dataesr/scientific-paragraphs-categorization huggingface dataset:
Processing of pseudo-bulk DNA accessibility data We retrieved sci-ATAC-seq3 mapped reads (dm6) from each of the 18 tissue pseudo-bulk (i. e. mapped reads from all cells combined) at the 10-12 hours timepoint from ref. 16  (downloaded from https://shendure-web. gs. washington. edu/content/members/DEAP_website/public/ on Feb. 1st 2022, BAM files available upon request; see also Extended Data Fig. 1A ). We generated coverage tracks for each tissue pseudo bulks, including the five tissues of interest: CNS, brain, epidermis, midgut and muscle (initially modeled seapartely for the somatic muscle and visceral muscle pseudo-bulks as these were annotated separately in the respective publication, we proceeded with only visceral as explained below). All read fragments from each pseudo-bulk were used for peak calling with MACS2  54, 55  with the following command: macs2 callpeak --nomodel --keep-dup all --extsize 200 --shift -100 --gsize dm -B.
Deep learning sequence-to-accessibility models Data preparation: We binned the dm6 genome into 1,001 bp windows with a stride of 50 bp, and filtered windows in the chromosomes chr2L, chr2R, chr3L, chr3R, chr4, chrX, chrY and chrM. For each window, we computed the log average of the depth-normalized ATAC coverage over the central 201 bp of the window. We combined the accessibility peaks of all scATAC-seq pseudobulks and selected all bins whose central 151 bp were within any 301 bp-centered peak region. We further added 144,424 random windows throughout the genome with a range of accessibility levels to obtain a dataset with reasonable class imbalances while maintaining high diversity in negative examples. Finally, we only included windows with non-zero ATAC signals across every pseudobulk and removed the ones with outlier values (quantile < 0.
01 or > 0. 999 in any pseudo-bulk). We augmented our dataset by adding the reverse complement of each original sequence, with the same output, ending up with 464,203 examples (928,406 post-augmentation). Cross-validation scheme: We used a cross-validation scheme to have a more robust model performance. We divided the sequences into 10 folds based on their chromosomal positions (considering chromosome halves; see Table  S3  for the specific folds used) and used a crossvalidation setup where we use 8 folds for training, 1 for validation, and 1 for testing. Each genomic window can serve as an example in a training, validation/tuning, or test set. Model architecture and training: We used the previously optimized DeepSTARR CNN architecture for predicting genome-wide enhancer activity from DNA sequence with minor adaptations  6.
22-24 ; update from https://github. com/AvantiShri/shap/blob/master/shap/explainers/deep/deep_tf. py) to compute contribution scores for all nucleotides in all sequences with respect to the accessibility predictions. We used 100 dinucleotide-shuffled versions of each input sequence as reference sequences. For each sequence, the obtained hypothetical importance scores were multiplied by the one-hot encoded matrix of the sequences to derive the final nucleotide contribution scores. We used one replicate model for each of the 10 folds of cross-validation and averaged the scores for each sequence in each cell type across all the 10 folds. The nucleotide contribution scores were visualized using the ggseqlogo function from the R package ggseqlogo (v. 0. 124). Motif discovery using TF-Modisco: To find important predictive motifs, we ran TF-Modisco (v.
0. 5. 12. 0 21 ) on the nucleotide contribution scores of one model fold for each tissue type separately, using the respective accessible regions. We specified the following parameters: sliding_window_size=15, flank_size=5, max_seqlets_per_metacluster=50000 and TfModiscoSeqletsToPatternsFactory(trim_to_window_size=15, initial_flank_to_add=5, final_min_cluster_size=30). We trimmed the PWM motifs by removing flanking positions with an information content lower than 0. 4. The TF-Modisco discovered motifs are detailed in Extended Data Fig. 2 , the converted PWM logo and the closest match from the TF motif database available at https://github. com/bernardo-de-almeida/motif-clustering  6  (similarity assessed using TOMTOM  60  with the following command: tomtom -dist kullback -motif-pseudo 0. 1 -text -min-overlap 1).
