In silico drug designing for Jaundice

 

Varsha Rani1 and Nand Lal2

Department of Biotechnology, College of Horticulture and Forestry Neri, Hamirpur, Himachal Pradesh, INDIA|

Department of Chemistry, Govt Degree College, Hamirpur, Himachal Pradesh, INDIA|

*Corresponding Author E-mail:

 

ABSTRACT:

Jaundice is a yellowing tinge to the skin, sclerae of eyes and body fluids. Jaundice is caused by increased level of bilirubin in the blood, a yellowish pigment, produced from the breakdown of heme, mostly from hemoglobin and red blood cells (RBCs). Protein responsible for causing jaundice is MRP2 (multiple resistance protein2). MRP2 is responsible for increasing the amount of bilirubin in blood. MRP2 amino acid sequence was retrieved from NCBI and 3D structure was modelled using Modeller software. 3D structure of MRP2 was further validated by Ramachandran plot. A drug named, 8-amido-dodec-4-ene was designed by in silico approach for Jaundice. MRP2 protein binds with drug 8-amido-dodec-4-ene effectively showing, Gibbs Binding Energy of -9.59KJ/mol. 8-amido-dodec-4-ene drug showed 0.09 drug likeness score and was found to be nonmutagenic, nonirritant, nontumerogenic and nonreproductive which means that this lead (8-amido-dodec-4-ene) molecule can be a better drug for jaundice after clinical trials. In silico studies are based upon the online tools and softwares which are designed by using different numerical and computational algorithms using Mathematics, so Mathematics plays a very important role in software development.

 

INTRODUCTION:

Jaundice is yellowish discoloration of the skin, sclerae (whites of the eyes) and mucous membranes caused by hyperbilirubinemia (increased levels of bilirubin in the blood). Hyperbilirubinemia increases levels of bilirubin in the extracellular fluids. Bilirubin is transported by blood to the liver, where it is excreted in bile, eventually reaching the small intestine. Jaundice may arise from a disorder at any point in the pathway and is usually a sign of problem that needs to be addressed. Jaundice is categorized into three different forms, depending on which part of the physiological mechanism the pathology affects. The pathology is occurring prior the liver in pre hepatic jaundice. The pathology is located within the liver in case of hepatic jaundice while pathology is located after the conjugation of bilirubin in the liver in post hepatic jaundice. Jaundice is the most common condition that requires medical attention in newborns. Jaundice in new born is the result of accumulation of unconjugated bilirubin which reflects a normal transitional phenomenon. However, in some infants, serum bilirubin levels may raise excessively, which can be cause for concern because unconjugated bilirubin is neurotoxic and can cause death in newborns and lifelong neurologic sequelae in infants who survive (kernicterus). For these reasons, the presence of neonatal jaundice frequently results in diagnostic evaluation.

 

The objective of this study was to design a drug for jaundice by in silico approach. Cause of jaundice at protein level was analysed and a drug molecule was designed to block the action of jaundice causing protein. Computational tools offer the advantage of delivering new drug candidates more easily and quickly and at lower cost.

 

METHODOLOGY:

Target identification:

For drug designing, first step is to identify a target molecule. Target molecule was identified based up on the research. One needs to have sound knowledge about the disease and the potential targets those can be used for the generation of drug molecule.  The most promising drug target is selected for the drug development. It must be present on necessary pathways not on alternative pathways and must be proteinaceous in nature.

 

Target structure retrival and Homology Modelling:

Sequence of protein encoding for the jaundice was retrieved from protein structure database Protein Data Bank (www.rcsb.org) and National Centre for Biotechnology Information (www.ncbi.nlm.nih.gov/blast). Homology Modelling was performed by using Modeller software.

 

Structure refinement:

Structure refinement, check the stability of a protein or target molecule by Ramachandran Plot. The prochecking of stereochemical parameter is product of UCLA (University of California and Los Angeles). This gives the idea of 3D configuration of protein generated. The models are generally similar having minor differences in their structure and orientation. This is calculated using Structural Analysis and Verification Server (SAVS).

