dataesr/scientific-paragraphs-categorization huggingface dataset:
GAGs: from sequences to 3D models About 50 GAG sequences interacting with proteins, identified by manual curation of the literature  (19) , and crossreferenced with the ChEBI database (https://www. ebi. ac. uk/chebi/ (20)) in agreement with the IMEx curation rules, have been added to MatrixDB. A further cross-reference to the major glycan repository GlyTouCan (https://glytoucan. org/ (13)) has been added to all GAG entries of MatrixDB in order to increase the interoperability of MatrixDB with glycobiology databases. The machine-readable GlycoCT format, a unifying sequence format for carbohydrates  (14) , and the images of GAG sequences based on the SNFG  (15)  have been added on the Biomolecule Report pages of GAG entries (Figure  1 ). These formats will allow users to computationally browse protein-GAG interaction data in order to identify the chemical groups of GAGs (N-sulfate, O-sulfate, and N-acetyl groups), and/or the uronic acid (glucuronic or iduronic acid), which are involved in protein binding, and to determine if they are specific of one structural and/or functional protein family.
Integration of gene expression and quantitative proteomic data The updated version of MatrixDB contains gene expression data imported from Expression Atlas (https://www. ebi. ac. uk/gxa/home), including data from 450 human donors and over 9600 RNA-seq samples across 51 tissue sites and 2 cell lines (transformed fibroblasts and EBV-transformed lymphocytes) from the Genotype-Tissue Expression (GTEx) Project (v7 release, https://gtexportal. org/home/). They are displayed as anatomograms, heatmaps and boxplots on the Biomolecule Report page of protein entries. Quantitative proteomic datasets of 14 different tissues and tumors imported from the ECM atlas (http://matrisomeproject. mit. edu/ecm-atlas/ (2)) have been added to the Biomolecule Report pages and are displayed as histograms. This allows the integration in the interaction networks of quantitative data reflecting the abundance of proteins expressed simultaneously in the same tissue in vivo.
Both gene expression and quantitative proteomic data can be used to build disease-specific or tissue-specific ECM interaction networks such as basement membrane networks (Figure  2 ). The largest interaction network comprises all human biomolecules retrieved by querying MatrixDB with 'basement membrane' in the advanced search (Figure  2A ). Proteomic data have then been used to select within this network the biomolecules identified in human glomerular basement membrane (Figure  2B ), human retinal vascular basement membrane (Figure  2C ), human lens capsule basement membrane (Figure  2D ), and human inner limiting membrane (Figure  2E ). Proteomic data are thus used to determine the biomolecules and the core network common to the studied basement membranes (e. g. COL4A1, COL4A2, COL4A3, COL4A4, COL4A5, NID2) and to identify biomolecules that are found only in a particular basement membrane (e.
A new visualization tool of the 3D structures of proteins, GAGs and interacting complexes The 3D structures of proteins and GAGs are visualized on the Biomolecule Report pages with a new visualization tool using the PDB Component Library (http://www. ebi. ac. uk/pdbe/pdb-component-library/index. html) and Lite-Mol  (18). In addition, protein sequences, secondary structures, topological diagrams, and domain annotations from CATH and SCOP, when available, are displayed on the Biomolecule Report pages thanks to this tool. 3D structure of complexes formed via interactions of two or more participants are displayed on the Experiment page when available in the Protein Data Bank (https://www. rcsb. org/ (  21 )).
Representation of n-ary interactions and homodimers The database schema has been improved. Indeed, the core classes that stored associations and experiments have been redesigned to speed up queries, ease data import and export and represent n-ary experiments. n-ary experiments are now represented as such and Spoke-expanded into binary associations when appropriate (e. g. when an n-ary experiment comprises a single bait Spoke expansion is performed around this bait  (22) ). The database schema now closely matches the PSI-MI 3. 0 XML specification  (23) , thus greatly facilitating data exchange with our partners within the IMEx consortium.
Conclusion The representation of GAG sequences binding to proteins in the machine-readable GlycoCT format is useful to browse MatrixDB to determine the chemical groups and sizes of GAGs contributing to their interactions with structural and/or functional protein families, and to decipher the GAG glycocodes. The possibility to build 3D models of GAGs from sequences written in the GlycoCT format using the GAG builder tool further refines our understanding of the molecular mechanisms of GAG-protein interactions and provides new insights into the 3D structure of GAGprotein complexes. The integration of quantitative ECM proteomic datasets is another major improvement, which allows the building of tissue-specific interaction networks based on the presence of the proteins and not only on expression data, which is an asset given the weak correlation between transcriptomic and proteomic datasets.