TF motif analyses across tissues: For the TFs that we could assign to the identified motifs, we retrieved their RNA in situ expression data at Drosophila embryogenesis stage 13-16 from the Berkeley Drosophila Genome Project (BDGP; https://insitu. fruitfly. org/cgi-bin/ex/insitu. pl) and matched their tissue annotation with the tissues used for the sequence-to-accessibility model (see Supplementary Fig. 1B  for summary results across tissues and Table  S1  for full annotation). In addition, we retrieved the TFs expression in matched single-cell RNA-seq clusters from the same publication where we retrieved the single-cell ATAC-seq data (ref. 16  ). The cluster assignment was done through nonnegative least square (NNLS) matrix factorization (see respective publication for details and data; https://shendure- A C C E L E R A T E D A R T I C L E P R E V I E W web.
Deep learning sequence-to-activity models Data preparation: We retrieved the in vivo enhancer activity data from the CAD4 database (available from ref. 33  , Table  S13 ), which also includes all enhancer activity data from the Vienna Tiles library (https://enhancers. starklab. org/). For each of the five tissues of interest (CNS, epidermis, gut, muscle, brain-specific), we defined sequences as active if they were active between stages 13 and 16 in any of the related tissue annotation terms: CNS -ventral nerve cord, neuroblast of ventral nerve cord primordium, embryonic brain, embryonic central brain, embryonic central brain glial cell, embryonic central brain neuron; epidermis -embryonic dorsal epidermis, embryonic ventral epidermis, embryonic head epidermis, lateral head epidermis, embryonic lateral epidermis, embryonic ventral trunk epidermis, ventral head epidermis, dorsal head epidermis, embryonic epidermis; gut -embryonic hindgut, embryonic midgut chamber, hindgut, embryonic/larval midgut, foregut, midgut interstitial cell; muscle -embryonic/larval somatic muscle, somatic muscle, embryonic somatic muscle, visceral muscle, embryonic/larval visceral muscle, circular visceral muscle fiber, longitudinal visceral muscle fiber, esophageal visceral muscle, embryonic/larval muscle system, muscle system, dorsal pharyngeal muscle; brain-specific -embryonic brain, embryonic central brain, embryonic central brain glial cell, embryonic central brain neuron AND inactive in the VNC -ventral nerve cord, neuroblast of ventral nerve cord primordium.
We computed the predictions for each sequence using the respective cross-validation set where the sequence is held-out for testing. Using this set of active and inactive tiles per tissue, model performance was accessed using the area under the precisionrecall curve (AUPRC), accuracy, F1-scores (all calculated using confusionMatrix from R package caret v. 6. 0-90  61  ), and by estimating the positive predictive value (percentage of validated active sequences among all positive predictions) at different prediction thresholds. We also evaluated the sequence-to-activity models for known tissue-specific enhancers in marker gene loci of each tissue (enhancers in our database present in +/-50kb from TSS): elav (CNS), grh (epidermis), GATAe (gut), Mef2 (muscle) and tll (brain) (Extended Data Fig.
Statistics and data visualization All statistical calculations and graphical displays have been performed in R statistical computing environment (v. 3. 5. 1  66  ) and using the R package ggplot2 (v. 3. 2. 1  67  ). Coverage data tracks have been visualized in the UCSC Genome Browser  68  and used to create displays of representative genomic loci. In all boxplots, the central line denotes the median, the box encompasses 25th to 75th percentile (interquartile range) and the whiskers extend to 1. 5× interquartile range. A C C E L E R A T E D A R T I C L E P R E V I E W A C C E L E R A T E D A R T I C L E P R E V I E W.
