In Silico Exploration of the Molecular Mechanism of Cassane

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Computational Biochemistry

In Silico Exploration of the Molecular Mechanism of Cassane Diterpenoids on Anti-inflammatory and Immunomodulatory Activity Ying Wang, Baichun Hu, Yusheng Peng, Xin Xiong, Wenhua Jing, Jian Wang, and Hui-yuan Gao J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00862 • Publication Date (Web): 25 Feb 2019 Downloaded from http://pubs.acs.org on February 27, 2019

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Journal of Chemical Information and Modeling

In Silico Exploration of the Molecular Mechanism of Cassane Diterpenoids on Anti-inflammatory and Immunomodulatory Activity

Ying Wang1,2, Baichun Hu1,3, Yusheng Peng2, Xin Xiong1,2, Wenhua Jing1,2, Jian Wang1,3*and Huiyuan Gao1,2

1. Key Laboratory of Structure-Based Drug Design ＆ Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang, 110016, People’s Republic of China； 2. School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, 110016, People’s Republic of China； 3. School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang, 110016, People’s Republic of China

*Correspondence: Huiyuan Gao, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang, 110016, P. R. China. E-mail: [email protected]; Fax: +86-24-23986460, Tel: +86-24-23986481. *Correspondence: Jian Wang, School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang, 110016, P. R. China. E-mail: [email protected]; Fax: +86-24-23995043. Tel: +86-24-52430227. 1

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Abstract Cassane diterpenoids (CA), recognized as main constituents of many medical plants of Caesalpinia genus, exhibit diverse bioactivities including anti-inflammatory and immunomodulatory activity, which also showed a therapeutic effect on rheumatoid arthritis (RA) according to the previous work including ours. In this study, 102 CA compounds were selected to explore the possible molecular mechanism of this type of natural products on anti-inflammatory and immunomodulatory activity using RA as a disease model through a series of in silico methods: chemical similarity-based target prediction, molecular docking and molecular dynamics (MD) simulation. As a consequence, four signaling pathways (TCR signaling pathway, TLR signaling pathway, VEGF signaling pathway and osteoclast differentiation pathway) were picked out for CA exerting their effect on inflammation and immunomodulation. Furthermore, the binding modes of CA complexing with several crucial targets, which were picked out by credible docking results and took part in these signaling pathways, were explored by MD simulations. It is the first time for natural CA to be investigated on the molecular mechanism on anti-RA activity with in silico methods, and these findings might to explain the activity for CA on anti-inflammation and immunomodulation, which could supply for a valuable reference for their drug-design researching.

Keywords: Cassane diterpenoids; Anti-inflammatory and immunomodulatory activity; Molecular dynamics simulations; Network pharmacology 2


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1. Introduction Cassane diterpenoids (CA) are a group of natural products with a basic molecular skeleton fused by three cyclohexane rings, a furan ring or a lactone with substitutions such as alkyl, hydroxyl, acetyl groups, and the skeleton for CA compounds is predicted to be derived from pimarane through methyl migration1. CA exhibit wide range

of

pharmacological

activities

such

anti-tumor,2

as

anti-malarial,3

anti-inflammatory,1, 4 immunomodulatory,1 anti-microbial,5 and anti-viral. 6 They are the main constituents of traditional national medicines belonging to Caesalpinia genus, including C. bonduc, pulcherrima,

11

7

C. sappan Linn,

8

C. Lamk,

9

C. major,

10

C.

C. minax Hance1 and so on. From our previous work on C. minax

Hance (Ku-shi-lian), it was found that the extract full of cassane constituents showed a significant anti-rheumatoid arthritis (RA) activity using complete Freund’s adjuvant (CFA) induced arthritis in rat, and the overproduction of TNF-α, IL-1β and IL-6 were remarkably suppressed in the serum. CA compounds isolated from this extract showed promising inhibitory activity against the expression mRNA of cytokine IL-1β, IL-6 and TNF-α of macrophages cells as well.6, 12 RA is a typical disease of immune-mediated inflammatory diseases (IMIDs) characterized by synovial inflammation and progressive destruction of cartilage and bone. Inflammatory cytokines, such as TNF-α, IL-1β, IL-6, and IL-17A, as well as inflammatory mediators, such as cyclooxygenases and lipoxygenases, play an important role in RA.13 The main therapeutic agents on RA treatment consist of steroidal or non-steroidal anti-inflammatory drugs (NSAIDs), biological agents, disease-modifying anti-rheumatic drugs (DMARDs), glucocorticoids, etc, with the function of inflammation reduction, pain alleviation, joint damage prevention and reducing the progress of RA.14 However, these drugs are good at relieving the 3



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symptoms rather than underlying causes, as a consequence, poor efficacy, adverse effect and drug resistant of them cause a severe problem for their clinical treatment. Therefore, it is a formidable task for researchers to discovery new drugs with more safety, and much attention has been paid on the natural products resource. According to the activity of CA derivatives on anti-RA, it has become a priority for these compounds to be developed as promising anti-RA agents with anti-inflammatory and immunomodulatory activity. The apparent character for traditional medicines on their curing effect lies in the active part containing a number of compounds towards multiple targets and related signaling pathways. Network pharmacology, a system biology-based methodology that assists to depict the relationships between active ingredients and their corresponding targets, is a forceful tool for researching the mechanism of action for traditional formula and novel bioactive ingredients, especially for a series of active compounds sharing similar structures with similar therapeutic functions.15,

16

Therefore, in silico method used in network pharmacology is a quite considerable and comprehensive strategy for pharmaceutical substances to be predicted on their effect, mechanism of action and ADMET characters in vivo. In

order

to

understand

the

possible

molecular

mechanism

on

CA’s

anti-inflammatory and immunomodulatory activity, especially the key targets for CA-linking, herein, RA was chosen as a disease model for in silico investigation. Meanwhile, to obtain a credible predicting result, target prediction was performed based on the ligand structures; and molecular docking was used based on receptor structures. Besides, in order to explore the interactions between protein targets and their ligands at a condition closer to the physiological environment, molecular dynamics (MD) simulations were conducted to access the stability of the binding 4


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modes generated by docking studies, and enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was also conducted to analyze the biological association among the potential targets of CA compounds. As far as we know, this is the first time to systematically investigate the possible molecular mechanism and potential targets for CA’s anti-RA effect with in silico strategy, and the findings here could supply a valuable reference for CA’s development for the treatment of RA.