FUNDING Fondation pour la Recherche Médicale  [ DBI20141231336  to M. D. , N. T. M. , S. R. B. ];  Institut Francais de Bioinformatique  [ ANR-11-INBS-0013 ,  Glycomatrix  project, call 2015 to O. C. , S. P. , N. T. M. , S. R. B. ]; GDR GAG [CNRS, GDR 3739, Structure,  Fonction et Régulation des Glycosaminoglycanes  to S. R. B. , S. P. ];  Cross Disciplinary Program Glyco@Alps, within the framework 'Investissements d'Avenir' program  [ ANR-15IDEX-02  to S. P. ]. Funding for open access charge:  Fondation pour la Recherche Médicale  [ DBI20141231336 ].
DATA AVAILABILITY MatrixDB interaction data are available at http://matrixdb. univ-lyon1. fr/ The ECM atlas is available at http://matrisomeproject. mit. edu/ecm-atlas/ The CT23D converter tool is an open source collaborative initiative available in the GitHub repository (https: //github. com/OlivierClerc/convert-glycoct-inp). The GAG builder tool, integrated into MatrixDB database, is also available at http://glycan-builder. cermav. cnrs. fr/gag/.
While some computationally-driven strategies make use of known metabolic reactions albeit not necessarily in the same species  (Rodrigo et al. , 2008; Moriya et al. , 2010)  others allow to design pathways that encompass novel reactions not stored in metabolic databases. These latter tools make use of retrosynthesis algorithms  (Marchant et al. , 2008; Moriya et al. , 2010; Henry et al. , 2010; Carbonell et al. , 2011b Carbonell et al. , , 2014a;; Yim et al. , 2011; Liu et al. , 2014; Campodonico et al. , 2014; Hadadi et al. , 2016a). Retrosynthesis algorithms take as input a set of metabolites, for instance the metabolites in a growth medium or the metabolites of a chassis strain model, and the set of target compounds to bioproduce. Ideally the target compounds could be any molecule in the chemical space.
Enzymatic promiscuity Reactions for retrosynthesis applications should be modelled with a controlled degree of generalization for their substrates and products. Indeed, reaction rules containing a full description of substrates and products chemical structures cannot be applied on new compounds. This is the case for classic metabolic models and database and their lack of generalization prohibits the generation of novel pathways. The use of generalized chemical transformations is required in order to be able to predict new metabolic transformations. Such predictions are necessary since reaction databases are not complete  (Altman et al. , 2013; Chang et al. , 2015)  and side enzymatic activities are often underestimated. This lack of knowledge on alternative enzymatic activities is currently a critical limiting factor for metabolic engineering since it has been estimated that 37% of E.
coli K12 enzymes have a promiscuous activity for other substrates structurally similar to their main known substrate  (Nam et al. , 2012). In order to be able to generate new metabolic transformations (and new compounds) one thus needs to use generalized reactions to model enzymatic promiscuity, i. e. rules that can be applied to different substrates, and eventually on compounds absent from the databases. For instance, BNICE  (Henry et al. , 2010; Jeffryes et al. , 2015; Hadadi et al. , 2016a)  and SimPheny  (Yim et al. , 2011)  use a collection of reaction rules that, as depicted in Fig. 1 , can be applied to any ketones (including oxaloacetate) since their encoding is focused on the reaction centre.
Systematic rule generation Reaction rules can be computed in principle by processing the set of reactions stored in metabolic databases. However there are some Fig. 2. RetroPath2. 0 rules and corresponding SMARTS for reaction 2. 6. 1. 1 at various diameters. A. Full reaction 2. 6. 1. 1 with atom mapping. B. The list of broken bonds (-1) and bonds formed (+1) is given by their atom numbers. C. The corresponding SMARTS for the component modelling promiscuity on L-glutamate: Substrate + Oxaloacetate = Product + 2-Oxoglutarate. D. The corresponding SMARTS for the component modelling promiscuity on oxaloacetate: Substrate + L-Glutamate = L-Aspartate + Product. C and D. Rules are encoded as reaction SMARTS and characterized by their diameter (∞ purple, 4 blue or 0 green), that is the number of bonds around the reaction centre (atoms 6, 10 and 14, 19) defining the atoms kept in the rule.
