Abstract
Natural medicines (i.e., herbal medicines, traditional formulas) are
useful for treatment of multifactorial and chronic diseases. Here, we
present KampoDB ([40]http://wakanmoview.inm.u-toyama.ac.jp/kampo/), a
novel platform for the analysis of natural medicines, which provides
various useful scientific resources on Japanese traditional formulas
Kampo medicines, constituent herbal drugs, constituent compounds, and
target proteins of these constituent compounds. Potential target
proteins of these constituent compounds were predicted by docking
simulations and machine learning methods based on large-scale omics
data (e.g., genome, proteome, metabolome, interactome). The current
version of KampoDB contains 42 Kampo medicines, 54 crude drugs, 1230
constituent compounds, 460 known target proteins, and 1369 potential
target proteins, and has functional annotations for biological pathways
and molecular functions. KampoDB is useful for mode-of-action analysis
of natural medicines and prediction of new indications for a wide range
of diseases.
Introduction
Traditional medicines are used clinically in many areas of the world,
including in Japan (Kampo), China, Korea, India (Ayurveda) and
Perso-Arabic countries (Yunani). Traditional medicines usually comprise
mixtures of the crude extracts from several medicinal herbs, each of
which contains multiple components. The World Health Organization took
the initiative to promote the globalization of traditional medicine in
1972 by founding a Division of Traditional Medicine. Approximately 45
years later, traditional medicines are widely available and are
commonly used in many parts of the world. Recently, there has been a
dramatic worldwide increase in the number of patients suffering from
complex diseases, such as lifestyle-related diseases, cardiovascular
diseases, diabetes, and immune-mediated diseases. It can be difficult
to cure these complex diseases effectively with Western medicines by
using the “one disease, one target, one drug” approach, and there are
growing expectations toward the “one disease, multiple targets,
multiple drugs” approach with multi-effective drugs such as traditional
medicines used in combination therapies with Western medicines.
Kampo medicine originated from ancient Chinese medicine but evolved
independently over a long period of time (more than 1500 years) to
become a style individual to Japan. Kampo formulas often differ from
Chinese or Korean traditional formulas, although many of the same
medicinal herbs are used for traditional medicines across eastern Asian
countries. Kampo medicines are decoctions or dry powders that include
pharmaceutical active ingredients extracted by boiling from a mixture
of naturally derived medicinal herbs. They are generally
factory-produced by pharmaceutical companies in Japan and provided in a
ready-to-use form. To assure the quality of Kampo products, the
Japanese Ministry of Health, Labour and Welfare published their
“Guideline on Data Requirements for Ethical Kampo Formulation” in 1985,
resulting in Kampo medicines becoming standardized with respect to the
quality and quantity of their ingredients. The Ministry maintains
oversight of Kampo medicines. Japanese traditional formulas Kampo
medicines are prescribed in hospitals in Japan as either monotherapy or
harmoniously combined therapy with standard western therapy and >80% of
medical doctors prescribe Kampo medicines in Japan^[41]1. Thus, Kampo
medicines are established as a pivotal part of mainstream medicine in
Japan and the cost of Kampo medicines is covered by the National Health
Insurance. On the other hand, in the United States, National Institutes
of Health (NIH) now supports clinical and basic research on the
traditional medicine. In recent years, NIH support and the US Food and
Drug Administration (FDA) guideline on investigating botanical drug
products, including complex formulas containing many constituents, has
fostered the development of botanical drugs in the United
States^[42]1,[43]2. Presently, randomized, double-blind,
placebo-controlled clinical trials of some Kampo medicines (e.g.,
Daikenchuto for bowel diseases) are underway for phase II or phase III
studies for FDA approval in the United States.
However, the pharmacotherapy with Kampo medicines greatly depends on
the empirical knowledge of medical doctors in practice, and there is
insufficient scientific evidence explaining the underlying molecular
mechanisms of Kampo medicines. The mechanisms of Kampo medicines are
different from those of ordinary medicines. The efficacies of Kampo
medicines stem from multiple compound–multiple target interactions.
