dataesr/scientific-paragraphs-categorization huggingface dataset:
While we focused on cases for which the ligands are known, we are confident that our EDES approach should also be valuable in the context of virtual screening, with the enhanced binding pocket sampling leading to a potentially higher number of interesting lead candidates. As a long-term goal, we aim to create a database of protein structures that should help in reducing the cost associated with the generation of the structures for ensemble docking runs, allowing for a single target virtual screening of thousands of compounds in a reasonable amount of time. The ensemble of targets could also be used to reposition existing drugs for new therapeutic uses, as recently suggested. 51 MATERIALS AND METHODS Standard MD Simulations. Standard all-atom MD simulations were carried out using the pmemd module of the AMBER16 99 molecular modeling software.
Topology files were created for each system using the LEaP module of AmberTools17 starting from the experimental structures available in the Protein Data Bank (PDB IDs 1JEJ  70  and 1JG6  70  for BGT and BGT-UDP, 1RTC  73  and 1BR5  74  for RIC and RIC-NEO, and 1GUD  78  and 1RPJ  78  for ABP and ABP-ALL, respectively). The ff14SB 100 and GAFF  101  force fields were used for proteins and ligands, respectively. Missing parameters for the latter were generated using the antechamber module of AmberTools17. In particular, atomic restrained electrostatic potential charges were derived after a structural optimization performed with Gaussian 09. 102 Each structure was solvated with the explicit TIP3P water model, and its net charge was neutralized with the required number of randomly placed K + or Cl -ions.
After equilibration, four production runs of 2. 5 μs each (for a total of 10 μs for each system) were performed for the apo systems, while a single 1 μs-long simulation was performed for each complex. A time step of 2 fs was used for preproduction runs, while equilibrium MD simulations were carried out with a time step of 4 fs in the NPT ensemble (using a MC barostat) after hydrogen mass repartitioning. 103 Coordinates from production trajectories were saved every 100 and 10 ps for MD apo and MD holo , respectively. Metadynamics Simulations. Bias-exchange well-tempered metadynamics simulations  57, 68, 69  were performed on the three apo proteins using the GROMACS 2016. 5 package  104  and the PLUMED 2. 3. 5 plugin. 105 The last conformation saved from the equilibration step from MD apo was used as the starting structure for each simulation.
Very similar definitions were found using the COACH-D Web server with the apo structures (Table  S2 ). The CVs were calculated by an in-house tcl script based on the VMD orient function. Namely, residues lining the binding site were split into two lists A and B according to the positions of the geometrical centers of their backbones on each of the two sides of the inertia plane, and the overall number of pseudocontacts N c between the two groups was calculated through the coordination keyword of PLUMED, which implements a switching function such as the following: ∑ ∑ = ∈ ∈ N s i j ij c A B where Ä Ç Å Å Å Å Å Å Å Å Å Å Å i k j j j j j y { z z z z z É Ö Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ä Ç Å Å Å Å Å Å Å Å Å Å Å i k j j j j j y { z z z z z É Ö Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ Ñ = - - s r r r r 1 1 ij ij n ij m 0 0 with r 0 = 8 Å, n = 6, and m = 12.
The resulting N c clusters were used as starting point to perform a second cluster analysis with the K-means method and requiring a total of N c clusters (with the maximum number of iterations set to 10 000). Despite not making any use of specific knowledge of the structure of the complexes, our informed strategy was able to generate a larger fraction of cluster structures displaying RMSD BS < 2 Å than that obtained from the standard application of K-means using randomly selected conformations as starting points (Figure  S5 ). In fact, this latter initialization strategy is considered one of the most unreliable ones on the basis of a comparison of several alternative algorithms on a range of diverse data sets. 108 We compared our clustering protocol to a more standard calculation based dRMSD at the binding site (using the hierarchical agglomerative method implemented in the cpptraj module of the AMBER package).
The scripts to divide the binding site residues according to the CIP scheme and perform the cluster analysis according to our ad hoc scheme are available at the webpage https://github. com/haddocking/EDES. Molecular Docking. Molecular docking calculations were performed with AutoDock4 82 and the HADDOCK Web server version 2. 2. 81, 109 This choice allowed our methodology to be validated against two programs differing in their search algorithms, scoring functions, and pose selection schemes. Both programs were first validated for redocking against experimental structures (Table  S8 ). Next, they were used to perform docking from the generated ensembles using default settings, apart from the following changes. In AutoDock4, we used the Lamarckian genetic algorithm (LGA) to perform a hybrid global-local search of the docking poses.