, 2014; Sivakumar et al. , 2016). Ideally, the diameter used should directly be linked to known promiscuity of an enzyme's sequence. In our experience, a diameter of 6-8 (see Supplementary Note 1 for a detailed discussion on diameter selection and promiscuity) is generally a good trade-off to cover known reactions' specificity with a reasonable amount of promiscuity predictions (see Section 4. 1. 3 for an evaluation of rules performance for promiscuity classification and  (Carbonell et al. , 2011a; Faulon et al. , 2008) ). Using the procedure outlined in the caption of Fig. 2 , when applied to the MetaNetX database  (Moretti et al. , 2016)  the number of rules returned is between 6900 and 19,000 depending on the parameters used to model enzymatic promiscuity (diameter) for the 31,527 reactions stored in MetaNetX (MNXR identifiers, v.
Method The workflow proposed in this section can a priori be used to all systems presented in Table  1  to construct retrosynthesis maps as long as reaction rules can be coded by reaction SMARTS. As examples, we provide such set of rules extracted from the SimPheny, BNICE and RetroPath systems. Our computational methods make use of in-house algorithms  (Carbonell et al. , 2014a) , RDKit routines  (Landrum, 2016)  and KNIME nodes  (Berthold et al. , 2008). They have been implemented in the form of a KNIME workflow -called RetroPath2. 0-(Fig. 3 ) that we provide in the Supplementary materials, in addition of sets of rules, examples, and useful data files. Updates will be published on myExperiment. org (https://www. myexperiment. org/workflows/4987. html).
Collected reaction rules Rules for SimPheny and BNICE were extracted from  Yim et al. (2011)  and  Henry et al. , (2010)  respectively, and manually entered by using Chemaxon Marvin Sketch software products (15. 5. 18, 2015, http://www. chemaxon. com). For each rule atom mapping was calculated by Marvin Sketch and the resulting rules were stored in SMARTS format as in Fig. 2.
Generated reaction rules We used MetaNetX version 2. 0  (Moretti et al. , 2016)  as the reference database for metabolic reactions that we encoded in rules. MetaNetX is a meta-database that compiles into a single reference namespace both reactions and metabolites extracted from main metabolic databases such as KEGG, Metacyc, Rhea or Reactome. Reactions can contain many substrates and many products. We performed an Atom-Atom Mapping (AAM) using the tool developed by  (Rahman et al. , 2016)  on all Me-taNetX reactions (Fig. 2A ). We filtered out transports reactions and those involving compounds with incomplete structures (class of compounds, R-groups, etc. ). Stereochemistry was removed. Multiple substrates reactions were decomposed into components (panel C and D in Fig. 2 ). There are as many components as there are substrates and each component gives the transformation between one substrate and the products.
We provide in Supplementary a subset of 14,300 rules for E. coli metabolism, both in direct and reversed direction. The rules were selected based on the MetaNetX binding to external databases and the iJO1366 whole-cell E. coli metabolic model  (Orth et al. , 2011). 3. 2. Building (retrosynthesis) reaction networks between two pools of compounds using the RetroPath2. 0 workflow The RetroPath2. 0 workflow essentially follows an algorithm proposed by some of us  (Carbonell et al. , 2011b). After removing all source compounds already in the sink set, the workflow applies the rules to each of the compounds of the source set. For each compound, the products are computed using the RDKit KNIME nodes  (Landrum, 2016). Products are standardised and duplicates are merged. All pairs substrate-product are added to the growing network along with the reaction rules linking them.
Score rules by enzyme sequence consistency Predicted reactions in the final graph generated by the RetroPath2. 0 retrosynthesis workflow need to be associated with enzyme sequences in the final engineering of the pathways. The selection of such sequences should look for a trade-off between the specificity of the reaction rule and the information available in enzyme databases for the reaction through the EC classification. Whereas the EC classification has traditionally provided a hierarchical numerical classification of enzyme-catalysed reactions to progressively describe reactions in finer detail, RetroPath2. 0 introduces a similar hierarchical classification that is controlled by the diameter used in rule generation. In some cases the diameter of the reaction rule found by the RetroPath2.
