Organic Chemicals

A taxonomy requires a well-defined, structured hierarchy. Following standard notation, we use the term “category” to refer to any chemical class (at any level), each of which corresponds to a set of chemicals. These categories are arranged in a tree structure (Additional file 1). The main relationship type connecting these different categories is the “is_a” relationship. The rationale behind the choice of a tree structure was to provide a detailed annotation represented via a simple data structure, which could be easily understandable by humans. Moreover, as described in the results section, ClassyFire provides a list of all parents of a compound, which makes it easy to infer all of its ancestors. Inspired by the original Linnaean biological taxonomy [4 (link)], we assigned the terms Kingdom, SuperClass, Class, and SubClass to denote the first, second, third and fourth levels of the chemical taxonomy, respectively. The top level (Kingdom) partitions chemicals into two disjoint categories: organic compounds versus inorganic compounds. Organic compounds are defined as chemical compounds whose structure contains one or more carbon atoms. Inorganic compounds are defined as compounds that are not organic, with the exception of a small number of “special” compounds, including, cyanide/isocyanide and their respective non-hydrocarbyl derivatives, carbon monoxide, carbon dioxide, carbon sulfide, and carbon disulfide. For the complete current list of exceptions, please see Additional file 1. The classification of compounds into these two kingdoms aligns with most modern views of chemistry and is easily performed on the basis of a compound’s molecular formula. The other levels in our classification schema depend on much more detailed definitions and rules that are described below. SuperClasses (which includes 26 organic and 5 inorganic categories) consist of generic categories of compounds with general structural identifiers (e.g. organic acids and derivatives, phenylpropanoids and polyketides, organometallic compounds, homogeneous metal compounds), each of which covers millions of known compounds. The next level below the SuperClass level is the Class level, which now includes 764 nodes. Classes typically consist of more specific chemical categories with more specific and recognizable structural features (pyrimidine nucleosides, flavanols, benzazepines, actinide salts). Chemical Classes usually contain >100,000 known compounds. The level below Classes represents SubClasses, which typically consist of >10,000 known compounds. There are 1729 SubClasses in the current taxonomy. Additionally, there are 2296 additional categories below the SubClass level covering taxonomic levels 5–11.
Altogether this extensive chemical taxonomy contains a total of 4825 chemical categories of organic (4146) and inorganic (678) compounds, in addition to the root category (Chemical entities). As a whole, this chemical taxonomy can be represented as a tree with a maximum depth of 11 levels, and an average depth of five levels per node (Fig. 2). As with any structured taxonomy, the creation of a well-defined hierarchical structure offers the possibility to focus on a sub-domain of the chemical space, or a specific level of classification. A more complete description of this taxonomic hierarchy can be found in the Additional file 1: Table S1. The chemical taxonomy and its hierarchical structure provided using the Open Biological and Biomedical Ontologies (OBO) format [33 (link)], which may help with its integration with respect to semantic technology approaches. The resulting OBO file was generated with OBO-Edit [34 (link)], and can be downloaded from the ClassyFire website.Fig. 2