 

Active site and lead identification:

Active site in protein was identified using LIGSITE, an online tool for the prediction of different active sites in a protein.

 

Growing of lead into ligand:

This step was performed by using software Ligbuilder. It is developed in open source environment with G compiler. Open source environment are Linux, Unix and Fedora.

 

RESULTS AND DISCUSSION:

Target identification:

Target molecule identification was done on the basis of thorough search about the research done by the scientists about the target for jaundice.  MRP2 protein was found to be the target molecule for the cause of jaundice. Several different mutations in MRP2 gene have been observed in patients with Dubin-Johnson syndrome (DJS), an autosomal recessive disorder characterized by conjugated hyperbilirubinemia.

 

Target sequence retrival and Homology Modelling:

Target (MRP2) sequence was reterived from NCBI database and 3D structure (Fig. 2) was modelled based on the template sequence by Modeller software. Homology modeling involves taking a known sequence with an unknown structure and mapping it against a known structure of one or several similar (homologous) proteins. It would be expected that two proteins of similar origin and function would have reasonable structural similarity. Therefore it is possible to use the known structure as a template for modeling the structure of the unknown structure. All homology modeling approaches consists of three steps:

i.         Finding homologous PDB files.

ii.        Creation of alignment, using single or multiple sequence alignments. Analysis of alignments; gap deletion and addition; secondary structure weighting.

iii.      Structure calculation and model refinement.

 

3D structure (Figure 1) was validated using the Ramachandran plot in SPDB Viewer software. Structural Analysis and Verification Server was used for structure refinement which includes, loop generation and energy minimization. In loop generation, residues from disallowed region were converted to the allowed regions while in energy minimization step, bad contacts was done zero. The energy of whole molecule is minimized and grooms were converted from higher state to the lower state.

 

Figure 1. 3D structure of MRP2 Protein showing helix and β sheets. 

 

Active site and lead identification:

Active site (Figure 2) was identified by using Ligsite online tool. Lead is a small molecule which provides the bases for the drug molecule. Lead is a molecule already present in natural form and has a natural tendency to bind with its own protein. It should be small and should not contain any metal ion. Lead identified for MRP2 protein was 8-amido-dodec-4-ene (Figure 3). This molecule was drawn by using Chemsketch software and SPDB Viewer was used for the visualization of protein as well as for ligand molecule. Chemsketch software also predicted some physical properties (Figure 4) of the ligand molecule like molecular formula (C13H25NO), formula weight (211.3437), composition (C (73.88%), H (11.92), N (6.63), O (7.57)), Molecular refractivity (65.85 cm3), Molar volume (237.9 cm3),  Parachor (568 cm3), Index of refraction (1.465 cm3), surface tension (32.4 dyne/cm), density (0.888 g/cm3), polarizability (26.10 x 10-24/cm3), Monoisotopic mass (211.19 Da), nominal mass (211 Da) and average mass (211.34 Da).

 

Figure 2. Predicted active site in the protein MRP2 by using Ligbuilder online tool.

 

Figure 3. Structure of Ligand (8-amido-dodec-4-ene) drawn by using Chemsketch software and molecule is visualize through SPDB Viewer software.

 

Figure 4. Structure of ligand (8-amido-dodec-4-ene) drawn in chemsketch software with its physical properties.

 

Growing of lead into ligand

This step was performed by Ligbuilder software using three steps:

1.)      Pocketing: To search vacant space in active site. Command used for pocketing was,  pocket pocket.index

2.)      Growing:  For growing lead molecule into ligand inside the active site. Command used for growing was, grow grow. index

3.)      Processing: Processing was used to check the parameters of the molecule like molecular weight, molecular volume, binding affinity, toxicity, parent molecule, log p (partition coefficient), HBD(Hydrogen bond  donar), HBA(Hydrogen bond acceptor), TPSA(Total or Topology Polar Surface Area). Command used for processing was, process process.index.