0 workflow might be high, i. e. highly specific to that reaction. However, it often occurs that there is no annotated enzyme sequence for the rule. In order to find some candidate sequences, we look into reactions that are close according to the EC hierarchy for each EC class containing at least one instance of the rule at given diameter. Traversing both rules diameter hierarchy and the underlying EC classes allows the selection of plausible sequence candidates for each reaction rule. We compiled the set of Uniprot sequence identifiers annotated for reactions by looking at the cross-link annotations in MetaNetX for Rhea and MetaCyc databases. In total 208,980 sequences from 5388 organisms were associated to 7793 reactions. At a given diameter of the rule, we iteratively assigned sequences to rules.
First, reactions with annotated sequences were collected for each generated rule. Since a rule can represent one or more reactions at a given diameter, sequences coming from different reactions sharing the same rule were aggregated into a single set for that rule. These direct annotations only provided a partial coverage for the total rules in the database. For instance, at diameter d = 8, there were 7898 orphan rules, i. e. rules that were generated from reactions lacking sequence annotation (Supplementary Table  1 ). Similarly, there were 6280 orphan reactions at diameter d = 8. In order to increase the coverage, we considered the EC class of the reaction when such information was available. Sequences associated with reactions sharing strictly the same EC class were combined together. Adding together such annotations for the same EC class fixed issues related to partial annotations for the less common reactions.
According with this algorithm, our database of 208,980 amino-acid sequences was clustered into 22,221 clusters for a similarity threshold of 0. 5. We used a penalty score for the rules based on the number of sequence clusters n rule contained in the sequences selected for a given rule: = score rule log n ( ) ( ) rule 10 (1) where the logarithm is applied for regularization. A penalty score of 0 implies high specificity, as this means that all sequences belong to a single cluster, while high penalty scores imply multiple clusters and therefore low specificity in the sequence annotation.
RetroPath2. 0 computes the scope for each queried compound. It can be visualized and explored to retrieve the pathways thanks to ScopeViewer, a humble web-application that we provide in Supplementary. Note that the provided workflow does not explicitly extract the pathways and does not rank them. Yet, we provide at https://github. com/brsynth/rp2paths a separate utility program "RP2paths" allowing one to enumerate pathways from the results generated by RetroPath2. 0.
Results We validated our set of rules with RetroPath2. 0 by checking that they were able to reproduce the known metabolic space, and that they could be used to perform reaction classification. The capability of RetroPath2. 0 to perform retrosynthesis was confronted to in vivo experiments by counting the number of bioproduction pathways found for targets extracted from a database of metabolic engineering successes. We also emphasized the versatile usage of RetroPath2. 0 by an original application to design biosensors (Supplementary Note 3).
Coverage of known metabolic space In order to check the potency of the rules, i. e. that they could indeed be used to predict reactions, we tried to retrieve all reference reactions of MetaNetX from the rules. We compared three dataset of monosubstrate rules according to their origin: SimPheny  (Yim et al. , 2011) , BNICE  (Henry et al. , 2010)  and RetroPath2. 0. To make a fair comparison we selected from all MetaNetX reactions a subset of 13,000 reactions having an associated EC number and a structure for all its compounds (SimPheny and BNICE rules are based on EC numbers). We extracted from those 6000 substrates and 7000 products (MetaNetX identifiers) excluding cofactors. For each rule dataset, all rules were applied on the set of substrates using the workflow with default parameters. We counted the number of products that could be regenerated and the number of generated compounds that were referenced in Me-taNetX.
Remarkably given the number of rules considered, 34% of MetaNetX products were recovered by SimPheny rules (50), and 41% by BNICE rules (86). They respectively generated 75,400 and 59,000 compounds, among which 5% and 7% could be found in MetaNetX and are thus connected to a biological database. Since RetroPath2. 0 rules were generated from MetaNetX data we expected a better coverage over the products. This was indeed the case with 96% recovered products from MetaNetX's reactions. The few missed products originated from reactions that could not be encoded in rules due to atom-atom mapping issues. Additionally, 63% of the 17,500 compounds generated by RetroPath2. 0 are new to MetaNetX, which highlights the capability of our rule dataset to generate a reasonable amount of new compounds. The fact that RetroPath2.
