowest free-energy minimum. However, it is interesting that not all the particles belong to this state, and that the Ca +2 -unbound state also has metastability.
In BioEM round 2, a finer quaternion grid of 125 points was created around the ten best orientations (i. e. , with the highest probability) selected from BioEM round 1. In total, a 1250 quaternion grid were used for the second BioEM orientation round. For this round, the Gaussian prior for the defocus was centered at the synthetic/experimental value of each particle and its scale was 0. 3 µ m. This procedure is similar to that described in Refs. 47, 58 ; however, here we calculated BioEM rounds 1 and 2 independently for each node of the path. We used the BioEM code from Ref. 40  with CPU and GPU acceleration. For one node along with the path and 10000 particles of 128 × 128 size, BioEM round 1 takes ∼ 6 h on 24 CPU cores + 2 GPUs, and BioEM round 2 takes ∼ 3 h on 24 CPU cores. Recalling Eq.
We performed the R-hat diagnostic  59  , which compares the inter-chain variance to the variance within each chain to monitor convergence of the MCMC using the arviz package  60. R-hat values ≤ 1. 1 indicate convergence of the sampling. The MCMC code was written in Python3. 5. It was optimized with the Numba compiler, taking approximately 2 h on 24 CPU cores for I = 13,000 particles, M = 20 nodes, and R = 8 replicas each with N MC = 200,000 MCMC iterations.
Synthetic particles. We used a modification of the BioEM program  40  to generate the synthetic cryo-EM particles following similar ideas to those described in Ref. 43. Each image was created by coarse-graining the molecular configuration (e. g. one taken from an MD simulation) on the residue level. Each residue was represented as a sphere with a corresponding radius and number of electrons  39. The contrast transfer function (CTF) was modeled on top of the ideal image given a defocus, amplitude and B-factor (for details see the SI of Ref. 39  ). For the synthetic particles, the amplitude was 0. 1 and the B-factor was 1 Å. Gaussian noise was added on top of the CTF convoluted image. The standard deviation of the noise was determined (as in Ref. 43  ) using the SNR and variance of the image without noise (calculated within a circle of radius 40 pixels centered at the box center).
All synthetic images were 128 × 128 pixels, however, the pixel size varied for each system. Benchmark systems. Hsp90 system. The Hsp90 chaperone is a flexible protein involved in several biological processes related to protein folding  46. When bound to certain ligands, its conformational landscape can be approximated by two relative motions of its chains (A and B)  43. The Hsp90 dynamics was reduced to a 2D dimensional phase space, where both chains are rotated in mutual normal directions and perpendicular to the axis of symmetry. In this work, we first assessed conformations from just one degree of freedom (1D analysis), and then we assessed images from conformations belonging to the 2D conformational space (2D analysis). To generate the conformations for the first degree of freedom (1D case), we started from the closed state (PDB ID 2cg9  61  ), removed the ATP ligand and residues 1-11 to avoid overlapping crashes.
From each MD conformation, we generated a synthetic image with pixel size of 0. 3 Å and with uniformly distributed random orientations in SO(3), SNR in log 10 [0. 01, 0. 1] and defocus in [0. 1, 1. 0] µ m. Using the 5688 synthetic images and the same ten nodes of the path, we performed the cryo-BIFE analysis. TMEM16F: experimental cryo-EM data. Cryo-EM particles. The cryo-EM particles of the TMEM16F membrane channel used to generate the calcium bound state  44  from the EMPIAR dataset  45  with code EMPI-AR-10278 were used. See Ref. 44  , for information about the experimental conditions. The images were recorded with a pixel size of 1. 059 Å box size of 256 × 256 pixels, with defocus values within the interval [0. 5, 2. 7] µm. For this work, we randomly selected 15,000 images from this Ca +2 -bound (Digitonin_Ca) set.
Note that these images represent the entire set and not only those used for the final reconstruction. Since only 13% of the particles from the EMPIAR-10278 set are used to create the Ca +2 -bound reconstruction  44  , our hypothesis is that not all imaged particles belong to this state. Our aim was to extract a free-energy profile from the Ca +2 -bound to the Ca +2 -unbound states using only the cryo-EM particles from the Ca +2 -added set. Steered MD for creating the TMEM16F path. To generate the path, we used steered MD simulations from the Ca +2 -bound to the Ca +2 -unbound state. The simulations were performed as follows. We started from the Ca +2 -bound structure (PDB ID 6p46). Since the structure has atoms missing, we added these using the Swiss model webserver  68. We note that because some residues have to accommodate to fit the missing residues the full atom structure was not identical to the PDB.
Starting from the full atom model of 6p46, we added the membrane using CHARMM-GUI  69  , in a 3:1:1 ratio of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1-palmitoyl-2-oleoylsn-glycero-3-phosphoethanolamine (POPE), and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphol-serine (POPS), respectively. A box size of 16. 8076 × 16. 8076 × 17. 2012 nm was used with periodic bound- ary conditions and 122923 TIP3P water molecules were inserted. We used the GROMACS program  62  with the CHARMM36M force field  70. The temperature was controlled in the simulation with the Berendsen thermostat at 300 K, whereas the pressure was controlled with the Berendsen barostat at 1. 0 atm  71. The energy was then minimized using the steepest descent algorithm and stopped when the maximum force was ≤ 1000 kJ/mol nm.
