Department of Bioinformatics, Sathyabama Institute of Science and Technology

Corresponding author email: jemmychristy.bioinfo@sathyabama.ac.in

Article Publishing History

INTRODUCTION

A stroke is a cerebrum assault, which happens when the blood stream to the regions of the brain is confined prompting limited oxygen supply that is required for cellular metabolism to keep the tissues alive. When the tissues become ischemic, except if the blood stream is quickly reestablished it leads to brain death. Ischemic stroke is brought about by the blockage of artery in the brain regions, representing around 87% of stroke cases, which adds to the major death cases and post-stroke inability in patients. Whereas, hemorrhagic strokes are caused when blood vessels blast inside (which is known as intracerebral drain) or on the surface of the brain (which is known as subarachnoid discharge). Hemorrhagic strokes are the most serious form and are related to higher danger within months. In cardioembolic stroke cases, the heart pumps unwanted materials into the brain circulating regions, bringing about the occlusion of the brain vessel and damage to the brain tissues (Arboix and Alió, 2010, Simats,  et al., 2016; Chandra, et al.,2017, Agrawal et al.,2018).

According to the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC), 15 million people suffer stroke worldwide each year. In affected population, five million people  die and a further five million have been permanently disabled. Stroke costs the United States an estimated $34 billion each year and £26 billion a year in the United Kingdom. By 2035, the prevalence of stroke will increase by more than 20% over 2016 and the direct medical costs are projected to reach $184.1 billion, which is a great challenge to human society and healthcare system. There are very limited number of FDA approved medications available for the treatment of stroke. A combination of medications is generally used to treat the condition and to prevent it happening again of such as alteplase (intravenous tissue plasminogen activator) that can dissolve the clots and restore the blood flow to brain is given via injection, antiplatelet, anticoagulants help to reduce the chances of another clot formation (such as aspirin, warfarin) is prescribed. Similarly, statins are prescribed to reduce the level of cholesterol in blood (French et al., 2016).

This gives wide opportunity for the natural compounds to act as protective agents, and which many experimental and clinical trials being performed. Marine compounds have diverse chemical structures, functions and therapeutic properties due to the physical and chemical marine environmental conditions ( Agrawal et al.,2018) which have led to the successful clinical studies of  Xyloketal B and CEP 1347 for the development of marine derived anti-inflammatory drugs targeting ischemia conditions. Xyloketal B is a natural compound isolated from the mangrove fungus, Xylaria sp and CEP- 1345, isolated from Nocardiopsis sp widely found in the South China Sea and the compounds Xyloketal B is been widely studied for free radical damage, atherosclerosis, and CEP- 1347 for Parkinson disease (Maroney et al., 2001 Gong et al., 2018 Huang et al., 2019 ).

MATERIALS AND METHODS

Identification of the potential anti-inflammatory targets: The differentially expressed genes were obtained for all the 3 ischemia datasets such as ischemic stroke (GSE37587), cardio embolic stroke (GSE58294) and hemorrhagic stroke (GSE13353) from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo)and analyzed using R programming, GEO2r, is a web tool based on  t-test (ANOVA) or analysis of variance and is useful to compare two or more groups of samples in a GEO series, to identify differentially expressed genes. The DEG’s are then screened out through the cut off criteria of adjusted Log2 (FC)| > 1 (Fan et al., 2017).

Similarly, the inflammatory properties were also studied using inflammatory genes obtained from Gene cards and Harmonizome database. Then, the 2,422 overlapping genes were obtained using Venn diagram, for which protein- protein interaction reactome network has been constructed having 7,411 nodes and 61,158 edges. Subsequently, using graph theory approach 63 clusters were identified using MCODE analysis to detect the dense and closely related group of proteins that share similar functionality with Degree Cut-off as2, K-Core as 2(Christy and Priyadharshini,2018). Out of which, 10 clusters have been selected having nodes <= 10, which are classified as group of proteins that share a similar functionality (Myers et al.,2015). Now, pathway analysis and enrichment analysis are made to study the functional associations of the genes and the conditions. This has led to the identification of the potential anti-inflammatory targets based on further refinement and target proteins were listed in Table 1.

