dataesr/scientific-paragraphs-categorization huggingface dataset:
Results E6 PBMs show parallel binding profiles to 14-3-3 isoforms. Among all 225 HPV E6 proteins curated in the PaVE database (https://pave. niaid. nih. gov/, Accessed December 9, 2020), 31 E6 proteins from mucosal α-genera HPV possess a C-terminal PBM with the class 1 consensus (X(S/T)X(L/V/I/C)-COOH, where X is any amino acid residue  30, 31  ). E6 PBMs are phosphorylatable by protein kinases at their conserved antepenultimate S/T residue  15, 16, 32. This phosphosite is preceded by arginine residues in most of the HPV E6 PBM sequences with recognizable basophilic kinase substrate consensus motifs, R(X/R)X(S/T) and RXRXX(S/T)  33, 34. The E6 PBMs can be classified in three subgroups: subgroups 1 and 2 prone to phosphorylation by the basophilic kinases and orphan subgroup 3 with a less predictable phosphorylation propensity (Supplementary Fig.
The domains of the E6 protein are shown by green (E6N) and beige (E6C) colors, the C-terminal tail containing the phosphorylatable residue (red circle) is cyan. The positions are numbered above the sequences, according to conventional PBM numbering, with the phosphorylatable antepenultimate residue (position -2) indicated by red. b Affinities of four selected HPV E6 phospho-PBMs, p35E6 mutant variants, and RSK1 phosphopeptides toward the seven human 14-3-3 isoforms as determined by fluorescence polarization using FITC-labeled HSPB6 phosphopeptide as a tracer. Apparent K D values determined from competitive FP experiments are presented. The heatmap representation of the data on b shows the affinity trends in the interaction profiles between 14-3-3 isoforms and four HPV E6 phospho-PBMs from strongest (red) to weakest (white).
4c ). Furthermore, the average maximal K D ratio between the strongest-binding and the weakest-binding 14-3-3 in the literature is around 12-fold, like in our present work (~11-fold). Moving further, we wondered whether the observed trends would be conserved at a full proteome-wide scale. Recently, a massive parallel affinity-purification approach coupled to massspectrometry (AP-MS) was applied to decipher the complexomes of more than 10,000 recombinantly expressed bait proteins in two orthogonal cell lines  29. We retrieved from the BioPlex 3 database (a compendium of the abovementioned complexome study) the numbers of detected interaction partners for each 14-3-3 isoform (Fig. 4d-g ). In total, 547 unique proteins were detected as an interaction partner of at least a single 14-3-3 isoform.
This allowed us to distinguish, among the 14-3-3 binders, those identified as baits retaining 14-3-3 preys, from those identified as preys retained by 14-3-3 baits. In both situations, the linear correlation of the numbers of 14-3-3 binders with the relative phosphopeptide-binding affinity scale of 14-3-3 proteins remained very strong (R 2 = 0. 96 and R 2 = 0. 90, respectively) (Supplementary Fig  7a). In further support of these interrelations, the number of 14-3-3 prey-binding baits also indicated correlation with the affinity trend of 14-3-3 isoforms when using data from a recent independent study (https://sec-explorer. shinyapps. io/Kinome_ interactions/ and  49  ) that used AP-MS to uncover the interactions of more than 300 protein kinases (R 2 = 0. 64) (Supplementary Fig. 7a ). The BioPlex database  29  also contains all peptide-spectrum matches (PSM) values for all the preys retained by each and every bait.
According to the integrated whole human body dataset of the PAXdb (https://paxdb. org and 3 ), 14-3-3ε is the 48th most abundant human protein (2479 ppm) and 14-3-3ζ is the 72nd (1680 ppm) out of 19,949 proteins. Considered as a whole, the cumulated seven 14-3-3 isoforms even rank within the top 20 (i. e. , top 0. 1%) most abundant human proteins. However, 14-3-3 isoforms are not uniformly distributed across tissues. Each human cell type displays a specific distribution of the 14-3-3 family (Fig. 5a ). We took advantage of the quantified hierarchized affinity profile of 14-3-3 isoforms to build a predictor tool that estimates the fraction of a given phosphoprotein that is engaged with each distinct 14-3-3 isoform (Supplementary Data File 2). As an input, the predictor requires (i) the K D of that phosphoprotein for at least one 14-3-3 isoform, and (ii) the cellular concentrations of the seven 14-3-3 isoforms and of the phosphoprotein of interest.