Figure [44]1 shows an illustration of the difference of the
mode-of-action between ordinary medicines and Kampo medicines. It is,
therefore, indispensable to establish fundamental technologies to
comprehensively analyze the underlying mechanisms of every
pharmacological action of multicomponent Kampo medicines in the human
body as a complex system.
Figure 1.
[45]Figure 1
[46]Open in a new tab
An illustration of the difference of the mode-of-action between
ordinary medicines and Kampo medicines. In ordinary medicines, the
efficacies stem from one compound–one target interaction. On the other
hand, in Kampo medicines, the efficacies stem from multiple
compound–multiple target interactions.
In recent biomedical science, clinical and molecular data for Kampo
medicine-based pharmacotherapy have been accumulated, and a variety of
omics data are becoming available in the genome, transcriptome,
proteome, metabolome, phenome, and diseasome. These “big data” are
useful resources for mode-of-action analysis of Kampo medicines; thus,
there is a strong need to develop databases and associated tools for
Kampo medicines. Many databases for Western medicines exist (e.g.,
DrugBank^[47]3, KEGG DRUG^[48]4, Matador^[49]5, SuperTarget^[50]5,
ChEMBL^[51]6, Therapeutic Target Database^[52]7, BindingDB^[53]8,
PubChem^[54]9, Comparative Toxicogenomics Database^[55]10). However,
there is no integrated database of Kampo medicine-related chemical and
biological data, and clinical research data and clinical findings.
There is a wiki-system database of Kampo medicines and crude
drugs^[56]11, but it is mainly Kampo medicine-related pharmacognostical
and chemical database and thereby cannot help to understand the
mode-of-actions and further clinical applications of Kampo medicines.
Here, we present KampoDB
([57]http://wakanmoview.inm.u-toyama.ac.jp/kampo/), a novel platform
for the analysis of natural medicines, which provides various useful
scientific resources on Kampo medicines, constituent herbal medicines,
constituent compounds, and target proteins of these constituent
compounds. Potential target proteins of these constituent compounds
were predicted by docking simulations and machine learning methods
based on large-scale omics data (e.g., genome, proteome, metabolome,
interactome). Therefore, KampoDB is useful for understanding the
mode-of-action of natural medicines in terms of biological pathways and
molecular functions of target proteins, which can lead to new
indications for a wide range of diseases. The present study aims to
elucidate the underlying mechanisms of Kampo medicines, while
predicting their target proteins and new indications, thereby
repositioning Kampo medicines for their extensive application in
clinical practice, with a view toward using them more effectively in
clinical practice.
Results
Data collection
The current version of KampoDB contains 42 Kampo medicines, 54 crude
drugs, 1230 constituent compounds, 460 known target proteins, and 1369
potential target proteins and has functional annotations for biological
pathways and molecular functions. The molecular information on natural
medicines in KampoDB was collected and digitized from scientific
literature, molecular databases, and clinical reports. We collected the
relationships between Kampo drugs and crude drugs (and also below
layers) from the Traditional Medical & Pharmaceutical Database of the
Institute of Natural Medicine, University of Toyama
([58]http://wakankensaku.inm.u-toyama.ac.jp/). As the information was
provided in Japanese, we translated it to English. The correlation
between Kampo drugs and crude drugs was not based on the computational
predictions. The mode-of-action was elucidated by applying the
state-of-the-art computational methods (see the METHODS section for
more details). KampoDB is compatible with other molecular biology
databases (e.g., KEGG^[59]12, ChEMBL^[60]6, UniProt^[61]13,
KNApSAcK^[62]14) by using the same identifiers (compound IDs, protein
IDs, disease IDs).
Inputs and outputs
KampoDB consists of three components: 1) natural medicines list, 2)
functional analysis, and 3) target prediction. Figure [63]2 shows a
diagrammatic representation of KampoDB. All of the resources are
accessible via the website
([64]http://wakanmoview.inm.u-toyama.ac.jp/kampo/).
Figure 2.
[65]Figure 2
[66]Open in a new tab
A diagrammatic representation of KampoDB that consists of three
components: (1) natural medicines list, (2) functional analysis, and
(3) target prediction.