2.

Materials and methods

2.1 Structural Preparation 102 structures of CA were chosen from our previous chemical work on C. minax Hance, C. bonduc, C. sappan Linn and compounds from Caesalpinia genus plants with anti-inflammatory and immunomodulatory activity.13 These structures were then classified into five types according to the structural characters: 59 cassane furanoditerpenoids, 21 cassane furan-lactonediterpenoids, 10 norcassane diterpenoids, 11 rearrange diterpenoids and one cassane tricyclic diterpenoid. Representative structures of each group were shown in Figure 1 and the details for structures and classifications were represented in Table S1. All structures were constructed and optimized in SYBYL 6.9.1 software package (Tripos Inc.) with Tripos force field and Gasteiger–Hückel charges. In the process of optimization, the Steepest Descent algorithm was utilized, followed by the Conjugate Gradient and Adopted Basis Newton-Raphson algorithms, with convergence gradient values of 0.1 kcal/mol, 0.01 kcal/mol and 0.001 kcal/mol, separately.

5



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Figure 1. Representative structures of the five classes of CA. The numbers of compound in each class are indicated with brackets.

2.2 Target prediction and classification As a common molecular skeleton was shared by CA, 47 structures (22 cassane furanoditerpenoids, 9 cassane furan-lactonediterpenoids, 6 norcassane diterpenoids, 9 rearrange diterpenoids and one cassane tricyclic diterpenoid) were picked out to conduct target prediction by chemical similarity searching (details were summarized in Table S2). The selected structures were then submitted to two online target prediction servers: SwissTarget Prediction (http://www.swisstargetprediction.ch/) and ROCS-based target prediction (http://targetfishing.molcalx.com.cn/inflam.html).17,

18

UniProtKB (https://www.uniprot.org/) database was utilized to receive official gene symbols of the targets.19 According to the structural information of each target combined with the detailed information of each crystal structure recorded in RCSB Protein Data Bank (PDB) (http://www.rcsb.org/),20 the targets were classified based on the availability of the protein crystal structures and information of their binding 6


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pocket. 2.3 Molecular docking studies Experimental crystal structures of the 75 targets with clear structural information were retrieved from RCSB Protein Data Bank. To improve the accuracy of docking, all the target structures were prepared by Discovery Studio 3.0 software package before calculation, the processes of which mainly include: removing water molecules from structures, building missing amino acids, adding hydrogen atoms to the protein, and completing loop segments. The native ligand structure in each protein was prepared with the protein at the same time, then they were separated from protein and saved as MOL2 files. Subsequently, molecular docking was performed here to verify the possible targets of CA compounds. The binding energies calculation and binding modes prediction were conducted at the same time by AutoDock Vina 1.1.2.21 In addition, the AutoDockTools 1.5.4 (ADT) was utilized to prepare input files and calculate a grid box for docking, which was set to expand 15 Å in each direction and its center coordinate was defined with native ligand and other parameters were set as default.22 The lowest binding energy of each complex was recorded and its corresponding binding mode was analyzed using Discovery Studio Visualizer 2017. 2.4 Enrichment analysis and construction of networks A compound-target (C-T) network was constructed according to the docking results by Cytoscape 3.2.1.23 To further investigate the biological functions on a larger scale, enrichment analysis of the 138 targets predicted by two websites was conducted with ClueGo v2.3.5 and CluePedia 1.3.5, plugs-in of Cytoscape, were applied to reproduce the enrichment result with KEGG served as its database.24, 25 MCODE, a plug-in of Cytoscape, was used to aggregate the nodes of related function into some individual modules or clusters, which facilitated the analysis of the complex network.26 7



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2.5 Molecular dynamics simulations Desmond v3.7 was used to execute the MD simulations for further validation of the binding modes as well as the stability of the three complexes.27 Processed with Protein Preparation Wizard, the refined structures were embedded into the simple point charge (SPC) water model within an orthorhombic box, which extended approximately 10 Å in each direction. The simulation box size was calculated by the given buffer distance between the solute structures and the simulation box boundary. A suitable number of Na+ counter-ions were added to neutralize the complexes with physiological salt concentration kept at 0.15 M, which was approximately the physiological concentration of monovalent ions. OPLS_2005 forcefield was applied to the protein-ligand systems.28 The system containing the complex of protein and ligand, water molecules, and counter-ions was then subjected to a minimization job to relax the system into a local energy minimization, where the maximum interactions were set as 2000, and convergence threshold was set as 1.0 kcal/mol/ Å as default. A hybrid

method

of

the

steepest

descent

and

the

limited-memory

Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithms was used to minimize the systems.29 A cut-off of 9.0 Å was set to define the short-range region while the long-range region was handled by the method of Smooth particle mesh Ewald. The minimized and later relaxed systems were subjected to simulation time of 100 ns, with a time step of 2 fs, which was performed in NPT ensemble, using a Nose-Hoover thermostat at 300 K and Martyna-Tobias-Klein barostats at 1.01325 bar pressure. During the simulation, the energies were calculated every 1.2 ps while every 8