Table 1: Anti-inflammatory targets

Genes	Names	FDA Approval	PDB ID	Favourable Region
PGR	Progesterone receptor	Yes	2OVH	95.6%
STAT3	Signal transducer and activator of transcription 3	Yes	1BG1	80.6%
MMP9	Matrix metalloproteinase-9	Yes	IGKC	90.4%
VEGFA	Vascular endothelial growth factor A	Yes	1FLT	92.6%
ErbB4	Receptor tyrosine-protein kinase erbB-4	Yes	2R4B	81.9%
CDK2	Cyclin-dependent kinase 2	Yes	1URW	89.3%
SRC	Proto-oncogene tyrosine-protein kinase Src	Yes	2SRC	89.4%

Target Protein retrieval and pre pre-processing : The structure of the anti-inflammatory targets such as PGR, STAT3, MMP9, VEGFA, CDK2, ErbB4, SRC were downloaded from PDB in PDB format. The hetero atoms and alternative conformers are then deleted to provide enrichment to the receptor- ligand interaction study. To make the protein stable, we have minimized the energy by applying force field CHARMm (Brooks et al.,2009) (Chemistry at HARvard Macromolecular Mechanics) and energy minimization algorithm conjugate gradient with the help of DS 2.1. The binding site of the proteins has also been cross validated with Cavity plus web tool, which provides potential binding sites on the surface of a given protein and rank them with ligandability and druggability scores based on structural geometry and CAVITY algorithm. The ligandability value represents the possibility of designing small ligands with high binding affinities and the druggability predicts the possibilities of the cavity being a good target. It predicts maximum pKd, DrugScore and Druggability of all the detected cavities and this cavity score is influenced by cavity volume, pocket lip size, hydrophobic volume, cavity surface area, and hydrogen-bond-forming surface area (Xu et al.,2018)

Therapeutic ligand PK/PD property screening: The 3D structure of the Xyloketal B and CEP 1347 is obtained from PubChem in SDF format. The obtained structure is then screened for its drug likeliness property using Discovery studio 2.1. The properties such as absorption, distribution, metabolism, excretion and toxicity (ADMET), which are the important parameters to determine a compound for therapeutic use. These properties are co-related with the descriptors such as BBB, which determines the distribution of compounds in human body, low molecular weight for absorption. The output ellipses describe the regions where well absorbed compounds are expected to be found, and 95%, 99% confidence limit ellipse corresponding to BBB and intestinal absorption models are indicated (Ponnan et al.,2012)

Intermolecular Docking assessment: The targets are downloaded from Protein data bank in PDB format and the potential binding sites are identified using DS 2.1. A total of 7 proteins were docked with Xyloketal B and CEP-1347 using Ligand Fit protocol and CHARMm force field in DS which provides shape-based docking for accurately docking the ligand into the protein active sites. The active sites are predicted using grid search and ERASER algorithm. This method then combines monte carlo conformational search along with shape comparison filter for generating ligand poses consistent with active site shape. Then the docked poses are analyzed using potential of dock score and non-bonded interactions (Meng et al.,2011). The scoring functions are used to identify correct poses from incorrect poses, which is the sum of all non-bonded interactions such as hydrogen bond, hydrophobic effect, ionic interactions and binding entropy expressed in terms of like LigScore ,PLP, JAIN, PMF, and dock score (Ferreira  et al.,2015).

