Abstract
Alzheimer’s disease (AD) is a common neurodegenerative disease having
complex pathogenesis, approved drugs can only alleviate symptoms of AD
for a period of time. Traditional Chinese medicine (TCM) contains
multiple active ingredients that can act on multiple targets
simultaneously. In this paper, a novel algorithm based on entropy and
random walk with the restart of heterogeneous network (RWRHE) is
proposed for predicting active ingredients for AD and screening out the
effective TCMs for AD. First, Six TCM compounds containing 20 herbs
from the AD drug reviews in the CNKI (China National Knowledge
Internet) are collected, their active ingredients and targets are
retrieved from different databases. Then, comprehensive similarity
networks of active ingredients and targets are constructed based on
different aspects and entropy weight, respectively. A comprehensive
heterogeneous network is constructed by integrating the known active
ingredient-target association information and two comprehensive
similarity networks. Subsequently, bi-random walks are applied on the
heterogeneous network to predict active ingredient-target associations.
AD related targets are selected as the seed nodes, a random walk is
carried out on the target similarity network to predict the AD-target
associations, and the associations of AD-active ingredients are
inferred and scored. The effective herbs and compounds for AD are
screened out based on their active ingredients’ scores. The results
measured by machine learning and bioinformatics show that the RWRHE
algorithm achieves better prediction accuracy, the top 15 active
ingredients may act as multi-target agents in the prevention and
treatment of AD, Danshen, Gouteng and Chaihu are recommended as
effective TCMs for AD, Yiqitongyutang is recommended as effective
compound for AD.
1. Introduction
Alzheimer’s disease (AD) is a persistent and irreversible
neurodegenerative disease, whose main clinical features are progressive
memory loss, cognitive declination, functional independence loss and so
on [[30]1]. The pathogenesis of AD is complex, and not fully understood
yet. The incidence of AD has been increasing in recent years, which
brings a heavy economic burden to the healthcare system around the
world. However, current clinical AD drugs approved by the U.S. Food and
Drug Administration (FDA) only relieve related symptoms within a
certain period, but none of these therapies can effectively halt the
development of the disease. Other single-target AD drugs have failed in
clinical trials. Therefore, it is essential to identify efficient and
low-toxicity AD drugs with multi-targets [[31]2].
Traditional Chinese medicine (TCM) contains multiple active ingredients
that can act on multi-targets simultaneously, some active
pharmacological compounds of herbs have been proven to be applied to
the treatment of many diseases [[32]3]. TCM has a long history of AD,
many active ingredients extracted from herbs have fewer side effects
and are regarded as potential anti-AD drugs [[33]4]. However, the
potential molecular mechanism of TCM for AD is still unclear, which
limits further clinical applications. Network pharmacology is integral
and systematic by integrating the ideas of pharmacology with network
science, systems biology, and bioinformatics, which can be used to
screen active ingredients and understand the mechanism of
multi-ingredient, multi-target, and multi-pathway of TCM [[34]5].
Therefore, network pharmacology can be used to reveal the associations
between AD, active ingredients and targets, which open up a new way to
study the mechanism between TCMs and AD.
With the development of high-throughput biomedical data, network-based
propagation methods are often used to predict associations among
biological components. Random Walk is one of the typical methods based
on network propagation. The traditional restart random walk (RWR)
algorithm only carries out random walks in a single network [[35]6],
many scholars have proposed improved algorithms for RWR on
heterogeneous networks. Luo et al. proposed a random walk-based
algorithm on the Reliable Heterogeneous Network (RWRHN) to prioritize
potential candidate genes for inherited diseases [[36]7]. Li et al.
presented a superimposed local random walk algorithm called LRWHLDA to
predict the associations between LncRNAs and diseases. Their algorithm
overcomes the limitation of lack of known association between nodes
[[37]8]. The topological and structural properties of different
networks are different. Wang et al. quantified the individual walk
length of nodes by using the improved Jaccard index, and proposed an
individual bi-random walk algorithm called DR-IBRW for drug
repositioning [[38]9]. Luo et al. put forward a bi-random walk
algorithm (MBIRW) for drug repositioning, random walks are conducted to
predict potential drug-disease associations in the drug similarity
network and disease similarity network respectively [[39]10]. The
algorithm based on network inference is also applied to the prediction
of biological information association. Cheng et al. proposed an
algorithm based on network-based inference (NBI), known drug-target
association information was used as the initial resource allocation,
the final resource allocation information was obtained through two-step
propagation to predict drug-target associations [[40]11]. Wang et al.
proposed a method based on the guilt-by-association principle, called
HGBI for prediction of novel drug-target associations [[41]12]. In
addition, KATZ is also a network-based algorithm. Zhu et al. considered
the contribution of different walk lengths to the similarity network,
and proposed the HMDAKATZ algorithm to predict bacteria-drugs
associations [[42]13].
