Insilico 3D Structure Modeling and Docking to Find ...

Research & Reviews: A Journal of Bioinformatics ISSN: 2393-8722 (online) Volume 1, Issue 3 www.stmjournals.com

Insilico 3D Structure Modeling and Docking to Find Potent Inhibitors for Thrombocytopenia Pranati Swain1*, Jasnaik D2 1

Bioinformatics, Orissa University of Agriculture and Technology, Orissa, India School of Biotechnology and Bioinformatics, DY Patil University, Maharashtra, India

2

Abstract Lots of people are getting affected by various kinds of auto-immune disease in their day to day life and thrombocytopenia is one of them, which occurs due to lack of thrombocytes or platelets. Various kinds of vaccines, drugs are used to stop the disease, but the effect varies from person to person depending upon the immune system. In thrombocytopenia disease the integrin beta protein of human affects the person’s own immune system leading in the less production of thrombocytes. Here the main objective of the study is to analyse the interaction between the disease stimulating proteins and the ligands or drugs bind to the protein at its active site. The structural prediction along with the stereo-chemical evaluation of protein helped to understand the function of the protein. The active sites of protein show the ligand binding regions. 14 different drugs were considered for the study. Out of which AC1NS627 drug showed best interaction with the protein as its binding perfectly at the active site of the protein. So from the study, we can conclude that AC1NS627 is the better drug which can be used for inhibiting the activity of integrin beta protein.

Keywords: In silico homology modeling, thrombocytopenia, molecular docking *Author for Correspondence E-mail: [email protected]

INTRODUCTION Platelets are also known as thrombocytes. Thrombocytes are the blood cells which act as blood coagulating factor. Thrombocytes are the fragments of cytoplasm without nucleus derived from the megakaryocytes of bone marrow. It reduces the chances of death due to hemorrhage during child birth. Thrombocytes are dark purple in colour. The normal thrombocyte count is 150,000 to 450,000 platelets per microlitre(µl) of blood. Thrombocytes control hemostasis [1]. Primary thrombocytes are formed by the formation of coagulation cascade with resultant fibrin deposition. Thrombocyte production is controlled by a hormone thrombopoietin. Thrombopoietin is produced in kidney and liver. Old Thrombocytes are destroyed by phagocytosis in liver and spleen. Decrease in the count of thrombocytes causes thrombocytopenia. Bleeding gums, nose bleed, and gastrointestinal bleeding; menorrhagia; and intraretinal and intracranial bleeding are

the symptoms of thrombocytopenia [2]. Activation of thrombocyte is inhibited by nitric oxide, endothelial-ADPase, and PGI2. Endothelial-ADPase. Drugs like Aspirin, Valproic acid, Methotrexate, Carboplatin, Interferon, Isotretinoin, Panobinostat causes thrombocytopenia by inhibiting the function of cyclooxygenase-1 (COX1), and hence normal hemostasis. thrombopoietinmimetics, desmopressin, factor VIIa drugs stimulate the production of Thrombocytes. Thrombocytopenia may be caused by less production of Vitamin B12 or folic acid and thrombopoietin, Leukemia, Congenital amegakaryocytic thrombocytopenia, Grey platelet syndrome, Bernard-Soulier syndrome, Wiskott–Aldrich syndrome [3]. Some other reasons of thrombocytopenia are pseudothrombocytopenia, Lyme Disease. Bone marrow biopsy is used for diagnosis of the disease. Idiopathic thrombocytopenic purpura (ITP), also known as immune thrombocytopenia, primary immune thrombocytopenia, primary immune thrombo-

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Insilico 3D Structure Modeling And Docking

cytopenic purpura or autoimmune thrombocytopenic purpura. This is an Autoimmune diseases in which purpuri crashes, petechiae are formed and lead to bleed [4]. As it is an Autoimmune diseases the antibodies like glycoproteins IIb-IIIa or Ib-IX in ITP antiADAMTS13 in TTP, and HUS anticardiolipin (anti-cardiolipin antibodies), β2 glycoproteinI in Antiphosphol ipid syndrome anti-HPA-1a, anti-HPA-5b, and others in NAIT act against the person’s own immune system. The treatment is carried out by the use of Steroids dexamethasone or methylprednisolone. Anti D, Immunosuppresants such as mycophenolate mofetil and azathioprine, Splenectomy, Platelet transfusion etc. Here the main objective is to analyse the interaction between the protein causing thrombocytopenia and the drugs used for the treatment. Thrombocytopenia is caused by one of the glycoprotein GPIIIa. HPA produces HPA-1a, HPA-1b, which further produce glycoprotein GPIIIa. We considered the integrin beta protein of homo sapiens as the target protein. It combines with an extracellular or intracellular messenger which brings changes in the cellular activity. Itegrin beta protein’s cell surface has heterodimeric receptors which mediates dynamic cell-to-cell as well as cellto-matrix adhesion. The integrin beta protein is able to make rapid and reversible changes in its adhesive function as it acts as mechanochemical sensors and transducers by modulating their ligand-binding affinity. All the subunits possess a large N-terminal extracellular domain followed by a transmembrane domain and a short C-terminal cytoplasmic region. Some subclasses of integrins have common beta chain and different alpha chains. The 3D structure of integrin beta protein of human was constructed by using MODELLEER9.12 tool. Best quality model was checked by using PROCHECK, PROSA, ERRAT, VERIFY 3D and WHATIF. Among of all the models best model was selected by using overall scores. Proteinligand docking was performed between the disease stimulating protein and the drugs used to inhibit the production of that stimulating proteins. 11 different drugs were selected from drug bank for analysis.