The concentrations of a given protein species in a given cell type can be roughly estimated from protein abundance databases (such as PAXdb 3 ), by using a simple conversion rule (see Methods). We used this approach to predict the proportions of each 14-3-3 isoform among the overall 14-3-3/phosphoprotein complexes Fig. 4  Hierarchized target binding by 14-3-3 isoforms is a general trend. a Affinity maps of 14-3-3 interactions based on experimentally determined dissociation constants against the 14-3-3ome, as obtained in this study (white background) and in others  40, [44] [45] [46] [47] [48]  (gray background). The color scale is either based on affinity values or on K D ratios. √ denotes affinities weaker than the limit of quantitation of the experimental assays. b Same map as in a, normalized to the strongest 14-3-3-binding motif.
An average 34,000-fold K D ratio is observed between the strongest and weakest 14-3-3-binding peptide. c Same map as in a, normalized to the strongest phosphopeptide-binding 14-3-3 isoform. Note that all peptides follow very similar affinity trends between the different 14-3-3 isoforms, with an average 12-fold K D ratio between the strongest and weakest-binding 14-3-3 isoform. d Number of unique partners detected according to the BioPlex database  29  for each 14-3-3 isoform, taken individually (left) or grouped in three subsets (right) following their relative affinity trends (strong, intermediate, and weak binders). e Number of 14-3-3 partners in BioPlex, which bound to 1, 2, 3, 4, 5, 6, or all 7 isoforms, respectively. Within each bar, the proportion of partners that bound to each individual isoform is indicated (same isoform color code as in d).
f Venn diagram showing repartition of the 14-3-3 partners from BioPlex among the strong, medium, and weak phosphopeptide-binding subsets, defined as in d. g Correlation between the number of binders of 14-3-3 isoforms, according to BioPlex, and ΔΔG av between the strongest phosphopeptide-binder, 14-3-3γ, and all individual isoforms (same color code as in d). ΔΔG values were calculated from the average K D ratios from c. h The average amounts of prey proteins from BioPlex (normalized to the amount captured using 14-3-3γ) that interact with at least five different 14-3-3 baits, deduced from their PSM values, also show a correlation with the ΔΔG av values of the same proteins. i Correlation of the sequence identity of human 14-3-3 isoforms relative to 14-3-3γ with the ΔΔG av values from g. Source data are provided as a Source Data file.
formed in various tissues, including uterus, rectum, and oral cavity, which are all susceptible to hrm-HPV infection, as well as in five other organs (esophagus, skin, lung, brain, and heart) (Fig. 5b  and Supplementary Fig. 9 ). Conversely, the free fraction of each phosphoprotein depends on absolute affinity constants. HPV-positive cell lines have been estimated to produce an average of ~1 ng of E6 per 10 6 cells, corresponding to an approximate intracellular concentration of 25 nM  50. In a situation where the E6 PBM would be fully phosphorylated, we can estimate, using the predictor, that 87%, 35%, 14%, or 5% of phosphorylated 33E6, 18E6, 16E6 and 35E6, respectively, would be engaged in 14-3-3 complexes in cells containing the average 14-3-3 concentrations found in human cells (estimated from integrated human data in PAXdb database 3 ) (Supplementary Fig.
1 ). In the literature, interactions of phosphorylated peptides with 14-3-3 even cover a wider ~40,000-fold affinity range, from low nanomolar to low millimolar (Fig. 4 ). As shown in the present work, very modest sequence variations of a phosphopeptide can be sufficient to alter its unbound and/or bound states in a way that greatly impacts binding affinity (Fig. 2c ). Similar principles may govern 14-3-3binding affinity variations of many other phosphopeptides. Conversely, the seven 14-3-3 isoforms bound each E6 phosphopeptide following a conserved hierarchized profile, with an approximate 11-fold K D ratio between the strongest-binding and the weakest-binding 14-3-3 isoform. Remarkably, 14-3-3 proteins obey the same hierarchy when binding to most of their targets, as supported by our own data on RSK1 and HSPB6 peptides, by our literature curation  40, [44] [45] [46] [47] [48]  , and by the unbiased proteome-wide complexome datasets such as the BioPlex 3 database (https:// bioplex.