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trajectory were recorded with a time interval of 4.8 ps. During the process of MD simulation, potential energy (U), root mean square deviation (RMSD), root mean square fluctuations (RMSF) and protein-ligand interactions were monitored for the stability of the docked complexes using the following equations: (1) (1) (2) (2) (2) (3) (3)

U = Ubonded + UvdW + Ues RMSDx = RMSFi =

1 N ′ ∑ N i = 1(ri(tx) 1 T ′ ∑ T t = 1(ri(tx)

― ri(tref)

― ri(tref)

2

2

(3)

Here Ubonded, UvdW, Ues represent the energies involved in bonded, van der Waals, and electrostatic, respectively. N is the number of atoms in the atom selection, tref is the reference time (the first frame is used typically), r' is the position of the selected atoms in frame x after superimposing on the reference frame, where frame x is recorded at time tx. T is the trajectory time over which the RMSF is calculated, ri is the position of atoms in residues i after superposition on the reference. 2.6 Binding free energy calculation The binding free energy estimation was carried out using Molecular Mechanics Generalized Born Surface Area (MM-GBSA) algorithm implemented in Schrodinger package.30 The calculation of binding free energy for protein-ligand complexes follows: ∆𝐺𝑏𝑖𝑛𝑑 = ∆𝐺𝑐𝑜𝑚𝑝𝑙𝑒𝑥 ―(∆𝐺𝑟𝑒𝑐𝑒𝑝𝑡𝑜𝑟 +∆𝐺𝑙𝑖𝑔𝑎𝑛𝑑)

(4) (5)

∆𝐺𝑡𝑜𝑡𝑎𝑙 = ∆𝐺𝑀𝑀 +∆𝐺𝑠𝑜𝑙 ―𝑇∆𝑆 ∆𝐺𝑠𝑜𝑙 = ∆𝐺𝐺𝐵 +∆𝐺𝑆𝐴

(6)

Where ∆Gbind is the binding free energy, whereas ∆Gcomplex, ∆Greceptor and ∆Gligand represents the free energy of complex, receptor and ligand, respectively. ∆GMM is the molecular mechanics components in gas phase interaction between protein and the ligand; ∆Gsol is the solvation free energy and T∆S is computed based on the change of 9



conformational entropy due to ligand binding. 3.

Results and discussion

3.1 Target prediction of CA There were 100 and 49 targets in total were predicted by servers of SwissTargetPrediction and ROCS-based target prediction, respectively, which based on the chemical similarity searching method.31 Several targets such as HMGCR, NOS2, PIK3CA, PIK3CD and PPARG were predicted by both websites. After deleting the non-homo sapiens targets, the remaining targets’ names were transformed into official gene symbols with checked UniProt codes, which were classified into three categories according to the availability of the crystal structures. There were 75 targets existed protein structures in complex with bound co-crystallized molecules to define the active site (class 1), 20 targets had protein structures without proper co-crystallized molecules (class 2) and 43 targets without available structures (class 3). Detailed information of the 138 targets was listed in Table S3. Considering the 75 targets were also used for further investigation, a certain crystal structure for each target was selected taking several factors into consideration, such as the resolution of the crystal structure, the structural similarity between the co-crystal ligand with CA, and the information of the active site. The crystal structural information of the 75 targets in class 1 was represented in Table S4.

3.2 Molecular docking studies Contrasting the binding affinities of CA compounds with those of native ligands from each target, 28 targets existed at least one compound with binding energy lower than 10


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those of native ligands, among which, 15 targets were considered as potential targets for all of 102 CA structures since the small difference between the affinity of CA and their co-crystal ligands. Along with structural information of targets participated in docking, all of docking results were summarized in Table S5. Finally, we applied three selection criteria to determine the key protein targets, i.e. whether the target: (1) plays a role in the four signaling pathways correlating to RA, (2) belongs to class 1 and (3) owns satisfactory binding affinities as well as binding modes with CA compounds close to the native ligands. Combining the information given by enrichment analysis and the docking results, we found that there were 17 targets totally in the four signaling pathways correlated with RA, among which 13 targets in class 1 were chosen to detailed analysis (GSK3B, NOS3, PIK3CG, MAPK14, PIK3CA, PIK3CD, PPARG, PRKCA, PRKCB, PRKCQ, PTGS2, SYK and TNF). All of these targets exited CA meet our selection criteria that the binding affinity was better than the native ligand of the corresponding target or worse than it but no more than 3 kcal/mol, and three targets were picked out as the crucial residues of them were observed to participate the interaction with CA and their binding modes were similar to the native ligand. Therefore, the complexes with the best binding affinity of the three targets were picked out to perform further MD simulations to examine their binding mode and consistency in detail. Endothelial NOS (NOS3), an isoform of NOS that generate nitric oxide (NO), which served as a mediator of the bone and tissue damage process related to the inflammation.32 The crucial residues participated in the hydrogen bond were consistent between the native ligand and minaxin A as they interacting with NOS3, 11



surprisingly, the binding energy of compound 46 was -9.5 kcal/mol, better than -6.7 kcal/mol calculated by the co-crystal ligand. Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform (PIK3CG), a class Ⅰ B phosphoinositide 3-kinases (PI3Ks) that had been reported to be closely related to immunity and inflammation,33 the binding affinity of the native ligand was -10.7 kcal/mol, while it was -9.0 kcal/mol as for compound 18, the residues participated in the hydrophobic interactions were similar and Tyr867 formed a hydrogen bond with both of them. Glycogen synthase kinase-3 beta (GSK3B), a serine-threonine kinase, regulated NF-κB at the transcriptional level and it was required for the NF-κB mediated anti-apoptotic response to TNFα.34 The binding energy of compound 55 complexed with GSK3B was close to the native ligand and they were -8.2 kcal/mol and -8.9 kcal/mol, respectively. Meanwhile, the residues they interacting with had a lot in common. 3.3 C-T network analysis To reveal the synergistic effect of CA interacting with multiple targets and present their relationship in a more intuitive way, a compound-target (C-T) network was constructed by comparing the binding affinity of CA with the native ligand for each target based on molecular docking results. As shown in Figure 2, several targets (CHRM2, HTR2B, MMP9, DHODH, PPARA) were considered to have no interaction with any of CA as the binding affinities of CA with these targets were higher than the native ligands of the corresponding target more than 3 kcal/mol, therefore, they were removed from the construction of C-T network because of their zero degrees. On the contrary, some targets (such as CASP3, EGFR, GSK3B, NOS3, TNF) were found to exhibit similar binding affinities close to the corresponding native ligand with all of 102 CA compounds, while others differed a lot in the binding 12