Swain and Jasnaik

The protein-ligand interaction was studied by using autodock and igemdock tools. Both the results were analysed to find the best interaction.

MATERIALS AND METHODS Sequence Collection The sequence of the disease stimulating protein was retrived from uniprot (http://www.rcsb.org/pdb/home/home.do). The uniprot ID of the disease stimulating protein is L7UUZ7, which is an integrin beta protein of homo sapiens present on ITGB3 gene. The sequences were downloaded in fasta format for further study. Sequence Alignment The basic local alignment similarity search is generally done to find out the matching amino acid and nucleotide sequences. BLAST are of five different types [5]. The collected sequence was further subjected to BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cg) to find the percentage of identity with other species. Dimensional Protein Structure Secondary structure prediction of protein gives idea about structural pattern from the protein sequence in terms of helix, sheets and coils [6]. For this GOR method was used to predict the secondary structure of HUMAN Integrin beta (Figure 1). 3D Structure Prediction The tertiary structures of the protein was generated by using various bioinformatics tools i.e. modeller 9.12, swiss-model and phyre2 server. Three different models were generated to select the best model by checking the sterio-chemical evaluation. Generating Model by using Modeller9.12 The FASTA format sequence were subjected to BLAST against PDB. The first template was selected which showed 99% similarity with 3IJEB. Then the alignment files, model evaluating files and model single files were modoified depending upon the target and template. This process is known as homologymodelling. After running the commands on the modeller screen it generates the best model. The best model was selected on the basis of lowest DOPE score. Only one model was selected out of 25 models.

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Research & Reviews: A Journal of Bioinformatics Volume 1, Issue 3 ISSN: 2393-8722 (online)

Genearating Model by using Swiss-model This is a protein homology modeling tool (http://swissmodel.expasy.org/interactive). This modeling server is accessible via the expasy web server. It gives the clear tertiary structure of a protein model showing the regions of alpha helix, beta sheets, beta turns, coils, helix etc. The FASTA format sequences were submitted in the work space and this generated model [7–9]. Genearating Models by using Phyre2 Here also the fasta format sequences were given as an input. The phyre2 server (http://www.sbg.bio.ic.ac.uk/phyre2/html/page .cgi?id=index)also generates 3D model of a protein [10]. Structure Validation The structure validation generally involves the sterio-chemical evaluation along with the ramachandran plot analysis [11]. PROSA((https://prosa.services.came.sbg.ac.at/ prosa.php) generally helps in the structural validation of a protein PDB file of the model is given as an input. It can also be applied to low resolution structures and approximate models obtained early in the structure determination process. The PROCHECK saves server (http://services.mbi.ucla.edu/SAVES/Ramacha ndran/) is generally used to find the region of allowed and disallowed regions of a protein. It also shows the phi-psi angles. It gives the sterio-chemical evaluation. The model file is generally uploaded in the server, which analyses the backbone conformation of a protein [12]. Protein-ligand Binding Here the drugs were considered as ligands and the disease stimulating protein was considered as protein component. The interaction between protein and lignad was studied by using autodock vina. Both the pdb files of ligands and proteins were uploaded and by setting the parameters the interaction was studied. The best interaction was selected on the basis of least binding energy [13,14].A docking study was conducted to find the binding sites of the disease stimulating proteins and to check

which all ligands are binding to that protein [15–17]. For this analysis 14 different drugs were collected from drug bank (Table 1). Table 1: Lists of Ligands Considered for Docking. Sl. no

Ligands/drugs

SID

1

AC1NS627

113911626

2

CCG205186

124750244

3

DO5819

47207480

4

HMS3229013

99302833

5

LS_193151

50076383

6

NSC-696819

525180

7

QCR-86

164041897

8

RT-016226

204356888

9

SEMAXANIB

135141433

10

SEMAXINIB

53789746

11

SU-5416

163686018

12

SUGEN-5416

5329098

13

UNII-71149535AJ

198984189

14

VEGFR2

204356888

Prediction of Active Site/ Binding Site of the Protein Active site or binding site is the position at which an enzyme binds to the substrate. The cast P server (http://stsfw.bioengr.uic.edu/castp/calculation.php) was used to find out the binding regions of the protein. Checking the Protein-ligand Interaction The interaction can be studied directly from autodock vina or by using discovery studio visualization tool. Then the interaction among the protein-ligand was performed manually by selecting the highest volume of the generated model.

RESULT AND DISCUSSION Secondary Structure of Protein The GOR online server identified the secondary structure of HUMAN Integrin beta protein with distinct regions of helices and strands. Thus, overall the helix 18.65%, Strand 20.81 and coils 60.53% was calculated (Figure 1).