The concentration of host proteins can be inferred from the protein abundance databases such as PAXdb (https://pax-db. org and 3 ), whereas the affinity to a 14-3-3 isoform can easily be obtained using state-of-the-art quantitative in vitro binding assays. While 14-3-3 proteins predominantly exist as dimers, the predictor deals with concentrations of 14-3-3 isoform monomers. The calculation assumes that the affinity of each monomer molecule toward a single phospholigand corresponds to the affinity measured for homodimers and is not influenced by the nature of the neighbor monomer, be it the same isoform (homodimeric species) or of another isoform (heterodimeric species). This assumption is plausible, considering the very high conservation of the amphipathic grooves of 14-3-3 proteins (Supplementary Fig.
8 ), responsible for ligand binding and facing each other in the dimeric structures. Anyhow, our Fig. 5  Cellular 14-3-3/phosphotarget complexomes. a Abundance of the seven 14-3-3 isoforms across different human tissues and in the whole human organism, according to the PAXdb database (https://pax-db. org and 3 ). Colors correspond to the protein abundances, according to the scale provided on the right. b Predicted proportions of 14-3-3-bound phosphoproteins that would be engaged with each individual isoform in different tissues, assuming that the majority of 14-3-3 molecules are available for interaction (same color code as in Fig. 4d ). Source data are provided as a Source Data file. predictor should be mainly intended as a rough trend estimator to stimulate thinking and explore hypotheses, rather than an accurate descriptor of the actual precise proportions of 14-3-3 complexes in cells.
Then, this mixture was used for creating a dilution series of the unlabeled competitor (i. e. , the studied peptides) and the measurement was carried out identically as in the direct experiment. All FP experiments were done in triplicates. Analysis of FP experiments were carried out using ProFit, an in-house developed, Python-based fitting program  63. ProFit utilizes a Monte Carlo approach to take into account experimental variability. It generates hundreds of simulated datasets, based on the experimental data variance and fits direct and competitive measurements in pairs. The experimental polarization window is first determined in the direct experiment, then this is either used as a fixed restraint in competitive fits or as a reference value to validate the result of unrestrained fits. In cases when restrained fit was necessary, and where we observed a slight increase in the base polarization (10-15 mP) in competitive fitting with other competitors, we used this modified window as a restraint.
The reported affinities and their standard deviations are the averages or standard deviations of 250-500 independent fits of simulated datasets. The dissociation constant of the direct and competitive FP experiment was obtained by fitting the measured data with quadratic and competitive equation, respectively  63, 64. ΔG values at 295 K were calculated using the equation: ΔG ¼ ÀRT Ã ln K D ð Þ ð1Þ ΔΔG av values were obtained by calculating the average and the standard deviation of all obtained individual ΔΔG values (between different motifs or different proteins), excluding cases when K D > 300 μM. All binding data and the obtained fits are provided as Supplementary Data File 1. Crystallization and structure determination. Crystallization conditions were screened using commercially available and in-house developed kits (Qiagen, Hampton Research, Emerald Biosystems) by the sitting-drop vapor-diffusion method in 96-well MRC 2-drop plates (SWISSCI, Neuheim, Switzerland), using a Mosquito robot (TTP Labtech, Cambridge, UK) at 4 °C.
The optimized condition of the crystals consisted of 19% polyethylene glycol 4000, 0. 1 M cacodylate buffered at pH 5. 5. For soaking, crystals were transferred to a mother-liquor solution containing (saturated, partially precipitated) 5 mM FSC and crystals were harvested after an 18-h incubation period. All crystals were flash-cooled in a cryoprotectant solution containing 20% glycerol and stored in liquid nitrogen. X-ray diffraction data were collected at the Synchrotron Swiss Light Source (Switzerland) on the X06DA (PXIII) beamline and processed with the program XDS  65. The crystal structure was solved by molecular replacement with a highresolution crystal structure of 14-3-3ζ (PDB ID 2O02) using Phaser 66 and structure refinement was carried out with PHENIX  67. TLS refinement was applied during the refinement.
The crystallographic parameters and the statistics of data collection and refinement are shown in Table  1. Predictions of proportions of 14-3-3 isoform complexes. We built a simple predictor tool that can be run using Microsoft Excel (Supplementary Data File 2). As an input, the predictor requires (i) the K D of binding of the phosphoprotein of interest to at least one 14-3-3 isoform, and (ii) the cellular concentrations of the seven 14-3-3 isoforms and of the phosphoprotein of interest. As an output, the predictor estimates the fraction of phosphoprotein that is engaged with each distinct 14-3-3 isoform. From the provided K D value(s), the predictor derives all K D values for the remaining 14-3-3 isoforms, using the average relative affinity ratios described in our results. The cellular protein concentrations required by the predictor can either be determined experimentally or fixed arbitrarily to explore hypotheses.