RESULTS AND DISCUSSION

ADMET Screening of ligands : In silico ADME analysis is performed using ADME Descriptors using Accelrys Discovery studio 2.1, in which various pharmacokinetics parameters such as Blood brain barrier (BBB), Hepatotoxicity, Plasma protein binding (PPB), cytochrome p450 inhibition were predicted for Cep-1347 and Xyloketal B. These levels influence the drug intensity and drug exposure to the tissues and thereby influencing the pharmacological activity of the compounds (Li ,2001). The results of the pharmacokinetic screening revealed that xyloketal B and CEP- 1347 followed Lipinki’s rule of five for bioavailability. However compound CEP 1347 shows high lipophilic nature due to high log p value, whereas, xyloketal B has shown 99% and 95% confidence level for intestinal absorption and BBB and falls inside the ellipse model. Figure1 ADMET result for Xyloketal B showing 95% and 99% confidence limit ellipse corresponding to Blood brain barrier and intestinal absorption models. Red and green color indicates absorption and blue and pink indicates BBB. ADMET result for CEP 1347 showing 95% and 99% confidence limit ellipse corresponding to Blood brain barrier and intestinal absorption models. Red and green colour indicates absorption and blue and pink indicates BBB and the Figure 2 depicted the details. Table 2 ADMET descriptors for Xyloketal B and CEP 1347

Table 2. ADMET descriptors for Xyloketal B and CEP 1347

Parameters	Xyloketal B	CEP 1347
BBB	2	4
PPB	0	2
CYP450	0.30	0.33
Hepatotoxicity	0.6	0.6
Intestinal absorption	0	1
Aq. Solubility and drug likeliness	0	0

Figure1: ADMET result for Xyloketal B and CEP 1347 showing 95% and 99% confidence limit ellipse corresponding to Blood brain barrier and intestinal absorption models. Red and green color indicates absorption and blue and pink indicates BBB

Figure 2

Figure 3: Docked poses of PGR druggable cavity with xyloketal B. Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Lys 885) mediates the hydrogen bond,then His 881,Thr 829,Leu 825 formed the carbon hydrogen bond and residues Leu 880, Val 884, Val 925, His 888, Lys 919, Tyr 753, Pro 918 involves in pi-Alkyl interaction with the receptor PGR.

Study of interactions between Xyloketal B, CEP 1347 and anti-inflammatory targets

Possible binding modes of Xyloketal B with the anti-inflammatory target receptors PGR, STAT3, VEGFA, SRC, CDK2, ErbB4, MMP9 were studied using Ligand Fit module in DS 2.1. The best docking pose and their dock score (Ligand/ Receptor interaction study + Ligand internal energy) as well as the non-bonded interactions (strong hydrogen bonds such as carbon and conventional hydrogen bonds) are noted to assess the affinity of the bonding. Higher the number of hydrogen bonds, more the solubility and more permeability for passive diffusion of the lead compounds. For cross validation of receptor ligand docking, cavity plus results has been used to show the interactions more favourable for validation.

Cavity module screening of anti-inflammatory targets revealed their draggability as well as ligandability nature. Xyloketal B intermolecular interactions with PGR listed in Table 4 and depicted in the Figure 3. Lys885, His881, Thr 829, Leu825 were the residues from druggable cavity mediated the hydrogen bond interaction within 2.5A^o distance. Within the same cavity pi-alkyl bonds interactions mediated pocket residues stabilize the intermolecular interactions. In general Pi-sigma interactions like Pi-alkyl and Pi-Sulphur which mainly largely comprise charge transfer aids in intercalating the drug within the druggable cavity of the receptor. Similarly, with STAT3 and other anti-inflammatory targets also mediated Hydrogen bond in addition to pi cation and pi alkyl bonds and interacting residues were listed in table 4 and depicted in figure 4. Residues Leu 438, His 437, Ser 381, Leu 378, Gly 373, Lys 383, Val 490, Ala377, Asp369 favoured the maximum number of interactions in the proposed target Table 5 and Figure 4. Glu 144, Ile 142 mediates the hydrogen bond, then Pro 143, Tyr 220, Pro 157, Glu 137 formed the carbon hydrogen bond interaction with the receptor VEGFA depicted in figure 5 and Table 6. Xyloketal B affinity to SRC analysed based on molecular docking and the interactions depicted in Figure 6 and residues involved in interactions are listed in Table 7.CDK2 intermolecular interactions with Xyloketal B mediated by hydrogen bonds and listed in Table 8, interacting residues and bonding distance were depicted figure 7.Similarly ErbB4  and MMP9 mediated interaction with Xyloketal B were depicted in Figure 8 and Table9 , Figure9 and Table 10 respectively.

