Deep Learning and Random Forest Approach for Finding Optimal

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Deep Learning and Random Forest Approach for Finding Optimal TCM Formula for Treatment of Alzheimer’s Disease Hsin-Yi Chen, JianQiang Chen, Jun-Yan Li, Hung-Jin Huang, Xi Chen, Hao-Ying Zhang, and Calvin Yu-Chian Chen J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.9b00041 • Publication Date (Web): 19 Mar 2019 Downloaded from http://pubs.acs.org on March 19, 2019

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Journal of Chemical Information and Modeling

The Random Forest model and Deep Learning model were utilized to build AD predicted models to calculate the possibility of our TCM formula. The result showed Methyl 3-O-feruloylquinate contained in Phellodendron amurense and Cynanogenin A contained in Cynanchum atratum are capable of forming stable interactions with GSK3β.

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Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Deep Learning and Random Forest Approach for

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Finding Optimal TCM Formula for Treatment of

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Alzheimer’s Disease

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Hsin-Yi Chen†#, Jian-Qiang Chen†#, Jun-Yan Li†#, Hung-Jin Huang†#, Xi Chen†,

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Hao-Ying Zhang†, Calvin Yu-Chian Chen†,‡,§*

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†

School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 510275, China

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‡

Department of Medical Research, China Medical University Hospital, Taichung 40447,

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Taiwan

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§Department

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Taiwan

of Bioinformatics and Medical Engineering, Asia University, Taichung 41354,

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#

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* Corresponding Authors

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Calvin Yu-Chian Chen, Ph.D.

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School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China.

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TEL: 15626413023

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E-mail: [email protected]

Equal contribution

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Abstract:

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It has demonstrated that Glycogen synthase kinase-3β (GSK3β) is related with Alzheimer’s

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disease (AD). Based on the world largest traditional Chinese medicine (TCM) database, network

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pharmacology-based approach were utilized to investigate the TCM candidates that can dock well

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in multiple targets. Support Vector Machine(SVM), multiple linear regression (MLR) methods

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were utilized to obtain predicted models, Specially, The Deep Learning and The Random Forest

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algorithm were adopted, We achieved R2 of 0.927 on training set and 0.862 on the test set in Deep

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Learning, The latter’s R2 on the training set is 0.869 and on the test set is 0.890.Besides,

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comparative molecular similarity indices analysis (CoMSIA) was performed to get a predicted

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model. All the training models achieved good results on test set.100ns MD simulation evaluated

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the stability of GSK3β protein-ligand complexes. Methyl 3-O-feruloylquinate and Cynanogenin

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A both induced more compactness to GSK3β complex and stable condition among all simulation

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times, the GSK3β complex also had no substantial fluctuation after simulation time of 5 ns. For

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TCM molecules, we used trained models to calculate predicted bioactivity values.Thus, the

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optimum TCM candidates were obtained by ranking the predicted values.The result showed

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Methyl 3-O-feruloylquinate contained in Phellodendron amurense and Cynanogenin A contained

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in Cynanchum atratum are capable of forming stable interactions with GSK3β.

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Keywords: Glycogen synthase kinase-3β (GSK3β); TCM formula; Network pharmacology;

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Deep Learning; Random Forest; MD simulation

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1. Introduction

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Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases of the central

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nervous system (CNS) that affects people over the age of 65. 1 The feature of Alzheimer’s disease

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(AD) is the abnormal accumulation and processing of mutant or damaged intra and extracellular

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proteins.2While the detailed molecular mechanism underlying the onset and progression of AD

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remains elusive, previous studies have correlated it with the accumulation of amyloid beta (Aβ),

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phosphorylated tau, 3 mitochondrial dysfunction, and neurofibrillary tangles in brains. 4 Targeting

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of β-amyloid (Aβ) has been the focus in the development of improved treatments for AD, but

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these have not yet shown any clinical benefit.5

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Regarding the Aβ pathway, Aβ peptides accumulation formed by sequential cleavage of β-

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amyloid precursor protein (APP) induces dementia syndromes in patients. 6 The cleaving enzyme,

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β-site amyloid precursor protein cleaving enzyme 1 (BACE1), cleaves APP at two β-sites to

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generate β peptide. 7 Inhibition of GSK-3 may help in the treatment of various diseases, such as

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Alzheimer's disease. 8 A known contributor to this pathway is the glycogen synthase kinase 3 beta

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(GSK3β), a proline-directed serine/threonine protein kinase,9 mainly known for its function in

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regulating glycogen metabolism by inactivating glycogen synthase through phosphorylation.

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Aberrant high phosphorylation activity of GSK3β has been linked to elevated BACE1 expression

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and Aβ production.10 Therefore, inhibiting the phosphorylation activity of GSK3β can be a viable

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and promising strategy to develop novel drugs and therapeutics for AD. 11Using multi-layer neural

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network

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(RF) model can successfully identify natural proteins from bait proteins. 14

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Over the past few years,

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2011, and we have developed computer-aided drug design15 webservers such as iScreen16 and

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iSMART17, which help the related institutions and researchers improve their efficiency.

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In this effort, we sought to develop novel formula with natural active ingredients capable of

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,Deep Learning model shows a satisfactory predictive ability. 13 The Random Forest

we established the TCM database (TCM Database@Taiwan) since


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inhibiting GSK3β and other key components/proteins in the pathogenic pathway of AD by

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exploiting the TCM database.

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drug strategy may not affect entire biological system19-21, a specific target still not fully to discover

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effectively drugs for treating diseases in organisms. Recent researches of drug design have been

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based on multi-target concept as strategy in the developments of effective drugs22, 23. Network

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analysis of signaling pathway were wildly used to identify potential factors for many diseases. 24-

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26

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AD was explored by utilizing the STRING database and the associated analyzing tools and a

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collection of promising target proteins along with GSK3β was pinned down. TCM database

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virtual screening, bioactivity prediction (QSAR modeling) and drug-target analysis were applied

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to identify lead compounds in TCM database. Molecular docking was ensued to find out the

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possible interaction modes between the active ingredients (small molecules) and the protein

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targets. Then the stability of protein-ligand complex was verified by molecular dynamics (MD)

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simulations and the detailed interactions between the ligands and the target proteins were

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delineated. In the end, we presented these verified compounds from the TCM database that

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possesses potential bioactivities against AD. (Figure 1)

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2. Material and methods

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2.1 Pathway enrichment analysis and network construction

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The biological pathways was constructed using STRING database (STRING v10.5, http://string-

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db.org/).27 It provides functional association between two proteins, the protein–protein interaction

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(PPI) information were retrieved from physical interaction databases and databases of curated

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biological pathway knowledge, such as IntAct, MINT, BioGRID, DIP, KEGG, Reactome, GO,

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etc. It can be queried from inside the Cytoscape software framework. The PPI network of AD was

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generated and visualized using the Cytoscape STRING app with data associated with Homo

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The trend of drug development has reveal that one target one

Specifically, the protein-protein interaction (PPI) network associated with the pathogenesis of



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sapiens. Top 10 proteins with confidence score of 0.8 was set to construct the PPI network and

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the network was expanded by adding new nodes. Functional enrichment analysis was conducted

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using the information from KEGG database and the results were visualized using Cytoscape in

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order to infer the target-disease relationship.