In the “Natural medicines list” component, a user can input a natural
medicine name (e.g., “kakkonto”) as a query. Kakkonto is one of the
most frequently used Kampo medicines in Japan, because it is a highly
effective and safe medicine against the common cold^[67]15,
influenza^[68]16 and allergic rhinitis^[69]17 either as sole therapy or
in combination with modern Western medicines. Kakkonto is composed of
seven Japanese Pharmacopoeia standard medicinal herbs: Puerariae Radix,
Cinnamomi Cortex, Zizyphi Fructus, Paeoniae Radix, Ephedrae Herba,
Zingiberis Rhizoma and Glycyrrhizae Radix. The main bioactive compound
in kakkonto is thought to be puerarin, which is an isoflavonoid derived
from Puerariae Radix that exhibits many pharmacological properties,
including such as anti-inflammation, vasodilation, neuroprotection,
antioxidant and anticancer effects (Supplementary Fig. [70]1)^[71]18.
Clicking on the search button, the user can obtain the corresponding
information on Kampo medicines, crude drugs, constituent compounds and
target proteins. Note that compound IDs correspond to KNApSAcK
IDs^[72]14, and protein IDs correspond to KEGG GENES IDs^[73]12. The
user can see a global classification of Kampo medicines, crude drugs,
constituent compounds, and target proteins in a hierarchical manner
(Kampo medicines on the 1st layer; crude drugs on the 2nd layer;
constituent compounds on the 3rd layer; target proteins on the 4th
layer). Note that each Kampo medicine consists of multiple crude drugs,
each crude drug consists of multiple compounds, and each constituent
compound is supposed to interact with its target proteins. If the
proteins are therapeutic targets of diseases, the corresponding
diseases are shown.
In the “Functional analysis” component, the user can input natural
medicine names. The output is the summary of the mode-of-action
analysis of the corresponding natural medicines, which provides
molecular function annotations of target proteins (e.g., molecular
functions in Gene Ontology^[74]19) and biological pathway annotations
(e.g., biological pathways in KEGG PATHWAY^[75]12). A visualization of
the results at different layer levels enables the user to see the
mode-of-action information in a hierarchical manner within a natural
medicine classification. The user can select one option from the
following four categories and click on the corresponding button: (1)
Pathway: biological pathways in the KEGG PATHWAY, (2) Brite: protein
classifications in KEGG BRITE, (3) Process: biological process terms in
GO, and (4) Function: molecular function terms in GO. For example, in
the case of “Pathway”, the output is the list of pathway names with
high enrichment ratio scores and low p-values (See the METHODS section
for more details).
In the “Target prediction” component, the user can see the results of
newly predicted target proteins of major constituent compounds by
performing docking simulations and machine learning techniques. The
user can select a query compound by clicking on a compound name of
interest in the list of the constituent compounds that are defined as
standard compounds in the Japanese pharmacopoeia (see the METHODS
section for more details). The outputs are the list of predicted human
proteins for the query compound and associated information. In the
docking simulation method, docking was performed for the constituent
compounds with each human protein 3D structure. In the machine learning
method, supervised classification with compound chemical structure
similarity was performed for each human protein (see the METHODS
section for more details).
Possible applications
An application of the “Natural medicines list” component in KampoDB is
to view a hierarchical classification of natural medicines.
Figure [76]3 shows an example of the output page of the query
“kakkonto” (an example of Kampo medicines) as an input. The 2nd and 3rd
layers show the crude drugs (e.g., “Ephedra herb”) constituting the
Kampo medicine (“kakkonto” in this case) and the compounds (e.g.,
“Methylephedrine”) constituting the crude drug (“Ephedra herb” in this
case), respectively. The 4th layer shows the target proteins (e.g.,
“ADRA1D”) that are known to interact with the constituent compound
(“Methylephedrine” in this case). The output enables the user to
investigate the hierarchical relationship between Kampo medicines,
crude drugs, constituent compounds and target proteins.
Figure 3.
[77]Figure 3
[78]Open in a new tab
An example of the output page of the query “kakkonto” (an example of
Kampo medicines) as an input in the “Natural medicines list” component.