In the present work, we used integrated protein abundance data from the PAXdb database  3. In this database, abundance of a given protein is expressed as the ppm fraction of the number of molecules of that protein species relative to the cumulated number of all molecules of all protein species detected in the sample  68. For instance, if the abundance of a protein species Prot n (Ab Protn ) is 1000 ppm, this means that out of a total one million  (10 6  ) counted proteins, one thousand (10 3 ) correspond to the protein species of interest. Furthermore, the total intracellular protein concentration Prot Tot has been estimated to be around 3 mM  69. Therefore, for any protein Prot n of interest, one can use the ppm abundance value, Ab Protn , to roughly estimate the cellular molar concentration of that protein (Prot n ) using Eq.
Acknowledgements We would like to thank  Prof. Lawrence Banks  for the shared plasmids,  Prof. Alexey Babakov  for the provided fusicoccin preparations,  Dr. Yaroslav Faletrov  for fusicoccin identity verification, and  Dr. Ed Huttlin  for guidance on the BioPlex database. N. N. S. is grateful to the  Russian Science Foundation  for the grant no. 19-74-10031  covering studies of 14-3-3 proteins. Protein purification and characterization was partially done in the framework of the Program of the  Russian Ministry of Science and Higher Education  (K. V. T. and N. N. S. ). We thank the support of the  Swiss Light Source synchrotron (P. Scherrer Institute, Villigen, Switzerland ) and the help of the beam-scientist at the PXIII beamline. The work was also supported by the Ligue contre le cancer (équipe labellisée 2015 to G.
Data availability All data supporting the findings of this study are available within the paper and its supplementary information files. Protein abundance data were taken from PAXdb (https://pax-db. org), HPV types were analyzed using the PaVE database (https://pave. niaid. nih. gov/), protein-protein interactions data were retrieved from the BioPlex 3. 0 (https://bioplex. hms. harvard. edu) and kinome interactome databases (https://secexplorer. shinyapps. io/Kinome_interactions/), and protein sequences and parameters were taken from Uniprot (https://www. uniprot. org; Uniprot IDs P31947, P63104). The refined models and the structure factor amplitudes have been deposited in the PDB with the accession codes 6ZFD (14-3-3ζ/18E6 complex; https://doi. org/10. 2210/pdb6ZFD/ pdb) and 6ZFG (14-3-3ζ/18E6/FSC complex; https://doi.
83 0. 71 Ramachandran 99. 36 98. 51 favored (%) Ramachandran 0. 64 1. 49 allowed (%) Ramachandran 0 0 outliers (%) Rotamer outliers (%) 1. 65 2. 13 Clashscore 6. 68 3. 17 Average B-factor 33. 39 29. 6 Macromolecules 32. 42 28. 49 Ligands 62. 93 32. 67 Solvent 41. 20 38. 11 Number of TLS groups 15 11 PDB ID 6ZFD 6ZFG.
Simulation We validate and characterized ONN with simulation software tools using a testbench before being implemented on FPGA. We use a testbench on the software Xilinx's Vivado Design Suite 2018. 2. We also perform post-place&route simulations to characterize ONN designs following Vivado's default strategies for synthesis and implementation. We set the target device to the Xilinx 7-series FPGA, the XC7Z020-1CLG400C since it is used for implementation afterward. We carry out simulations with a 5 × 3 ONN and a 10 × 6 ONN configured for pattern recognition using both Hebbian and Storkey learning rules. We configure the 5 × 3 ONN design with three stored patterns with standard 5 × 3 bitmap representations of digits 0, 1, and 2 (see Figure  5A ). Each pixel of the image corresponds to a neuron.