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affinities with CA despite of their structural similarity, such as ADRB2 with a degree of 16, AR with a degree of 6, CES1 with a degree of 97, DRD3 with a degree of 17 and MAOB with a degree of 2. After the targets without binding molecules were removed, a C-T network containing 172 nodes and 29412 edges was obtained. Some active compounds were found to hit more than one target, for instance, caesalpinone (91) had the most potential targets with a degree of 67, following by macrocaesalmin (16) with a degree of 62, caesalmin B (2) with a degree of 61, and both of phanginin A (57) and phanginin B (63) with a degree of 60, demonstrating that these compounds played an important role for CA to intervene the progress of inflammation and immunomodulation by disrupting multi-targets.

Figure 2 C-T network generated by the compounds with their potential targets, which was evaluated by the affinity comparison with the native ligands for each target. 13



3.4 Enrichment analysis by ClueGo and CluePedia To further decipher the function of 138 potential targets on a systematic level, ClueGo was applied to clarify the biological interpretation and interrelations of functional groups in biological networks, and main items of which were displayed with their accountants in Figure 3, while the original network and the items of it presented in histogram were shown in Figure S1 and Figure S2, respectively. MCODE was applied to cluster the network into several modes based on the topological property of the network as the original network derived from ClueGo was too complicated to analyze. Six clusters were derived from MCODE and their topological parameters were displayed in Table 1. Among the six sub-networks split from original network, Cluster 1 was the largest as well as the most important one because of its connection with RA. As displayed in Figure 4, a network of Cluster 1 was reconstructed with the nodes divided into four classes: targets, signaling pathways, biological processes and diseases. Imitating the analytic procedure of Cluster 1, other networks of clusters were reconstructed as well and displayed in Figure 5.

14


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Figure 3 ClueGo analysis of the 138 predicted targets for 102 CA. The pie chart represents major GO terms and their proportions in the whole biological network formed by the potential targets.

Table 1 Cluster of the network generated by ClueGo enrichment with MCODE Cluster

Scores

Nodes

Edges

1 2 3 4 5 6

36 12 10 6 3 3

65 13 25 6 3 3

1162 72 124 14 3 3

Figure 4 Cluster1 of the ClueGo enrichment network, the nodes presented in a shape of V painted yellow stands for the targets that connected this module with others, the nodes represented in blue 15



round rectangle, green diamond, and red ellipse stand for the signaling pathways, biological processes and diseases in this module, representatively.

Figure 5 Cluster 2-6 of the ClueGo enrichment network (A, B, C, D, E stand for Cluster 2, 3, 4, 5, 6), the meaning of nodes each display style was the same as Cluster 1.

3.4.1 Clusters generated with MCODE Cluster 1, the largest module of the network generated by enrichment analysis with ClueGo, was also the most crucial one since almost all of the diseases, biological processes and signaling pathways involved in this module are closely associated with the occurrence and development of RA. As is known to all, RA is a complex disease with several systems and progresses involved. There were some comorbidities of RA with higher prevalence that had been illustrated by many literatures such as several types of cancer, cardiovascular disease (CVD), infections, type 2 diabetes (T2D) and psychiatric disorders, which were found in our network as well and they were displayed with red ellipses in Figure 4. As for the biological processes in this module, most of them were closely connected with the diseases described above, for more detail, proteoglycans in cancer, central carbon metabolism in cancer and choline 16


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metabolism in cancer were related to cancer obviously, fluid shear stress and atherosclerosis were connected with CVD, autophagy, apoptosis, natural killer cell mediated cytotoxicity, FcγR-mediated phagocytosis, leukocyte trans endothelial migration were mainly associated with various immunoreactions caused by infections, pancreatic secretion was related to T2D and several other diseases. Signaling pathways, the most important part in this module, were discussed in detail following section. There were six targets found in cluster 1 (PIKCA, PIK3CB, PIKCD, PRKCA, PRKCB and PRKCG), they belonged to Phosphoinositide 3-kinase and protein kinase C family, respectively. Both of them were known to be involved in diverse cellular signaling pathways, which play a role in regulating transcription and cell growth, in mediating immune response and in learning and memory. Cluster 2 was closely associated with drug metabolism, the targets involved in which were four members in the UDP-glucuronosyltransferase 2B family. There were some biological processes associated metabolism in this module, such as chemical carcinogenesis, pentose and glucuronate interconversions, ascorbate and aldarate metabolism, retinol metabolism, porphyrin and chlorophy Ⅱ metabolism, metabolism at xenobiotics by cytochrome P450, the except one was steroid hormone biosynthesis. Cluster 3 was the second largest network, RELA, MAPK12, MAPK14, ADCY1, ADCY5 and ADCY6 served as connective nodes with other networks. Signaling pathways involved in this network were: neurotrophin signaling pathway, AGE-RAGE signaling pathway in diabetic complications, HIF-1 signaling pathway, longevity regulating pathway, oxytocin signaling pathway, FoXO signaling pathway, signaling pathways regulating pluripotency if stem cells and two diseases relevant: amoebiasis and amphetamine addiction. Cluster 4 was connected by TNF with other sub-networks, a popular target emerged 17