The 1st layer shows the Kampo medicine query (“kakkonto” in this case).
The 2nd layer shows the crude drugs (e.g., “Ephedra herb”) that form
the Kampo medicine (“kakkonto” in this case). The 3rd layer shows the
constituent compounds (e.g., “Methylephedrine”) that form the crude
drug (“Ephedra herb” in this case). The 4th layer shows the target
proteins (e.g., “ADRA1D”) that are known to interact with the
constituent compound (“Methylephedrine” in this case).
An application of the “Functional analysis” component in KampoDB is to
perform the mode-of-action analysis of natural medicines in terms of
biological pathways and molecular ontologies. Figure [79]4 shows an
example of the output page of the query “Methylephedrine” (an example
of constituent compounds of “kakkonto”) as an input in the “Functional
analysis” page. In the case of pathway enrichment analysis, biological
pathways with high enrichment ratios and low p-values can be thought of
as candidates for the associated pathways. For example, the “cGMP-PKG
signaling pathway”, “Calcium signaling pathway”, and “Adrenergic
signaling in cardiomyocytes” were detected as the pathways associated
with the term “Methylephedrine.” This is a reasonable result because
target proteins of methylephedrine (e.g., ADRA1D, ADRA1B, ADRA1A) are
known to be involved in the adrenergic signaling process^[80]4.
Figure 4.
[81]Figure 4
[82]Open in a new tab
An example of the output page of the query “Methylephedrine” (an
example of constituent compounds of “kakkonto”) as an input in the
“Functional analysis” component. The 1st column shows the pathway ID in
KEGG, the 2nd column shows the pathway name, the 3rd column shows the
enrichment ratio, and the 4th column shows the p-value for a
hypergeometic test.
An application of the “Target prediction” component in KampoDB is to
predict unknown target proteins of the constituent compounds of natural
medicines. Figure [83]5 shows an example of the output page of the
query “shikonin” (a constituent compound of “Lithospermum
erythrorhizon”) with the docking simulation option in the “Target
prediction” component. The left panel in Fig. [84]5 shows the binding
form of the predicted interaction between shikonin with FK506-binding
protein (FKBP). The graphical picture enables the user to investigate
the ligand binding sites on the protein 3D structure. The validity of
the shikonin-FKBP interaction and its pharmacological effects were
experimentally confirmed in a previous work^[85]20.
Figure 5.
[86]Figure 5
[87]Open in a new tab
An example of the output page of the query “shikonin” (a constituent
compound of “Lithospermum erythrorhizon”) with the docking simulation
option in the “Target prediction” component.
Figure [88]6 shows an example of the output page of the query
“Sinomenine” (a constituent compound of “boiogito”: see red rectangle
in Supplementary Fig. [89]2) with the machine learning option in the
“Target prediction” component. Boiogito is prescribed as a Kampo remedy
for arthritis, nephrosis, edema, hyperhidrosis and obesity. Boiogito is
composed of six Japanese Pharmacopoeia standard medicinal herbs:
Sinomeni Caulis et Rhizoma, Astragali Radix, Atractylodis Lanceae
Rhizoma, Zizyphi Fructus, Glycyrrhizae Radix and Zingiberis Rhizoma.
Sinomenine, an ingredient extracted from the Sinomenium Stem, exerts
anti-inflammatory effects through inhibiting lymphocyte
proliferation^[90]21, and decreasing eicosanoid synthesis and nitric
oxide production^[91]22. Furthermore, sinomenine ameliorates
experimental arthritis in an animal model^[92]23. The list of target
candidate proteins and the associated information (e.g., molecular
functions, biological pathways, applicable diseases) are shown with a
ranking from the highest prediction score. Examples of predicted
applicable diseases of sinomenine are adiposity and type II diabetes
mellitus, implying that sinomenine is effective for treatment of
adiposity and type II diabetes mellitus based on the target proteins:
GAA, OPRM1, OPRD1, OPRK1. These observations are reasonable, because
Kampo medicine “boiogito” that includes sinomenine as a constituent
compound is known to be useful for adiposity. These results also
suggest that GAA, OPRM1, OPRD1, and OPRK1 may play key roles in the
pharmacological action of “boiogito”. This is how the method can be
used for the mode-of-action analysis of Kampo medicines.