The grid density (spacing parameter changed from 0. 375 to 0. 25 Å) and number of energy evaluations (ga_num_evals increased by a factor of 10 from the default value) were both increased, with the purpose to avoid repeating each calculation several times to obtain converged results. For each set of structures, 500 rigid docking independent calculations were performed using an adaptive grid enclosing all of the residues belonging to the binding site. Next, the top poses (in total 500, one for each docking run) were clustered using the cpptraj module of AmberTools17 with a hierarchical agglomerative algorithm and a cutoff of 1. 5 Å for the RMSD distance matrix. In HADDOCK, a single docking run was performed per case, starting from the various ensembles of 500 conformations, with increased sampling (10000/400/400 models for rigid-body docking (it0 step), semiflexible refinement (it1 step), and final refinement in explicit solvent (wat step).
The weight of the intermolecular van der Waals energy for the initial rigid-body docking stage was increased to 1. 0 (from the default value of 0. 01), RMSD-based clustering was selected with a cutoff of 1 Å, and the docking was guided by ambiguous distance restraints defined for the residues of the binding site and the ligand. 110 In the rigid-body stage, the protein binding site residues were defined as "active", effectively drawing the ligand into the binding site without restraining its orientation. For the subsequent stages, the restraints were such that only the ligand was active, allowing it to explore the binding site better while maintaining at least one contact with its residues. Druggability Calculations. For each structure within the four ensembles (MD apo , MD holo , EDES 3w , and EDES 4w ) generated for the three proteins, we used the open-source pocket detection package f-pocket  84  to identify and characterize putative protein binding sites.
Results Design workflow for cell-free biosensors. Synthetic metabolic cascades have been used by the synthetic biology community for a wide range of applications, including production of biofuels, pharmaceuticals, and biomaterials  [15] [16] [17]. As such, there is a wide variety of well-characterized enzymes catalyzing various reactions transforming one molecule into another. Our framework harnesses this power by using metabolic enzymes as transducers to allow us to "plug in" a given enzyme into our characterized biosensor modules to detect a ligand with no known corresponding TF (Fig. 1a ). Specifically, the metabolic enzyme converts the undetectable molecule into one for which we have an existing TF-based genetic circuit (Fig. 1b ). We used the SensiPath webserver that we previously designed and validated in vivo to determine the required metabolic cascade  18, 19  (Supplementary Note 1).
The workflow to engineer a cell-free biosensor detecting a novel molecule is straightforward (Fig. 1c ). First, possible metabolic pathways to convert the molecule of interest into a detectable ligand are identified using SensiPath. Second, the genes coding for the metabolic transducer enzyme, the TF sensor, and the reporter module are synthesized and cloned into cell-free expression vectors. Finally, the DNA concentration of each plasmid is titrated in cell-free reactions to optimize signal strength and dynamic range in response to the molecule of interest (Fig. 1c ). Optimization of cell-free benzoic acid sensor. BenR is a member of the AraC/XylS TF family, originally isolated from Pseudomonas putida. In the presence of benzoate, BenR binds to the P Ben promoter and activates transcription (Fig.
1  A modular design workflow for engineering scalable cell-free biosensors. a Cell-free biosensors are composed of three modules: a generic sensor module linked to an output module and a metabolic transducer module transforming different molecules into ligands detectable by the sensor module. b An undetectable ligand is converted into a detectable ligand by the enzyme from the transducer module. Binding to the transcription factor controls the sensor module and downstream gene expression. c The biosensor design workflow starts with retrosynthetic pathway design using the SensiPath server  19. Once the transducer and sensor modules are determined, the genes encoding enzymes, transcription factors, and target promoters driving a reporter are cloned into cell-free expression vectors. The sensor is calibrated by titrating the concentrations of each plasmid to maximize signal output and dynamic range The white square represents the optimal condition (100 nM reporter and 30 nM BenR plasmid) with the highest relative fluorescence (see Supplementary Fig.