We used the leapfrog algorithm to propagate the equations of motion. The long-range electrostatic interactions are calculated using a PME scheme with a 1. 2 nm cutoff. We performed two consecutive equilibrations, of 125 ps each, in an NVT ensemble with a time step of 1 fs. Then, we performed two equilibrations in an NPT ensemble, where the first was of 125 ps and time step of 1 fs, and the last was of 1. 5 ns, with a time step of 2 fs. For the equilibration in the NPT ensemble, the pressure coupling was of semi-isotropic type. The backbone atoms of the protein were restrained throughout the equilibration runs. After the MD equilibration, we performed steered MD simulations 72 using the GROMACS program 62 patched with the PLUMED 2. 5 library  73. The first target structure for the steered MD was the Ca +2 -unbound state (PDB ID 6p47).
Acknowledgements J. G-B. , S. O. and P. C. were supported by  MinCiencias ,  Ruta N, University of Antioquia, Colombia , and the  Max Planck Society, Germany. The  Flatiron Institute  is a division of the  Simons Foundation. The authors also acknowledge  Naomi Latorraca ,  Ron Dror  for the availability of the MD trajectories;  Cristian Rocha  for help setting up the TMEMF16F membrane; and  Johans Restrepo ,  Yifan Cheng ,  Ahmad Reza Mehdipour , and  Gerhard Hummer  for useful discussions.
As a result, cells incorporate the electroporated fluorescent nucleotides in replication forks that initiated during the aphidicolin arrest and any that are initiated immediately after release. However, the transfected nucleotides are rapidly depleted, preventing incorporation at later initiation sites  (Wilson et al. , 2016). In our primary experiment, 87% of cells were synchronized in mitosis and, after release from aphidicolin, 94% incorporated the fluorescent label, with 100% of labeled cells showing an earlyreplication pattern of replication foci (Table  S1 , Figures  S1A, B ), indicating a high degree of synchrony and <1% of contaminating asynchronous S phase cells. We collected a dataset of 728 Gb across 2. 9 million fibers ranging in length from 150 kb to 1. 2 Mb, with an average fiber length of 250 kb (Table  S1  and Figure S1C ).
Therefore, we combined the four experiments into one dataset of 11. 8 million fibers ranging from 150 kb to 2. 2 Mb in length, averaging 285 kb and constituting 1,109-fold coverage of the human genome (Table  S1 ). The replication-incorporation signal we detect is punctate in nature and uniform in intensity  (Figures 1A and S2A). From these observations, we conclude that these incorporation signals consist of individual fluorescent nucleotides. Furthermore, the distributions of signal intensity (Figure  S2A ) and inter-signal distances (Figure  S2B ) are consistent with sparse labeling by a pool of fluorescent nucleotides that is depleted by replication, as expected for a one-time bolus of nucleotides delivered by electroporation. The distribution of the signals is predicted by inefficient, random incorporation of fluorescent nucleotides at an initial rate of about 1/900 thymidines that is depleted with a half length of about 75 kb (Figure  S2B ).
Given the lengths of the replication tracks and the sparse nature of the signal within them, we estimate that we can localize initiation events with a resolution of about 15 kb (14. 2±0. 1 kb, see Methods for details). The fluorescent signals in our initial ORM dataset labels are enriched in replication initiation zones, as identified by replication timing peaks  (Chen et al. , 2011) , consistent with our prediction that they represent nucleotide incorporation near sites of replication initiation (Figure  1B ). To test whether the incorporation signals truly reflected initiation events, we modified our protocol to allow for a delay, ranging from 5' to 90', after aphidicolin release and before transfection (Table  S1 ). We predicted that this approach would result in a gap between two symmetric populations of incorporation signals surrounding the replication initiation sites.
In our combined 0' dataset, we observed 1,035,587 replication tracks. Because of the sparse labeling, 58% of our replication tracks are labeled with only one signal. To test whether these solo signals are sparsely labeled replication tracks or spurious background noise, we calculated the enrichment of solo signals at early replication sites. Solo signals are enriched at such sites, demonstrating that they, too, mark regions of replication initiation (Figure  S2C ) and that noise, if any, is very low with this method. For visualization, we used only tracks labeled with at least two signals, which contain an average of 6. 2±3. 8 signals and average 38. 4±25. 8 kb in length. For fibers on which we identify more than one track, the average distance between track centers is 111±78 kb (Figure  S2D ), consistent with previous measurements  (Cayrou et al.
in prep, personal communication, J. Ma). We observe 1. 0 million initiation events in our combined 0' dataset. From the average replication track length of 38. 4 kb, we infer that 46. 6 Gb of DNA is labeled, which is ~1. 5% of the 3. 36 Tb in the dataset. Therefore, we are labeling the DNA replicated in the first ~1. 5% of S phase and during that time, about 1000 initiation events occur per genome (1,035,587 initiation events observed in 1,109 genome equivalents).