In this paper, we develop a novel algorithm based on entropy and random
walk with the restart of heterogeneous network (RWRHE) for predicting
active ingredients associated with AD and screening out the effective
TCMs for AD. Firstly, the similarity measures of active ingredients are
calculated from two aspects, including the chemical structure and the
Gaussian interaction profile kernel (GIP kernel) similarity [[43]14].
The similarity measures of targets are calculated from four aspects,
including protein sequence, interaction score in String
database(release 2021–08) [[44]15], common neighbor, and GIP kernel
similarity. Secondly, based on the weight of each similarity measure
assigned by information entropy, similarity measures of active
ingredients and targets are integrated into comprehensive similarity
measures, respectively. Therefore, a heterogeneous network can be
constructed by connecting the comprehensive active ingredient network
and target network via known active ingredient-target associations.
Next, the active ingredients-target association score matrix is
calculated by bi-random walk on heterogeneous networks. Then, AD
related targets are selected as the seed nodes, and the random walk is
performed on the target similarity network to calculate the AD-target
association score vector. Finally, the relationships between AD and
active ingredients are predicted and scored, the effects of TCMs for AD
are scored and ranked. Gene ontology (GO) and Kyoto Encyclopedia of
Genes and Genomes (KEGG) enrichment analysis [[45]16] are performed on
the top 100 targets by different methods to illustrate the relationship
between topological properties and relevant biological functions.
Molecular docking is used to assess the ability of top 15 the active
ingredients to enter the active pocket of the key enzymes or proteins
of AD, the results show that the active ingredients may act as
multi-target agents in the prevention and treatment of AD.
2. Materials and methods
2.1. Datasets
Six TCM compounds from the AD drug reviews in the CNKI (China National
Knowledge Internet) are selected, including Dangguishaoyaosan,
Yigansan, Yiqitongyutang, Gubenjiannaoye, BKHJ TCM and Yizhi
[[46]17–[47]20]. A total of 20 herbs are involved. The active
ingredients of these herbs are retrieved from Traditional Chinese
Medicine System Pharmacology (TCMSP) database (release 2014–05)
[[48]21]. Based on the condition that oral bioavailability (OB) is more
than 20% and drug-likeness (DL) is more than 0.1, 583 active
ingredients are screened out. The herbs contained in TCM compounds and
the active ingredients contained in the herbs are shown in [49]S1
Table.
The datasets used in this study include active ingredients, targets and
known active ingredient-target associations, which are collected from
two databases: TCMSP and HERB (release 2021–01) [[50]22]. Targets
associated with active ingredients are mapped into genes through
UniProt database (release 2022–05) [[51]23] for “Homo sapiens”
organism. 4313 active ingredient-target associations involving 387
active ingredients and 374 targets are retrieved from TCMSP database.
Simultaneously, 5823 active ingredient-target associations involving
453 active ingredients and 642 targets are obtained from HERB database.
We take the union of these two datasets as the total dataset, which has
6973 active ingredient-target associations involving 457 active
ingredients and 731 targets, as shown in [52]S2 Table.
2.2 Similarity measures
2.2.1 Active ingredient similarity measures
Let
[MATH: R=r1,r2
,⋯,rnr :MATH]
be the set of n[r] active ingredients and
[MATH: T=t1,t2
,⋯,tnt :MATH]
be the set of n[t] targets. The matrix A[rt] represents known active
ingredient-target associations, and its dimension is n[r] * n[t]. The
value of A[rt](i, j) is 1 if active ingredient i and target j have a
known association, otherwise is 0.
The first similarity measure is calculated based on the chemical
structure. The SMILES (Simplified Molecular Input Line Entry
Specification) describes the chemical structure of active ingredients
[[53]24], which could be retrieved from PubChem database (release
2022–06) [[54]25]. For the active ingredients that cannot be retrieved,
the SMILES can be obtained from their 2D structure through the Swiss
Target Prediction database (release 2019–05) [[55]26]. The Chemical
Development Kit is used to compute chemical fingerprints of active
ingredients [[56]27]. The similarity between active ingredients r[i]
and r[j] is calculated by the Tanimoto score of the binary fingerprint
vector [[57]28], the formula as follows:
[MATH: Srf(ri,rj)=fri*f<
mrow>rjfri2+fri2−f<
mi>ri*frj
:MATH]
(1)
where
[MATH:
fr<
mi>i :MATH]
represents the chemical fingerprint vector of the active ingredient
r[i].
Based on the finding that weak similarity between active ingredient
pairs provides less information for prediction, the logistic function
is used to convert those small similarity values into values close to
zero and expand those large similarity values simultaneously [[58]10].
The improved similarity value by the logistic function is redefined as
follows:
[MATH: L(Srf(ri,rj))=11+
exp(c⋅Srf(ri,rj)+d) :MATH]
(2)
where c and d are the parameters. According to the previous research
[[59]29, [60]30], we set d as log(9999), which represents L(0) =
0.0001. We set L(0.3) < 0.01, which determines c as -15. The improved
similarity value
[MATH: LSrfri,rj
:MATH]
is denoted as
[MATH:
Sr1
ri,rj
:MATH]
.