Figure 6.
[93]Figure 6
[94]Open in a new tab
An example of the output page of the query “Sinomenine” (a constituent
compound of “boiogito”) with the machine learning option in the “Target
prediction” component.
A case study
As a case study, we show here how KampoDB could be used with
daikenchuto, one of the most frequently used Kampo medicines in Japan.
Daikenchuto is beneficial for postoperative complications such as ileus
and abdominal bloating. Although the mechanisms of daikenchuto are not
fully understood, it has been reported that daikenchuto ameliorates
these intestinal motility disorders via the release of serotonin and
suppress the inflammation via the inhibition of cyclooxygenase-2
activity^[95]24,[96]25.
When “daikenchuto” was entered as a query in KampoDB, the “Functional
analysis” component predicted “serotonergic synapse” and “arachidonic
acid metabolism” as the associated pathways. It also predicted “Wnt
signaling pathway”, “T cell receptor signaling pathway”, and “TNF
signaling pathway” as candidates for target pathways associated with
the mechanisms of daikenchuto. This suggests that daikenchuto derives
its anti-inflammatory activity via arachidonic acid metabolism^[97]26
and several other pathways. “T cell receptor signaling pathway” and
“TNF signaling pathway” have been supported by previous
reports^[98]27,[99]28 as the underlying mechanisms of daikenchuto.
However, to the best of our knowledge, there is no report on the role
of daikenchuto in the “Wnt signaling pathway”.
We previously showed that daikenchuto markedly alleviated dextran
sulfate sodium (DSS)-induced experimental colitis in mice. Ulcerative
colitis is a chronic inflammatory bowel disease (IBD) in which patients
experience intermittent remission and relapse over decades. The
long-term chronic inflammation elevates the risk of colitis-associated
cancer (CAC) and can lead to CAC-related death. Therefore, CAC is
regarded as the most serious complication of IBD. However, not all
medicines effective against experimental colitis are necessarily
effective against CAC. Indeed, it has been reported that an agonist for
a prostaglandin E2 receptor subtype suppresses DSS-induced colitis and
also prevents the development of colorectal carcinogenesis in a murine
CAC model, whereas sulfasalazine, a prodrug of 5-aminosalicylic acid
with efficacy against DSS-induced colitis, did not prevent colorectal
tumor formation in a murine CAC model^[100]29.
Using KampoDB, “Wnt signaling pathway”, “T cell receptor signaling
pathway”, and “TNF signaling pathway” were predicted as candidates for
target pathways associated with the underlying mechanisms of
daikenchuto that contribute to the development of CAC. In particular,
the contribution of the Wnt signaling pathway to the colorectal
carcinogenesis is established^[101]30. Recently, it was reported that
the activation of Wnt/β-catenin signaling is essential for the early
phase development of IBD-associated colorectal cancer^[102]31,[103]32.
Additionally, Wnt signaling-initiated tumorigenesis has been reported
in a murine CAC model^[104]33.
Taking all together, these findings suggest that daikenchuto attenuates
the development of chronic inflammation-associated cancer. It has the
potential to be a new therapeutic strategy while repositioning the use
of Kampo medicine. While testing this hypothesis, we found that the
daikenchuto treatment indeed significantly suppressed the development
of chronic colitis-associated colon cancer in a murine experimental
model, as shown in Fig. [105]7.
Figure 7.
[106]Figure 7
[107]Open in a new tab
Effect of daikenchuto on the development of colitis-associated cancer
(CAC) in mice. CAC was induced in mice by intraperitoneal injection of
azoxymethane (AOM) (10 mg/kg) followed by repeated exposure to a 2%
dextran sulfate sodium (DSS) in drinking water. Daikenchuto (300 mg/kg)
was orally administered during experiment. (A) Schematic drawing of the
experimental design for the evaluation of daikenchuto in the CAC model.
