Abstract

   Isoegomaketone is a water-soluble natural ketone compound that is
   commonly present in Rabdosia angustifolia and Perilla frutescens. At
   present, it is known that isoegomaketone has a wide range of
   pharmacological activity, but there has been no thorough investigation
   of its potential targets. As a result, we examined the potential
   targets of isoegomaketone using the network pharmacology approach. In
   our study, the TCM Database@Taiwan was utilized to search for the
   chemical formula. The pharmacological characteristics of isoegomaketone
   were then evaluated in silico using the Swiss Absorption, Distribution,
   Metabolism, and Excretion (Swiss ADME) and Deep Learning–Acute Oral
   Toxicity (DL-AOT) methods, and the potential isoegomaketone target
   genes were identified using a literature study. Additionally, using the
   clusterProfiler R package 3.8.1, the Gene Ontology (GO) enrichment
   analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
   enrichment analysis of target genes were performed. In order to obtain
   the protein interaction network, we simultaneously submitted the
   targets to the STRING database. After this, we performed molecular
   docking with respect to targets and isoegomaketone. Finally, we created
   visual networks of protein–protein interactions (PPI) and examined
   these networks. Our results showed that isoegomaketone had good
   drug-likeness, bioavailability, medicinal chemistry friendliness, and
   acceptable toxicity. Subsequently, through the literature analysis, 48
   target genes were selected. The bioinformatics analysis and network
   analysis found that these target genes were closely related to the
   biological processes of isoegomaketone, such as atherosclerotic
   formation, inflammation, tumor formation, cytotoxicity, bacterial
   infection, virus infection, and parasite infection. These findings show
   that isoegomaketone may interact with a wide range of proteins and
   biochemical processes to form a systematic pharmacological network,
   which has good value for the creation and use of drugs.

   Keywords: isoegomaketone, network pharmacology, molecular docking,
   biological activity, active molecule

1. Introduction

   Traditional Chinese medicine contains a large number of active drug
   molecules [[30]1]. Through the mining of traditional Chinese medicine,
   the development of drugs can be greatly enriched [[31]2].
   Isoegomaketone is a water-soluble natural ketone compound [[32]3]. It
   is commonly present in traditional Chinese medicines Rabdosia
   angustifolia and Perilla frutescens [[33]4]. Recent studies have shown
   that isoegomaketone has various types of biological activity, such as
   anti-inflammatory, anti-tumor, anti-rheumatoid arthritis, etc.
   [[34]5,[35]6,[36]7,[37]8,[38]9]. Thus, isoegomaketone is considered to
   be an important active component of Perilla frutescens and has great
   drug development potential. However, the potential targets of
   isoegomaketone and its potential for further development are not yet
   fully understood. At present, the extraction of potential targets from
   existing research and the use of computer methods to predict the
   potential development directions has become the main method
   [[39]10,[40]11,[41]12]. Compared with traditional methods, this method
   provides more convenient technology and a clearer direction for drug
   design and development.

   As a result, we applied the network pharmacology approach to
   comprehensively examine the pharmacological effects of isoegomaketone.
   Firstly, the Swiss Absorption, Distribution, Metabolism, and Excretion
   (Swiss ADME) and Deep Learning–Acute Oral Toxicity (DL-AOT) methods
   were used to evaluate in silico the drug properties of isoegomaketone.
   Furthermore, we provided candidate target genes through literature
   analysis. Additionally, Gene Ontology (GO) enrichment analysis, Kyoto
   Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and
   molecular docking were performed on these potential target genes.
   Finally, we thoroughly demonstrated the probable targets of the
   compound by creating the isoegomaketone pharmacological interaction
   network.

2. Method

2.1. Molecular Formula and In Silico Drug Properties of Isoegomaketone

   An important database resource that is free to use and readily
   available is the TCM Database@Taiwan ([42]http://tcm.cmu.edu.tw/
   (accessed on 25 March 2022)), which houses sources of chemical and
   pharmacological information on isoegomaketone [[43]13]. The Swiss ADME
   ([44]http://www.swissadme.ch/ (accessed on 25 March 2022)), which
   supports drug development, enables researchers to calculate the
   physicochemical descriptors and predict the ADME parameters,
   pharmacokinetic features, drug-like nature, and medicinal chemistry
   friendliness of one or more small compounds [[45]14]. Thus, in our
   study, the in silico drug properties of isoegomaketone were analyzed
   using the Swiss ADME.