Based on the assumption that similar active ingredients tend to
associate with similar targets, GIP kernel similarity is used to
calculate the similarity between active ingredients. The interaction
profile IP(r[i]) of active ingredient r[i] is a binary vector
representing the known associations between the active ingredient r[i]
and targets. The GIP kernel similarity between active ingredient r[i]
and active ingredient r[j] is computed as follows:
[MATH: Sr2(ri,rj)=exp(−ϒrIP(ri)−IP(rj)2)ϒr
=ϒr′/1nr∑i=1nrIP(ri)2 :MATH]
(3)
where IP(r[i]) denotes the i-th row of the matrix A[rt].
[MATH: Υr′ :MATH]
is set to be 1 according to previous research [[61]14]. The similarity
values of active ingredients based on the above two similarity measures
are shown in [62]S3 Table.
2.2.2 Target similarity measures
The first target similarity measure is calculated based on the protein
sequences, which can be retrieved from UniProt database. The
Smith-Waterman local alignment algorithm [[63]31] is used to calculate
the sequence similarity of targets, and the similarity matrix is
denoted as
[MATH:
St1
:MATH]
.
The second target similarity measure is based on the interaction
confidence scores of the String database. The gene symbols of targets
are entered into the String database, and the organisms are selected as
"Homo sapiens". Then the interaction scores of target pairs are as
elements of the second similarity matrix
[MATH:
St2
:MATH]
.
The third target similarity measure is calculated based on the common
neighbors of targets. To filter out the target interaction
relationships with low confidence, target pairs with interaction scores
greater than 0.4 in the String database are regarded as neighbors. The
contribution of the common neighbor targets with small degree is
greater than that of the common neighbor targets with large degree,
each common neighbor target is assigned a weight based on its degree
value. The target similarity measure based on the common neighbors is
defined as:
[MATH: St3(ti,tj)=∑z∈Γ(ti)∩Γ(tj)1k(z) :MATH]
(4)
where Γ(t[i]) is the set of neighbors of target t[i], z are the common
neighbors of targets t[i] and t[j], k(z) is the degree of z. If two
targets have more common neighbors and the degree of common neighbors
is small, the similarity between them will be greater than 1, it will
be replaced by 0.99.
Based on the assumption that similar targets tend to associate with
similar active ingredients, the fourth target similarity measure based
on GIP kernel is defined. The interaction profile IP(t[i]) of target
t[i] is a binary vector representing the known associations between
target t[i] and active ingredients. The GIP kernel similarity between
targets t[i] and t[j] is computed as follows:
[MATH: St4(ti,tj)=exp(−ϒtIP(ti)−IP(tj)2)ϒt
=ϒt′/1nt∑i=1ntIP(ti)2 :MATH]
(5)
where IP(t[i]) denotes the i-th column of the matrix A[rt].
[MATH: Υt′ :MATH]
is set to be 1 according to previous research [[64]14]. The similarity
values of targets based on the above four similarity measures are shown
in [65]S4 Table.
2.2.3 Integrating similarity measures based on entropy
Different similarity measures contain different similarity information
and play different roles in measuring the similarity of node pairs. The
information entropy is used to select similarity measures in the
previous research [[66]32]. In this study, the weights of different
similarity measures are further calculated based on entropy.
For the m-th similarity matrix for active ingredients, the entropy
[MATH:
Eim
:MATH]
of the i-th row is calculated as follows:
[MATH:
Eim=−<
/mo>∑j=1ksij∑j=1ksi<
/mi>jlogsij∑j=1ksi<
/mi>j :MATH]
(6)
where s[ij] represents the similarity value between active ingredients
i and j. k indicates the number of active ingredients. We average the
entropy of all rows as the final average entropy value. The average
entropy of the m-th similarity matrix is calculated as follows:
[MATH:
Em=mean∑i=1kEik :MATH]
(7)
The smaller the average entropy of the similarity matrix is, the less
random information is delivered by the similarity measure. The
similarity measure with small average entropy occupies a significant
proportion of the comprehensive similarity measure. The average entropy
of each similarity matrix is normalized after taking the reciprocal,
then the weight of the m-th similarity measure is defined as follows:
[MATH:
ωrm=1/Emeanm∑n1/Emea
nn
:MATH]
(8)
where ω[rm] represents the weight of the m-th similarity measure of
active ingredients. In the same way, the average entropy and weight of
each target similarity measure can be obtained. The average entropy and
corresponding weights of the similarity matrix of two active
ingredients and four targets are shown in [67]Table 1.
Table 1. Entropy and weight of similarity measure.