recently mainly involved in immune therapy, which has gained more and more attention. The only signaling pathway of this module was RIG-I-like receptor signaling pathway and diseases involved in this module were four types of infectious disease: leishmaniasis, pertussis, shigellosis and toxoplasmosis. Cluster 5 and Cluster 6 were relatively simple as there were only three nodes in them. IL-17 signaling pathway and two diseases associated with it (tuberculosis and amyotrophic lateral sclerosis (ALS) were included in Cluster 5, while estrogen signaling pathway and two biological processes (progesterone-mediated oocyte maturation and platelet activation) were integrated in Cluster 6. 3.4.2 Enrichment analysis of signaling pathway in Cluster 1 As a matter of fact, targets could not regulate a certain type of disease individually, they usually interact with other biomacromolecules and make up signaling pathways, network pharmacology provided an intuitionistic and systematic view of the signal transduction among various targets and it is helpful for a thorough understanding of a disease. As displayed as rounded rectangle in Figure 4, signaling pathways included in Cluster 1 were mainly involved in immune (B cell receptor signaling pathway, Fc epsilon RI signaling pathway), signal transduction (TNF signaling pathway, ErbB signaling pathway, Ras signaling pathway, Rap1 signaling pathway, Sphingolipid signaling pathway, mTOR signaling pathway, AMPK signaling pathway, JAK-STAT signaling pathway) and others (insulin signaling pathway, GnRH signaling pathway, prolactin signaling pathway, thyroid hormone signaling pathway, longevity regulating pathway). What's more, four major signaling pathways participating in the pathology of RA according to KEGG database were observed in Cluster 1, they were VEGF, Toll-like receptor, T cell receptor and Osteoclast differentiation signaling pathways. 18


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Subsequently, all targets in these pathways were compared with the targets predicted by CA, as a consequence, some key targets were picked out for they were shared by both of the target prediction and the RA related pathways, which included: PIK3CG, NOS3, RELA and etc. As depicted in Table 2, these key targets mainly enriched into the four signaling pathways, and they could be deemed as crucial pathways for CA treating RA. In order to describe their relationships in a more intuitive way, the signaling pathways directly related to these targets were extracted as presented in Figure 6. Table 2 Enrichment results of the four crucial signaling pathways from DAVID by mapping on KEGG Term

Count

P Value

hsa04370: VEGF signaling pathway

11

4.47E-08

hsa04660: T cell receptor signaling pathway

10

4.53E-05

hsa04380: Osteoclast differentiation

10

2.90E-04

hsa04620: Toll-like receptor signaling pathway

8

1.75E-03

Genes PRKCA, PIK3CG, MAPK12, PTGS2, PIK3CB, MAPK14, PIK3CD, PIK3CA, PRKCG, NOS3, PRKCB PIK3CG, PRKCQ, TNF, MAPK12, PIK3CB, MAPK14, RELA, GSK3B, PIK3CD, PIK3CA PIK3CG, TNF, MAPK12, PIK3CB, MAPK14, RELA, PIK3CD, PPARG, PIK3CA, SYK PIK3CG, TNF, MAPK12, PIK3CB, MAPK14, RELA, PIK3CD, PIK3CA

19



Figure 6 Associated signaling pathways of CA against RA. Four signaling pathways derived from enrichment analysis of potential targets by mapping on KEGG, which are important in the pathology of RA as well. They are VEGF signaling pathway (A), T cell receptor signaling pathway (B), Toll like receptor signaling pathway (C) and osteoclast differentiation (D).

VEGF signaling pathway (hsa04370) is mainly involved in the biological progress of angiogenesis, where new blood vessels produced from an existed vascular network.35 While aberrant activation of this signaling pathway is one of typical pathologic conditions of RA, it could also be found in several related types of disease such as diabetic retinopathy and cancer.36 Initiated by the activation of VEGFR2 with PKC, RAF1, MEK, ERK participated in, proliferation of the vascular endothelial cell 20


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was deemed as the main effect of this pathway. In addition, the generation of NO as well as the actin reorganization were also important effects of VEGF signaling pathway in RA, which was mediated by PI3K-Akt signaling pathway and MAPKs, respectively. T cell receptor (TCR) signaling pathway (has04660) plays a crucial role in the process of antigen recognition and the initiation of adaptive immune response.37 Although there are many elements in this signaling pathway, potential targets mainly involved in two parts initiated with different transmembrane receptors. TCRs were complexed with CD3 and ZAP70, the activation of it could cause the transcription of DNA mediated by downstream targets such as p38 MAPK and NFAT. CD28 was another type of transmembrane receptor similar to TCRs, the effects of which including proliferation, differentiation and a series of immuno-responses. Different from TCRs, signaling transduction initiated by CD28 was mediated by PI3K, Akt and NF-κB. Toll-like receptor (TLR) signaling pathway (hsa04620) participates in the recognition of peptidoglycan (LPS) by TLR2 and TLR4 in synovial fibroblasts. Leading to the production of proinflammatory cytokines and type I Interferons (IFNs), this signaling pathway is essential for a host to defense against pathogens.38 However, the aberrant activation of this pathway might be responsible for pathogenesis of autoimmune, chronic inflammation, chronic pain and infection.39,

40

There are 10

functional TLRs in human, TLR1, TLR2 and TLR4 among which involve in the signaling transduction of the potential targets and they could be divided into two groups. One group was initiated through the activation of TLR1 and TLR2, both of which were complexed with TLRAP, MyD88, Rac and PI3K, and the transcription of DNA can be activated mediating by PI3K, Akt and NF-κB in this signaling pathway. 21