The image streaming from the camera to the screen comes from a Digilent Github project named Zybo-Z7-Pcam-5c (Digilent, 2020), compatible with the Xilinx's Vivado software 2018. 2. We embed the digital ONN inside the image treatment flow. To do so, we convert the camera's image in greyscale, binarize it in black/white pixels, and scale it down to a 10 × 6 pixels image taking the primary color between black/white pixels. We rescale ONN output into a 1280x720 pixels image to display it on the screen. Both rescaling steps use the Vivado HLS tool from Xilinx. We also use development board processor resources to configure the camera. Based on our characterization results, we can parametrize the ONN to a certain training configuration. We choose to train the 10 × 6 ONN to recognize five digits, from 0 to 4, with the Hebbian learning rule.
, 2015; Berthelot et al. , 2018). These paradoxical observations warrant further exploration, to better understand the determinants of gene expression robustness in the presence of rapidly evolving regulatory landscapes. However, so far few attempts have been made to directly connect the evolution of gene expression to the evolution of regulatory mechanisms, at a genome-wide scale  (Berthelot et al. , 2018; Wong et al. , 2017). These studies proposed that the presence of complex regulatory landscapes, involving numerous expression enhancers with potentially redundant roles, is the key driver of the robustness of gene expression levels  (Berthelot et al. 2018). that restriction maps are identical across all samples within a species (Methods). The dataset included interactions for 19,389 baited fragments for human and 21,858 for mouse.
We focused on intra-chromosome (cis) interactions occurring at a linear genomic distance comprised between 25 kb and 2 Mb, and involving a baited and an un-baited restriction fragment (Methods). To evaluate the significance of the observations obtained with this data, we simulated interactions that reproduce the distribution of distances between baited fragments and contacted fragments, as well as the numbers of contacts per baited region, for each sample (Fig. 1A-C , Supplemental Fig. S1 , Methods). The simulated interactions involve the same set of baited fragments and are constructed on the same restriction map as the PCHi-C data (Fig. 1A , Methods). We designed this simulated dataset to account for the effect of the genomic distance between promoters and contacted regions, which is traditionally the main criterion for inferring regulatory interactions, in addition to being the main factor driving the likelihood of observing a chromatin contact  (Cairns et al.
, 2016). However, our simulations cannot reproduce other characteristics of the PCHi-C data, such as the number of contacts per un-baited genomic fragment (Supplemental Fig. S1 ) or the number of contacts per baited region across all samples (Supplemental Fig. S2 ). The simulated interaction data cover a larger fraction of the genome than the PCHi-C data, for which contacted regions are more often shared among baits and among samples (Supplemental Fig. S2 , Supplemental Text). Moreover, restriction fragments contacted in PCHi-C data may differ from those included in the simulations in terms of sequence uniqueness or mappability, because interactions can only be detected if sequencing reads can be correctly aligned to restriction fragments. To minimize this possible source of discrepancy between the PCHi-C and the simulated data, we filtered interactions to keep only those involving restriction fragments with a sufficient theoretical mappable fraction and PCHi-C read coverage (Methods, Supplemental Text).
, 2003) ), even where SHH expression is not detected (Fig. 1F , Supplemental Fig. S5 ). This result confirms previous reports that promoter-centered interactions can be observed in PCHi-C data even in the absence of gene expression  (Schoenfelder et al. , 2015). Thus, PCHi-C data provide a broad overview of promoter-centered chromatin interactions, likely including pre-formed contacts, which precede gene activation  (de Laat and Duboule, 2013). These data thus extend beyond regulatory interactions that function exclusively in the sampled cell types. For interactions shared across cell types, differences between the human and mouse PCHi-C datasets are genuine between-species differences, rather than consequences of unequal cell type sampling. Thus, although similar cell types were not always available for human and mouse, we are confident that this dataset is suited for between-species comparisons.
Inference of cis-regulatory landscapes from PCHi-C chromatin contact maps The promoter-centered chromatin contacts defined with PCHi-C data are known to be enriched in regulatory interactions  (Schoenfelder et al. , 2015). We validated this observation by jointly analyzing PCHi-C contact maps and genome-wide enhancer prediction datasets (Methods). For all datasets, the average restriction fragment length covered by predicted enhancers is significantly higher in the PCHi-C data than in the simulated data (Fig. 2A , Wilcoxon rank sum test, p-value <10 -10 ). For example, for ENCODE data (N=408,738 enhancers), the average length fraction that is covered by enhancers is 3. 5% in the PCHi-C data, compared to 2. 4% in the simulated data. Moreover, the proportion of restriction fragments that overlap with at least one enhancer is significantly higher in PCHi-C data than in simulations.