Density plots show the respective distributions for both predictions for inactive and inactive sequences. Right: precision-recall curves for the sequence-to-accessibility and sequence-to-activity models on test data, plus two additional controls: models trained directly on the in vivo enhancer activity data starting from random initialization and models pre-trained on ATAC-seq data from an unrelated tissue (salivary gland). Respective areas under the precision-recall curve (AUC) are shown. Predictions for all models were computed for each sequence only using the respective cross-validation set where the sequence is held-out for testing. Extended Data Fig. 4. Metric evaluation of the different models. The performance of different models (x-axis) per tissue (column) was evaluated on test data with five different metrics: area under the precision-recall curve (AUPRC), F1-score, accuracy across all sequences, only among positive, or only among negative sequences.
Data availability The Drosophila embryo scATAC-seq data used in this study is publicly available at https://shendure-web. gs. washington. edu/content/members/DEAP_website/public/. The Drosophila dm6 genomic sequence was downloaded as a fasta file from https://hgdownload. soe. ucsc. edu/goldenPath/dm6/bigZips/dm6. fa. gz. The TF motif database is available at https://github. com/bernardo-de-almeida/motif-clustering. TF expression data was retrieved from BDGP (RNA in situ; https://insitu. fruitfly. org/cgi-bin/ex/insitu. pl) and https://shendure-web. gs. washington. edu/content/members/DEAP_website/public/ (single-cell RNA-seq). The enhancer activity data from the Vienna Tiles library is available at https://enhancers. starklab. org/, and the enhancer activity database CAD4 is available from ref.
33  (Table  S13 ). The final pre-trained accessibility and enhancer activity models, as well as the data used to train and evaluate the models, can be found on zenodo at https://doi. org/10. 5281/zenodo. 8011697. Reporter DNA constructs and transgenic flies for the synthetic enhancers are available from the Vienna Drosophila Resource Center (VDRC) at https://shop. vbc. ac. at/vdrc_store/vdrc-fly-stocks/other-resources/a-stark-stocks-as-stock. html.
Spectroscopic and Crystallographic Characterization of the Cycloadducts 4 The structure, the purity and the relative configuration of all products were elucidated from their 1D and 2D NMR spectroscopic data and were unequivocally confirmed by an X-ray data analysis performed on cycloadduct 4i. Relevant 1 H and 13 C chemical shifts of dispiropyrrolidine 4i are shown in Figure  2. The 1 H NMR spectrum of the cycloadduct 4i shows a doublet at δ H 3. 75 (J = 9. 6 Hz) corresponding to H-4 proton of the pyrrolidine ring which showed 1 H- 13 C HSQC correlation (Figure  S20 ) with the doublet of a doublets at δ 5. 40 ppm (J = 9. 6 Hz and J = 4. 8 Hz), attributed to the pyrrolidine proton H-5. The multiplicity and the coupling constant values of these protons confirm the formation of the regioisomer 4i as in the case of hypothetical regioisomer 4i (Scheme 1), pyrrolidinyl protons should exhibit a singlet instead of a doublet in the 1 H NMR spectrum.
Spectroscopic and Crystallographic Characterization of the Cycloadducts 4 The structure, the purity and the relative configuration of all products were elucidated from their 1D and 2D NMR spectroscopic data and were unequivocally confirmed by an X-ray data analysis performed on cycloadduct 4i. Relevant 1 H and 13 C chemical shifts of dispiropyrrolidine 4i are shown in Figure  2. The 1 H NMR spectrum of the cycloadduct 4i shows a doublet at δ H 3. 75 (J = 9. 6 Hz) corresponding to H-4 proton of the pyrrolidine ring which showed 1 H- 13 C HSQC correlation (Figure  S20 ) with the doublet of a doublets at δ 5. 40 ppm (J = 9. 6 Hz and J = 4. 8 Hz), attributed to the pyrrolidine proton H-5. The multiplicity and the coupling constant values of these protons confirm the formation of the regioisomer 4i as in the case of.