E [mean ]ω
Active ingredient
[MATH: Sr1
:MATH]
3.9734 0.5986
[MATH: Sr2
:MATH]
5.9265 0.4014
Target
[MATH: St1
:MATH]
6.5938 0.2071
[MATH: St2
:MATH]
4.5929 0.2974
[MATH: St3
:MATH]
4.9661 0.2750
[MATH: St4
:MATH]
6.1959 0.2204
[68]Open in a new tab
Finally, the two similarity measures of active ingredients are
integrated into a comprehensive active ingredient similarity measure
S[r], and the four similarity measures of targets are integrated into a
comprehensive target similarity measure S[t], which are calculated as
follows:
[MATH:
Sr=
ωr1Sr<
/mi>1+ωr2
Sr2St=ωt1St
1+ωt
2St2+ωt3St3+ωt4St4
:MATH]
(9)
where ω[r1] and ω[r2] are the weights of active ingredient similarity
matrix
[MATH:
Sr1
:MATH]
and
[MATH:
Sr2
:MATH]
, respectively. ω[t1], ω[t2], ω[t3], ω[t4] are the weights of target
similarity matrix
[MATH:
St1
,St<
/mi>2,St3
,St
4 :MATH]
, respectively. The comprehensive similarity values of active
ingredients and targets are shown in [69]S5 Table.
2.3 Construction of the heterogeneous network
The similarity networks of active ingredients and targets are
constructed based on the comprehensive similarity measures of active
ingredients and targets.
[MATH: R=r1,r2
,⋯,rnr :MATH]
is the node set of n[r] active ingredients. The comprehensive
similarity S[r](r[i], r[j]) is the weight between active ingredients
r[i] and r[j].
[MATH: T=t1,t2
,⋯,tnt :MATH]
is the node set of n[t] targets. The comprehensive similarity
S[t](t[i], t[j]) is the weight between targets t[i] and t[j].
In addition, the active ingredient-target bipartite graph G(V, E) is
constructed based on the known active ingredient-target associations. V
= {R, T} is the node set containing active ingredient nodes and target
nodes. E = {A[rt](i,j)} is the edge set. If active ingredient i and
target j have a known association, the weight of edge between them is
1, otherwise is 0.
The active ingredient-target heterogeneous network can be constructed
by integrating the active ingredient similarity network, target
similarity network, and the known active ingredient-target association
network. The heterogeneous network is illustrated in [70]Fig 1. The
yellow circles and green rectangles represent active ingredients and
targets, respectively. Solid lines represent known active
ingredient-target associations, dashed lines indicate the predicted
active ingredient-target associations.
Fig 1. The active ingredient-target heterogeneous network.
[71]Fig 1
[72]Open in a new tab
Yellow circles and green rectangles represent active ingredients and
targets respectively.
2.4 Bi-random walk on the heterogeneous network
On the heterogeneous network, a bi-random walk algorithm is used to
predict the active ingredient-target associations score matrix. As the
previous research [[73]33], the Laplacian normalization is used to
normalize the weight matrix of a network. For the active ingredient
similarity matrix S[r], the Laplacian normalization is divided into two
steps:
[MATH: Sr′(ri,rj)=Sr(ri,rj)D(ri,ri)*D(rj,rj) :MATH]
(10)
[MATH:
Sr″(ri,rj)=Sr<
/mi>′(ri,rj)∑jSr′(ri,rj) :MATH]
(11)
where D is a diagonal matrix, and the elements of the main diagonal are
the sum of corresponding rows of the matrix S[r], i.e
[MATH: D(ri,ri)=∑k=1<
/mn>nrSr(ri,rk) :MATH]
. The matrix S[r] after Laplacian normalization is denoted as
[MATH:
Sr″
:MATH]
. Likewise, the target similarity matrix S[t] after Laplacian
normalization is denoted as
[MATH:
St″
:MATH]
. In addition, the adjacency matrix A[rt] of the active
ingredient-target associations is normalized as follows:
[MATH:
Art′=Artsum(Art) :MATH]
(12)
A random walk begins from an active ingredient, then traverses to other
target nodes based on its associated targets. The probabilistic
associations between the active ingredient and all targets are
obtained. The left random walk is performed on the target similarity
network to simulate this process:
[MATH:
leftRTt=α×RTt−1*St″+(1−α)×Art′ :MATH]
(13)
where α = 0.3 is the restart probability, which controls the
probability for the walker staying at the starting node. leftRT[t] is
the predicted association matrix between active ingredients and targets
in the t-step iteration,
[MATH:
RT0=Art′
:MATH]
. The left random walk stops until |leftRT[t+1] − leftRT[t]| < 10^−6,
the resulted matrix is denoted leftRT.
Likewise, a random walk starts from a target node, then traverses other
active ingredient nodes based on its known associated active
ingredients. Then another active ingredient-target association matrix
is obtained. The right random walk is conducted on the active
ingredient similarity network to mimic this process:
[MATH:
rightRTt=α×
Sr″×RTt−1+(1−α)×Art′ :MATH]
(14)
where α = 0.3 is the restart probability, the right random walk stops
until |rightRT[t+1] − rightRT[t]| < 10^−6, the resulted matrix is
denoted rightRT. The final result is the average of the left and right
random walk results, and the final predicted active ingredient-target
association matrix is denoted as RT as follows:
[MATH:
RT=leftRT+rightRT2 :MATH]
(15)
The final matrix RT is shown in [74]S6 Table.