(B) Macroscopic changes in the colon. Colons were removed from vehicle-
or daikenchuto-treated mice at day 70, and representative results from
5 independent animals are shown. (C) The number of tumors. Colons were
removed at 70 days to determine the number of macroscopic tumors. The
data are presented as means ± SE of 5 mice. ^†Significant difference
from vehicle at p < 0.05.
Daikenchuto comprises three medicinal herbs: ginseng root, processed
ginger, and Zanthoxylum peel (Supplementary Fig. [108]3). KampoDB was
able to predict the possibility that the Wnt signaling pathway was a
target of ginseng root and that the T cell receptor and TNF signaling
pathways were underlying mechanisms of the anti-CAC effects of
processed ginger and Zanthoxylum peel. These results suggest that the
additive or synergistic actions of constitutive medicinal herbs
contribute to the suppressive effect of daikenchuto on the development
of CAC. Therefore, KampoDB can be useful for predicting new roles or
aspects of traditional medicines, helping to clarify the underlying
mechanisms of traditional medicines.
Discussion
KampoDB is the first platform for the analysis of natural medicines for
mode-of-action analysis and repositioning of natural medicines in the
world. The primary contribution of this study is to propose
computational methods for the mode-of-action analysis and repositioning
of Kampo medicines. In this study, we put great efforts on establishing
a methodology for the computational prediction of target proteins and
new indications of Kampo medicines. We established a useful web service
that makes it easier for medical doctors to use Kampo medicines in
clinical practice. The methods are expected to be useful for analyzing
the complex systems of natural medicines. Thus, the technologies should
contribute to innovation in the field of health science.
A related work of this study is a wiki-system of Kampo medicines and
crude drugs^[109]11 and the Kampo section of the KNApSAcK^[110]14
database that enables group search of medicinal plants, formula search
by a medicinal plant, and medicinal plant search by a Kampo formula.
However, these existing databases do not provide the information on
potential target proteins, target pathways, and applicable diseases.
Thus, they cannot help to understand the mode-of-actions and further
clinical applications of Kampo medicines and crude drugs.
The performances of the target prediction and indication prediction
depend heavily on the data representation of Kampo medicines, crude
drugs, constituent compounds, and proteins. In this study, we used
chemical structures of the constituent compounds and protein
structures, but another approach would be to use other omics data.
Recently, compound-induced transcriptome data (e.g., chemical treatment
on human cell lines) and genetically-perturbed transcriptome data
(e.g., gene knockdown, gene overexpression) have been utilized in
various pharmaceutical applications. Similarity, the analysis of gene
expression profiles by perturbations with Kampo medicines and crude
drugs would be an interesting approach for target prediction and
indication prediction. The inclusion of these gene expression data will
be one of our important future works.
Traditional medicines have considerable advantages, such as the
abundance of clinical experience gained over a long time, the diversity
of chemical structures of the constituent compounds, and their
biological activity in humans, providing an incomparable source of new
drug leads for effective drug development. The results of the present
study provided possible concepts and methodologies from traditional
medicine that could help the discovery and development of new drugs.
We plan to maintain KampoDB by updating the molecular data on a regular
basis and by analyzing the data using more sophisticated computational
methods. For the “Natural medicine list” and “Functional analysis”
components, we intend to incorporate the latest information from the
literature and from other molecular databases. For the docking
simulation analysis in the “Target prediction” component, we plan to
perform docking simulations for missing compound–protein pairs as soon
as the information on protein structures becomes available and to
investigate the possibility of using other docking software, such as
myPresto. For the machine learning analysis in the “Target prediction”
component, we plan to use more sophisticated machine learning methods
(e.g., deep learning, support vector machine, and logistic regression)
to improve its accuracy in predicting target proteins and the
applicable diseases. Currently, the prediction results for applicable
diseases are presented at the level of the constituent compounds of the
Kampo medicines and crude drugs, but we intend to develop integrative
methods to show the prediction results for applicable diseases at the
level of Kampo medicines and crude drugs themselves. In Japanese
traditional medicines, various kinds of Kampo medicines, crude drugs,
and constituent compounds exist. Our KampoDB is just the first version
and does not cover all Kampo medicines and diseases. In our future
versions, we will add more Kampo medicines, crude drugs, constituent
compounds, and diseases.