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2.2 Screening and molecular docking

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The crystal structure of potential targets for Alzheimer’s disease such as GSK3β, Nicastrin and

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APBB1 were obtained from PDB database (PDB accession code: 4ACC, 5FN5 and 3D8D

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respectively).28, 29 The Prepare Protein module of Accelrys Discovery Studio 2.5.5.9350 (DS 2.5)

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was employed to insert missing atoms, model missing loops, and remove crystal waters.

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Protonation state of each protein was assigned at pH 7.4. Total number of 61,000 TCM

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compounds obtained from the website of TCM Database@Taiwan

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criteria and Lipinski's rule of five. A number of 18776 of the resulting compounds were then

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prepared for docking by energy minimization using the smart minimizer algorithm before the

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subsequent docking studies, in which Monte-Carlo sampling with the CHARMm force field

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(Chemistry at HARvard Molecular Mechanics) was chosen for ligand conformation generation,

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using the LigandFit module of DS 2.5. 30 The active site of GSK3β ATP binding site was defined

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based on the interaction of 7YG binding pose (the compound 23 in Berg's study,

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(PDB code: 4ACC), the 7YG is selective for GSK-3β ATP binding site. During the docking

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process, DREIDING force field, containing Gasteiger charges was chosen to calculate interaction

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energy for each ligand.

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2.3 Construction of QSAR model


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were filtered by ADMET

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to GSK3β

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2.3.1 2D-QSAR model

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30 GSK3β inhibitors including 24 training molecules and 6 test molecules with bioactivity (Ki

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value) from Berg's study 28 were utilized to build QSAR models (Table 1). All Ki values (nM)

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were converted to pKi values using equation (1):

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pKi = 9 - log(Ki)

(1)

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The 2D structure of each molecule was created using ChemBioOffice 2010 and the molecular

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properties of GSK3β inhibitors, the independent property, were calculated with the Calculate

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Molecular Properties module of DS 2.5. The pKi of each ligand was treated as a dependent

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property, as opposed to the calculated properties from chemical scaffold as independent ones in

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the regression step. The Genetic Function Approximation (GFA) algorithm was employed to

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select suitable properties related to activity value. Then the 2D-QSAR models were used to

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predict the bioactivity of TCM candidates with the selected properties and bioactivity of each

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GSK3β inhibitors.

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2.3.1.1 Deep Learning model

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.Aiming to enhance the accuracy of predicted activity value, the Deep Learning (DL) method

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was applied to build a new model. We construct a simple 4-layers full connected neural network

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using ReLu (Rectified Linear Units) function as activation function. Dropout technique was also

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applied in the second and third layer (with rate 0.4 for the second and 0.6 for the third) to reduce

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over-fitting. The loss function is mean squared error (MSE). We use the Adam optimizer

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(Kingma & Ba, 2014) with learning rate 0.0001 and other parameters used in the original paper.

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2.3.1.2 Random Forest model

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Similarly, the Random Forest was also applied in this paper. About the 204 properties calculated

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by the Genetic Function Approximation (GFA) algorithm, we used Pearson correlation

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coefficient matrix to analyze the relationship between these properties. The result was presented 6 ACS Paragon Plus Environment


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as a picture with the python package Yellowbrick. About the 30 data set, we used principal

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component analysis including 2D and 3D to analyze the relationship between properties. The

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preprocessing of the data set involved the following steps. Firstly, the numerical value of

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properties was be zoomed to 0 to 1, then the 54 properties with variance greater than 0.05 were

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be chosen. About these 54 properties, we did normalization to get the data with mean is 0 and

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variance is 1. Finally, we got 9 properties using Lasso feature selection. The Pearson correlation

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coefficient matrix heatmap of 9 properties shows the correlation coefficient of 9 properties

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selected is small enough.

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2.3.1.3 SVM and MLR model

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The regression of models were generated by support vector machine (SVM) and multiple linear

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regression (MLR), which were generated by LibSVM and MATLAB, respectively. The equation

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(2) expresses the squared correlation coefficient (r 2) of SVM: 31

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r =

̅ ̅ ̅ (l̅ ∑li=1 f(xi )yi −∑li=1 f(xi ) ∑li=1 yi )

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̅ ̅ ̅ ̅ (l̅ ∑li=1 f(xi )2 −(∑li=1 f(xi ))2)(l̅ ∑li=1 yi 2 −(∑li=1 yi )2)

(2)

The expression of MLR model is described by equation (3): Y = b0 + b1×X1 + b2×X2 + ...+ b7×X7

(3)

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In particular, lead drugs of GSK3β ATP binding site were also utilized to generate predict model

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for candidate validation, a number of 117 chemical compounds (Table 1, Table S1) were used to

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build property-DockScore relationship models.

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were calculated with the activity site of GSK3β ATP binding site by using docking study.

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2.3.2 3D-QSAR model

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26 GSK3β inhibitors including 21 training molecules and 5 test molecules with bioactivity (Ki

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value) from Berg's study 28 were utilized to build CoMSIA model with Sybyl-X 1.1. (Table 1).

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The CoMSIA model was constructed according to their activity and structural characteristics

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The DockScore of all chemical compounds


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including stereoscopic fields, electric fields, hydrophobic fields, and hydrogen bonds for

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acceptors and donors. The models were evaluate through cross-validation (CV). The residual

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between the measured and predicted values were calculated. The square sum of explained

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(SSE), F-test, coefficient of determination (R2), and q2 for cross-validation were referred as

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evaluation indexes of CoMSIA model. Various field combinations had been studied separately.

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2.4 MD simulation

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To prepare the protein-ligand system for MD simulations, the topology files and parameters for

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the ligands were obtained from SwissParam web server. 34 The periodic cubic box for simulation

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had the margin of 1.2 nm and was solvated using TIP3 water molecules. To mimic the

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physiological condition, we included 0.145 M Na+ and Cl-ions to neutralize the system charge.

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The systems were minimized for 5,000 steps using steepest descent followed by equilibration in

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canonical ensemble (NVT, T = 310K) for 100 ps with positional constraints applied and stages of

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equilibration in isothermal-isobaric ensemble (NPT) to release the constraints. We extended the

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NPT production runs to 100,000 ps. The van der Waals interactions were evaluated by Lennard-

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Jones potential 35. The bond lengths between each pair of atoms were constrained by the linear

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constraint solver (LINCS) algorithm. We sampled every 20 ps over the simulations to analyze the

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variation of our protein-ligand systems using GROMACS 4.5.5. All simulations were performed

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using the charmm27 force field. 36, 37

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3. Results and discussion

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3.1 Potential target proteins for Alzheimer’ disease form PPI network analysis

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Protein-protein interaction (PPI) is known to be a critical component in cell signaling transduction,

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thereby, plays important roles in the pathogenesis of many diseases. Over the years, researchers

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have acquired a substantial amount of data on PPI in various forms, which contributed greatly to

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the quest for understanding the molecular mechanism for diseases and the ultimate remedies. To 8 ACS Paragon Plus Environment


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identify target proteins for Alzheimer’s disease, we utilized website of STRING v10.5 to construct

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a PPI network for target protein selection. (Figure 2) We then performed functional enrichments

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analysis with this PPI network to sort out the protein interactions associated with Alzheimer’s

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disease. The resulting proteins were ranked/categorized by interaction score, amongst them the

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GSK3β, NCSTN, MAPT and PSEN1 were related with Alzheimer’s disease from Kyoto

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Encyclopedia of Genes and Genomes (KEGG) database (Table S2), with false discovery rate

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(FDR) values all below 0.05. We then interrogated the relationships between the four proteins and

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their related pathway, and constructed an association network using Cytoscape with the data of

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the functional enrichment from KEGG database (Figure 3). It then became clear that the Notch

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signaling pathway, Neurotrophin signaling pathway and Wnt signaling pathway (colored in red)

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converged to AD though the four proteins. Many research have been reported that these three

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signaling pathway play important roles in pathogenesis of AD. 38-41 Therefore, GSK3β, NCSTN,

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MAPT and PSEN1 are potential effective molecular targets for disrupting the pathogenic pathway

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of Alzheimer’s disease, and we believe it is advisable to focus our drug selection on them.