 

Docking and Molecular properties of the drug molecule

Docking of lead (8-amido-dodec-4-ene) molecule with MRP2 protein was done by using Hex software. It was observed that lead molecule is binding effectively with the MRP2 protein having -9.59KJ/mol Gibbs Binding Energy. Molecular visualization was done by using Pymol software after docking as shown in figure 5.

 

Figure 5. Figure is showing the binding of ligand molecule (8-amido-dodec-4-ene) with the MRP2 protein. Ligand molecule is binding near the pocket in the 3D structure of MRP2 protein.

 

PASS software was used for the calculation of drug likeness score and drug likeness score was predicted to be 0.903 (figure 6). This score showed that the ligand molecule (8-amido-dodec-4-ene) can be considered as a drug.

 

Figure 6. Software Pass gave the drug likeness score of 0.903 showing that this ligand (8-amido-dodec-4-ene) can be used as a drug against Jaundice after proper clinical trials.

 

Toxtree software predicted that the ligand molecule (8-amido-dodec-4-ene) is a low class I drug (figure 7) which means that this is non toxic in nature. Molsoft software predicted that ligand molecule fall under the category of drugs having score of 0.09 (figure 8). These results predict that the selected ligand molecule as a drug can be considered as a drug molecule and is safe for use as a drug against jaundice. This is non toxic and nontumerogenic, nonmutagenic and nonreproductive but found irritant in nature (figure 9) as predicted by Osiris software. With the addition of o (oxygen) molecule in the structure of drug molecule, drug became non irritant. So the final drug molecule with oxygen molecule is considered as a drug against Jaundice.

 

Figure 7: Toxtree software predicted that the ligand molecule (8-amido-dodec-4-ene) belongs to low class I in the toxicity level which means that this ligand molecule is non toxic in nature.

 

Figure 8: Molsoft predicted that the ligand molecule (8-amido-dodec-4-ene) fall under the category of drugs having drug likeness score of 0.09, which means this ligand molecule can be considered as a drug.

 

Figure 9: Osiris software predicted that the ligand molecule (8-amido-dodec-4-ene) is nontumerogenic, nonmutagenic, nonreproductive in nature which means it can be safe for use as a drug against Jaundice.

 

CONCLUSION:

Aim of designing a drug against Jaundice using computational Biology and Bioinformatics has been accomplished by this project. The inhibitor molecule (8-amido-dodec-4-ene) was grown successfully inside the active site and has made the protein- inhibitor complex with effective Gibbs Binding Energy of -9.59KJ/mol. Drug molecule (8-amido-dodec-4-ene) was found to be nontoxic, nontumerogenic, nonmutagenic, nonreproductive and non irritant in nature having drug likeness score of 0.09. Thus it can be concluded that through proper clinical researches drug molecule designed can be modified and used for the effective treatment of jaundice.             

 

REFERENCES:

1.        https://www.ncbi.nlm.nih.gov

2.        https://www.rcsb.org

3.        https://www.ncbi.nlm.nih.gov/gene?Db=gene&Cmd=ShowDetailView&TermToSearch=1244

4.        http://www.news-medical.net/health/What-is-Jaundice.aspx

5.        http://www.proteinstructures.com/Modeling/Modeling/Modeling/model-quality2.html

6.        https://services.mbi.ucla.edu/SAVES/

7.        http://emedicine.medscape.com/article/974786-overview

8.        https://bioinformatictools.wordpress.com/tag/pocket-finder

9.        http://pubs.acs.org/doi/abs/10.1021/ci100350u

10.     hex.loria.fr/

11.     https://www.ncss.com/software/pass/

12.     toxtree.sourceforge.net

13.     www.ncbi.nlm.nih.gov/projects/SNP/osiris/

 

 

 

Received on 19.11.2016       Modified on 28.11.2016

Accepted on 04.12.2016      ŠA&V Publications All right reserved

DOI: 10.5958/2349-2988.2017.00025.0

Research J. Science and Tech. 2017; 9(1):155-159.