Table 3. Output of cavity module for anti-inflammatory targets

Proteins	Druggability
PGR	Druggable
STAT3	Druggable
MMP9	Druggable
VEGFA	Druggable
ErbB4	Druggable
CDK2	Druggable
SRC	Druggable

Table 3. Non-bonded interactions between Xyloketal B and PGR

Ligand	Dock score	Amino acid (PGR)	Atom		Distance
			RECEPTOR	LIGAND
Xyloketal B	66.019	LYS 885 LYS885 HIS 881 THR 829 LEU 825	HZ1 HE2 HA OG1 O	O5 O5 O4 H36 H37	2.96989 2.28316 2.09333 2.25979 2.76227

Table 4. Non- bonded interactions between Xyloketal B and STAT3

Ligand	Dock score	Amino acid (STAT3)	Atom		Distance
			RECEPTOR	LIGAND
Xyloketal B	52.471	LEU 438 LEU 378 LEU 378 GLY373 SER 381 HIS 437	HN O 0 HA2 HB2 HA	O4 H34 H35 O1 O5 O4	2.5787 2.58422 2.67216 2.35742 2.23962 2.43909

Table 5. Non- bonded interactions between Xyloketal B and VEGFA

Ligand	Dock score	Amino acid (VEGFA)	Atom		Distance
			RECEPTOR	LIGAND
Xyloketal B	28.394	GLU 144 ILE 142 PRO 143 PRO 157 TYR 220 GLU 137 PRO 157	HN O HA O OH OE1 O	O5 H51 O5 H36 H36 H37 H37	2.09583 1.68155 2.36209 2.44219 2.25501 2.59563 2.9771

Table 6. Non- bonded interactions between Xyloketal B and SRC

Ligand	Dock score	Amino acid (SRC)	Atom		Distance
			Ligand	Receptor
Xyloketal B	69. 839	ASP 386	H34	OD2	2.94002

Figure 4: Docked poses of STAT3 druggable cavity with xyloketal B.Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 438) mediates the hydrogen bond, then His 437, Ser 381, Leu 378 and Gly 373 formed the carbon hydrogen bond and residues Lys 383, Val 490, Ala 377 involves in pi-Alkyl interaction pink colored dashed lines,Asp 369 mediated pi cation interaction  with the receptor STAT3.

Figure 5: Docked poses of VEGFA druggable cavity with xyloketal B.Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Glu 144, Ile 142) mediates the hydrogen bond, then Pro 143,Tyr 220,Pro 157,Glu 137 formed the carbon hydrogen bond interaction  with the receptor VEGFA.

Figure 6: Docked poses of SRC druggable cavity with xyloketal B. Interactions are shown as dashed lines between receptor residues and ligand atoms. Asp386 formed the carbon hydrogen bond interaction, Leu 407, Cys 277, Val 281, Phe 278 were the residues involved in pi-alkyl bonding pink colored dashed lines, then Lys 295 mediated pi cation interaction with the receptor SRC.

Figure 7: Docked poses of CDK2 druggable cavity with xyloketal B. Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 54) mediates the hydrogen bond, then His 121 formed the carbon hydrogen bond and residues Leu 124, Val 123, Arg 150 involves in pi-Alkyl interaction with the receptor CDK2.

Figure 8: Docked poses of ErbB4 druggable cavity with xyloketal B. Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Ala 749,Thr 860,Asp 861) mediates the hydrogen bond,Leu 794,Val 781,Leu 783,Val 732,Leu 850,Leu 864,Met 772 involves in pi-Alkyl interaction.Lys 751 mediates the pi-cation interaction with ErbB4.