Spectroscopic and Crystallographic Characterization of the Cycloadducts 4 The structure, the purity and the relative configuration of all products were eluci dated from their 1D and 2D NMR spectroscopic data and were unequivocally confirmed by an X-ray data analysis performed on cycloadduct 4i. Relevant 1 H and 13 C chemica shifts of dispiropyrrolidine 4i are shown in Figure  2. The 1 H NMR spectrum of the cycloadduct 4i shows a doublet at δ H 3. 75 (J = 9. 6 Hz corresponding to H-4 proton of the pyrrolidine ring which showed 1 H- 13 C HSQC correla tion (Figure  S20 ) with the doublet of a doublets at δ 5. 40 ppm (J = 9. 6 Hz and J = 4. 8 Hz) attributed to the pyrrolidine proton H-5. The multiplicity and the coupling constant val ues of these protons confirm the formation of the regioisomer 4i as in the case o The regio-and stereochemistry of the 1,3-dipolar cycloaddition was further corroborated by a single-crystal X-ray analysis of spiropyrrolidine 4i, whose molecular structure is depicted in Figure  3.
Computational Approach In an attempt to rationalize the observed bioactivity for the newly synthesized derivatives, molecular docking studies were performed. The most antibacterial spirooxindolehydantoins, i. e. , 4c, 4g, and 4h were docked into the active site of three bacterial targets: Thymidylate Kinase (TMK) (PDB ID 4QGG) from S. aureus, (3-oxoacyl-[acyl-carrier-protein) synthase 3 (PDB ID 4EWP) from M. luteus, DNA gyrase (PDB ID 4DUH) from E. coli. The ligand 4e was docked to the target secreted aspartic protease (PDB ID 3Q70) from C. albicans and C. krusei. The Ligands 4i and 4l with the most promising ant-inflammatory potential were docked into the active sites of LOX-5 (PDB ID: 3V99). The PDB binding structures of different target enzymes are presented in Figures S31-S42 of the Supplementary Material.
Orientation and Bonding Interaction Compound 4g in the Active Site Cavity of Thymidylate Kinase (PDB: 4QGG) The molecular docking results shows that the compound 4g is the most potent antibacterial agent against TMK (ID: 4QGG) from S. aurues, revealing the highest negative score of -153. 700 kcal/mol, comparable to the standard drug Tetracycline with the score of -112. 465 kcal/mol (Table  7 ). Comparatively, compound 4g being the most active against the antibacterial agent against TMK (ID: 4QGG) from S. aurues which showed that was formed almost the same number of interactions with the TMK protein of S. aureus compared to the Tetracycline with a small difference in the type of residue (Table  7 ). Compound 4g displayed five strong hydrogen bonds (two Conventional H-Bond types and three Carbon H-Bond types) with crucial residues: ARG36 (1.
74 Å), GLU37 (2. 26 Å), PRO38 (2. 12 Å) and ARG48 (2. 73 and 3. 05 Å) of TMK protein from S. aureus (Table  7  and Figure  6 ). As illustrated in Figure  6 , two 6-rings of the compound 4g displayed two hydrophobic interactions (Pi-Pi T-shaped) with the residues PHE66 and TYR100. In addition, two other hydrophobic interactions (Pi-Pi Stacked) were formed between compound 4g and residue PHE66. While four hydrophobic interactions (Pi-Alkyl) with active site residues: ARG36, ILE47, ARG48 and VAL51 (Table  7  and Figure  6 ). We note that these results confirmed by previous studies  [61] [62] [63]  which concluded that inhibition of the both amino groups: ARG48, PHE66 and GLU37 of TMK protein might be responsible for their potent antibacterial activity. Orientation and Bonding Interaction Compound 4h in the Active Site Cavity of (3-Oxoacyl-[acyl-carrier-protein) Synthase (PDB: 4EWP) Due to the existence of only one crystal structure of the M.
luteus receptor in the RCSB Protein Data Bank. Initially, we faced a great challenge to identify the active site of the receptor (PDB ID 4EWP) for molecular docking analysis. After solving that problem, we launched molecular docking simulation and as presented in Table  7 , the compound 4h fits well in the binding pocket of M. luteus receptor (PDB ID 4EWP) with highest negative score of -147. 875 kcal/mol, which is comparable of Tetracycline with a score of -96. 245 kcal/mol. Moreover, the docking conformation of compound 4h showed that this compound established three hydrogen bonds and five hydrophobic interactions with active site residues of the target protein (Figure  7 ). The hydrogen bonds were observed between the compound and active site residues SER120(2. 58 Å), TRP197(1. 70 Å) and SER120(2.
