dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular network analysis. After the preprocessing of the LC-MS/MS data with MZmine 2. 37, the output mgf file was then processed with MetGem software  28  to give a network containing 15,261 nodes. Molecular network was generated using the following parameters: m/z tolerance set to 0. 02, Minimum Matched Peaks set to 6, topK set to 10, Minimal Cosine score Value of 0. 7 and Max. Connected Component Size of 100. Associated CSV file was then loaded. For the mapping process, relative quantification of each ion was represented as pie chart-diagrams, whose proportions were based on respective areas of the corresponding extracted ion chromatograph area (EIC). The spectra in the network were then searched for analogues against the spectral libraries available. The library spectra were filtered in the same manner as the input data.
All matches kept between network spectra and library spectra were required to have a score above 0. 7 and at least 6 matched peaks. m/z Tolerance for the search of analogues was set to 100. For t-SNE output, cosine score threshold (0. 7) was similar to molecular network view. The number of iterations, perplexity, learning-rate and early exaggeration parameters were set to 1000, 6, 200 and 12, respectively. These values were previously optimized on another complex set of data  28. For a more advanced retreatment of the network, MN was also exported on Cytoscape 3. 7. 0 software  63. Large-scale cultivation and isolation. Strains were cultivated during 15 days at 28 °C in 14 cm Petri dishes of PDA (Potato Dextrose Agar) media. Each strain was extracted three times consecutively with ethyl acetate (EtOAc) at room temperature.
sequences from closest representatives species available in the GenBank database were retrieved for comparison (Colletotrichum gloeosporioides (MH864569), Colletotrichum sp. strain LGMF1580 (MG976389), C. gloeosporioides (MH865232), C. gloeosporioides (MH866036), C. fragariae (MH864138), C. vietnamense (MH863700), Colletotrichum sp. strain AHGB10 (MH267867), C. gloeosporioides (KT004429), C. gloeosporioides (KR995714), Colletotrichum sp. strain AHGB15 (MH267881), C. horii (KR995727), C. horii (LC186042), C. karstii (MG602059), C. fructicola (MH865616), C. siamense (MG807423), C. queenslandicum (JX010184)). Monilochaetes infuscans (JQ005780) was used for the output group. Sequences were aligned in MAFFT version 7 (https ://mafft. cbrc. jp/align ment/serve r/).
Acknowledgements This work has benefited from a joint  Agence Nationale de la Recherche-Swiss National Science Foundation (ANR-SNF ) grant ( SECIL , ref  ANR-15-CE21-0016  and  SNF N° 310030E-164289 ) and from an  "Investissement d' Avenir" grant  ( CEBA , ref  ANR-10-LABX-0025 ) managed by the  ANR.
Computational programs used in this study Reliable identification of miRNA targets is still a major challenge. Reporter assays and Western blotting are the most reliable methods for demonstrating direct interactions between an miRNA and its target but can still generate false positives  [Kuhn et al. , 2008]. In addition, the interactions that are supported by strong experimental evidence are limited. MiRTarBase database release 7. 0 from September 15, 2017  [Chou et al. , 2018] , which includes all experimentally verified interactions, comprises the interactions between 735 miRNAs and 2766 targets obtained by reporter assays or Western blotting for Homo sapiens. This is far to small a scope for our purposes since, according to these data, the majority of genes are not considered to be targeted by miRNAs.
Collection of miRNA-target interactions The content of miRDB database v6 was downloaded using the link provided on the miRDB website (http://mirdb. org/downl oad/miRDB_v6. 0_prediction_result. txt. gz). The data are presented in a single file that lists for each miRNA its predicted targets and the associated score. For Homo sapiens, miRDB records more than 15 million interactions between 2656 miRNAs and 17,464 targets. After removing the predictions associated with a score below 80, 825,626 associations between 2638 miRNAs and 16,732 targets remained for Homo sapiens. As targets are identified by their NCBI gene IDs, we used the following file downloaded from the NCBI website to map the NCBI identifiers to gene symbols: ftp://ftp. ncbi. nlm. nih. gov/gene/DATA/gene2refseq. gz. This file also allowed us to obtain a list of all the genes indexed by NCBI (which number 20,227); thus, by differentiating between this list and the list of genes referenced in miRDB, we identified 3,495 genes predicted to not be targeted by miRNAs.