2.5 Prediction of AD-target association
In this study, the targets from the Alzheimer’s disease pathway
(hsa05010) in the KEGG and the targets contained in the AD mini
metabolic network [[75]34] are selected. The intersection of these
targets and aforementioned 731 targets contains 72 targets, which form
the seed node set. The initial probability vector p[0] of targets is
constructed such that equal probability is assigned to each target
seed, and probability 0 is assigned to other targets, with the sum of
the probabilities equal to 1. Starting from the target seed node,
according to the topological property of the target similarity network,
random walks are carried out to traverse other targets. Then, the
AD-targets association probabilities vector could be predicted as
follows:
[MATH:
Pt=α*St″*<
msub>Pt−1+<
/mo>(1−α)P0 :MATH]
(16)
where α = 0.3 is the restart probability. P[t] is the probability
vector of the t-step iteration, and its value represents the
association probabilities between targets and AD. The iteration stops
until |P[t+1] − P[t]| < 10^−6, the resulted probability vector is
denoted as P, and its values are as shown in [76]S7 Table.
2.6 Prediction of AD-active ingredient association
Combined with the final predicted active ingredient-target association
matrix RT and the AD-targets association vector P, the association
scores of active ingredients and AD are predicted as follows:
[MATH: Pr(ri)=∑jRT(ri,tj)*P(tj) :MATH]
(17)
where RT(r[i], t[j]) represents the final predicted association score
of active ingredient r[i] and target t[j], P(t[j]) represents the score
of the target t[j] associated with AD. P[r](r[i]) represents the score
of the active ingredient r[i] associated with AD, as shown in [77]S8
Table.
3. Results and discussion
3.1 Measuring the effect of similarity by ablation analysis
Different similarity measures in a network can lead to differences in
the topology and structure characteristics of the network. Therefore,
the iterative process of random walk and the accuracy of prediction
results are affected by different measures. Ablation analysis is
performed on the comprehensive similarity measure to study the
influence of different similarity measures on the performance of the
RWRHE algorithm. We implement two simplified variants of RWRHE.
1. RWRHE_GIP: By removing GIP kernel similarity, the active ingredient
similarity
[MATH:
Sr2<
/mn> :MATH]
and target similarity
[MATH:
St4<
/mn> :MATH]
based on GIP kernel similarity are removed.
2. RWRHE_E: By removing entropy, each similarity measure is weighted
equally.
The Receiver Operating Characteristic Curve (ROC) reflects the
relationship between True Positive Rate (TPR) and False Positive Rate
(FPR) at different thresholds. The area under the ROC curve (AUC) is
used as an evaluation metric to measure the accuracy of prediction
results. “Alzheimer’s Disease” is used as the keyword, 63 active
ingredients associated with AD are retrieved from the HERB database as
the positive control group. At the same time, the active ingredients
related to leukemia, mammary carcinoma, fever, and acute pharyngitis
are retrieved, and the active ingredients related to AD are excluded,
66 active ingredients are obtained as the negative control group. The
positive and negative control groups are shown in [78]S9 Table. TPR,
also known as sensitivity, represents the ratio of active ingredients
in positive controls that rank for association with AD above the
specified threshold. FPR, also known as 1-specificity, denotes the
percentage of active ingredients in negative control group that rank
for association with AD above the specified threshold. ROC curves of
RWRHE, RWRHE_GIP, and RWRHE_E algorithms are shown in [79]Fig 2. The
results show that the RWRHE algorithm achieves higher prediction
accuracy, and its AUC value is 0.914. The AUC values of RWRHE_GIP and
RWRHE_E algorithms are 0.910 and 0.905, respectively. The number of
nodes and edges in the network corresponding to the RWRHE_GIP and the
RWRHE_E is the same as that of the RWRHE, but each edge has different
weight. The GIP kernel similarity integrates the known active
ingredient-target associations information, which makes the prediction
results more reliable. The importance of each similarity measure is
quantitatively weighted based on entropy, which makes the similarity
network more complete. Therefore, integrating GIP kernel similarity and
information entropy can improve the performance of prediction results
to a certain extent.
Fig 2. The ROC curves of RWRHE, RWRHE_GIP and RWRHE_E.
[80]Fig 2
[81]Open in a new tab
3.2 Comparison with other methods
To further evaluate the performance of RWRHE algorithm, other five
network-based algorithms RWR [[82]6], RWRHN [[83]7], HGBI [[84]12],
LRWHLDA [[85]8] and HMDAKATZ [[86]13] are compared with RWRHE. RWR
algorithm is implemented on the node similarity network. The walker
returns to the seed node with a certain probability in the iterative
process. All nodes are ranked according to the final probability. The
RWRHN algorithm takes into account the jump probability of nodes in
heterogeneous networks when constructing the transition matrix. Active
ingredients related to AD are retrieved from the SymMap
database(release 2019–01) [[87]35], and the top 50 active ingredients
in the inferred evidence score are selected as seed nodes. The RWR and
RWRHN algorithms are used to calculate the association scores between
AD and all active ingredients. The HGBI algorithm based on
heterogeneous graph reasoning infers potential node associations by
constructing heterogeneous networks. The LRWHLDA algorithm is a
superimposed local random walk algorithm. The HMDAKATZ algorithm
considers the contribution of different walks to the association
probability, and considers that the longer walks tend to contribute
less to similarity. We apply HGBI, LRWHLDA and HMDAKATZ algorithms to
calculate the active ingredient-target association probability matrix,
which is compared with the bi-random walk part in this study. The ROC
curves of RWRHE and the other five algorithms are shown in [88]Fig
3(a). The results show that the RWRHE algorithm has excellent
prediction performance, and its AUC value is 0.914, which is higher
than the AUC values of the other five algorithms.