41 Å) of the target protein, (3-oxoacyl-[acyl-carrier-protein) synthase (Figure  7 ). While five hydrophobic interactions with active site residues: two with TRP197, two with VAL92 and one with ALA338 were observed (Table  7  and Figure   7 ). On the other hand, the results of the docking thus indicated a slight difference in the binding of the compound 4h to the (3-oxoacyl-[acyl-carrier-protein) synthase enzyme vs. the Tetracycline. Furthermore, previous papers  [64, 65]  confirmed that the residues Val92 and ALA338 are responsible for the formation of complex: 4EWP-compounds. Molecules 2023, 28, x FOR PEER REVIEW 16 of 29 receptor (PDB ID 4EWP) for molecular docking analysis. After solving that problem, we launched molecular docking simulation and as presented in Table  7 , the compound 4h fits well in the binding pocket of M.
luteus receptor (PDB ID 4EWP) with highest negative score of -147. 875 kcal/mol, which is comparable of Tetracycline with a score of -96. 245 kcal/mol. Moreover, the docking conformation of compound 4h showed that this compound established three hydrogen bonds and five hydrophobic interactions with active site residues of the target protein (Figure  7 ). The hydrogen bonds were observed between the compound and active site residues SER120(2. 58 Å), TRP197(1. 70 Å) and SER120(2. 41 Å) of the target protein, (3-oxoacyl-[acyl-carrier-protein) synthase (Figure  7 ). While five hydrophobic interactions with active site residues: two with TRP197, two with VAL92 and one with ALA338 were observed (Table  7  and Figure  7 ). On the other hand, the results of the docking thus indicated a slight difference in the binding of the compound 4h to the (3-oxoacyl-[acyl-carrier-protein) synthase enzyme vs.
the Tetracycline. Furthermore, previous papers  [64, 65]  confirmed that the residues Val92 and ALA338 are responsible for the formation of complex: 4EWP-compounds. Orientation and Bonding Interaction Compound 4c in the Active Site Cavity of DNA Gyrase (PDB: 4DUH) The obtained results revealed that the compound 4c binds into the DNA Gyrease enzyme. The MolDock Score energy and types of possible interactions in addition to, the interacting moiety of our docked compound is represented in Table  7  and Figure  8. The obtained results revealed that the compound 4c binds into the DNA Gyrease enzyme. The MolDock Score energy and types of possible interactions in addition to, the interacting moiety of our docked compound is represented in Table  7  and Figure  8. Furthermore, the compound 4c proved a good ability to the binding into DNA Gyrase binding site and this is confirmed by forming 6 strong hydrogen bonding interactions with Gly 101(1.
Apparatus and General Information The NMR (400 MHz for 1 H NMR, 101 MHz for 13 C { 1 H} NMR) spectra were recorded on Bruker Avance 400 machine (Rheinstetten, Germany). The chemical shifts δ were reported in ppm relative to tetramethylsilane (TMS). Data were described as follows: chemical shift (δ in ppm), multiplicity (s = singlet, d = doublet, m = multiplet, dd = doublet of doublet), coupling constants (Hz), integration. Optical rotations were determined by a Perkin Elmer polarimeter. Elemental analyses were performed on a Perkin Elmer 2400 Series II Elemental CHNS analyzer (Waltham, MA, USA). Thin-layer chromatography (TLC) was performed on silica gel plates (Merck, silica gel 60 F 254 0. 2 mm, 200 × 200 nm) (Darmstadt, Germany) using UV light at 254 nm.