Analysis of the distribution of miRNA target associations With a Python script that we developed, we counted the predicted number of miRNAs that target each gene. This allowed us to draw the plot presented in Fig. 1  and to note that the shape of the distribution is typical of a power law function. We performed the same processing steps to determine the number of predicted targets for each miRNA (data are presented in Supplemental Table  S2 ) and to draw the plot shown in Fig. 2. To study possible correlations between the susceptibility of genes to be controlled by miRNAs and their functions, we categorized the genes according to their degree of targeting by miRNAs. The 3495 genes that were not targeted by any miRNA constituted the first category. To avoid a population size bias, we defined the other categories such that each category contained a similar number of genes, i.
PPI enrichment To determine whether each of the categories includes genes that exhibit more interactions with each other than could be due solely to chance, using the data from the String database  [Franceschini et al. , 2012] , we determined the number of corresponding proteins for each category and the number of interactions between the proteins in the same category associated with a high confidence score (minimum required interaction score ≥ 0. 700). We performed the same procedure for 1000 randomly picked sets of genes of similar size and averaged the number of interactions found (Supplemental Table  S4 ). The average degree of the networks constructed from the genes in each category was significantly higher than that of random networks of the same size. To determine if this difference is sufficient to be considered representative, we computed the PPI enrichment p-value for the six categories as described by Franceschini et al.
[Franceschini et al. , 2012]. The obtained p-values were very low, well below 10E-16, meaning that the genes in each category show more interactions among themselves than would be expected for a random set of genes of similar size. It can also be seen that the average degree correlates with the number of miRNAs that target genes in each category. This number ranges from 5. 82 for highly targeted genes to 3. 65 for nontargeted genes. However, although the "nontargeted genes" network is less dense, it still presents more connections than a random network, and it is apparent from the visualization of this network using interaction data from the String database with a high confidence (minimum required interaction score ≥ 0. 700) that there are modules of genes that seem to present a definite arrangement (Supplemental Figure  S1 ).
4a ). From the genes in this network, we reproduced the known protein-protein interaction network using the interactions described in the String database  [Franceschini et al. , 2012 ] associated with a high confidence score (minimum required interaction score ≥ 0. 700) (Fig. 4b ). The interaction enrichment p-value was lower than 10E-16. This means that the proteins encoded by the genes cotargeted by more than 50 miRNAs exhibit more interactions among themselves (687 edges in the network) than what would be expected for a random set of proteins drawn from the genome (368 edges).
b) PPI network for the same set of genes as in panel a, built using the interactions described in the String database associated with high confidence (minimum required interaction score ≥ 0. 700). the distribution of these pairings. The results are summarized in Fig. 1  and Supplemental Table  S1. While roughly one-sixth of the genes were not targeted by miRNAs, a number of genes were targeted by hundreds of miRNAs (approximately 450 genes showed potential pairing with more than one hundred miRNAs). In parallel, we determined the reverse distribution, i. e. , the number of miRNAs per gene (Fig. 2  and Supplemental Table  S2 ). As shown in Fig. 1 , a power law distribution was observed, indicating that few genes were hypothetically targeted by a large number of miRNAs with different combinatorial assortments for each gene in this category.
e. , two nodes linked by a vertex refer to two genes sharing at least 50 identical motifs theoretically annealing with different miRNAs (Fig. 4a ). All of these comatching genes (two by two) constitute the bricks for subsequently building a gene network using interactions stored in the String database with high confidence. The graphical results are presented in Fig. 4b. The PPI enrichment p-value of this network is below 10E-16, meaning that these genes exhibit more interactions among themselves than would be expected for a random set of genes of similar size drawn from the genome. The two overlapping networks (the network of genes with miRNA motifs and the network of interacting genes taken from the former network) indicate that in this large group of genes, biochemical/ genetic interactions are likely orchestrated by a layer of miRNAs acting at the network level.
, 2009; Tarang and Weston, 2014]. One of the most sensitive computational scoring tools, developed by Weijun Liu and Xiaowei Wang in 2019, has allowed the systematic retrieval of a large catalog of candidates in several vertebrates (human, mouse, rat, dog and chicken)  [Lim et al. , 2003; Lee et al. , 2003]. We relied on the results of this tool, stored in the mirDB database, to perform our analyses  [Lee et al. , 2003]. Noncanonical pairing might undermine powerful prediction tools, and additional experiments will be needed to validate the prediction results.