Illustration of the taxonomy as a tree

An overarching goal of this work is to create a platform for exploring the chemistry of the phytochemicals of Indian medicinal plants. Evaluation of the phytochemicals of Indian medicinal plants for their druggability or drug-likeliness will facilitate the identification of molecules for drug discovery. We would like to emphasize that synonymous chemical names are pervasive across the literature on traditional Indian medicine which were mined to construct this database. In order to remove redundancy, we manually annotated the common names of phytochemicals of Indian medicinal plants compiled from literature sources with documented synonyms and standard chemical identifiers (Fig. 1) from Pubchem^{44 (link)}, CHEBI^{45 (link)}, CAS (https://www.cas.org/), CHEMSPIDER⁴⁶ , KNAPSACK^{47 (link)}, CHEMFACES (http://www.chemfaces.com), FOODB (http://foodb.ca/), NIST Chemistry webbook⁴⁸ and Human Metabolome database (HMDB)^{49 (link)}. While assigning standard identifiers to phytochemicals in our database, we have chosen the following priority order: Pubchem^{44 (link)}, CHEBI^{45 (link)}, CAS, CHEMSPIDER⁴⁶ , KNAPSACK^{47 (link)}, CHEMFACES, FOODB, NIST Chemistry webbook⁴⁸ and HMDB^{49 (link)}. We highlight that this extensive manual curation effort led to the mapping of more than 15000 common names of phytochemicals used across literature sources to a unique set of 9596 standard chemical identifiers. Phytochemicals which could not be mapped to standard chemical identifiers were excluded from our finalized database. Our choice to include only phytochemicals with standard identifiers and structure information was dictated by our goal to investigate the chemistry and druggability of phytochemicals of Indian medicinal plants. We remark that the 2D structure information for the unique set of 9596 IMPPAT phytochemicals was obtained using the standard chemical identifiers from the respective databases. We have also determined the chemical classification of the IMPPAT phytochemicals using ClassyFire^{50 (link)} (http://classyfire.wishartlab.com/). ClassyFire^{50 (link)} gives a hierarchical classification for each chemical compound into kingdom (organic or inorganic), followed by super-class, followed by class, followed by sub-class. Note that ClassyFire classifies organic compounds into 26 super-classes. In a nutshell, this largely manual effort to compile a non-redundant chemical library of 9596 phytochemicals of Indian medicinal plants with standard identifiers and structure information will serve as valuable resource for natural product-based drug discovery in future. Moreover, the use of standard chemical identifiers will enable effortless integration of our IMPPAT database with other data sources.

To reveal the association between RKN parasitism and the variation in endophytic nitrogen-fixing bacteria, seedlings of tomato cultivar cv Xinzhongshu No.4 were planted in Meloidogyne sp.-parasitized soils by supplying different nitrogen sources, in pot experiments carried out from June to August 2020. The soil used was collected from a nursery field with a 3-year nematode parasitism history. In total, 11 different inorganic or organic nitrogen compounds and two biofertilizers were selected for testing (Additional Table S9). Nitrogen sources were separately applied to each plot at 300 mg N/Kg soil after tomato seeding (keeping 5 tomato plants per pot out of 8–10 seeds sowed). The two biofertilizers were fresh chicken manure (fermented) and commercial chicken manure-based biofertilizer. Each nitrogen amendment treatment was performed with three replicates. Pot-planted tomato plants in soil without nematode parasitism history were used as positive control, using tomato plants in soil with nematode parasitism history but no nitrogen supplementation as negative control. At 55 days after seeding, tomato plants were harvested for the evaluation of RKN parasitism, quantifying the attack severity using the number of galls per plant [22 (link), 49 (link)]. Subsequently, root and/or gall samples were separately collected from healthy or nematode-parasitized tomato plants, as described above. Together, 57 samples (45 root, and 12 gall samples) were collected from healthy and nematode-parasitized tomato plants, including healthy control, parasitized control, and plants treated with 13 different nitrogen sources (Additional Table S9). Furthermore, community analysis for the effect of nitrogen supplement on root endophytic microbiota was performed, following the procedure described above.

To explore the relationship and action mechanisms between candidate proteins and active ingredients, molecular docking simulations were conducted to evaluate the strength and mode of interactions between components and hub targets. Crystal structures of critical targets protein receptors were acquired from the Protein Data Bank database (http://www.rcsb.org/) in Protein Data Bank format. The active component structure as ligands was downloaded from the PubChem compound database (http://pubchem.ncbi.nlm.nih.gov/). After removing the water molecules and organic compounds from ligands and proteins and adding non-polar hydrogen bridge to them by PyMol 2.6.0 software, the format of the molecular ligands and proteins was transformed into pdbqt format. Subsequently, the docking of ligands and proteins was performed by AutoDockTools 1.5.7 software. Each group of molecular docking was run 50 times, and the ionization energy was calculated. The minimum energy value was selected as the docking affinity. Finally, the docking results were visualized using PyMol software.