Mining novel natural reactive oxygen species (ROS) inhibitors by targeting Rho Kinase for prevention of secondary spinal cord injury: An in-silico trial using traditional Chinese medicinal compounds

1 Introduction

Spinal cord injury (SCI) is an injury triggered event that is associated with permanent neurologic deficit. The deficit instigated by SCI leads to medical comorbidity, not only effecting sensory and motor capabilities, but also having impact on the physiological and economical condition of the patient (McDonald and Sadowsky, 2002). Edwin Smith papyrus, an Egyptian physician in 1700 BCE was the first one to document SCI as an “ailment not to be treated. Since then SCI has been recorded as one of the devastating conditions where most of the cases are exanimate before any patient care is given. The surviving SCI cases remain morbid and are more prone to mortality,in USA alone 10–40 people in a million in a year are effected by SCI. The total number of cases estimated in USA as reported in 2016 are at staggering 282,000, to which every year 11,000 new cases are added. Billions of dollars in USA alone are spend on this disease, making it one of the economically devastating diseases. (White and Black, 2016).

SCI is categorized into primary spinal cord injury(pSCI)and secondary spinal cord injury(sSCI), pSCI is defined as the injury inflicted at the time of trauma and sSCI is defined by the injury causedby the body’s response to initial trauma (Cadotte and Fehlings, 2011). The consequences of the SCI are defined by the extent of secondary damage, which is initiated by a cascade of molecular cellular events triggered by pSCI. The pSCI triggers glutamatergic excitotoxicity, free radical damage, cytokine production and inflammation, all of them effecting the survival of neurons and glial cells, thus setting a base for onset of sSCI, leading to other patho-mechanisims that trigger neuropathic pain and autonomic dysfunction. Use of free-radical scavengers, anti-inflammatory drugs and anti-apoptotic drugs are suggested to be effective therapeutic strategies for the inhibition of sSCI (Zhou et al., 2014).

In the pathogenesis of sSCI the role of reactive oxygen species (ROS) molecules in oxidative damage to spinal cord lipids, known as lipid peroxidation (LP) is well established Hall et al., 2016). The hydrogen peroxide and peroxinitrites are non-radical ROS reported to play important roles in post pSCI onset, among them OH, NO₂ and CO₃ peroxinitrites are more prominent to initiate LP.The expression of peroxinitrites is regulated by RhoA GTPase (RhoA) / Rho-associated kinase (ROCK) pathway, other than this ROCK is also associated with cytoskeletal rearrangement and cell movement function in a cell. This RhoA/ROCK pathway is implicated in disorders like cardiovascular disease (Budzyn et al., 2006) and central nervous system diseases (Yamamoto et al., 2014). Spinal cord injury is one of the prominent CNS disease among others like multiple sclerosis, Alzheimer’s disease, glaucoma and stroke to be regulated by RhoA/ROCK pathway (Tokushige et al., 2011).

Computer aided drug designing (CADD) is a promising strategy in novel drug discovery for rare diseases. This powerful tool is an established standard for novel drug designing and discovery, novel leads have been reported as enzyme inhibitors as well as protein − protein interaction disruptors using this method (Amin et al., 2016). In this study we are trying to look into novel natural lead compound of traditional Chinese medicine (TCM) origin that will alter RhoA/ROCK pathway by inhibiting the ATP binding site of ROCK. The main aim will be to discover a compound that will alter ROS based sSSI. We have used TCM database of 672 compounds for multistep structure based CADD method, virtual screening technique, followed by docking and simulation was employed for identifying promising lead compound, the approach is very well used method for identifying novel leads.

2 Material and method

3 Virtual screening drug likeliness prediction

A total of 128 TCM compounds were shortlisted after virtual screening based on their binding energy (ΔG) calculations (Trott and Olson, 2010). The selected compounds were further limited by subjecting them to rules set by lipiski (Lipinski, 2004). The Lipinki Rule of five (RO5) parameters gave us five compounds for further analysis.

4 Result and discussion

Virtual Screening: A database of in-house highly active TCM compounds were used to inhibit the ROCK protein by targeting its ATP binding site (Fig. 1). ROCK is composed of a ATP binding and a catalytic domain as where phosphorylation takes place, shown in Fig. 2. The ATP binding domain was used to generate inhibitors against ROCK protein. This inhibition has a role in reducing sSCI induced tissue damage via reduction of LP and decrease in oxidative stress. To come up with a novel ROCK protein inhibitor virtual screening, drug-likeliness, docking and molecular dynamics simulation methods were used. Virtual screening helped us to limit the number of compounds from the 672 natural productsto 128, based on their binding energy (ΔG Kcal/mol).

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Fig. 1

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Fig. 2

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Drug likeliness: To limit the focus on compounds that could be promising for further development, we checked each compound for drug-likeliness. Drug-likeliness of shortlisted compounds was defined by mutagenic and carcinogenic property and rule of five (RO5) set by Lipinski RO5 properties include number of hydrogen bond donor (HBD), number of hydrogen bond acceptor (HBA) molecular weight (MW) and octanol/water partition coefficient (logP), the permissible range is HBD ≤ 5, HBA ≤ 10, MW ≤ 500 Dalton and clog p ≤ 5. Table 2 shows drug-likeliness properties, five compounds were shortlisted on their drug-likeliness values. All the compounds are accommodating the values expected from typical drugs.

