alexa Analysis of Binding Properties of Phosphoinositide 3-kinase Through <em>In silico</em> Molecular Docking | Open Access Journals
ISSN: 0974-276X
Journal of Proteomics & Bioinformatics
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Analysis of Binding Properties of Phosphoinositide 3-kinase Through In silico Molecular Docking

P. Daisy*, R. Sasikala, A. Ambika

Bioinformatics centre (BIF), Department of Biotechnology & Bioinformatics, Holy Cross College, Tepakulam, Tiruchirapalli-620002, India

*Corresponding Author:
Dr. P. Daisy
Co-coordinator, Bioinformatics centre (BIF)
Department of Biotechnology & Bioinformatics
Holy Cross College, Tepakulam,Tiruchirapalli-620002
Tel: 0431-2700637
Fax: 0431-2713312
E-mail: [email protected]

Received Date: May 12, 2009; Accepted Date: June 18, 2009; Published Date: June 18, 2009

Citation: Daisy P, Sasikala R, Ambika A (2009) Analysis of Binding Properties of Phosphoinositide 3-kinase Through In silico Molecular Docking. J Proteomics Bioinform 2: 274-284. doi: 10.4172/jpb.1000086

Copyright: © 2009 Daisy P, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Proteomics & Bioinformatics

Abstract

Phosphoinositide 3-kinases (PI3-kinases) are increasingly considered to have a key role in intracellular signal transduction in health and disease. Particularly the enzymes plays vital role in wide range of cancer such as breast, ovarian, myeloid leukemia, prostate, Small Cell Lung cancer (SCLC) etc., Compounds such as Wortmannin, LYS2002 are the inhibitors of PI3-kinases but these compounds shown adverse side effects . Hence five natural flavanoids having inhibitory effects on PI3-kinase namely Andrographolide, Kaempferol, Luteolin, Quercetin and Gingerol were taken for in silico prediction of binding affinities of the protein PI3- kinase. Our reports can be used to develop new inhibitors with better binding affinities towards the protein PI3-kinase protein. For the binding analysis the catalytic subunit of the protein PI-3 Kinase p110? was taken for the study as it considered being a potential target in cancer treatment.

Keywords

PI-3 Kinase Inhibitors; In silico binding affinities; Molecular Docking

Introduction

Phosphoinositide 3-kinases (PI 3-kinases or PI3Ks) are a family of related enzymes that are capable of phosphorylating the 3 position hydroxyl group of the inositol ring of phosphatidylinositol (PtdIns). PTEN/PI3K/AKT constitutes an important pathway regulating the signaling of multiple biological processes such as apoptosis, metabolism, cell proliferation and cell growth (Carnero A et al., 2008). Genomic mutations, alterations of the PI3K-AKT regulatory network, underlie such diseases as cancer, glucose intolerance (diabetes mellitus), schizophrenia, and/or autoimmune diseases (Noguchi M et al., 2008). In particular the PI3-kinases generate and convey signals that have an important role in cancer (Stein 2001). PI3-kinases are ubiquitously expressed, are activated by a high proportion of cell surface receptors, especially those linked to Tyrosine kinases, and influence a bewildering variety of cellular functions and events. The majority of the research on PI 3-kinases has focused on the Class I PI 3-kinases. Class I PI 3-kinases are composed of a catalytic subunit known as p110. Many literature studies has proven that PI3 Kinases to be the most significant contributor to activation of cancer in human such as ovarian cancer (Bellacosa et al., 1995; Yuan et al., 2000; Shayesteh et al., 1999), breast cancers (Nakatani et al., 1999), myeloid leukaemia (Vanhaesebroeck et al., 1999), glioblastoma, prostatic, endometrial and endometroid ovarian cancer [Ali et al., 1999].Apart from these frequent and early involvement of the PI3-kinase pathway was observed in lung cancer specifically small cell lung cancer (SCLC)(Pierre et al., 2004; Moore et al., 1998). A number of compounds such as wotmannin (Powis et al., 1994), demethoxyviridin (Woscholski et al., 1994), LY294002 (a morpholino derivative of the broad-spectrum kinase inhibitor quercetin (Vlaho et al., 1994) that inhibit PI3-kinases have been identified. It is important to emphasize that wortmannin and, particularly, LY294002 display little selectivity within the PI3-kinase family. Both compounds lose specificity at high concentrations and showed less potent for this group of enzymes. More over Inhibitors of PI3 Kinase have unacceptable toxicity if administered continuously in protein trafficking and in DNA repair and cell cycle checkpoint control is likely to be undesirable.The potential toxicity of PI3-kinase inhibitors can probably best be limited by compounds extracted from natural source. Flavonoids provide a large number of interesting natural compounds that are consumed daily and exhibit more or less potent and selective effects on some signaling enzymes as well as on the growth and proliferation of certain malignant cells in vitro (Laurence et al., 1999). In silico molecular docking is one of the most powerful techniques to discover novel ligands for receptors of known structure and thus play a key role in structure-based drug design (Brooijmans et al., 2003).Investigators often use docking computer programs to find the binding affinity for molecules that fit a binding site on the receptor. Hence here we have taken Insilico molecular docking to analyze the binding properties of the enzyme PI3 kinase with the flavanoids.