Similar effects on the transcription of genes related to inflammatory cytokines were generated by TLR4 as well, and the downstream targets of it mainly included TRAF6, TAKs, MKKs, p38 MAPK and AP-1. Bone destruction has been recognized as a clinically serious symptom in RA, an effective treatment of which was the inhibition of osteoclast differentiation (hsa04380).41 Many factors involve in the destruction of this process and form a complicated network. According to the transmembrane receptors, potential targets of CA could be classified into three groups. SYK complexed with DAP12 and FcRγ, their combination caused the phosphorylation of BLNK and signaling transduction of some downstream signals.42 TRAF2/6 was activated once accepted the signals from TNFR1 or RANK, subsequently, PI3K-Akt signaling pathway involved in cell proliferation, cell survival, cytoskeleton rearrangement, while the other two pathways resulting in transcription of some osteoclast specific genes were mediated by NF-κB and p38 MAPK-AP-1 respectively.43 Apart from the signaling pathways related to RA described above, several other signaling pathways enriched were important for the pathology as well. It is an appealing treatment to therapeutically modify cellular signaling pathway with several vital targets, for which it was relatively well-targeted, mainly orally administered and lower cost compared with biologic therapies. Phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) pathway, a mediator of cell survival and proliferation, has been found to play an essential role in various biological process with many genes related as its downstream effectors, such as NF-κB, HIF-1α, TNF, PDGF, TGF β and so on.44 Another signaling pathway similar to PI3K-Akt pathway is mammalian target of rapamycin (mTOR) signaling pathway, which controls various cellular processes as well, such as apoptosis, autophagy, translation, energy metabolism, and 22


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PI3K-Akt-mTOR signaling pathway has also been researched widely in RA. MTOR signaling pathway has been found to be activated by some inflammatory stimuli with binding at their specific receptor, and several important genes related to RA are regulated by it such as IL-22, IL-17, IL-9, NF-κB and etc.45-47 TNF signaling pathway is an effective therapeutic choice for autoimmune disease and there were anti-TNF medicines have been applied in the treatment of RA for both inflammation and bone destruction.48 3.5 Molecular dynamics simulations According to the results generated by enrichment analysis, several targets were recognized to be important for CA to exert their therapeutic effects on RA. At the same time, some complexes formed by CA and these crucial targets with desirable binding modes were picked out on the basis of binding affinities and binding modes. Then MD simulations were performed to ensure the stability of the complexes at a condition closer to the physiological environment, where the system embedded with water molecules and added counter ions to neutralize the system. Then 100 ns of MD simulations for the three representative complexes were performed to explore their stabilities using the probable binding modes predicted by previous molecular docking studies as the starting and reference structures. The dynamical properties of three representative complexes were then subjected to the analysis of trajectories data obtained from 100 ns MD simulations. RMSD, RMSF and potential energy were used to evaluate the system in the molecular dynamics studies, as they were shown in Figure 7, Figure S3 and Figure S4, respectively. From RMSD recorded along the simulation, we could see that three complexes were relatively stable, therefore, detailed binding modes of them were further analyzed for the receptor-ligand interactions and their typical binding modes comparing with the native ligand were 23



represented in Figure 8.

Figure 7 RMSD values of protein backbone atoms and ligands monitored throughout the 100 ns molecular dynamics simulation. (A) minaxin A (46) and NOS3 complex, (B) norcaesalpinin A (18) and PIK3CG complex,(C) caesalminaxinK (55) and GSK3B complex.

24


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Figure 8 Representative binding modes of compound-target obtained from MD simulation. (A) NOS3 complexed with its co-crystal ligand and minaxin A (compound 46), (B) PIK3CG complexed with its original ligand and norcaesalpinin A (compound 18) (C) GSK3B complexed with its original ligand and caesalminaxinK (compound 55). The structures of protein were presented in cartoon style and the amino acids participate in interaction were represented as sticks and hydrogen bonding was shown as dotted green line. For each target, carbon atoms of native ligand were painted orange while they were painted blue in CA.

From the protein and ligand RMSD values of the three complexes over the 100 ns MD simulations displayed in Figure 7, all the complexes underwent modest shift in certain period of time. For the complex formed by minaxin A (46) and NOS3, although protein underwent small fluctuations at the period of the first 15 ns, 30-36 ns and 46-64ns, finally reached a plateau at about 3.0 Å and retained stable to the end of the simulation. The ligand was stable and fluctuated slightly around 0.2 Å over the whole time of the simulation, indicating the binding mode of this complex was stable for the duration of the MD trajectory. For the complex of norcaesalpinin A (18) binding with PIK3CG, the complex seemed to underwent more fluctuations relatively, and equilibrium stages were found at the periods of 20-38 ns, 62-80 ns and 83 ns to the last around 3.9 Å. Different from the first complex, the ligand represented relatively larger and substantial fluctuations during nearly half of the MD trajectory and reached a stable stage at 60 ns around 0.7 Å, which suggesting the binding mode of this system changed during the MD simulation. Analyzing the RMSD plot of the 25



complex of GSK3B and caesalminaxin K (55), the protein backbone underwent frequent fluctuations during first half of the MD trajectory and gradually reached equilibrium around 2.6 Å at 60 ns till the end. Analyzing the RMSD fluctuations of the three complexes provide insight for the further detailed binding modes analysis. The protein-ligand interaction monitored throughout the simulation was displayed in Table S5 and the residues with interaction fraction over 10% were represented in Figure 9. Four types of protein-ligand interactions were monitored throughout the molecular dynamic simulations: hydrogen bonds, hydrophobic, ionic and water bridges. Among them, hydrogen bonds played a significant role in ligand binding as their strong influence on drug specificity, metabolization and absorption, with major consideration for the selection of the molecules with preferred interactions.