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3.2 Results of predicted model generation

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3.2.1 Deep Learning model discussion

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In this paper, we applied deep learning method on our research and achieve R2 of 0.927 on

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training set and 0.862 on the test set. We used a simple 4-layers full connected neural network

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for our research (Figure 4). The activation function is ReLu (Rectified Linear Units). Our

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training samples are fewer, when training neural networks, the trained models are prone to over-

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fitting. Dropout can be used as a trick to train deep neural networks. In each training batch,

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over-fitting can be significantly reduced by ignoring half of the feature detectors (making half of

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the hidden layer nodes have a value of 0).Dropout technique is applied in the second and third

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layer (with rate 0.4 for the second and 0.6 for the third) to reduce over-fitting. The loss function 9 ACS Paragon Plus Environment

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is mean squared error (MSE). The Adam optimizer is suitable for unstable objective functions

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and its hyper parameters are well interpreted and usually need little or no adjustment. By setting

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Adam optimizer’s learning rate as 0.0001. We have done 120 times different attempts and

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achieve credible results. (Figure 5).

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3.2.2 Random Forest model discussion

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As for Random Forest model, the Pearson Ranking of 204 Features illustrates that high correlation

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between some features, which are bigger than 0.4(Figure 6). In order to reduce the number of

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related features, replace the original variable with fewer variables. Principal component analysis

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has no effect if the original variables are orthogonal to each other, ie, there is no correlation. The

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Two-dimensional principal component analysis and Three-dimensional principal component

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analysis demonstrate dimensionality reduction conduce to hunt out the most representative

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features (Figure 7). We scaled the number of 204 properties and filtered from them with variance

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greater than 0.05 to find out the most representative 54 properties. Then, we made the Lasso

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feature selection to get 9 features with small correlation coefficient and good orthogonality

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(Figure 8). Up to 20 trees are used for training, and samples that have not been extracted are used

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as verification sets. The mean square error (MSE) on the training set is 0.122, on the test set is

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0.049. The R2 on the training set is 0.869, on the test set is 0.890(Figure 9).

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3.2.3 MLR and SVM models discussion

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Among the property-activity relationship models we built by using Genetic Function

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Approximation (GFA) algorithm, the most reasonable one (Table 2) contains eight molecular

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properties: logD, Jurs_PPSA_3, Jurs_RPSA, Jurs_TPSA, Jurs_WPSA_3, Minimized_Energy,

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PMI_X, and Shadow_XYfrac. The multiply linear function of this model is expressed in equation

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(8)



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pKi = -28.251 + 0.79395 × logD + 1.292 × Jurs_PPSA_3 − 85.727 × Jurs_RPSA + 0.15375 ×

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Jurs_TPSA − 1.2504 × Jurs_WPSA_3 + 0.14322 × Minimized_Energy + 0.0055567 × PMI_X +

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25.714 ×Shadow_XYfrac

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In equation (8), logD is the octanol-water partition coefficient in pH 7.4, accounting for the

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ionization states of each ligand. Jurs_PPSA_3 is the positive surface area weighted by all

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positively charged atoms of each ligand. Jurs_RPSA stands for the polar surface area divided by

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all molecular of solvent-accessible surface area. Jurs_TPSA is the total polar surface area

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(summation of solvent-accessible surface areas of atoms). Jurs_WPSA_3 represents the surface-

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weighted charged partial surface areas. Minimized_Energy is the energy after a fast minimization

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procedure by using clean force field. PMI_X exhibits the orientation and conformational rigidity

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of each ligand. Shadow_XYfrac indicates the area of the molecular shadow of each ligand in the

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xz plane. The properties obtained from GFA algorithm showed that properties of each ligand with

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ionization states, solvent-accessible surface areas, and orientation of scaffold were significantly

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related to experimental pKi values.

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In order to construct a bioactivity predictive model for each molecule in TCM database, we

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computed a score by using these eight properties for all the training and test molecules. We then

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validated the prediction accuracy of these two models by using SVM and MLR models displayed

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accurate prediction ability indicated by the high values of correlation coefficient (r 2) observed in

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the SVM model (r2 = 0.7925) and MLR model (r2 = 0.8942) (Figure 10). Hence, we utilized the

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SVM and MLR models to predict the bioactivities of compounds from TCM database.

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We also utilize GFA algorithm to select reasonable property for property-DockScore relationship

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models generation (Table 3), the best model contains fifteen molecular properties are related to

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DockScote (R2 = 0.8494), and the multiply linear function of this model is expressed in equation

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(9):

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DockScore = -19.655 + 3.5212 *ALogP − 1.2877 × ES_Count_aaCH + 2.0698 × ES_Count_aaN

(8)


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+ 4.6548 × ES_Count_sssN + 0.31246 × ES_Sum_tN + 2.4761 × HBA_Count + 2.6922

×

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Num_Rings + 2.1511 × Num_RotatableBonds + 0.15699 × Molecular_PolarSASA − 0.35 ×

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Molecular_PolarSurfaceArea − 4.2065 × CHI_3_C − 11.063 × IAC_Mean − 1.4235 × Kappa_1

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+ 0.97326 × Dipole_mag + 29.415 × Jurs_FPSA_2 + 392.97 × Jurs_FPSA_3 + 0.26191 ×

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Jurs_PNSA_1 + 0.047399 × Jurs_WNSA_2 − 0.040872 × Strain_Energy + 18.979 ×

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Shadow_XYfrac

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In equation (9), ALogP is log of the octanol-water partition coefficient. The ES_Count property

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is calculate the E-state count for atom type in the molecule. The ES_Sum property is calculate the

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E-state sum for atom type in the molecule. HBA_Count is the number of hydrogen bond accepting

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groups. Num_Rings is defined as the number of rings in the smallest set of smallest rings.

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Num_RotatableBonds is the number of Rotatable bonds in the molecule. Molecular_PolarSASA

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is the value of the polar solvent accessible surface area for each molecule. CHI_3_C is a class of

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connectivity indices of topological descriptors. IAC_Mean is Graph-Theoretical InfoContent

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descriptors. Kappa_1 means Kappa Shape Indices. Dipole_mag is 3D electronic descriptors in an

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electrostatic field. Jurs_FPSA descriptor is fractional charged partial surface areas. Jurs_PNSA_1

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is sum of the solvent-accessible surface areas of all negatively charged atoms. Jurs_WNSA_2

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indicates surface-weighted charged partial surface areas. Strain_Energy is the difference between

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Energy and Minimized_Energy. Shadow_XYfrac indicates the area of the molecular shadow of

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each ligand in the xz plane.