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Insilico 3D Structure Modeling And Docking

Swain and Jasnaik

Fig. 1: Secondary Structure Prediction of HUMAN Integrin Beta Protein. Tertiary Structure Three different models were generated by using phyre2 server, swiss-model server and the modeller9.12 tool. The best model was selected on the basis of the lowest dope score from modeller9.12, however, the phyre2 and

Fig. 2: Structure Obtained from Phyre2.

swiss-model generated the best model by refining the loops. The structures were then visualized by using pymol, discovery studio visualiser and swiss pdb viwer, respectively to analyze the models more clearly.

Fig. 3: Structure Obtained from Modeller9.12.

Fig. 4: Structure Obtained from Swiss Model.

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Research & Reviews: A Journal of Bioinformatics Volume 1, Issue 3 ISSN: 2393-8722 (online)

Structural Evaluation The structural evaluation was performed by using the PROCHECK saves server, rampage server. The procheck saves server showed the backbone conformation of protein and the rampage showed the allowed regions, disallowed regions and the residues lying in the outer region. From the percentage of allowed and disallowed regions we can analyze which is the best protein structure

which is modelled. Normally the allowed region lies between 70–90%. The models which generated using modeller 9.12 lies in 82.0% of the favored region, a model which was generated using their lies in 75.6% of the favored region, a model which was generated using swiss-model lies in 74.00%. From this observation it was found that the best model was generated by using modeller9.12 tool (Table 2).

Fig. 5: Structure from Modeller.

Fig. 6: Structure from Phyre2.

Fig. 7: Structure from Swiss Model Server. Table 2: Sterio-Chemical Evaluation of Protein. Model

Residues in Favoured Region

Residues in Additional Allowed Region

Residues in Generously Allowed Region

Residues in Disallowed Region

Number of end Residues (excl. Gly and Pro)

Number of proline Residues

Total No. of Residues

Modeller Output

564 (82.0%)

97 (14.1%)

13 (1.9%)

14 (2.0%)

2

40

788

Phyre2 server Output

459 (75.6%)

116 (19.1%)

18 (3.0%)

14 (2.3%)

1

36

695

Swiss Model server Output

449 (74.00%)

129 (21.3%)

16 (2.6%)

13 (2.1%)

2

36

695

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Insilico 3D Structure Modeling And Docking

Active Site Prediction From the active site prediction we got the binding regions of the protein. The highest

Swain and Jasnaik

volume was selected to get the better binding sites.

Fig. 8: Binding Site of Protein. Docking The protein-ligand interaction was performed by using Auto Dock tool which showed the binding energies between the protein and ligands (Table 3). The least binding energy

was chosen as the best interaction. From the final output of the docking we can find the residues of the interactions which are given below (Figures 9–22).

Fig. 9: Receptor-AC1NS627. RRJoBI (2014) 31-40 © STM Journals 2014. All Rights Reserved

Fig. 10: Receptor-CCG205186. Page 36

Research & Reviews: A Journal of Bioinformatics Volume 1, Issue 3 ISSN: 2393-8722 (online)

Fig. 11: Receptor+DO5819.

Fig. 12: Receptor+HMS3229013.

Fig. 13: Receptor+LS_193151.

Fig. 14: Receptor+NSC-696819.

Fig. 15: Receptor+QCR-86.

Fig. 16: Receptor+RT-016226.

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Insilico 3D Structure Modeling And Docking

Swain and Jasnaik

Fig. 17: Receptor+SEMAXANIB.

Fig. 18: Receptor+SEMAXINIB.

Fig. 19: Receptor+SU-5416.

Fig. 20: Receptor+SUGEN-5416.

Fig. 21: Receptor+UNII-71149535AJ.

Fig. 22: Receptor+VEGFR2.

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Research & Reviews: A Journal of Bioinformatics Volume 1, Issue 3 ISSN: 2393-8722 (online)

Table 3: Auto-Dock Result Analysis. Name of the Lead Compound

AutoDock Binding Energy

AC1NS627

–8.88

CCG205186

–5.86

DO5819

–8.18

HMS3229013

–7.54

LS_193151

–7.1

NSC-696819

–8.49

QCR-86

–8.38

RT-016226

–8.55

SEMAXANIB

–8.07

SEMAXINIB

–6.56

SU-5416

–7.93

SUGEN-5416

–8.37

UNII-71149535AJ

–8.01

VEGFR2

–6.75

CONCLUSION In this study, we generated the best tertiary structure of the protein by using the template 3IJE with B chain This model has been qualified using several validation methods, including PROCHECK saves server, rampage, prosa. From the sterio-chemical evaluation of tertiary structure of protein, it was easy to study the function of the protein. All evidences suggested that the geometric quality of the backbone conformation, residue interaction and contact and the energy profile of the structure generated was well within the limits. The protein-ligand docking study has been elucidated for the purpose of finding the effective drug to inhibit the activity of integrin beta protein. The protein-ligand docking performance showed that the best interaction is between AC1NS627 and integrin beta protein. So we can conclude that this molecule could be further studied for its lead drug likeness.

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