Figure 9: Docked poses of MMP9 druggable cavity with xyloketal B. Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Arg 162,Gln 169) mediates the hydrogen bond, residues Val 167,His 203,Ile 198 involves in pi-Alkyl interaction  with the receptor MMP 9.

Study on non- bonded interactions between CEP-1347 and anti- inflammatory targets :CEP-1347 is a indolocarbazole derivatives derived from Marine bacterial sps Nocardiopsis. It is considered as potent inhibitor of SAPK/JNK pathway which activated after neuronal toxic insults in neurons. In our studies CEP-1347 was assessed for their affinity with anti-inflammatory targets namely  PGR, STAT3, MMP9, VEGFA, CDK2, ErbB4, SRC and MMP9.Pharmacophore features of this potent inhibitor showed better binding affinity and Figures 10,11,12,13,14,15,16 depicted the intermolecular interactions. Interaction mediated by hydrogen bonds, pi-cation, pi-alkyl and carbon hydrogen bonds. Interacting residues listed in Tables 11,12,13,14,15 and 16.Three different datasets on ischemia conditions such as ischemic stroke, cardio embolic stroke and haemorrhagic stroke were analysed. The control and diseased samples from each dataset are studied using techniques such as differential analysis of the genes, networking, pathway, functional analysis, hub genes validation, expression level studies using graph theory.

Figure 10: Docked poses of PGR druggable cavity with CEP 1347.Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 438) mediates the hydrogen bond, then His 437, Ser 381, Leu 378 and Gly 373 formed the carbon hydrogen bond and residues Lys 383, Val 490, Ala 377 involves in pi-Alkyl interaction,Asp 369 mediated pi cation interaction  with the receptor STAT3.

Figure 11: Docked poses of STAT3 druggable cavity with CEP 1347.Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 438) mediates the hydrogen bond, then His 437, Ser 381, Leu 378 and Gly 373 formed the carbon hydrogen bond and residues Lys 383, Val 490, Ala 377 involves in pi-Alkyl interaction represented in pink colored dashed lines ,Asp 369 mediated pi cation interaction  with the receptor STAT3.

Figure 12: Docked poses of VEGFA druggable cavity with CEP 1347.Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 438) mediates the hydrogen bond, then His 437, Ser 381, Leu 378 and Gly 373 formed the carbon hydrogen bond and residues Lys 383, Val 490, Ala 377 involves in pi-Alkyl interaction,Asp 369 mediated pi cation interaction  with  the receptor VEGFA

Figure 13: Docked poses of SRC druggable cavity with xyloketal B. Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 438) mediates the hydrogen bond, then His 437, Ser 381, Leu 378 and Gly 373 formed the carbon hydrogen bond and residues Lys 383, Val 490, Ala 377 involves in pi-Alkyl interaction,Asp 369 mediated pi cation interaction  with the receptor SRC.

Figure 14: Docked poses of CDK2 druggable cavity with xyloketal B.Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 438) mediates the hydrogen bond, then His 437, Ser 381, Leu 378 and Gly 373 formed the carbon hydrogen bond and residues Lys 383, Val 490, Ala 377 involves in pi-Alkyl interaction,Asp 369 mediated pi cation interaction  with the receptor CDK2

Figure 15: Docked poses of ErbB4 druggable cavity with xyloketal B.Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 438) mediates the hydrogen bond, then His 437, Ser 381, Leu 378 and Gly 373 formed the carbon hydrogen bond and residues Lys 383, Val 490, Ala 377 involves in pi-Alkyl interaction,Asp 369 mediated pi cation interaction  with the receptor ErbB4.