Molecular Docking: The five final shortlisted natural compounds from IBS database were docked using AutoDock 4.2 tool into the optimized binding site of ROCK protein (Fig. 3). In Table 3 we have shown the results generated. Three of the Five natural compounds were found to form hydrogen bond with ROCK protein (Table 4). AutoDock tool was used for molecular docking simulations, the top binding pose based on ΔG were taken for further analysis. Each binding pose was studied using discovery studio, the default parameters were used to calculate all the possible interactions. The interactions studied are van der waals, conventional hydrogen bond, carbon hydrogen bond, pi-cation, pi-donor hydrogen bond, alkyl and pi- alkyl interaction. The lead2-ROCK complex has binding energy of −7.34 Kcal/mol andis forming two conventional hydrogen bonds with TYR96 and HIS62 of cPLA2′s C2 domain. The binding pocket of lead2 (1-cyclohexyl-5-(4-methoxybenzyl)-5-(((1R)-8-oxo-5,6-dihydro-1H-1,5-methanopyrido[1,2-a][1,5]diazocin-3(2H,4H,8H)-yl) methyl)pyrimidine-2,4,6(1H,3H,5H)-trione) comprises following amino acids TYR96, VAL97, ASP40, LYS32, THR41, PRO42, ASP43, HIS62, ASN64, ASN65, ASP93, ALA94, and ASN95. The O₂₂ atomic site of lead2 shows hydrogen bond interaction with TYR96 and HIS62, with a distance between the lead and ROCK of 1.88 Å and 1.67 Å respectively. Lead 4 (3-(furan-2-yl)-N-(furan-2-ylmethyl)-3-(p-tolyl)propan-1-amine) shows three conventional hydrogen bond interactions with the ATP binding domain of ROCK. Three atoms of lead4, N_15, O₂ andO₅ are forming the bond with TYR96, HIS62 and ASN95 with a bond length of 1.82 Å, 2.14 Å and 1.97 Å respectively. The binding pocket of lead 4 comprises of nine amino acids: ASP40, THR41, ASN65, ASP43, ASN64, HIS62, ASN95, TYR96, and VAL97. Lead4 is having ΔG of −10.09 Kcal/mol, the best reported among the top ten compounds. The third compound showing interaction is Lead6 ((12bS)-7-(2-ethoxy-3-methoxyphenyl)-2-(3-isopropoxypropyl)-12b-methyl-2,3,6,7-tetrahydropyrazino [1′,2′:1,2] pyrido[3,4-b]indole-1,4(12H,12bH)-dione),the binding pocket of the lead 6 molecule with lipid binding C2 domain of CPLA2 comprises of following amino acids viz. TYR96, ALA94, ASN95, LEU39, LYS32, ASP40, ASP43, ASN65, ASN64 and HIS62. Out of them lead6 forms hydrogen bond with TYR96 and HIS62, the interaction of our interest here is formed by lead6 O₁₆ and O₂₃ position with TYR96 and HIS62 at bond length 1.31 Å and 2.01 Å respectively. Based on ΔG and the number of interactions lead 7 (1-((S)-2-amino-4-methylpentanoyl)-N-((S)-1-((4-fluorobenzyl)amino)-3-methyl-1-oxobutan-2-yl)piperidine-4-carboxamide hydrochloride) is the least ranked among the top ten natural compounds inhibiting ATP binding domain of ROCK. Its binding pocket comprises of eleven amino acids; ASN64, ASP43, ASN65, LYS32, MET38, GLY36, LEU39, ASP37, THR41, ASP93, ASN95, TYR96, and ALA94. Lead7 shows ΔG of −6.07 Kcal/mol and has single hydrogen bond interaction between lead4′s O₁₃ position and CPLA2′s TYR96 with bond length of 1.52.

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Fig. 3

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Novel ROCK Inhibitor: The top natural compound inhibiting ATP binding domain of ROCK. The compound has the best binding energy and forms maximum number of hydrogen bonds, thus jamming the important ATP binding site in ROCK protein. To look into Lead 4 drug-ability, its Absorption, Distribution, Metabolism and Excretion (ADME) properties were calculated using in-silico ADME/Tox server (https://preadmet.bmdrc.kr/). In this calculation features like Caco-2 cell permeability (Caco-2p), MDCK cell permeability (MDCKp), Human intestinal absorption, Plasma Protein Binding and Blood Brain Barrier values of Lead 4 were calculated. The results generated are shown in Table 5. For drug absorption Caco-2 cell model and MDCK cell model were used and the value ranges in permissible range, the human intestinal absorbance (HIA) of 92.59% shows that Lead 4 can be well absorbed and can reach the target site easily. The plasma protein binding of lead 4 is 17.71% and shows its availability to reach the target protein is high. Evaluation of cell cytotoxicity revealed the IC50 for lead4 at 134.2 ± 6.8 μg/ml.

Conflict of interest

The authors declared no conflict in this manuscript and publications.