Methods
Chemical structure representation
The chemical structures of constituent compounds were obtained from
KNApSAcK^[111]14 and PubChem^[112]9 and were represented by their KEGG
Chemical Function and Substructures (KCF-S) descriptors^[113]34. Each
compound was coded by a high-dimensional feature vector in which each
element indicates the frequency of a feature defined by KEGG Chemical
Function Substructures (KCF-S) (i.e., chemical substructures). The
number of features was 475,692. We computed chemical structure
similarity scores of compounds by using the generalized Jaccard
correlation coefficient.
Compound–protein interactions
Known compound–protein interactions were acquired from public
databases: ChEMBL^[114]6, MATADOR^[115]5, DrugBank^[116]3, the
Psychoactive Drug Screening Program Ki, KEGG DRUG^[117]4, the Binding
DB^[118]8, and the Therapeutic Target Database^[119]7. For the ChEMBL
data, we selected only compound–protein interaction pairs that were
clearly denoted as active interactions or had binding affinities of
<30 μM (e.g., IC[50]), which yielded 1,287,404 compound–protein
interactions involving 519,061 compounds and 3,735 proteins. Compounds
and proteins included in the chemical–protein interactome data are
referred to as interactome compounds and interactome proteins,
respectively.
Constituent compounds
Kampo formulas are recognized as official prescription drug and listed
in the Japanese pharmacopoeia. We selected 80 compounds derived from
constituent medicinal herbs of Kampo formulas that are most frequently
used for the medical treatment in Japan. 80 compounds are listed as
standard drugs for the crude drug analysis (medicinal herb analysis) in
the Japanese pharmacopoeia.
Target prediction by docking simulation
We performed a target prediction by performing a docking simulation.
Protein 3D structures were obtained from the PDB database^[120]35 and
SAHG^[121]36. In this study, we used AutoDock, which is a suite of
automated docking tools, to predict how compounds bind to a target
protein^[122]37. We performed a large-scale docking simulation for all
possible pairs of the constituent compounds and about 40,000 human
proteins. The predicted protein–ligand complexes were optimized and
ranked according to the empirical scoring function, which estimates the
binding free energy of the ligand receptor complex. We stored the
calculated numerical results in the platform.
Target prediction by machine learning
We performed a target prediction by using our previously developed
method, called TESS (target estimation based on similarity
search)^[123]38, to predict target proteins on the basis of compound
chemical structures and large-scale chemical–protein interactome data
in the framework of chemogenomics. We propose to apply the TESS
algorithm to each constituent compound of Kampo medicine. In the TESS
procedure, we calculated the similarity scores of compound chemical
structures by the Jaccard index based on the KCF-S descriptors^[124]34,
which were used as prediction scores.
First, we compute pairwise similarity scores for all pairs between a
query constituent compound and all of the interactome compounds in our
chemical–protein interactome data. Second, from the interactome
compounds known to interact with the k-th protein (k = 1, 2,…, p), we
select an interactome compound with the highest similarity to the query
constituent compound and use the corresponding similarity score as a
prediction score to assess the possibility that the query compound
interacts with the k-th protein. Third, we repeat this procedure for
all p interactome proteins and assign the prediction scores to pairs
between the query compound and all interactome proteins. Finally, high
scoring compound–protein pairs are predicted as candidates for
interaction pairs. Then, the predicted compound–protein pairs are
grouped into Kampo medicines based on their constituent compounds.
Figure [125]8 shows an illustration of the process. The details of the
performance evaluation can be found in the “Performance evaluation”
section in Supplementary Information.
Figure 8.
[126]Figure 8
[127]Open in a new tab
A workflow of the target perdition for Kampo medicines.
Compound–protein interactions are newly predicted using compound
chemical structure similarities in the framework of supervised
classification. Then, the predicted compound–protein pairs are grouped
into Kampo medicines based on their constituent compounds.