Figure 16: Docked poses of MMP9 druggable cavity with xyloketal B.Interactions are shown as dashed lines between receptor residues and ligand atoms. Residues highlighted in Green (Leu 438) mediates the hydrogen bond, then His 437, Ser 381, Leu 378 and Gly 373 formed the carbon hydrogen bond and residues Lys 383, Val 490, Ala 377 involves in pi-Alkyl interaction,Asp 369 mediated pi cation interaction  with the receptor MMP9.

These analyses helped us to predict that, the potential anti-inflammatory targets, according to Human Protein Atlas, the proposed targets are FDA approved, such as PGR(Liu et al.,2012; Zhu et al.,2017; Gibson et al.,2009),STAT3(Wang et al., 2017;Liang et al., 2016), MMP9 (Chaturvedi et al.,2014; Christy,2013), VEGFA(Greenberg et al.,2013), CDK2 (Osuga et al.,2000), ErbB4 (Robert et al.,2013, Qian et al.,2018) and SRC plays vital role in the neuro immunology, neuronal development such as angiogenesis, apoptosis, motor performances, pathological events through activation of multiple signalling pathways, and by regulating many numbers of proteins that participate in the post-ischemic events to can attenuate the expressions(. The inter-molecular interaction study revealed that Xyloketal B has higher interactions with PGR, STAT3, VEGFA, and ErbB4 with dock score 66.019, 52.471, 28.394, 66.695 and CEP 1347 having higher interactions with PGR, STAT3, SRC, ErbB4, and MMP9 with dock score 70.491, 57.725,99.144, 121.495 and 62.72 respectively.

Table 7. Non- bonded interactions between Xyloketal B and SRC

Ligand	Dock score	Amino acid (VEGFA)	Atom		Distance
			RECEPTOR	LIGAND
Xyloketal B	28.394	GLU 144 ILE 142 PRO 143 PRO 157 TYR 220 GLU 137 PRO 157	HN O HA O OH OE1 O	O5 H51 O5 H36 H36 H37 H37	2.09583 1.68155 2.36209 2.44219 2.25501 2.59563 2.9771

Table 8. Non- bonded interactions between Xyloketal B and CDK2

Ligand	Dock score	Amino acid (CDK2)	Atom		Distance
			Ligand	Receptor
Xyloketal B	65.243	LEU 54 HIS 121	H51 H34	O O	1.86881 2.8994

Table 9: Non- bonded interactions between Xyloketal B and ErbB4

Ligand	Dock score	Amino acid (ErbB4)	Atom		Distance
			Ligand	Receptor
Xyloketal B	66. 695	THR 860 ASP 800 ASP 861 ALA 749	O1 O1 02 H51	HG1 HN HN O	2.08283 2.52537 2.17772 2.57675

Table 10. Non- bonded interactions between Xyloketal B and MMP9

Ligand	Dock score	Amino acid (MMP9)	Atom		Distance
			Ligand	Receptor
Xyloketal B	61.088	ARG 162 GLN 169	H51 03	O HE22	2.36905 2.77257

Table 11. Non- bonded interactions between CEP-1347 and PGR

Ligand	Dock score	Amino acid (PGR)	Atom		Distance
			Ligand	Receptor
CEP-1347	70. 491	TYR 753 HIS 881 GLY 923 TYR 753 LEU 921 SER 757 THR 829 LEU 921	S1 O5 O7 H50 H57 O6 O4 H52	HH HD1 HN O O HB2 HB O	2.580589 2.79844 1.93441 1.72248 2.14658 2.51224 2.95172 2.06928

Table 12. Non- bonded interactions between CEP-1347 and STAT3

Ligand	Dock score	Amino acid (VEGFA)	Atom		Distance
			Ligand	Receptor
CEP 1347	36.199	GLN 22 ASN 219	O4 H64	HE21 OD1	2.65442 2.9773

Table 13. Non- bonded interactions between CEP-1347 and VEGFA

Ligand	Dock score	Amino acid (STAT3)	Atom		Distance
			Ligand	Receptor
CEP-1347	57.725	LYS 370 ASN 491 LYS 488 THR440 SER 372 LEU 438 LYS 370	O7 06 H50 H57 O3 H51 H52	HZ2 HD22 O OG1 HB2 O O	1.65372 1.86214 1.76279 2.09157 2.83771 2.37833 2.59893