4c , and Supplementary Table  5 ). These data are a promising step toward developing cellfree biosensors for biomarker detection in clinical samples. Finally, we aimed to detect cocaine in clinically relevant conditions. Cocaine rapidly enters the bloodstream after ingestion and is subsequently detectable in the urine for up to 10 h 28. To determine whether our system could detect clinically relevant cocaine levels, we spiked urine samples with increasing concentrations of cocaine and added 2 µL to 20 µL cell-free reactions with our optimized cocaine biosensor. Our initial experiment showed small but detectable sfGFP signal at urinary concentration of 1000 µM, but our system was unable to show adequate fold change at lower, clinically relevant concentrations (Supplementary Fig. 14 ). We found that cell-free reactions produce increasing low levels of noise over time in the GFP fluorescence channel (Supplementary Fig.
This is the first time, to our knowledge, that cell-free biosensors have been used to detect endogenous molecules in unprocessed samples. Using our workflow, this process should be applicable to a wide range of other sensor/reporter pairs. One constraint of our system is that the TF must respond only to the product of the enzymatic reaction and not the substrate. Such potential crosstalk can easily be checked by running a control reaction without the metabolic transducer module. We computed that 1205 disease-associated biomarkers from the Human Metabolome Database (HMDB) could be converted into detectable molecules by one enzymatic reaction (Supplementary Note 1 and Supplementary Data 1). In addition, 64 HMDB metabolites could be transformed into benzoate and thus theoretically connected via a metabolic transducer to our optimized sensor (Supplementary Note 1 and Supplementary Data 2).
Inducers were dissolved in ethanol and final reactions contained 0. 5% ethanol for all inducer concentrations including the negative control. Reactions were prepared in PCR tubes on ice and 20 µL were transferred to a black, clear-bottom 384well plate (ThermoScientific), sealed, and the reaction was carried out in a plate reader (Biotek; Cytation3 or Synergy HTX) to measure both endpoints and reaction kinetics. The subsequent data were processed and graphs created using custom Python scripts or Microsoft Excel. Reactions for the representative images in Fig. 2c  and Fig. 3b  were incubated in PCR tubes at 37 °C for 4 h and imaged on a UV table with either a Sony α6000 camera (benzoic and hippuric acid sensors) or a cell phone camera (cocaine sensor) and background subtracted with Adobe Photoshop.
Acknowledgements We thank the  Bonnet and Faulon lab members  for fruitful discussions. We are grateful to  Pau Bernado ,  Annika Urbanek , and  Anna Morato  for helpful discussions on cell-free systems and extract creation,  Gottfried Otting  for BL21 (DE3) Star::RF1-CBD 3 strain, and the patients for providing their samples. We thank  Gilles Valette  from the  "Laboratoire de Mesures Physiques" , the analytical facilities of the  University of Montpellier , where the spectrometry analyses were performed. This work was supported by an  ERC   starting grant  " COMPUCELL " to J. B. J. B. also acknowledges continuous support from the  INSERM Atip-Avenir program  and the  Bettencourt-Schueller Foundation. The CBS acknowledges support from the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01.
J. -L. F. acknowledges support from  BBSRC/ EPSRC  (grant number  BB/M017702/1 ) and from the  ANR  (grant number  ANR-15-CE1-0008 ). M. K. is supported by  DGA  ( French Ministry of Defense ) and  Ecole Polytechnique  and A. P. is supported by a scholarship from  INRA  and the  IDEX Paris-Saclay. All plasmids will be available from Addgene.
Data availability The source data underlying Figs. 2b, c , 3a, b, and 4a-d, and Supplementary Fig. 2  and 4 -17, and DNA sequences for all constructs are provided as a Source Data File. Plasmids used in this paper are available from Addgene (pBEAST-BenR: #114597; pBEAST-pBen-sfGFP: #114598; pBEST-HipO: #114599; pBEAST-J23101-CocE: #114600; pBEAST-sfGFP: #116916; pBEAST-J23101-sfGFP: #116917; pBEAST-pBen-Luc: #122694). All other raw data are available from the corresponding authors at reasonable request.
In the second system, any combination is attested. Clearly, the latter system would be more difficult to learn than the former, as it makes it harder to infer the plural form of a word from its singular form (or the singular from the plural for that matter). Nonetheless, both systems present the same degree of Enumerative complexity. Recently, information theoretic approaches have been proposed as a way to circumvent the limitations of pure feature counting methods. Following Kolmogorov  (Kolmogorov, 1968) , the complexity of a dataset of inflected forms can be measured by the shortest possible grammar needed to describe them, or their algorithmic complexity, in line with the Minimum Description Length (MDL) principle  (Rissanen, 2007). For example, following  Goldsmith (2001) , we can model the task of morphological induction as a data compression problem: find the collection of markers forming the shortest grammar that fits the empirical evidence best.