Acknowledgments This work was supported by an  ANR  grant, " Methylclonome " ANR-12-BSV6-006-01 , to  Alain Robichon  and  Claude Pasquier. This work was also supported by the  French National Research Agency (ANR)  through the  LABEX SIGNALIFE program  (reference #  ANR-11-LABX-0028-01 ).
Previously, MetRS and other synthetases have been redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. Here, we have redesigned MetRS computationally to bind several ligands: the Met analog azidonorleucine, methionyl-adenylate (MetAMP), and the activated ligands that form the transition state for MetAMP production. Enzyme mutants known to have azidonorleucine activity were recovered by the design calculations, and 17 mutants predicted to bind MetAMP were characterized experimentally and all found to be active. Mutants predicted to have low activation free energies for MetAMP production were found to be active and the predicted reaction rates agreed well with the experimental values. We suggest the present method should become the paradigm for computational enzyme design.
Structural models MetRS:AnL and MetRS:MetAMP complexes. For MetRS:AnL, we started from the crystal structure of a complex between a triple mutant of E. coli MetRS and AnL (PDB code 3H9B)  [41]. The protein mutations were L13S, Y260L, H301L. We refer to this mutant as SLL. The protein backbone was held fixed. Side chains more than 20 Å from the ligand were held fixed. The other side chains were allowed to explore rotamers, taken from the Tuffery library, augmented to allow multiple orientations for certain hydrogen atoms  [42, 43]. Side chains 13 and 301 were allowed to mutate into the following 14 types: ACDEHIKLMNQSTV; position 260 was allowed to mutate into the same types, except that Tyr replaced Asp. Thus, there were 14 3 = 2744 possible sequences in all. Histidine protonation states at non-mutating positions were assigned by visual inspection of the 3D structure.
System preparation was done using the protX module of the Proteus design software  [31]. For MetRS:MetAMP, we started from a crystal complex (PDB code 1PG0) between E. coli MetRS and a methionyl adenylate (MetAMP) analogue  [44]. The protein backbone was held fixed. Side chains more than 20 Å from the ligand were held fixed. The other side chains were allowed to explore rotamers  [42, 43]. Side chains 13, 256 and 297 were allowed to mutate into all types except Gly or Pro, for a total of 5832 possible sequences in all. Histidine protonation states at non-mutating positions were assigned by visual inspection of the 3D structure. Unfolded state. The unfolded state energy was estimated with a tri-peptide model  [45]. For each mutating position, side chain type, and rotamer, we computed the interaction between the side chain and the tri-peptide it forms with the two adjacent backbone and C β groups.
Stiffness parameters were assigned by analogy to existing parameters. The complete set of parameters for AnL and MetAMP is in Supplementary Material (S2 and S3 Files, respectively). Rotamers. AnL was positioned in the protein complex so that its backbone had the position occupied in the MetRS:AnL crystal structure  [41]. The ligand's side chain was allowed to explore rotamers. These were defined by the usual side chain rotamers of Met  [42, 43]. We started by positioning Met in the pocket by superimposing it on AnL in the mutant MetRS: AnL crystal complex (PDB code 3H9B). We then positioned the 17 Met side chain rotamers from the Tuffery library. We extracted the AnL side chain from the experimental complex and superimposed it on each of the 17 Met rotamers, producing 17 AnL conformers. Finally, for each one, we performed a short energy minimization with the AnL backbone held fixed.
Modeling the MetRS transition state complex MetRS catalyzes two reactions. In the first, Met reacts with ATP to give MetAMP and pyrophosphate. In the second, tRNA reacts with MetAMP. Here, we considered the first reaction, which occurs in the absence of tRNA. A model for the ground state ligands Met + ATP was first obtained, starting from the crystal complex between MetAMP and PP i (PDB code 3KFL). The covalent structure was reset to that of Met + ATP and the geometry was adjusted by a short energy minimization. The complex included a magnesium ion. Next, a model for the activated ligand [Met:ATP] ‡ was obtained, starting from the Met + ATP complex. First, a phosphate and carboxylate fragment were positioned in a geometry close to the expected pentacoordinate transition state arrangement  [44, [47] [48] [49]  and an ab initio energy minimization was done, including planarity constraints for the phosphorus and three oxygens.