Crystal Structure Determination Data collection was performed on a Bruker D8 Venture four-circle diffractometer from Bruker AXS GmbH (Karlsruhe, Germany). CPAD detectors used were Photon II from Bruker AXS GmbH; X-ray sources: Microfocus source IµS; and microfocus source IµS Mo and Cu, respectively, from Incoatec GmbH with mirror opticsHELIOS and a singlehole collimator from Bruker AXS GmbH. Programs used for data collection were APEX4 Suite  [76]  (v2021. 10-0) and integrated programs SAINT (V8. 40A; integration) and SADABS (2018/7; absorption correction) from Bruker AXS GmbH  [76]. The SHELX programs were used for further processing  [77]. The solution of the crystal structures was done with the help of the program SHELXT  [78] , the structure refinement with SHELXL  [79]. The processing and finalization of the crystal structure data was done with program OLEX2 v1.
5  [80]. All non-hydrogen atoms were refined anisotropically. For the hydrogen atoms, the standard values of the SHELXL program were used with U iso (H) = -1. 2 U eq (C) for CH 2 and CH and with U iso (H) = -1. 5 U eq (C) for CH 3. All H atoms were refined freely using independent values for each U iso (H). Crystal data for C 25 H 20 FN 4 O 3. 5 , M = 451. 45 g•mol -1 , red-orange plates, crystal size 0. 942 × 0. 789 × 0. 281 mm 3 , triclinic, space group P-1, a = 12. 1123(9) Å, b = 12. 5291(10) Å, c = 15. 0421(11) Å, α = 95. 793(3) • , β = 91. 329(3) • , γ = 99. 674(3); V = 2236. 9(3) Å 3 , Z = 4, D calc = 1. 341 g/cm 3 , T = 100 K, R 1 = 0. 0509, wR 2 = 0. 1291 for 59257 reflections with I ≥ 2σ (I) and 14895 independent reflections. GOF = 1. 052. Largest diff. peak/hole/e Å -3 0. 61/-0. 33.
Ligands and Targets Preparations The 3D structures of four most active compounds: 4c, 4g, 4h, 4e, 4i and 4l were preoptimized by means of the Molecular Mechanics using Force Field MM+. After that, all compounds optimized using the semi-empirical method AM1  [84]  which implemented in the Hyperchem 8. 0. 8 software  [85]. The X-ray crystallography structures of four different drug target proteins were downloaded from the RCSB Protein Databank (https://www. rcsb. org/, accessed on 20 May 2023), accessed on 10 May 2023 that is, Thymidylate Kinase (TMK) (PDB ID 4QGG) from S. aureus  [86] , (3-oxoacyl-[acyl-carrier-protein) synthase 3 (PDB ID 4EWP) from M. luteus  [65] , DNA gyrase (PDB ID 4DUH) from E. coli  [87]  were selected as antibacterial targets, and secreted aspartic protease (PDB ID 3Q70) from C.
albicans and C. krusei  [88]  was selected as the antifungal targets. The crystal structure of the human 5-LOX enzyme in complex with its substrate, Arachidonic acid PDB (PDB ID: 3V99)  [89] , was also downloaded from the same Protein Data Bank. Some information related to the selected targets was reported in Table  S1  in the Supplementary Materials). According to Table  S1 , we note X-ray crystals of the targets selected have resolution between 1. 5 and 2. 5 Å  [90] , confirming that these structures can be considered of good quality.
Molecular Docking Simulations Docking simulations were carried out on the five targets selected by using Molegro Virtual Docker v. 6. 0. 1(MVD)  [91]  software package knowing that the algorithm keeping the macromolecule rigid while allowing ligand flexibility. All X-ray crystals of the targets selected were simplified by removing water molecules, ions, cofactors and co-crystal ligands from their PDB structure and molecular docking protocol were followed and used in our previous studies  [92, 93]. In the first, The MolDock score [GRID] algorithm was used as the score function, and the MolDock was used as search algorithm. In the second, default parameter settings in the same software package (Score function: MolDock Score; Ligand evaluation: Internal ES, Internal HBond, Sp2-Sp2 Torsions, all checked; Number of runs: 10 runs; Algorithm: MolDock SE; Maximum Interactions: 1500; Max.