Fig 3.
[89]Fig 3
[90]Open in a new tab
(a)The ROC curves of RWRHE, RWR, RWRHN, HGBI, HMDAKATZ and LRWHLDA.
(b)The ranks of CDF.
In addition, the ranks of prediction results also play an important
role in evaluating the performance of all algorithms. The cumulative
distribution function (CDF) of the ranks is employed to compare the
performance of different algorithms. CDF refers to the proportion of
active ingredients whose ranking exceeds the top-r threshold in the
positive control group. 60 ≤ r ≤ 100 are reported as shown in [91]Fig
3(b). The results show that compared with the other five algorithms,
the RWRHE algorithm retrieves the largest proportion under the same
top-r threshold. The majority of the 63 active ingredients in the
positive control group are retrieved in the top 100. Therefore, RWRHE
has a good performance in predicting the relationship between AD and
active ingredients.
The RWRHE algorithm predicts the association scores and ranks between
active ingredients and AD. According to the active ingredients
contained in herbs and ranks of active ingredients associated with AD,
the effect score of herb k in treating AD is calculated based on ranks
as follows:
[MATH: Hk=∑i<
mn>1rank(i) :MATH]
(18)
where, rank(i) represents the rank of the active ingredient i
associated with AD. Finally, 20 herbs are scored and ranked according
to the predicted effect scores of herbs for AD, shown in [92]S10 Table,
the results show that Danshen have the best effect for AD, followed by
Gouteng and Chaihu. Similarly, based on the active ingredients
contained in the compound, the efficacy of the compound for AD is
scored and ranked according to [93]Eq (18), the results are shown in
[94]Table 2. The results show that Yiqitongyutang is the best compound
for AD compared with other compounds, which indicates that
Yiqitongyutang may have better therapeutic effect for AD.
Table 2. Ranking of efficacy of compound in treating AD.
Compound score rank
Yiqitongyutang 6.229121413 1
Yigansan 4.196828293 2
BKHJ TCM 2.394613178 3
Gubenjiannaoye 2.28103394 4
Dangguishaoyaosan 0.991858956 5
Yizhi 0.165683049 6
[95]Open in a new tab
3.3 GO enrichment analysis and KEGG pathway analysis
The top 100 targets associated with AD are regarded as potential
targets of AD. To learn more information about TCMs and AD,
bioinformatics analysis is used to find relevant information about
potential targets of AD. GO enrichment analysis and KEGG pathway
analysis are performed to clarify relevant biological information about
the potential targets of AD. GO enrichment analysis includes biological
process (BP), cell component (CC), and molecular function (MF). To
screen out the most significantly enriched biological annotations, the
top 11 entries with the lowest P value are selected respectively, as
shown in [96]Fig 4(a)–4(c). In biological process results, the most
enriched GO term ‘Positive regulation of transcription from RNA
polymerase II promoter’ may be involved in the transcription of the
substance related to synaptic connectivity. Synapses play a central
role in learning and memory, and disorders of these behaviors can lead
to neurological diseases, including AD [[97]36]. The targets are also
mainly concentrated on positive and negative regulation of gene
expression and apoptotic biological processes, suggesting that
apoptosis and gene expression regulation play an important role in the
mechanism of AD. In addition, the targets are involved in the
biological process of protein phosphorylation. Protein phosphorylation
is the key mechanism of AD [[98]37]. Highly phosphorylated tau protein
forms neurofibrillary tangles (NFTs), which is one of the main
histopathological features of AD [[99]38]. In cell component results,
the targets are mainly concentrated on cytoplasm, nucleus, plasma
membrane and so on. In molecular functional results, the targets are
mainly concentrated on protein kinase binding, protein kinase activity,
protein serine/threonine kinase activity and protein kinase activity.
Some researchers report that Glycogen synthase kinase-3β (Gsk3β),
Ccyclin-dependent kinase 5 (CDK5) and Microtubule affinity regulating
kinase (MARK) may hyper-phosphorylate tau and accelerate the formation
of NFTs [[100]39–[101]41]. Cyclin-dependent kinases (Cdks), which are
serine/threonine kinases, regulate cell cycle and neuronal
differentiation. Cdks pathway may have effects on neuron loss, which is
responsible for AD [[102]42].
Fig 4. GO enrichment analysis and KEGG pathway analysis of potential targets.