Pathway/ontology enrichment analysis
We performed the functional enrichment analyses for natural medicine
(e.g., Kampo medicines, crude drugs) by mapping a set of target
proteins of the constituent compounds of each natural medicine to
biological pathways or molecular ontology terms. There are four
options: (1) Pathway: biological pathways in KEGG PATHWAY, (2) Brite:
protein classifications in KEGG BRITE, (3) Process: biological process
terms in GO, and (4) Function: molecular function terms in GO. Here, we
focus on the explanation of the enrichment analysis for Pathway. Note
that the same procedure can be performed not only for Pathway but also
for other options (Brite, Process, and Function).
We used the 163 biological pathways in KEGG (except for Global and
overview maps). The enrichment ratio was calculated as the ratio of the
number of associated target proteins to the number of all proteins in
each pathway. The p-value was calculated by performing a hypergeometric
test^[128]39,[129]40. Let G[comp] denote a set of target proteins of
the constituent compounds of a natural medicine (e.g., Kampo medicines,
crude drugs) of interest, and let G[path] denote a set of target
proteins in a pathway map. Further, let r = |G[comp]|, k = |G[path]|,
z = |G[comp]
[MATH: ∩ :MATH]
G[path]|, and l equal the total number of genes in the entire dataset
(l = 460). We assumed that z follows a hypergeometric distribution. The
probability of observing an intersection of size z between G[path] and
G[comp] is computed as follows:
[MATH: p(Gpath,Gcomp)=∑i
=zmin(k,r)(ki)(l−kr−i)/(lr)⋅ :MATH]
1
The resulting p-values were corrected by using the false discovery
rate^[130]41. In this study, Kampo medicines were associated with all
possible target proteins through their constituent compounds. Several
proteins were overlapped between different pathways, and the activities
of protein-coding genes were not considered, rendering the enrichment
analysis likely to produce high values. To determine more specific
pathways, the mapping of Kampo medicines-induced gene expression data
onto biological pathway maps would be a solution; however, it was out
of this paper’s scope.
Disease–target associations
The information on therapeutic target proteins for each disease was
obtained from scientific literature and medical books. Drugs regulate
therapeutic target proteins known to be useful for the treatment of
each disease. Note that target proteins that are not known to be
associated with diseases are not taken into consideration. In total,
2,062 disease–target associations involving 250 diseases and 462
therapeutic target proteins were obtained.
Indication prediction by target matching
We performed a prediction of drug indications (i.e., applicable
diseases) of the query constituent compound based on its target
proteins (including known target proteins and newly predicted target
proteins by TESS) and the disease–target association set.
First, we take a target protein of the query constituent compound and
look for the same target protein in the disease–target association set.
Second, we select diseases associated with the matched target protein,
and link the query constituent compound to the selected diseases via
the matched target protein. The prediction scores are set to one if the
matched target proteins are known targets of the query drug, while the
prediction scores are set to the TESS score if the matched target
proteins are newly predicted.
In vivo experiments with a CAC mouse model
Male BALB/c mice (8–10 weeks) were purchased from Japan SLC (Shizuoka,
Japan). The mice were housed in the experimental animal facility at the
University of Toyama and given free access to food and water. All
experiments were performed in accordance with the Guide for the Care
and Use of Laboratory Animals of the National Institutes of Health and
the University of Toyama. The Animal Experiment Committee at the
University of Toyama approved all of the animal care procedures and
experiments (authorization no. A2015INM-2). CAC model was induced as
described previously^[131]42. The mice were administered azoxymethane
intraperitoneally (10 mg/kg; Sigma-Aldrich, St. Louis, MO). After 5
days, the mice were administered 2% DSS (36-50 kDa; MP Biomedicals,
Santa Ana, CA) in their drinking water for 5 days, followed by 16 days
of regular water. This cycle was repeated three times. The body weight
of each mouse was measured every other day, and its colonic mucosa was
monitored using a mouse endoscopy system (AE-C1; AVS, Tokyo, Japan). On
day 70 after the start of azoxymethane administration, the mouse colon
was excised for macroscopic evaluation and histological and biological
analyses. Visible tumors (>1 mm along the major axis) were counted in
the mid to distal colon of each mouse.
Electronic supplementary material
[132]Supplementary information^ (1.2MB, pdf)
Acknowledgements