Figure 9 Normalized stacked bar chart representation of interactions and contacts over the course of the MD trajectory. Values greater than 1.0 are possible as some residues make more than one contact of a particular type with the ligand. (A) minaxin A (compound 46) and NOS3 complex, (B) norcaesalpinin A (compound 18) and PIK3CG complex, (C) caesalminaxinK (compound 55) and GSK3B complex.

Analysis of the NOS3-compound 46 complex trajectory data showed an existence of hydrogen bonds between Glu327 and compound 46 remained stable throughout the simulation period. The hydrogen bond Glu327 participated account for 118.8%, indicating there were more than one hydrogen it bond formed, the two hydrogen bonds formed by Glu327 with two hydroxyls accounted for 39% and 79%, respectively. Besides, the hydrogen bonds formed by Arg149 and Cys150 were 26


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monitored to account for 89.6% and 62.3% of the entire MD trajectories. From the above analysis, we can find that the hydrogen bond interactions with Glu327 and Cys150 within this complex generated from molecular docking maintained well during the MD simulation. Another type of hydrogen bonds was those formed with water bridges, which was observed relatively significant with Arg149 and Met324 that were observed to account for 49.2% and 31.9% of the MD simulation period. As for hydrophobic contacts, the interactions between Trp144 and Phe319 were relatively stable as their interactions can be observed in 72.4% and 76.2% out of the whole MD simulations and there was no ionic contact found in this complex. From trajectory analysis of PIK3CG and norcaesalpinin A (18) complex, it was not surprise to find that the binding mode changed a lot for both of the protein and ligand suffering substantial fluctuations as we discussed above. As we can see from the RMSD plot, the complex tended to reach an equilibrium stage at 20 ns, when Val882 replaced a water molecule to form hydrogen bond interaction bond with the oxygen atom on furan ring of the ligand, and this interaction accounted for 41.3% of the entire MD simulation. It was found that water bridge contact made up a large proportion of protein-ligand interaction based on the MD simulation result, and several residues involved in hydrogen bond interaction were found to maintain the contacts but through mediating by water molecules. Asp964, Thr887, Asp950 and Thr886 were observed to form water bridge interactions, and they accounted for 96.4%, 73.6%, 37.8% and 30.5%, respectively. Several residues were found to participate in hydrophobic contacts with the compound, such as Trp812, Tyr867, Met804 and 27



Met953, and their interaction were observed in 47.1%, 45.0%, 27.9% and 24.4% of the whole MD trajectories, respectively. Through analyzing the MD trajectories of GSK3B complexed with caesalminaxin K (55), hydrogen bond interactions were found to play a vital role in sustaining the receptor-ligand interaction. Among them, the hydrogen bond interaction formed with Glu97 maintained strong interaction, and it was observed in 99.1% of the entire MD trajectories. Another hydrogen bond formed by Asp200 was less stable, which accounted for 66.5% of the whole MD simulation. Both of Glu97 and Asp200 were also found to participate in water bridge contacts, however, they were less stable and were found in few frames recorded. Gln185 and Cys199 formed water bridges interactions with the ligand, which accounted for 48.1% and 17.8% of the entire MD trajectories. 3.6 Binding free energy calculation As the complexes reached equilibrium stages through 100 ns MD simulations, the final binding modes derived from MD simulations were submitted to a binding free energy calculation with MMGBSA method. As the results shown in Table 3, the binding free energies were -66.8190 kcal/mol for NOS3-compound 46, -74.3333 kcal/mol for PIK3CG-compound 18, and -82.1562 kcal/mol for GSK3B-compound 55, respectively. The results generated by MMGBSA were consistent with the binding modes refined by MD simulations. While the van der waals energy contributed most in all of the three complexes, hydrogen bond interactions of NOS3-compound 46 and GSK3B-compound 55 were more important than that of PIK3CG-compound 18. The hydrogen bonds of PIK3CG formed with compound 18 were less stable during the process of MD simulation, and most of which were mediated by water molecules. 28


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High values of Generalized Born electrostatic solvation energy were disadvantageous for the binding free energy, which suggested that the binding pockets were large and much exposed to the solvent environment. Besides, the high attribution of lipophilic indicated that the hydrophobic properties possessed by the skeleton of CA played an important role in their binding with these targets. Table 3 Binding free energies of the three systems with the values of energies in kcal/mol Binding Free Energy Coulomb energy Covalent binding energy Van der waals energy Lipophilic energy Generalized Born electrostatic solvation energy Hydrogen bonding energy Pi-pi packing energy

NOS3-46

PIK3CG-18

GSK3B-55

-66.8190 -13.4774 0.3196 -43.0075 -29.6352 21.5294 -1.5719 -0.97588

-74.3333 -2.8175 0.8533 -44.0606 -45.5688 17.629 -0.1807 -0.1881

-82.1562 -10.56 3.6958 -50.4451 -42.4806 18.8172 -1.1835 0

4. Conclusion Four

signaling

pathways

for

cassane

diterpenoids

(CA)

exerting

the

anti-inflammatory and immunomodulatory activity to treat RA disease were picked out, that is TCR signaling pathway, TLR signaling pathway, VEGF signaling pathway and osteoclast differentiation pathway, and they are closely related to antigen recognition, inflammation, angiogenesis and osteoclastogenesis, respectively. Meanwhile, key targets were deemed by the credible docking results in these signaling pathways. It was found that structures of CA achieved the satisfied performance with key targets related to inflammation and immune mainly including PI3Ks, NOS, PKCs, and MAPKs. The molecular simulation studies were performed with several CA structures complexed with these targets to investigate their stability in the protein active sites. The results indicated that the hydrophobic groups in CA structures were main serving groups, while, the other substitutes such as hydroxyl or acetyl groups were participated in hydrogen bonding interactions mainly, and this find 29



can provide a valuable reference for the relationship structure-activity study of CA compounds. Through the molecular modeling work on the exploration of CA’s effect on inflammation and immunomodulation, a valuable reference was built up for their further development.