[103]Fig 4
[104]Open in a new tab
(a): the histogram of GO biological process; (b) the histogram of GO
cell component; (c) the histogram of molecular function; (d) the bubble
graph of KEGG pathway.
The involvement of pathways based on potential targets by KEGG pathway
enrichment analysis is plentiful. The top 10 pathways with the smallest
P value are drawn as bubble graphs in [105]Fig 4(d). The size of the
dots in the bubble diagram represents the number of targets enriched in
the pathway, and the depth of the color represents the size of the
statistical test P-value. The number of these targets enriched in the
AD pathway is the highest. In addition, the potential targets are
mainly concentrated on neurodegenerative diseases, apoptosis, and
cancer pathways.
To further illustrate the relationship between topological properties
and relevant biological functions, GO enrichment analysis and KEGG
pathway analysis are performed on top 100 targets predicted by the
RWRHE_GIP and RWRHE_E algorithm, respectively, shown in [106]S11 Table.
The results of the two algorithms do not include the GO term ‘Positive
regulation of transcription from RNA polymerase II promoter’, the GO
term ‘inflammatory response’ and the KEGG pathway ‘Pathways in cancer’.
The GO term ‘Positive regulation of transcription from RNA polymerase
II promoter’ have contribution to the pathogenesis of AD, which has
been discussed above. Numerous studies have revealed the strong
contribution of inflammation to AD pathogenesis, Aβ deposition in AD is
related to inflammatory response [[107]43–[108]45]. Some cancer-related
signaling pathways including FOXO, mTOR, SIRT1, HIF, oxidative stress,
inflammation, and metabolism have important roles in regulating aging
and AD [[109]46]. The GIP similarity and the weights based on entropy
are necessary in integrating networks, which could further display the
entries related to AD.
To further explain the mechanism of multi-pathway and multi-target in
the treatment of AD with TCMs, a target-pathway network is constructed
based on the top 10 pathways of KEGG enrichment analysis. The
relationships between the targets and the pathways are intuitively
visualized, as shown in [110]Fig 5. The same pathway is connected to
different targets, in the meantime, the same target is connected to
different pathways, which indicates that they are interactive
relationships. Different targets play a synergistic role in the same
pathway. And the pathway also regulates gene transcription and effects
gene expression. Compared with single-target drugs, TCMs show the
advantage of multi-target and multi-pathway.
Fig 5. The pathway-target network.
Fig 5
[111]Open in a new tab
The orange circles represent targets, the blue hexagons represent
pathways, and the gray lines represent the target-pathway connections.
3.4 Molecular docking
The components of TCM are complex and the targets are diverse.
Molecular docking can indicate the binding ability between active
ingredients and related targets. The Coach-D server is presented to
predict the protein-ligand binding sites and ligand-binding poses by
molecular docking [[112]47]. The minimum value of Energy^u by Coach-D
is regarded as the final docking energy to evaluate the binding ability
between active ingredient and target.
The panel of ligands is composed of the top 15 active ingredients by
RWRHE and several approved acetylcholinesterase (ACHE) inhibitors for
AD. Several approved ACHE inhibitors for AD clinically are Galantamine,
Donepezil, Rivastigmine, Tacrine and Huperzine A. The first four are
approved by FDA, and Huperzine A is approved in China.
76 key enzymes and receptors from AD metabolic network [[113]34] are
sorted out to form the panel of proteins, and they are divided into 12
categories according to their main functions in the AD metabolic
network, the structure versions of proteins are selected according to
better resolution from Protein data bank (PDB), shown in [114]S12
Table.
The minimum values of Energy^u for these possible complexes formed by
ligands and proteins in the panels are calculated by Coach-D. The
smaller the minimum value of docking energy is, the stronger the
stability of active ingredient-protein site binding is. The docking
energies of these five ACHE inhibitors and 12 categories of proteins
are computed and the box plots are shown in [115]Fig 6. The docking
energies of these five ACHE inhibitors and their common target ACHE are
used as the standard for active ingredients. The docking energies of
the top 15 active ingredients and 76 proteins in the panel are given by
Coach-D, then the average docking values of the top 15 active
ingredients and 12 categories of proteins are computed and represented
with red dots in [116]Fig 6. The five approved ACHE inhibitors’ common
target is the enzyme ACHE. BCHE is the isozyme of ACHE, the median
docking energies of these five ACHE inhibitors with ACHE/BCHE are
-8.7/-8.5, almost all docking values of the top 15 active ingredients
and ACHE are less than -8.7 and the average value is -9.73, all docking
values of the top 15 active ingredients and BCHE are less than -8.5 and
the average value is -10.10. The results indicate that the top 15
active ingredients can bind to ACHE/BCHE and improve the level of
acetylcholine (ACH), which is involved in both memory and learning.
Similarily, the average docking value of the top 15 active ingredients
and BACE1 is -9.45 and less than -8.7, the result shows that the top 15
active ingredients may bind to BACE1, which may reduce the generation
of Amyloid-β (Aβ), Aβ is the main component of senile plaques. Among
the top 15 active ingredients, some active ingredients are confirmed as
ACHE or BACE1 inhibitors in previous research through wet experiments.