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The 117 inhibitors of GSK3β ATP binding site were used to calculate the value of the fifteen

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properties. The SVM and MLR algorithm were employed to construct DockScore predicted

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models. We further validate the prediction accuracy of these two models, the high value of

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correlation coefficient (r2) displayed in the SVM model (r2 = 0.971) and MLR models (r2 = 0.822).

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CoMSIA model of 3D-QSAR were constructed (Figure 11). The value of CoMSIA were

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calculated and further to obtain the predicted activity. Partial least square (PLS) analysis and

(9)



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validation of CoMSIA model were provided the value of correlation coefficient (R2), standard

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error of estimate (SEE) and F test (F ratio) for evaluating, as well as the cross validation (q2cv)

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for assessing (Table 4). Large enough R2 and relatively small SEE could explain the rationality

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of the model. And the q2cv which greater than 0.5 were worth being considered. CoMSIA model

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suggested the hydrophobic group (blue area), hydrogen bonds donor (Magenta area) for drug

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design. Areas that were inappropriate for introducing hydrogen bond donor would be warned

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correspondingly (green area) (Figure 11B). External validations of the models were provided to

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identify its accuracy.

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3.3 TCM candidates selection via database screening and bioactivity prediction

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The compounds from TCM database were used in the high throughput virtual screening against

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GSK3β, Nicastrin and APBB1. Many small molecule GSK3β inhibitors have been reported in

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previous studies.

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prediction and filter out reasonable TCM candidates (Table 5). The ADMET description of each

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candidate were calculated (Table 6). As Nicastrin and APBB1 have no sufficient experimental

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data to determine the bio-activity of chemical compounds and there is no available control set to

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compare with TCM candidates, the QSAR model and MD simulation cannot be used to analysis

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chemical compounds. Therefore, the only way to investigate potent ligand is calculate binding

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score through docking study (Table S3). The drug-target network showed Methyl 3-O-

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feruloylquinate and Morusimic acid B have potential to target multi-protein (Figure 12). Methyl

20

3-O-feruloylquinate, Morusimic acid B and Cynanogenin A had high vote score among the top

21

eight candidates in the results of docking study and bioactivity prediction. Their chemical

22

scaffolds are displayed in Figure 13 and he docking pose for the chosen three candidates and

23

GSK3β complex are presented in Figure 14. These three TCM candidates have a common

24

property, a carboxylate group in their scaffolds. This functional group might increase the binding

25

affinity for R141 of GSK3β. Analyzing the docking poses revealed that all small molecules form

42

So they could be used to construct a QSAR model to performed the activity


Page 15 of 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

H-bond interactions with residues on GSK3β. Methyl 3-O-feruloylquinate interacts with Q185 of

2

GSK3β through H-bond interactions. Furthermore, residue I62 and R141, interact with docked

3

ligand, are close to Methyl 3-O-feruloylquinate. Morusimic acid B has H-bond interactions with

4

N186 and R141. Cynanogenin A forms two H-bonds with Q185 and R141. 7YG engages with

5

K85 and D133 through 2 H-bonds. Interestingly, there is no H-bond formed between 7YG and

6

residues of Q185 and R141. From the Table 5, the multiple QSAR validations (SVM, MLR,RF,DL

7

and CoMSIA), we integrate all the five methodology and select out the potent compounds(Table

8

7).

9

3.4 Stability of the protein-ligand complexes

10

To efficiently search over the large conformational space for the possible protein-ligand

11

interaction mode, most docking studies treat the receptor as a rigid body, which often results in

12

docking poses don’t necessarily represent the protein-ligand interactions under dynamic condition.

13

Therefore, we performed MD simulations with the protein-ligand complexes recovered from

14

docking to validate the stability of them by monitoring the structural variation over the

15

simulations. We plotted of root mean square deviation (RMSD) of each complex with reference

16

to the corresponding first frame in the simulation trajectory and shown them in Figure 15. The

17

fluctuation in the plot of RMSD tended to stabilize after 5 ns (Figure 15A), and the value of

18

RMSD is in average of 0.23 nm. The RMSD evolution profile of the GSK3β complex with Methyl

19

3-O-feruloylquinate, Morusimic acid B, or Cynanogenin A, resembles that of 7YG (Figure 16A),

20

suggesting an intrinsic stability of the protein structure in the complexes.

21

We also computed the radius of complex gyration for all the complexes over the simulations

22

(Figure 16B). The value of complex gyration is inversely proportional to the compactness of the

23

structure. The compactness of the GSK3β complexes with the three TCM candidates revealed less

24

variation than that of the GSK3β-7YG complex. The gyration values of GSK3β complexes with

25

Morusimic acid B, and Cynanogenin A did not change to a great extend during the simulation. 14 ACS Paragon Plus Environment


Page 16 of 64

1

The lowest value of gyration appeared in the GSK3β-Methyl 3-O-feruloylquinate complex after

2

80 ns of simulation, which indicated the protein structure of GSK3β become more compact at

3

final stage of MD simulation.

4

To analyze the energy of all simulation system with solvent and protein complexes, we calculated

5

total energy of simulation system during 100 ns (Figure 17), the value of total energy is sum of

6

potential energy and kinetic energy. We found that the total energy of all simulated systems are

7

within the average of -1.012 × 106 over all simulation time, this results denoted that situation of

8

simulation system is constant, this phenomenon also illustrated the solvent are not affect the

9

protein structure of GSK3β during MD process. Because all of the simulation systems of GSK3β

10

complexes are stable during MD simulation, we further analyzed the structural variation of each

11

ligand by RMSD calculation (Figure 18). We found that Cynanogenin A has large increased values

12

after 5ns. For 7YG, Methyl 3-O-feruloylquinate, and Morusimic acid B, the ligand RMSD value

13

were tend to stable during all simulation time. Therefore, we further measured distances between

14

interacted atoms in next studies to observe the variation of Cynanogenin A in GSK3β binding site.

15

3.5 H-bond distance analysis form dynamics protein-ligand complexes

16

To analyze the variation of Cynanogenin A, we found that two residues, R141 (ARG141) and

17

R144 (ARG144), can generate interactions. These residues are important for Methyl 3-O-

18

feruloylquinate, Morusimic acid B and Cynanogenin A to bind to GSK3β. Consequently, we

19

calculate the distance between Cynanogenin A and residues R141, R144 and D200 (ASP200) of

20

GSK3β throughout the simulation (Figure 19). About the H-bond distance analysis of

21

Cynanogenin A, we also found that the H-bond distances of R141 and R144 were increased after

22

30 ns and 60 ns, respectively. On the other hand, the distance between Cynanogenin A and D200

23

was decreased during 70 ns to 90 ns. The data illustrated that the Cynanogenin A was close to

24

D200 in GSK3β binding site; hence, we further analyzed the binding conformation of

25

Cynanogenin A in the binding site of GSK3β at initial stage (0 ns) and final stage (100 ns) of the 15 ACS Paragon Plus Environment

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1

MD simulation. The snapshots of Cynanogenin A were shown in Figure 14. As for snapshot of

2

initial stage, the Cynanogenin A interacted with D141 and D144 in GSK3β binding site, which

3

forms two H-bond interactions on both of the residues. At final stage of MD simulation time, the

4

position of Cynanogenin A was close to D200. Because the D200 located in GSK3β binding site,

5

the data of H-bond distance revealed that Cynanogenin A move into the binding pocket at final

6

stage of MD simulation time.