Double-circled nodes represent word-final states, i. e. , letters ending a word form; shaded nodes represent (possibly allomorphic) stems, according to the segmentation criteria of section 3. 2. In (A), we provide a word-tree (left) and a word-graph (right) for the same set of 9 form types of geben. Although forms are input to the map with no information about their internal structure, the dataset was annotated manually, to allow us to correlate variation in the map response after training with variation in the (morphological) structure of training forms. First, for each form, we marked up two uniqueness points: UP1 and CUP. UP1 was marked at the position in the verb form where the stem is uniquely identified, both relative to paradigmatically-unrelated onset-aligned forms in the dataset, and with respect to other possible stem allomorphs within the same paradigm.
Inflected forms containing unpredictable stems (e. g. , English past_part(drink) → drunk, German past_1S(geben) → gab, German pres_2S(geben) → gibst) are segmented into a stem and a suffix (whenever overtly realized) (e. g. , drunk-, gab-, gib-st). paradigm. Our operational definition of UP1 represents a natural extension of Balling and Baayen's criteria to a dataset containing irregularly inflected forms. Paradigms that undergo unpredictable stem formation processes are classified as irregular. Paradigms whose stems are formed transparently and systematically 9 are annotated as regular. In Standard Modern Arabic, where verbs appear to cluster into different inflectional classes depending on the vocalic patterns intercalated with the roots, irregular (or weak) verb paradigms have stems that undergo a reduction in the number of root consonants (traditionally referred to as first, second, third weak and doubled roots, depending on the position and number of the radical consonants being dropped)  (Marzi et al.
, 2017). 10 Table  1  shows that English and (to a lesser extent) German conjugations exhibit identical verb forms (morphological syncretism) in a sizable subset of the 15 paradigm cells we selected. It can be objected that we are imposing the paradigm structure of inflectionally richer languages on inflectionally more impoverished ones, with the effect of spuriously inflating the token distribution of our dataset in languages such as English, where the base form is presented to the TSOM five times as many as -s forms are. However, we contend that our training sets language-specific map was made to vary so as to keep constant the ratio between the map size and the number of nodes required to represent its training set with a word tree. Accordingly, a language-specific map size ranges between 35 × 35 nodes (English) and 42 × 42 nodes (Greek).
2e-16, t-test), with a tendency towards lower expression in highly-frequent     23  , repliSeq (DNA replication timing, ENCODE), 3D chromatin interaction loops (HiC  27  , and CTCF (ENCODE) over PMD borders. If available, data from the breast cancer cell line (MCF7) and mammary epithelial cells (HMEC) was used, otherwise data from fibroblasts (IMR90, Tig3) was used. e Gene distribution inside PMDs (top, as a fraction of all annotated genes; bottom, as gene coding density). f Gene expression inside PMDs. Gene expression (top) and standard deviation (bottom) for the 25 overlapping cases of our WGBS and the transcriptome cohorts  17  was plotted as a function of PMD frequency. g Somatic mutations inside PMDs. Substitutions, insertions, deletions, and rearrangements were calculated for the 25 overlapping cases of our WGBS and the breast tumor full genomes cohorts  15  , and plotted as a function of PMD frequency.
24/31 (74%) genes were downregulated (Supplementary Fig. 7A, B ), and an overall negative correlation between CGI-promoter methylation and expression was evident (Supplementary Fig. 7C ). For 16 out of these 24 genes we confirmed that significant downregulation also takes place in cancer relative to normal in an independent breast cancer expression dataset (TCGA, see examples in Supplementary Fig. 7D ). Among the downregulated genes in PMDs are EGFR   (epidermal growth factor receptor) and PDGFRA (plateletderived growth factor receptor α) that have tumor promoting mutations (Supplementary Fig. 7A-C ). Paradoxically, both genes are significantly downregulated in our as well as the TCGA breast cancer dataset (Supplementary Fig. 7D ). Taken together, despite the large number of hypermethylated CpG islands inside breast cancer PMDs (13,013 CGIs; 47%, Fig.