Álvarez-Berbel et al. show that Quercetin and apigenin are
characterized as inhibition of the enzymatic activity of ACHE
[[117]48]. Beg et al. report that the exposure of AD flies to
kaempferol reduces ACHE activity [[118]49]. In addition, Han et al.
report that Baicalein exhibits strong BACE1 and ACHE inhibitory
properties [[119]50]. Youn et al. show that oleic acid exerts
significant noncompetitive inhibitory activity against BACE1 [[120]51].
Also, the docking results show that the top 15 active ingredients may
bind to CDK5/MARK/GSK3β and restrain the phosphorylation of tau and
reduce the formation of NFTs. It is well known that Memory loss, Senile
plaques and NFTs are the main histopathological features of AD. The
results show that the active ingredients can enter the active pocket of
related targets well and act as multi-target agents in the prevention
and treatment of AD.
Fig 6. The box plot of molecular docking.
[121]Fig 6
[122]Open in a new tab
4. Conclusion
AD is a complex neurodegenerative disease with few approved drugs. TCM
has a long history and has a strong clinical basis for more than two
thousand years. TCM has the advantage of more active ingredients,
multi-target cooperative regulation and lower toxicity. Therefore, the
study of new active ingredients from herbs is of great significance for
the development of anti-AD drugs.
In this study, a novel random walk algorithm called RWRHE is devised to
predict active ingredients and effective TCMs associated with AD based
on entropy and random walk with the restart of a heterogeneous network.
The comprehensive heterogeneous network is constructed by integrating
the known active ingredient-target association network, active
ingredient similarity network and target similarity networks based on
entropy. The active ingredients and effective TCMs for AD are inferred
based on random walks. The results measured by machine learning and
bioinformatics show that the RWRHE algorithm achieves better prediction
accuracy. Particularly, the docking energies of the five approved ACHE
inhibitors and their common target ACHE are used as the standard for
active ingredients, the results show that the top 15 active ingredients
may improve the level of Ach, reduce the generation of Aβ and restrain
the phosphorylation of tau, which are involved in the main
histopathological features of AD. 20 herbs are ranked according to the
active ingredients contained in herbs and ranks of active ingredients
associated with AD, Danshen, Gouteng and Chaihu are recommended as
effective TCMs for AD. Yiqitongyutang is recommended as effective
compound for AD in the same way.
This study may provide new directions for building more effective
prediction models to identify novel AD drugs. But there are several
potential limitations in the current study that could be improved. In
future, more validated association data would be incorporated to
improve the quality of the heterogeneous network, and more useful and
detailed studies would be integrated to improve the prediction ability.
Only some predicted active ingredients have been validated for acting
on key enzymes of AD in different published papers, we will take
experimental validation into account and try to design wet experiments
to verify the effectiveness of the predicted active ingredients in
future.
Supporting information
S1 Table. TCM compounds-herbs-active ingredients.
(XLSX)
[123]Click here for additional data file.^ (46.7KB, xlsx)
S2 Table. Active ingredients, targets and known active
ingredient-target associations from TCMSP and HERB.
(XLSX)
[124]Click here for additional data file.^ (246.1KB, xlsx)
S3 Table. The similarity values of active ingredients based on two
similarity measures.
(XLSX)
[125]Click here for additional data file.^ (3.5MB, xlsx)
S4 Table. The similarity values of targets based on four similarity
measures.
(XLSX)
[126]Click here for additional data file.^ (13.9MB, xlsx)
S5 Table. The comprehensive similarity values of active ingredients and
targets.
(XLSX)
[127]Click here for additional data file.^ (9.2MB, xlsx)
S6 Table. The final active ingredient-target association matrix RT.
(XLSX)
[128]Click here for additional data file.^ (4.3MB, xlsx)
S7 Table. AD-target associations vector P.
(XLSX)
[129]Click here for additional data file.^ (38.3KB, xlsx)
S8 Table. AD-active ingredient association vector P[r].
(XLSX)
[130]Click here for additional data file.^ (30.7KB, xlsx)
S9 Table. The positive and negative control groups.
(XLSX)
[131]Click here for additional data file.^ (11.4KB, xlsx)
S10 Table. The predicted effect ranks of herbs in treating AD.
(XLSX)
[132]Click here for additional data file.^ (9.5KB, xlsx)
S11 Table. GO enrichment analysis and KEGG pathway analysis on top 100
targets predicted by RWRHE_GIP and RWRHE_E.
(XLSX)
[133]Click here for additional data file.^ (21.5KB, xlsx)
S12 Table. The panel of proteins.
(XLSX)
[134]Click here for additional data file.^ (10.3KB, xlsx)
Data Availability
All relevant data are within the paper and its [135]Supporting
information files.
Funding Statement
This research was funded The Science Fund of Tianjin Education
Commission for Higher Education, grant number 2019KJ025. The funders
had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
References