7

3.6 Tunnels prediction in protein structure and migration of docked ligands

8

Here, we analyzed the tunnels and channels in the structure of protein-ligand complexes for all

9

MD conformations (Figure 20), the possible channels were produced from the place of each 43

10

docked ligand by using CAVER 3.0 software.

11

individual clusters. The width of produced channels was very small in either of GSK3β-7YG

12

(Figure 20A) or Methyl 3-O-feruloylquinate (Figure 20B), which indicated that these two ligands

13

might not easily escape from the binding site. On the contrary, the produced channels from

14

GSK3β-Morusimic acid B (Figure 20C) performed more distinct predicted tunnels, suggesting

15

that Morusimic acid B has a higher chance to escape from the docking site. Interestingly, we found

16

the GSK3β-Cynanogenin A complex featured one channel and displayed from inside of protein

17

structure, which was colored in light blue channel in Figure 20D. This tunnel resides inside of

18

protein structure, suggesting that Cynanogenin A move from outside into GSK3β binding pockets.

19

We then utilized the DSSP analysis tool of GROMACS to delineate the evolution of the secondary

20

structure features of the protein-ligand complexes in our simulations (Figure 21). All our GSK3β-

21

ligand complexes displayed stable secondary structure during over the entire course of our

22

simulations (Figure 21A-C). It is worth noting that Cynanogenin A induced changes to the

23

secondary structure of residue 65 to 70 in the window of from 0 ns to 15 ns (Figure 21D),

24

suggesting that the binding of Cynanogenin A with the active site of GSK3β affected surrounding

25

residues, and altered the stability of secondary structure.

The calculated tunnels were group into five



Page 18 of 64

1

In the end, we measured the distance between protein and ligand to illustrate the migration of

2

each ligand over the simulations by using Ligplot plus (Figure 22).

3

GSK3β and Morusimic acid B exibits a large fluctuation 30 ns into the free MD simulation, which

4

suggested that Morusimic acid B cannot bind to GSK3β stably, the distance between protein and

5

ligand is in the average of 3.0 nm at final stage of MD simulation time. However, the distance

6

between GSK3β and Cynanogenin A reduced from 2.0 nm to 1.5 nm during 100 ns simulation

7

time. The phenomenon of Cynanogenin A is related with H-bond distance analysis, tunnels

8

prediction and DSSP analysis. During the process of Cynanogenin A migrated into the binding

9

site of GSK3β at final stage of MD simulation, the reduced distance of H-bond appeared between

10

D200 and Cynanogenin A, one possible channel was observed from the results of tunnels

11

prediction, and the results of DSSP analysis showed that secondary structure of binding region

12

was changed from residue 65 to 70. We also analyzed the MD snapshot with 2D diagram to

13

observer the variation of protein-ligand interactions at initial state (0 ns) and final state (100 ns)

14

in Figure 23. The key residues, R141 and R144, could interacted with each ligand at 0 ns, but

15

Morusimic acid B change interacted residues at 100 ns (Figure 23F), which was related to the

16

result of distance analysis from protein and each ligand. 7YG and Methyl 3-O-feruloylquinate did

17

not change the binding residues among all simulation times. The binding residues still had

18

hydrophobic and H-bond interactions with 7YG and Methyl 3-O-feruloylquinate. Since the

19

binding position of Cynanogenin A changed to binding site of GSK3β during dynamic condition,

20

the numbers of binding residues are increased at final stage of MD simulation (Figure 23H). These

21

results suggested that TCM candidates, Methyl 3-O-feruloylquinate and Cynanogenin A, might

22

be regarded as potential inhibitors for GSK3β. According to the information of herbal resource in

23

TCM database, Methyl 3-O-feruloylquinate could be isolated from Phellodendron amurense and

24

Stemona japonica,45, 46 Cynanogenin A from Cynanchum atratum.

25

Phellodendron amurense is Amur cork tree, which has been used to tread hematochezia, diabetes, 17 ACS Paragon Plus Environment

44

47

The distance between

The common name of

Page 19 of 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


48

1

mouth sores, and drench tur-bidity in medical therapy.

2

pathogenic microorganisms.

3

been used to treat skin inflammatory in folk medicine. 49

4

4. Conclusion

5

Identifying drug targets used to be an utterly empirical and experimental procedure, in which

6

researchers delved into literatures to fish out potential candidates and set up experiments to prove

7

or disprove, which were often laborious. Nowadays, as large-scale databases of protein-protein

8

interaction are established and ever increasing in the amount of information curated in them, the

9

quest for potential drug targets has become unprecedentedly efficient by integrating them into the

10

process. With the purpose of tackling Alzheimer’s disease, we performed the pathway network

11

analysis on PPI network to find out potential drug targets for it. Our PPI network studies revealed

12

the importance of GSK3β and Nicastrin in the pathogenesis of Alzheimer’s disease, the result of

13

network analysis highlighted the substantial involvement of these two proteins in Alzheimer’s

14

disease. With these the protein targets in hand, we opted to unearth natural compounds of potential

15

inhibitory effect to them from known herbs, mainly due to the readiness and safety associated

16

herb medicine application. Our group has made a major contribution in unifying traditional

17

Chinese medicine with modern medicinal chemistry by developing the TCM, which contains all

18

known herbs and the corresponding active compounds in it. Among the compounds from TCM

19

database, we determined that Methyl 3-O-feruloylquinate and Cynanogenin A were capable of

20

binding to GSK3β by predicting the bioactivities and performing molecular docking. We further

21

evaluated our results by MD simulations. The MD simulations shown that Cynanogenin A had

22

unstable Ligand RMSD after MD simulation, but the MD analyses showing that Cynanogenin A

23

migrated into the binding pocket after 5 ns. Methyl 3-O-feruloylquinate and Cynanogenin A both

24

induced more compactness to GSK3β complex and stable condition among all simulation times,

25

the GSK3β complex also had no substantial fluctuation after simulation time of 5 ns. In addition,

46

Stemona japonica has potential to anti-

Cynanchum atratum belong to Apocynaceae family, which has



Page 20 of 64

1

the MD simulation analysis displayed that these two GSK3β complexes can generate stable

2

conformations in the end of MD simulation time. We then identified that Methyl 3-O-

3

feruloylquinate could be isolated from Phellodendron amurense and Stemona japonica 45, 46 and

4

Cynanogenin A is available in Cynanchum atratum.

5

formula comprises Phellodendron amurense and Cynanchum atratum, to be applied in

6

Alzheimer’s disease treatment.

7

Acknowledgments

8

This work was supported by Guangzhou science and technology fund (Grant No 201803010072)

9

and China Medical University Hospital (DMR-106-151, DMR-106-071). We also acknowledge

47

Therefore, we propose this novel herb

10

the start-up funding from SYSU “Hundred Talent Program”.