RetroPath2. 0 rules for reaction classification We evaluated the ability of our rules to perform automated reaction classification. To that end, reactions in the database that contained EC class annotations were grouped into their corresponding EC class at the third level. We then computed the similarity between reactions based on the signature content of their rules. For a given diameter d, each rule was decomposed into its elementary signatures  (Carbonell et al. , 2014a)  and similarity between two given reactions R 1 and R 2 was computed by means of the Jaccard similarity coefficient T R R ( , ) d 1 2 applied to the two reaction rules: = ∩ ∪ T R R σ R σ R σ R σ R ( , ) ( ) ( ) ( ) ( ) d d d d d 1 2 1 2 1 2 (2) The previous expression ranges between 0 (minimum similarity) and 1 (maximum similarity) and has been often applied to compute similarity between compounds or even reaction that are described by binary fingerprints (EC-BLAST  (Moriya et al.
For each reaction in the database, we computed its corresponding rule and similarities based on a diameter range from 0 to 8. In total, rules were computed for 13,782 reactions contained in the database. We used a = 2 as regularization parameter. We then tested the discriminant ability of using such reaction global similarity measure for reaction classification. Our tests were performed using the R package ROCR. We created a positive and negative set for each EC class. The positive set was formed by the set of reactions annotated for this EC class. A balanced training set was then built by randomly selecting from the negative set. For each EC class containing at least 10 data points, as well as for the total set of balanced training set we computed the area under the ROC curve (AUC), resulting in an overall AUC of 0.
The algorithm traverses both the reaction and sequence space in order to score reaction specificity and more specific rules get lower scores. To evaluate the ability of the score to represent enzyme specificity, we have analysed a reference set of enzymes in E. coli that have been classified as either specific or generalist, i. e. if they can catalyse one or multiple reactions  (Nam et al. , 2012). For each gene, we took their associated reactions in the EcoCyc database  (Keseler et al. , 2013). Each reaction was mapped into their associated rule at several diameters d. The resulting scores for each gene were then aggregated. We mapped in total 787 E. coli genes, with 602 specific vs. 185 generalist enzymes, respectively. Notably, the scores computed in that way, as shown in Supplementary Fig.
Coverage of bioproduction pathways The Learning Assisted Strain EngineeRing (LASER) database is a repository for metabolic engineering strain designs  (Winkler et al. , 2015). It stores more than 600 successful metabolic engineering designs  (Winkler et al. , 2016)  that have been manually curated from the literature. Those examples are particularly appealing for testing retrosynthesis features since they include an ideal dataset of authentic positive examples of bioproduction pathways, sometimes involving heterologous enzymes. We extracted all compounds targeted for production described in the LASER database (release f6ce080a8993) and used them to assess the ability of RetroPath2. 0 to find retrosynthesis pathways for real-life applications when used with all the rules from MetaNetX. The structures of the target compounds were inferred from their name by querying PubChem and ChemSpider.
160 compounds targeted for bioproduction were extracted from LASER. To complete further this dataset, we extracted 68 compounds (MBE dataset) published in Metabolic Engineering in 2016 (volumes 33-38), a period not covered by LASER. These two datasets contained 203 distinct compounds in total once merged together based on their structure (standard InChI). Furthermore, we removed E. coli endogenous compounds that were used as our "sink". Finally, 146 distinct compounds were collected to serve as "source" compounds. Compounds from E. coli were extracted from iJO1366 whole-cell model  (Orth et al. , 2011)  and MetaNetX cross-references. We ignored compounds that belong to so-called "blocked pathways" which are by definition impossible to produce or consume at steady-state in a metabolic model.
Moreover, any other applications where the problem can be framed into source, sink and rule sets can be processed by the workflow including problems where compounds are not metabolites and reactions are not metabolic reactions. The most critical feature of a reaction network prediction system is certainly how the reactions are encoded and from where this knowledge was extracted. In our case, we choose to adopt a reaction encoding based on SMARTS, a widely accepted compound query language  (Daylight, 2017)  that was already used successfully in such context  (Hadadi and Hatzimanikatis, 2015). Unlike most rule-based reaction prediction systems, RetroPath2. 0 rules are not built around the Enzyme Commission nomenclature, but rather from an automatic translation of enzymatic reactions extracted from databases, which we believe offers a refined view of enzyme's capabilities.