Table 14 Non- bonded interactions between CEP-1347 and SRC

Ligand	Dock score	Amino acid (SRC)	Atom		Distance
			Ligand	Receptor
CEP- 1347	99.144	CYS 277 LYS 295 GLY 276 ASP 404 ARG 388 ARG 388	O4 O6 O4 H44 – –	HN HZ2 HA2 OD2 HH12 HH22	1.81155 1.62797 2.69631 2.85921 2.85918 2.69236

Table 15 Non- bonded interactions between CEP-1347 and CDK2

Ligand	Dock score	Amino acid (CDK2)	Atom		Distance
			Ligand	Receptor
CEP-1347	111. 05	ASP 145 ILE 10	O5 H51	HN O	2.87654 3.06647

Table 16 Non- bonded interactions between CEP- 1347 and ErbB4

Ligand	Dock score	Amino acid (ErbB4)	Atom		Distance
			Ligand	Receptor
CEP-1347	121.495	MET 799 GLN 797 GLY 725 GLY 725	O7 H57 O3 O6	HN O HA2 HA2	1.91772 2.67839 2.92012 2.57601

Table 17  Non- bonded interactions between CEP- 1347 and MMP9

Ligand	Dock score	Amino acid (MMP9)	Atom		Distance
			Ligand	Receptor
CEP-1347	62.72	ASP 800 ASP 800 LEU 718 LEU 788	H61 H56 H57 H69	OD2 OD2 O O	2.5093 1.75975 2.90109 3.03434

Through Cavity plus results, we ensured that the ligands are docked on the druggable cavities (having positive amino acids) of the receptor which has led to higher number of hydrogen bonds based on drug actions. This indicates that the intermolecular interactions are strong between the targets and the lead compounds, high solubility, high permeability across the membranes leading to good bioavailability of the compounds. There are many experimental and clinical studies being performed on xyloketal B and CEP-1347 as an anti-oxidative, anti-apoptotic showing positive results, which gives us a possibility for bring better stroke recovery (Xiao et al.,2014).

CONCLUSION

Computational analysis of Xyloketal B and CEP 1347 and their interactions with anti-inflammatory targets such as PGR, STAT3, MMP9, VEGFA, CDK2, ErbB4, and SRC have shown higher binding affinity with strong favourable hydrogen bond interactions. Cerebral ischemia/reperfusion injury generates various cell apoptotic pathways, it’s evident that ischemia-stimulated oxidative stress as well as inflammation are primarily responsible for the following cell death by necrotic or apoptotic processes. Reactive oxygen species (ROS) control cell survival/death by enabling several cells signaling process and related pathways, particularly JAK2/STAT3 stimulation makes a significant contribution to cell apoptosis subsequent ephemeral focal cerebral ischemia. In generic expression of VEGF-A in ischemic stroke group resulted in a decrease state as compared with the non-stroke group, thus VEGF signaling process correspond to vital targets for treatment of stroke.  CDK inhibitors blocks pRb phosphorylation and the increase in E2F1 levels and dramatically reduces neuronal death by 80%. These results suggest that CDKs are a promising therapeutic target for the treatment of ischemia. Here we have reported that the protective effects of Xyloketal B and CEP-1347 in brain injury and neuroinflammation by inhibiting STAT3 activation. PR is a key factor for endogenous neuroprotection; thus, they are deemed to be pharmacological target to treat stroke. The docking studies have shown that these compounds having anti-inflammatory, anti-apoptotic, anti-oxidative abilities can serve as treatment for ischemic conditions. Also, many experimental studies and clinical studies have been performed for reperfusion conditions, neurological disorders and extensive studies on stroke can be carried out for prevention and better recovery.

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