11

Disclosure

12

The author reports no conflicts of interest in this work.

13 14 15 16 17 18 19 20 21 22 23 24 25


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Phellodendron Amurense Bark. Phytochemistry 1994, 35 (1), 209-215.

15

46. Ge, F.;

16

X.; Zuo, J.; Ye, Y., Isolation of chlorogenic acids and their derivatives fromStemona japonica

17

by preparative HPLC and evaluation of their anti-AIV (H5N1) activityin vitro. Phytochemical

18

Analysis 2007, 18 (3), 213-218.

19

47. Bai, H.; Li, W.;

20

novel pregnane glycosides from Cynanchum atratum. Tetrahedron 2005, 61 (24), 5797-5811.

21

48. Akihisa, T.;

22

Koike, K.; Abe, M., Melanogenesis-Inhibitory and Cytotoxic Activities of Limonoids, Alkaloids,

23

and Phenolic Compounds from Phellodendron amurense Bark. Chemistry & biodiversity 2017,

24

14 (7).

25

49. Choi, Y. Y.; Kim, M. H.; Lee, H.;

Satoh, Y.;

Ohtsuka, M.;

Nagasao, M.; Shoji, J., Phenolic Constituents of

Ke, C.; Tang, W.; Yang, X.; Tang, C.; Qin, G.; Xu, R.;

Li, T.; Chen,

Koike, K.; Satou, T.; Chen, Y. J.; Nikaido, T., Cynanosides A-J, ten

Yokokawa, S.;

Ogihara, E.;

Matsumoto, M.;

Zhang, J.;

Kikuchi, T.;

Ahn, K. S.; Um, J. Y.; Lee, S. G.; Kim, J.; Yang, 25


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1

W. M., Cynanchum atratum inhibits the development of atopic dermatitis in 2,4-

2

dinitrochlorobenzene-induced mice. Biomedicine & pharmacotherapy = Biomedecine &

3

pharmacotherapie 2017, 90, 321-327.

4



Page 28 of 64

Table 1. Chemical scaffold of GSK3β inhibitors of Berg’s study for QSAR model generation. Core

Comp. R1

R2

R3

pKi

0

--

--

7.39

H

--

7.13

--

6.52

--

1 2*

H

3*

F

H

8.89

4

CH3

H

9.34

5

CF3

H

8.96

6

CH3

CH3

8.82

7

H

F

8.05

8

H

CH3

8.20

9

H

H

8.70

10

H

H

7.74

11

H

H

8.31

12*

H

H

8.36

13*

H

H

9.40

14

H

H

15*

H

H 27


SO2NH2

7.92 9.17

Page 29 of 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


16

H

H

7.70

17*

H

H

8.08

18

H

H

8.51

19

H

H

7.80

20

C

C

NH2

6.92

21*

N

C

NH2

7.19

22

N

N

H

6.43

23

--

9.66

24

--

9.32

25#

--

7.92

26

--

6.16

27#

--

9.00

28#

--

7.13

29#

--

7.05

30#

--

7.66

Compound 1-30 without * for SVM, MLR model, compound 0-21 for CoMSIA model, * test compound for SVM, MLR model, # test compound for CoMSIA.


Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

Page 30 of 64

Table 2. The top 10 models to predicted related properties of chemicals of Berg’s study by GFA algorithm R2

Equation of Model Model_1=

-28.251 + 0.79395 * logD + 1.292 * Jurs_PPSA_3 − 85.727 * Jurs_RPSA + 0.15375 * Jurs_TPSA − 1.2504 * Jurs_WPSA_3 + 0.14322 * Minimized_Energy + 0.0055567 * PMI_X + 25.714 * Shadow_XYfrac

Model_2 = -113.98 + 0.79395 * logD + 1.292 * Jurs_PPSA_3 + 85.727 * Jurs_RASA + 0.15375 * Jurs_TPSA − 1.2504 * Jurs_WPSA_3 + 0.14322 * Minimized_Energy + 0.0055567 * PMI_X + 25.714 * Shadow_XYfrac Model_3 = -113.71 + 0.67595 * logD + 3.5206 * Jurs_PPSA_3 + 12.758 * Jurs_RPSA + 0.13481 * Jurs_SASA − 4.6307 * Jurs_WPSA_3 + 0.13264 * Minimized_Energy + 0.0053795 * PMI_X + 21.569 * Shadow_XYfrac Model_4 = -100.95 + 0.67595 * logD + 3.5206 * Jurs_PPSA_3 − 12.758 * Jurs_RASA + 0.13481 * Jurs_SASA − 4.6307 * Jurs_WPSA_3 + 0.13264 * Minimized_Energy + 0.0053795 * PMI_X + 21.569 * Shadow_XYfrac Model_5 = -22.063 + 0.57547 * logD − 0.15598 * Num_Chains + 259.93 * Jurs_FPSA_3 − 68.494 * Jurs_RPSA + 0.12083 * Jurs_TPSA + 0.15364 * Minimized_Energy + 0.0061851 * PMI_X + 27.547 * Shadow_XYfrac Model_6 = -90.557 + 0.57547 * logD − 0.15598 * Num_Chains + 259.93 * Jurs_FPSA_3 + 68.494 * Jurs_RASA + 0.12083 * Jurs_TPSA + 0.15364 * Minimized_Energy + 0.0061851 * PMI_X + 27.547 * Shadow_XYfrac Model_7 = -26.332 + 0.72646 * logD − 1070.6 * Jurs_FPSA_3 + 3.7603 * Jurs_PPSA_3 + 0.024697 * Jurs_TPSA − 2.6268 * Jurs_WPSA_3 + 0.14699 * Minimized_Energy + 0.0061307 * PMI_X + 25.083 * Shadow_XYfrac Model_8 = -33.207 + 0.68468 * logD − 1390.9 * Jurs_FPSA_3 − 0.050945 * Jurs_PNSA_3 + 4.9397 * Jurs_PPSA_3 − 3.4294 * Jurs_WPSA_3 + 0.1476 * Minimized_Energy + 0.0062177 * PMI_X + 29.63 * Shadow_XYfrac


0.863

0.863

0.861

0.861

0.861

0.861

0.860

0.859

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Model_9 = -24.483 − 0.94715 * ES_Count_sNH2 + 0.67404 * logD − 0.12282 * Num_Chains + 0.41936 * Jurs_PPSA_3 + 0.023046 * Jurs_TPSA + 0.1639 * Minimized_Energy + 0.0073735 * PMI_X + 28.03 * Shadow_XYfrac Model_10 = -31.01 − 1.1799 * ES_Count_sNH2 + 0.86682 * logD + 0.78994 * Jurs_PPSA_3 + 0.029885 * Jurs_TPSA − 0.4277 * Jurs_WPSA_3 + 0.15829 * Minimized_Energy + 0.0073822 * PMI_X + 27.477 * Shadow_XYfrac


0.856

0.856


Page 32 of 64

Table 3. The top 10 models to predicted related properties of chemicals of Berg’s study by GFA algorithm R2