   The DL-AOT prediction server
   ([46]http://www.pkumdl.cn:8080/DLAOT/DLAOThome.php (accessed on 25
   March 2022)) is a tool used to evaluate the acute oral toxicity (AOT)
   of small molecules [[47]15]. Thus, in our study, the in silico toxicity
   (LD50) of isoegomaketone was analyzed using the DL-AOT prediction
   server.

2.2. Target Gene Screening for Isoegomaketone

   All candidate potential targets are derived from literature analysis.
   Literature analysis was carried out through various academic databases,
   with the keyword “isoegomaketone”
   [[48]5,[49]6,[50]16,[51]17,[52]18,[53]19,[54]20,[55]21,[56]22,[57]23,[5
   8]24]. Finally, eleven published, peer-reviewed pharmacological studies
   on isoegomaketone were selected. All eleven selected pharmacological
   studies on isoegomaketone demonstrated high research quality and were
   all carried out via in vivo or in vitro methods.

2.3. Analysis of PPI Network

   One of the online databases that compiles all publicly available
   sources of knowledge of PPI is STRING 11.0 ([59]https://string-db.org/
   (accessed on 29 March 2022)), which enables users to supplement the
   knowledge already known about PPI with computational predictions
   [[60]25]. We uploaded 48 putative targets of isoegomaketone to the
   STRING database in order to build a PPI network, with the species
   defined as “Homo sapiens” and the minimum interaction score set to 0.4.
   After this, the outcomes were imported into Cytoscape 3.7.2 for visual
   evaluation. Cytoscape, an open-source software platform for visualizing
   complex networks, can calculate the parameters of each node in the
   network diagram, such as the degree, betweenness centrality (BC), and
   closeness centrality (CC) [[61]26]. We utilized the cytoHubba plug-in
   topological method to identify the significant protein nodes and
   subnetworks in the network, after filtering the target nodes by the
   matching median values of degree values, BC and CC in the PPI network
   [[62]27].

2.4. Gene Function and Pathway Enrichment Analysis

   GO functional annotation analysis is a standard method used to carry
   out large-scale functional enrichment analyses of genes, and it
   comprises biological processes (BP), molecular functions (MF), and
   cellular components (CC) [[63]28]. It is possible to assign functional
   meanings to genes and genomes at the molecular and higher levels by
   adopting the widely used Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway [[64]29]. Using the clusterProfiler R package 3.8.1 with FDR <
   0.2 (FDR, false discovery rate) and p value < 0.05, KEGG pathway
   analysis and GO analysis were carried out, and the most important
   targets engaged in the pertinent biological processes were examined.

2.5. Compound–Target Molecular Docking

   The 2D structure of isoegomaketone, downloaded from PubChem
   ([65]https://pubchem.ncbi.nlm.nih.gov/ (accessed on 26 March 2022)) in
   mol2 format, was imported into the AutoDockTools 1.5.6 software. After
   checking its spatial structure, adding atomic charges, assigning atomic
   types, and changing all flexible bonds to rotatable by default, it was
   saved in pdbqt format as a docking ligand. The 3D structures of
   target-gene-associated proteins were downloaded from the Uniprot
   database ([66]https://www.uniprot.org/ (accessed on 26 March 2022)) and
   the pdb database ([67]https://www.rcsb.org/ (accessed on 26 March
   2022)), and they were then saved in pdb format. AutoDockTools 1.5.6
   software was used to carry out the removal of origin ligands, removal
   of water molecules, hydrogenation, charge addition, and various
   optimizations. The grid box was set as the default value and the final
   optimized proteins were saved in pdbqt format as docking acceptors.
   Molecular docking was performed using AutoDock Vina 1.1.2. The protein
   structure was set during molecular docking to be a rigid macromolecule,
   and the Genetic Algorithm Parameters algorithm was used. PyMOL was used
   to illustrate the outcomes for the group in which each protein had the
   lowest binding energy.

3. Result

3.1. Molecular Formula and In Silico Drug Properties of Isoegomaketone

   We obtained the chemical formula for isoegomaketone using the TCM
   Database@Taiwan database ([68]Figure 1). Through Swiss ADME, we
   obtained essential ADME-related data on isoegomaketone, and the
   drug-likeness as well as the medicinal chemistry of isoegomaketone were
   subsequently evaluated via the classic formula. The results are
   provided in [69]Table 1. Regarding the drug-likeness, lead-drug
   likeness, and medicinal chemistry friendliness, the pharmacokinetic
   characteristics of isoegomaketone were consistent with Lipinski’s rule
   of five, the Ghose filter, Veber’s Rule, and the RO (3) rule, and the
   synthetic accessibility was 2.9. Definitions of these rules and
   parameters can be found in References