Acknowledgements This work has been funded through the  ICGC Breast Cancer Working group  by the  Breast Cancer Somatic Genetics Study (BASIS) , a European research project funded by the  European Community 's  Seventh Framework Program  ( FP7/2010-2014 ) under the grant agreement number  242006 ; the  Triple Negative  project funded by the  Wellcome Trust  (grant reference  077012/Z/05/Z ). For contributions towards specimens and collections:  Tayside Tissue Bank ,  OSBREAC consortium, Icelandic Center for Research (RANNIS) ,  Swedish Cancer Society, Swedish Research Council, Foundation Jean Dausset-Center d'Etudes du polymorphisme humain, Icelandic Cancer Registry ,  Brisbane Breast Bank ,  Breast Cancer Tissue  and  Data Bank  and  ECMC (King's College London) ,  NIHR Biomedical Research Center (Guy's and St Thomas's Hospitals) ,  Breakthrough Breast Cancer, Cancer Research UK.
We thank  E. M. Janssen-Megens ,  K. Berentsen ,  H. Kerstens  and  K. J. Francoijs  for technical support. We thank  H. Kretzmer  and  R. Siebert  for providing processed data files of the lymphoma dataset. A. B. B. was through the  Dutch Cancer Foundation (KWF)  grant  KUN 2013-5833. SN-Z is personally funded by a  CRUK   Advanced Clinician Scientist Award  ( C60100/A23916 ). M. S. was supported by the  EU-FP7 -DDR response project. L. B. A. is supported through a J. Robert Oppenheimer Fellowship at  Los Alamos National Laboratory. A. L. R. is partially supported by the  Dana-Farber/Harvard Cancer Center SPORE in Breast Cancer  ( NIH/NCI 5 P50 CA168504-02 ). J. A. F. was funded through an  ERC Advanced Grant  ( ERC-2012-AdG-322737 ) and  ERC   Proof-of-Concept Grant  ( ERC-2017-PoC-767854 ).
Data availability Tables containing CpG methylation values (bigwig), genomic coordinates and mean methylation values of PMDs and CGIs are available via https://doi. org/10. 5281/ zenodo. 1467025 or https://doi. org/10. 17026/dans-276-sda6. Raw data for whole-genome bisulfite sequencing of the 30 breast tumor samples of this study is available from the European Genome-phenome Archive (https://www. ebi. ac. uk/ega) under dataset accession EGAD00001001388 (Study EGAS00001001195, Data Access Committee EGAC00001000010). External data resources used in this study are listed in Supplementary Data 5.
Hypervariable T-cell receptors (TCR) play a key role in adaptive immunity, recognising a vast diversity of pathogen-derived antigens. High throughput sequencing of TCR repertoires (RepSeq) produces huge datasets of T-cell receptor sequences from blood and tissue samples  [1, 2]. However, our ability to extract clinically relevant information from RepSeq data is limited, mainly because little is known about TCR-disease associations. Here we present a statistical approach called ALICE (Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences) that identifies TCR sequences that are actively involved in the current immune response from a single RepSeq sample, and apply it to repertoires of patients with a variety of disorders -autoimmune disease (ankylosing spondylitis [3]), patients under cancer immunotherapy  [4, 5] , or subject to an acute infection (live yellow fever vaccine  [6] ).
The method's robustness is demonstrated by the agreement of its predictions with independent assays, and is supported by its ability to selectively detect responding TCR in the memory but not in the naïve subset. ALICE requires no longitudinal data collection  [6, 7]  nor large cohorts [8-10], and is thus directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with a wide variety of diseases and conditions, which can be used for diagnostics, rational vaccine design and evaluation of the adaptive immune system state.
2e, top). By contrast, our selection logo highlights amino acids that are enriched relative to that baseline (Fig. 2e , bottom), showing a high enrichment in aromatic (Y and F) residues at CDR3 position 8. We speculate that these residues form contacts with the antigen that are crucial for TCR recognition. By robustly identifying expanded clonotypes in vivo from a single RepSeq dataset without any information about the antigens of interest, ALICE overcomes a major limitation of MHC-multimer staining  [11] [12] [13] 26 ]a popular ex vivo assay commonly used for identifying condition-associated TCR sequences -which requires prior knowledge of the antigenic peptide and HLA allele presenting it. Our method can thus be applied to variety of conditions for which this information in not available, from autoimmune disease to infection models.