Supporting Information Detailed tables showing the structure and classification information of 102 compounds (Table S1) and their representative ones used for target prediction (Table S2), a detailed summary of potential targets of CA compounds (Table S3) and their corresponding docking results (Table S4), the results of enrichment analysis (Figure S1 and S2), and relevant data for molecular dynamics simulations of the three representative complexes such as interaction fraction (Table S5), RMSF plots (Figure S3) and energy plots (Figure S4).

Author Contributions Conceived and designed the experiments: J.W. and H.-Y.G., Performed calculation: Y.W. and B.-C.H., Analyzed Data: Y.W., Y.-S. P., X.X., and W.-H. J., Wrote paper: Y.W. Conflict of interest The authors have declared no conflict of interest.

Acknowledgements Authors Y.W., X.X., W.-H.J. and H.-Y.G acknowledge the support from the Project of National Natural Science Foundation of China (No. 31670359), Program 30


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for Liaoning Innovative Talents (No. LR2017041). B.-C.H. and J.W. acknowledge the Natural Science Foundation of Liaoning province (No. 20170540854) and long-term training of young teachers in Shenyang Pharmaceutical University (No. ZQN2015002). Y.-S. P. acknowledges the University Students Innovation and Entrepreneurship Training Program (201810163).

Abbreviations AD : ADCY1 : ADCY5 : ADCY6 : ADRB2 : ADT : AGE : Akt : ALS : AMPK : AP-1 : CA : CASP3 : CD28 : CD3 : CES1 : CHRM2 : CVD : DAVID : DHODH : DMARD : DRD3 : EGFR : ERK : FcRγ : FDR : FoXO : GAD : GnRH : GO : GSK3B :

Alzheimer’s disease Adenylate cyclase type 1 Adenylate cyclase type 5 Adenylate cyclase type 6 Beta-2 adrenergic receptor AutoDockTools advanced glycation endproducts Protein kinase B amyotrophic lateral sclerosis AMP-activated protein kinase Activator protein 1 cassane diterpenoids Caspase-3 Cluster of Differentiation 28 cluster of differentiation 3 Liver carboxylesterase 1 Muscarinic acetylcholine receptor M2 cardiovascular disease Database for Annotation, Visualization and Integrated Discovery Dihydroorotate dehydrogenase (quinone), mitochondrial disease-modifying anti-rheumatic drug D(3) dopamine receptor Epidermal growth factor receptor extracellular signal-regulated kinases Fc receptor γ-chain false discovery rate Forkhead box O Genetic Association Database Gonadotropin-releasing hormone Gene Ontology Glycogen synthase kinase-3 beta 31



HIF-1 : HMGCR : HTR2B : IFN : IL-17 : IL-22 : IL-9 : IMID : JAK : JAK3 : KEGG : LBFGS : LPS : MAOB : MAP3K5 : MAPK : MAPK12 : MAPK14 : MD : MEK : MKK : MMP9 : mTOR : MyD88 : NFAT : NF-κB : NO : NOS : NOS1 : NOS2 : NOS3 : NSAID : PDB : PDGF : PI3K : PIK3CA : PIK3CB : PIK3CD : PKC : PPARA : PPARG : PRKCA : PRKCB : PRKCG :

Hypoxia-inducible factor -1 3-hydroxy-3-methylglutaryl-coenzyme A reductase 5-hydroxytryptamine receptor 2B Interferons Interleukin-17 Interleukin-22 Interleukin-9 immune-mediated inflammatory disease janus kinase Tyrosine-protein kinase JAK3 Kyoto Encyclopedia of Genes and Genomes limited-memory Broyden-Fletcher-Goldfarb-Shanno peptidoglycan Amine oxidase [flavin-containing] B mitogen-activated protein kinase kinase kinase 5 serine/threonine mitogen-activated protein kinase Mitogen-activated protein kinase 12 mitogen-activated protein kinase 14 molecular dynamics Mitogen-activated protein kinase kinase MAPK kinase Matrix metalloproteinase-9 Serine/threonine-protein kinase mTOR Myeloid differentiation primary response 88 Nuclear factor of activated T cells nuclear factor kappa light chain enhancer of activated B cells Nitric oxide NO synthase Nitric oxide synthase, brain Nitric oxide synthase, inducible Nitric oxide synthase, endothelial non-steroidal anti-inflammatory drugs Protein Data Bank Platelet-derived growth factor phosphoinositide 3-kinase Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit beta isoform Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit delta isoform Protein kinase C Peroxisome proliferator-activated receptor alpha Peroxisome proliferator-activated receptor gamma Protein kinase C alpha type Protein kinase C beta type Protein kinase C delta type 32


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PRKCQ : PTK : RA : RAC : RAF1 : RAGE : RANK : RELA : RIG : RMSD : RMSF : ROCS : SPC : STAT : SYK : T2D : TAK : TCR : TGF β : TIRAP : TNF : TNFR1 : TRAF6 : Tregs : ZAP70 :

Protein kinase C theta type protein tyrosine kinases rheumatoid arthritis Ras-related C3 botulinum toxin substrate RAF proto-oncogene serine/threonine-protein kinase receptor for advanced glycation endproducts Receptor Activator of Nuclear Factor κB Transcription factor p65 retinoic acid-inducible gene root mean square deviation root mean square fluctuations Rapid Overlay of Chemical Structures simple point charge signal transducer and activators of transcription Tyrosine-protein kinase SYK type 2 diabetes TGF-β-activated protein kinase T cell receptor Transforming growth factor beta Toll/interleukin-1 receptor domain-containing adapter protein Tumor necrosis factor Tumor necrosis factor receptor superfamily member 1A TNF receptor associated factor 6 regulatory T cells Zeta chain associated protein kinase 70

33



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