0. The magnesium ion, which bridges the α, β and γ phosphates, was included in the calculation. The resulting charges were applied to atoms close to the α phosphate group, while other atoms kept their usual Met or ATP charges. Small manual adjustments were made to establish the correct total charge of -4. The final Mg charge was +1. 5. Charges are in Supplementary Material (S3 File). The geometry of the protein around the ligands was relaxed slightly by performing a short, restrained molecular dynamics simulation, with the ligands held fixed. The entire system was placed in a large box of explicit TIP3P water  [50]. Harmonic restraints were applied to nonhydrogen atoms, with force constants that decreased gradually from 5 to 0. 5 kcal/mol/Å 2 over 575 ps of dynamics, performed with the NAMD program  [51].
1A ). Crystallization trials conducted on both forms resulted in suitable tetragonal crystals for OrfG64-204 while no positive result was obtained for OrfG223-331. Experimental phases were determined by using a singlewavelength anomalous dispersion method (SAD) on crystals soaked with an osmium derivative. A native crystal allowed final model refinement to 1. 75 Å resolution (Rfree = 21. 2 %). Detailed data and refinement statistics are given in Table  S1. The polypeptide chain adopts an alpha+beta fold, composed of two α-helices named α1 and α2, followed by a β-sheet of four strands (β1 to β4), the last of which has a marked disruption into two subparts β4a and β4b (Fig. 2 ). Cross-comparisons performed by DALI  [30]  and PDBefold  [31]  classify the spark OrfG central domain in the NTF2-like superfamily.
Its folding pattern mainly consists in a highly curved antiparallel beta sheet that wraps around a central alpha helix surrounded by shorter additional ones. As expected from sequence analysis, the closest VirB8like structural neighbours of OrfG64-204 is the TcpC central domain (TcpC104-231, i. e. the first domain of TcpC99-309, PDB entry 3ub1  [20] ), with an rmsd of 1. 77 Å for 92 aligned amino acids. This search also highlighted the structure of the C-terminal domain of TraM from pIP501 (TraM190-322, PDB entry 4ec6  [19] , rmsd of 1. 90 Å for 96 aligned amino acids) as a close structural homolog (Fig. 2 ). The folds of TcpC104-231, TraM190-322 and OrfG64-204 differ by the length of their secondary structures and connecting loops. Furthermore, TcpC104-231 has an additional helix α3 prior to the β-sheet.
Both TcpC and TraM were described as Gram-positive equivalents of VirB8 from VirB/D4 T4SS from A. tumefaciens. Despite their low sequence identity and their different domain organization, it was proposed that proteins from Grampositive Conj-T4SSs that adopt a NTF2-like fold form the "VirB8-like" family  [23]. This family also includes the soluble domain of the conjugative component TraH from pIP501 (TraH57-183, PDB entry 5aiw  [32] ) (Fig. 2 ). Since the structure of OrfG64-204 belongs to the NTF2-like superfamily as well, we propose that OrfG from ICESt3 is a new member of the VirB8-like family found in Gram-positive Conj-T4SS.
The results emphasized a surprising diversity despite the simplicity and small size of the NTF2-like fold. The set of Gram-negative VirB8 structures encompasses 13 independent entries in the protein database (Protein Data Bank). That of known Gram-positive VirB8-like structures is poorer: the crystallographic analysis of OrfG64-204 brings their number to four. The NTF2-like pattern is globally conserved in the 17 structures. On average, their fold consists in three antiparallel alpha helices followed by an antiparallel four-stranded sheet, for which we propose the nomenclature α1α2α3β1β2β3β4. However, the length of the secondary structures (especially beta strands) and their relative orientation, the curvature of the beta sheet, plus possible decorations inserted in the minimal fold bring enough diversity to make the structural superimposition delicate.