Equation of Model Model_1=

-28.251 + 0.79395 * logD + 1.292 * Jurs_PPSA_3 − 85.727 * Jurs_RPSA + 0.15375 * Jurs_TPSA − 1.2504 * 0.863 Jurs_WPSA_3 + 0.14322 * Minimized_Energy + 0.0055567 * PMI_X + 25.714 * Shadow_XYfrac

Model_2 = -113.98 + 0.79395 * logD + 1.292 * Jurs_PPSA_3 + 85.727 * Jurs_RASA + 0.15375 * Jurs_TPSA − 1.2504 * 0.863 Jurs_WPSA_3 + 0.14322 * Minimized_Energy + 0.0055567 * PMI_X + 25.714 * Shadow_XYfrac Model_3 = -113.71 + 0.67595 * logD + 3.5206 * Jurs_PPSA_3 + 12.758 * Jurs_RPSA + 0.13481 * Jurs_SASA − 4.6307 * 0.861 Jurs_WPSA_3 + 0.13264 * Minimized_Energy + 0.0053795 * PMI_X + 21.569 * Shadow_XYfrac Model_4 = -100.95 + 0.67595 * logD + 3.5206 * Jurs_PPSA_3 − 12.758 * Jurs_RASA + 0.13481 * Jurs_SASA − 4.6307 * 0.861 Jurs_WPSA_3 + 0.13264 * Minimized_Energy + 0.0053795 * PMI_X + 21.569 * Shadow_XYfrac Model_5 = -22.063 + 0.57547 * logD − 0.15598 * Num_Chains + 259.93 * Jurs_FPSA_3 − 68.494 * Jurs_RPSA + 0.12083 * 0.861 Jurs_TPSA + 0.15364 * Minimized_Energy + 0.0061851 * PMI_X + 27.547 * Shadow_XYfrac Model_6 = -90.557 + 0.57547 * logD − 0.15598 * Num_Chains + 259.93 * Jurs_FPSA_3 + 68.494 * Jurs_RASA + 0.12083 * 0.861 Jurs_TPSA + 0.15364 * Minimized_Energy + 0.0061851 * PMI_X + 27.547 * Shadow_XYfrac


Page 33 of 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46


Model_7 = -26.332 + 0.72646 * logD − 1070.6 * Jurs_FPSA_3 + 3.7603 * Jurs_PPSA_3 + 0.024697 * Jurs_TPSA − 2.6268 * 0.860 Jurs_WPSA_3 + 0.14699 * Minimized_Energy + 0.0061307 * PMI_X + 25.083 * Shadow_XYfrac Model_8 = -33.207 + 0.68468 * logD − 1390.9 * Jurs_FPSA_3 − 0.050945 * Jurs_PNSA_3 + 4.9397 * Jurs_PPSA_3 − 3.4294 * 0.859 Jurs_WPSA_3 + 0.1476 * Minimized_Energy + 0.0062177 * PMI_X + 29.63 * Shadow_XYfrac Model_9 = -24.483 − 0.94715 * ES_Count_sNH2 + 0.67404 * logD − 0.12282 * Num_Chains + 0.41936 * Jurs_PPSA_3 + 0.856 0.023046 * Jurs_TPSA + 0.1639 * Minimized_Energy + 0.0073735 * PMI_X + 28.03 * Shadow_XYfrac Model_10 = -31.01 − 1.1799 * ES_Count_sNH2 + 0.86682 * logD + 0.78994 * Jurs_PPSA_3 + 0.029885 * Jurs_TPSA − 0.4277 * 0.856 Jurs_WPSA_3 + 0.15829 * Minimized_Energy + 0.0073822 * PMI_X + 27.477 * Shadow_XYfrac



Page 34 of 64

Table 4 PLS Analysis and Validation of CoMSIA Models. q2cv

ONC

r2

SEE

F ratio

S

0.363

5

0.942

0.214

48.840

H D A

0.523 -0.120 0.325

5 5 5

0.973 0.566 0.835

0.147 0.587 0.362

106.686 3.919 15.211

S+H S+D S+A

0.526 0.307 0.375

5 5 5

0.971 0.940 0.920

0.142 0.218 0.252

99.824 47.212 34.643

H+D H+A D+A

0.545 0.477 0.336

5 5 5

0.977 0.950 0.889

0.134 0.199 0.297

129.898 57.303 23.977

S+H+D S+H+A S+D+A

0.500 0.466 0.394

5 5 5

0.973 0.952 0.935

0.146 0.195 0.227

108.700 59.928 43.147

H+D+A S+H+D+A

0.462 0.468

5 5

0.952 0.955

0.196 0.189

59.928 63.420

Parameter

q2cv: Correlation Coefficient (cross validation) r2: Correlation Coefficient (non-cross validation) ONC: Optimal number of components SEE: Standard error of estimate F ratio: F-test value


Page 35 of 64 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46


S: Steric H: Hydrophobic D: Hydrogen bond donor A: Hydrogen bond acceptor H+D Filed proportion: H: 90.0%; D:10.0%



Page 36 of 64

Table 5 Docking results of top ten candidates and 7YG, activity value of each chemical molecule was predicted by QSAR models. Predicted value (pKi)

d

SVM

MLR

RF

DL

Nodifloridin A

8.325

9.128

8.064

8.073

137.700

0

4

0

1

4

2,2'-[Benzene-1,4diylbis(methanediylo xybenzene-4,1-

8.366

8.806

7.451

8.501

126.304

0

4

1

1

4

7.561

8.933

8.591

6.063

105.216

0

3

0

0

4

4

methoxyphenyl) propionic acid] Prostaglandin E1

7.689

8.245

8.077

10.670

98.746

0

3

0

1

4

5 6 7

Tianshic acid Methyl 3-O-feruloylquinate Morusimic acid B

7.908 9.664 8.148

9.560 7.584 9.233

7.833 8.584 8.820

7.655 8.291 9.098

95.808 94.757 82.394

0 1 0

3 4 3

0 0 0

0 1 0

4 4 4

8

Cynanogenin_A 7-Methoxy-aristolochiac acid

8.694 8.082

9.443 8.327

8.296 8.487

6.523 10.304

81.451 79.165

0 0

3 4

0 1

0 1

4 3

1

2

3

9

Name

diyl]bis(oxoacetic acid) Dihydroferulic acid[3-(4-hydroxy-3-

a

Absorption bBBB


c

CYP2D6

Hepatoto xicity

e

Dock Score

Ranking

Solubility Level

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10 Control set

3,15,19-trihydroxy8(17),13-ent-labdad ien-16-oic acid

8.367

7.381

8.623

5.935

71.920

0

3

0

1

4

7YG

7.499

7.341

7.852

7.304

67.118

1

4

0

1

3

Notes: a

Predicted by Structure-binding affinity predictive model

Abbreviations: SVM, support vector machine; MLR, multiple linear regressions; CoMSIA, comparative molecular similarity indices analysis.



Page 38 of 64

Table 6 The predicted ADMET descriptors of top eight candidates and 7YG.