Determination of Genome-Wide Replication Kinetics in Asynchronous Cells The experiments described above provide unprecedented single-molecule mapping of initiation events at the onset of S phase. However, most cell types are not amenable to such precise cell-cycle synchronization. To map replication kinetics in unperturbed cells, we labeled asynchronous HeLa cells and H9 human embryonic stem cells, and mapped replication incorporation tracks by ORM (Figure  2A ). We identified 412,115 replication tracks in two biologically-independent HeLa replicates totaling 1. 4 Tb of data and 299,595 tracks in one H9 dataset totaling 738 Gb of data (Table  S1 ). Using the same analysis as for our synchronous datasets, we infer a similar nucleotide-labeling frequency of 1/1025 and 1/850 thymidines, respectively.
The replication tracks average 37. 1±30. 0 kb in length in the HeLa data and 44. 0±38. 7 kb in length in the H9 data, which is comparable to the length of tracks in the synchronized data. Thus, forks released from aphidicolin arrest synthesize at about the same rate as untreated replication forks. The tracks are uniformly distributed across the genome, as predicted for asynchronous replication forks, with an average density of 1. 3±0. 5%. In particular, in contrast to our synchronous dataset, and as expected, we see no enrichment at replication timing peaks in early-or latereplicating regions (Figure  2B ). In order to use this data to infer replication kinetics, it is necessary to determine the polarity of each incorporation track, so that the direction of each mapped replication fork is known.
Genome-Wide Mapping of Early-Firing Human Initiation Zones To map the genome-wide distribution of early-firing human initiation sites, we analyzed the incorporation signal in our combined 0' dataset and identified 4,628 initiation zones, defined as peaks of replication signal density (Figure  3A , see Methods for details). The initiation zones are 40±13 kb in length (Figure  S2E ), comparable to initiation zones mapped by OK-seq  (Petryk et al. , 2016). The initiations zones cover about 6% of the genome, however, they contain only about 40% of the ORM signal. Much of the rest of the ORM signal is directly adjacent to defined initiation zones, but much is distributed elsewhere at lower density, particularly in late-replicating regions on the genome (Figure  3A ). Plotting the initiation efficiency of 50 kb genomic bins show that there is a continuous distribution of initiation efficiency between the higher-efficiency regions we define as initiation zones and lowerefficiency regions, with no obvious break between them (Figure  S2F ).
The median genome-wide inter-initiation-zone distance is 294 kb. However, since our dataset is biased towards early replicating initiation zones, we suspect that we underestimate the number of initiation zones in late-replicating parts of the genome. Consistent with that contention, limiting our analysis to the earliest quarter of the genome leads to a median inter-initiation-zone distance of 174 kb. The inter-initiation-zone distances in the other three quarters of the genome are 270, 362 and 319 kb, respectively. We suspect that the lower density of observed initiation zones in the later-replicating regions of the genome results from having less data rather than fewer initiation events in these regions (Figure  1G ). A major advantage of ORM is that it records the number of fibers that both have and do not have replication tracks, allowing for the calculation of the absolute efficiency with which replication initiates at each locus in the genome.
E) The estimated minimal number of initiation sites per initiation zone. The estimate was made by calculating minimum number of replication tracks in each initiation zone whose centers are more than 15 kb apart. Most of the IZs for which all track centers are with in 15 kb of each other are small and contain few replication tracks (Figure  S2B ). F) The distribution of signal across initiation zones. The ratio of signal frequency at the IZ center to the IZ boundary is plotted versus IZ length. This value is expected to decrease more quickly in IZs that predominantly have a single initiation site (Eq. 21) than if initiation is distributed across the IZ (Eq. 24, Supplemental Mathematical Methods). See Supplemental Mathematical Methods for details. G) The distribution of the inter-initiation distances in the combined 0' dataset measured as the distance from the middle of neighboring replications tracks for fibers that have multiple tracks.
Comparison of Different Origin Mapping Datasets We compared our optical replication mapping data to four published genome-wide HeLa replicationinitiation-mapping datasets: OK-seq  (Petryk et al. , 2016) , SNS-seq  (Picard et al. , 2014) , Ini-seq  (Langley et al. , 2016)  and Orc1 ChIP-seq  (Dellino et al. , 2013). By visual inspection, replication tracks appear to be enriched around the initiation zones identified by OK-seq (Figure  3A ). Across the genome, we see colocalization of OK-seq initiation zones and ORM signal. We see similar colocalization of ORM signal with Ini-seq signal, a cell-free initiation mapping approach, Orc1 ChIP-seq peaks, and, to a lesser extent, SNS-seq peaks, which maps initiation events by sequencing short nascent strands produced by replication initiation (Figure  3A ).