We found the mTM-align algorithm as the most suited one to deal with these differences. The global superimposition of the 17 structures and the corresponding structural alignment provided by mTM-align were analyzed the using an in-house developed script to allow their visualization in Pymol (see Materials and Methods) (Fig. 3 ). The structure-based tree obtained using mTM-align indicated that Gram-positive VirB8-like structures clearly separate from the group formed by the Gram-negative ones (Fig. 4 ). The tree also shows different classes in both Gram-negative and Gram-positive VirB8-like structures, which seem to have a real structural meaning since they contain members whose sequences share modest to low similarities (Table  S2 ). At least three classes appear in the Gram-negative VirB8s (named I -, II -and III -for the convenience of their description) and one in the Gram-positive VirB8-like structures (I + ), while the three other proteins and did not belong to identified classes (Fig.
4 ). In the Gram-negative group, the three classes differ by the way their helix α3 is disrupted in two parts α3a and α3b, separated by a protruding loop of 4 residues with two distinct conformations in classes I -and II -, or by a strong kink in class III -(Fig. 4 ). This disruption of α3 is not a distinctive trait of the Gram-negative proteins since CagV from Helicobacter pylori (PDB entry 6iqt  [33] ), not assigned to a class, has a straight helix. All members of classes I -and II -have a small additional helix α4 between β3 and β4, which is involved in protein dimerization, while members of class III -have a loop instead. Furthermore, class III -and CagV display a bulge that separates β1 in two parts β1a and β1b (Fig. 4 ). Strikingly, the four Gram-positive VirB8-like proteins have no specific structural feature that unambiguously distinguishes them from the Gram-negative ones.
However, the overall structure superposition separated them in the structural tree, which seems to be explained by the small differences disseminated along their polypeptide chains (Fig. 4 ). Indeed, OrfG64-204 has no α3 (replaced by an unobservable region) and a strand β4 that clearly splits in two short portions β4a and β4b separated by a bulge that ends with Pro200 (Fig. 2 ). By comparison, TraM190-322 (PDB entry 4ec6) has a shorter α2, also misses α3 and has a slightly longer loop between β1 and β2. In contrast, TcpC104-231 (PDB entry 3ub1) possesses α3 and has much longer strands β1, β2 and β3, together with a longer loop between them. TraH57-183 is the most distant structure, as it is the only one with a continuous strand β4 with no bulge insertion, while on the contrary β1 possesses two parts β1a and β1b with a bulge in between (PDB entry 5aiw) (Fig.
The script that we developed to analyse the structural alignment of the 17 structures easily identified the conserved or similar residues within the different sets of proteins that we considered. Interestingly, we found no conserved residue common to all 17 structures. In the subset of the 13 Gram-negative VirB8 structures, once again, no amino acid is conserved. On the contrary, up to 30 residues are conserved within each Gram-negative class, among which many form hydrophobic contacts where the β-sheet faces α1 (Fig. S1 ). By comparison, in the four Gram-positive VirB8-like structures, only six residues share similar natures and constitute a hydrophobic patch within the core of the protein near the C-terminal side of α1. In addition, it is worthy to notice the presence of a conserved glutamate residue (Glu154 in OrfG) at the end of β1, with its side chain towards the surface.
Strikingly, although they are subtle, the differences observed when comparing the structures of all the NTF2-like domains of VirB8-like proteins lead again to distinguish Gram-positive and -negative bacteria. The tree based on structural similarities seems significant as it groups structures in a way comparable to the one obtained through phylogenetic analysis (when applicable i. e. for sequences that belong to the PFAM group PF04335, see Fig. 4 ). Moreover, this tree, strictly based on the spatial superposition of atoms without valuing sequence conservation, associates in a same class, proteins that share low sequence similarity. For instance, in the case of Gram-negative bacteria with more known structures, VirB8s from A. tumefaciens and from Bartonella quintana gather in class II -although they only have 22% of sequence identity; Class III -contains TraM from Plasmid R64  [39]  and the IcmL-like protein from Legionella pneumophila despite only 13% of identity (Table  S2 ).
Thus, the partition of VirB8-like structures from Gram-negative bacteria on one side, and Gram-positive ones on the other side in the tree is highly significant since the low sequence similarity of the latter with any other could have distribute them anywhere in the tree. However, if clear in the tree, this distinction is less apparent when looking at the structures themselves, so that it is hard to define peculiar features that cause this distinction. Enrichment of the database by new structures of VirB8-like proteins from Gram-positive bacteria will probably bring elements that underpin this partition, as well as it will certainly draw several new classes in this part of the tree. The current distribution separates the monomeric E. faecalis TraH/Orf8 from the class I + which contains TcpC from C.