Name

a

Absorption

b

c

BBB

CYP2D6

d

Hepatotoxicity

e

Solubility Level

Nodifloridin A

0

4

0

1

4

2,2'-[Benzene-1,4diylbis(methanediylo

0

4

1

1

4

xybenzene-4,1-diyl]bis(oxoacetic acid) Dihydroferulic

0

3

0

0

4

acid[3-(4-hydroxy-3-methoxyphenyl) propionic acid] Prostaglandin E1 Tianshic acid

0 0

3 3

0 0

1 0

4 4

Methyl 3-O-feruloylquinate

1

4

0

1

4

Morusimic acid B

0

3

0

0

4

Cynanogenin_A 7-Methoxy-aristolochiac acid

0 0

3 4

0 1

0 1

4 3

3,15,19-trihydroxy-8(17),13-ent-

0

3

0

1

4

labdad 37 ACS Paragon Plus Environment

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ien-16-oic acid 7YG

1

4

0

1

3

a

Absorption: Good absorption = 0; Moderate absorption = 1; Low absorption = 2;

b

BBB (Blood Brain Barrier): Very high penetration = 0; High penetration = 1; Medium penetration = 2; Low penetration = 3; Undefined

penetration = 4 c

CYP2D6: Non-inhibitor = 0, Inhibitor = 1

d

Hepatotoxicity: Non-inhibitor = 0, Inhibitor = 1

e

Level 3: Drug-likeness with good solubility; Level 4: Drug-likeness with optimal solubility



Page 40 of 64

Table 7 Vote score of top eight candidates

Vote score

pKi

Dock Score

SVM

MLR

RF

DL

1

1

0

0

0

0

0

0

2

1

0

0

1

0

0

0

0

2

0

0

1

0

0

1

0

0

2

Prostaglandin E1 Tianshic acid Methyl 3-O-feruloylquinate

0 0 1

0 1 0

0 0 1

1 0 1

0 0 1

0 0 0

1 0 0

0 0 1

2 1 5

Morusimic acid B Cynanogenin_A

0 1

1 1

1 1

1 0

1 0

0 1

1 0

1 0

6 4

Name Nodifloridin A 2,2'-[Benzene-1,4diylbis(methanediyloxybenzene4,1-diyl]bis(oxoacetic acid) Dihydroferulic acid[3-(4-hydroxy3-methoxyphenyl) propionic acid]

CoMSIA

SVM

MLR

Multi- Totaltarget score

Vote score: For all activity values predicted by one algorithm, the top 50% were voted 1 point, and others were voted as 0 point.


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Figure legends

Figure 1. Flowchart of experiment process. Abbreviations: TCM, traditional Chinese medicine; AD, Alzheimer's disease; GFA, genetic function approximation; SVM, support vector machine; MLR, multiple linear regressions; PLS, partial least square; CoMSIA, comparative molecular similarity indices analysis. MD, molecular dynamics;



Figure 2. PPI networks obtained from the Cytoscape STRING app. Each network nodes represent proteins. Colorful network nodes are first shell of interactors for AD.


Page 42 of 64

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Figure 3. Association network generated form functional enrichments data under Cytoscape tool. The investigated matching proteins were ellipse, and correlative pathways for the matching proteins were rectangle. The width of each edge in this network was depended on the value of FDR score, a thick edge has low FDR score than a thin edge.



Figure 4. Deep learning network diagram including four layers, the activation is ReLu, and the optimizer is Adam Optimizer with learning rate is 0.0001.


Page 44 of 64

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Figure 5. Scatter plots to present the results of 120 experiments.



Figure 6. The relation of 204 features ranked by Pearson Correlation Coefficient.


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A

B

Figure 7. The Principal component analysis (PCA) visualization by Yellowbrick . (A) 2D, (B) 3D. 46 ACS Paragon Plus Environment


Figure 8. The Pearson correlation coefficient matrix heatmap of 9 selected features.


Page 48 of 64

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Figure 9. Scatter plots to identify relationships between observed activity and predicted activity for Random Forest model, all unit of activity value were represented by pKi. The values of MSE of the model on train and on test are 0.122 and 0.049, respectively. The values of R2 of the model on train and on test are 0.869 and 0.890, respectively.



Figure 10. Scatter plots to identify relationships between observed activity and predicted activity for (A) SVM and (B) MLR models, all unit of activity value were represented by pKi. The values of R2 of the two models are 0.793 and 0.894, respectively. 49 ACS Paragon Plus Environment

Page 50 of 64

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Figure 11. CoMSIA model constructed by GSK3β inhibitors. (A) Scatter plots to validate CoMSIA model, (B) CoMSIA model.



Figure 12. The results of drug-target network analyze from database screening, the protein targets were denoted by red rectangle.


Page 52 of 64

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Figure 13. The chemical scaffold of (A) Methyl 3-O-feruloylquinate, (B) Morusimic acid B, (C) Cynanogenin A and (D) 7YG



Figure 14. The docking poses of (A) Methyl 3-O-feruloylquinate (green), (B) Morusimic acid B (orange), (C) Cynanogenin A (purple) and (D) 7YG (yellow) in GSK3β binding site.


Page 54 of 64

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Figure 15. The snapshots of Cynanogenin A in GSK3β binding site at (A) 0ns and (B) 100ns, the docked ligand are colored in purple.



Figure 16.

Figure 16. The MD trajectory analysis of (A) Protein RMSD and (B) Radius of Gyration for GSK3β and TCM ligands: 7YG, Methyl 3-O-feruloylquinate, Morusimic acid B, and Cynanogenin A during 100ns simulation time.


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Figure 17. The total energy calculations of GSK3β and TCM ligands: 7YG, Methyl 3-O-feruloylquinate, Morusimic acid B, and Cynanogenin A during 100ns simulation time.



Figure 18. The MD trajectory analysis of Ligand RMSD for 7YG, Methyl 3-Oferuloylquinate, Morusimic acid B, and Cynanogenin A in GSK3β structure among 100ns simulation time.


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Figure 19. The H-bond distance between Cynanogenin A and residues of ARG141, ARG144 and ASP200 during 100ns simulation times of GSK3β.



Figure 20. The possible tunnels identified from all protein-ligand complexes with: (A) 7YG (B) Methyl 3-O-feruloylquinate, (C) Morusimic acid B, and (D) Cynanogenin A, by 100ns MD simulation times. All of tunnels grouped into three clusters, which are colored by red, blue and green, respectively.


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Figure 21. DSSP analysis of five snapshots among 20 ns simulation times: proteinligand complexes of (A) 7YG, (B) Methyl 3-O-feruloylquinate, (C) Morusimic acid B, (D) Cynanogenin A. Residues from 35 to 85 on GSK3β are colored in blue, which were used to observe the variation of secondary structures during all MD simulations.



Figure 22. Distance between GSK3β and docked ligands during 100 ns simulation times.


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Figure 23. Snapshot analysis of GSK3β complexes for comparison between initial (0 ns) and final states (100 ns) from MD simulation: (A) Initial state and (B) final state of 7YG. (C) Initial state and (D) final state of Methyl 3-O-feruloylquinate. (E) Initial state and (F) final state of Morusimic acid B. (G) initial state and (H) final state of Cynanogenin A. Blue chemical scaffold were used to display each ligand in 2D 62 ACS Paragon Plus Environment


diagram. Hydrophobic residues were represented by eyelash. H-bond interactions were indicated by green dash line. All difference of each residue from comparison between initial and final states was marked by red circle.


Page 64 of 64

Deep Learning and Random Forest Approach for Finding Optimal

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