Flavanoids Taken for Binding Analysis with PI3 Kinase

Natural flavanoids such as Andrographolide from Andrographis paniculata, Gingerol from Zingiber officinale , Kaempferol from tea, broccoli, Delphinium, Witch-hazel, grapefruit etc. , Luteolin from Chromolaena odorata and Quercetin from Allium cepa were taken. All these compounds were shown to exhibit anticarcinogenic, anti diabetic and antimicrobial effects and their references were shown in Table I. For all the four compounds namely andrographolide, kaempferol, luteolin and quercetin except gingerol literature proof has been available to shown inhibitory effects towards PI3 kinase except for Glycerol. Hence the compounds were taken for the study of binding affinities towards the protein PI3 kinase, despite their lack of strict specificity, the study provided valuable bases for the prediction of natural compounds could be specific inhibitors of PI 3-kinase. Through this we could predict that these compounds exhibit diverse effects by inhibiting PI-3 kinase.

S.No Compound Biological Effects
1 Andrographolide PI3 kinase (Yu BC et al., 2003).
Anti Diabetic(Tsai HR et al., 2004)
Antioxidant(Lin FL et al., 2009)
Anticancer(Rajagopal S et al., 2003)
Antimicrobial(Chang RS et al., 1991)
2 Kaempferol PI3 Kinase (Labbé D et al., 2009 )
Antimicrobial(Tereschuk ML et al.,2004 )
Anticancer(Jeong JC et al., 2009)
Antidiabetic(Fang XK et al., 2008 )
Antioxidant(Verma AR et al., 2009)
3 Luteolin PI3 Kinase (Zhong Yao Cai, 2006)
Antimicrobial, Anticancer, Antidiabetic, Antioxidant
(López-Lázaro M, 2009 )
4 Gingerol Antimicrobial(Park M et al., 2008 )
Anticancer(Lee SH et al., 2008 )
Antidiabetes(Sekiya K et al., 2004)
Antioxidant(Masuda Y et al., 2004 )
5 Quercetin PI3 Kinase (Labbé D et al., 2009 )
Antimicrobial(Tereschuk ML et al.,2004 )
Antidiabetic(Fang XK et al., 2008 )
Antioxidant(Verma AR et al., 2009
Anticancer (Kim EJ et al., 2008)

Table I: Inhibitors taken for the study and their Multiple Biological Effects.

Materials and Methods

Bioinformatics online databases such as pubmed, PDB and Pubchem, were used. PubMed database developed by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM) is designed to provide access to citations from biomedical journals. From PubMed we have collected literatures on PI3 Kinase, and flavanoids.Understanding the interactions between proteins and ligands is crucial for the pharmaceutical and functional food industries. The experimental structures of these protein/ligand complexes are usually obtained, under highly expert control, by time-consuming techniques such as X-ray crystallography or NMR. These techniques are therefore not suitable for routinely screening the possible interaction between one receptor and thousands of ligands. To overcome this limitation, computational algorithms (i.e. docking algorithms)have been developed that uses the individual structures of the receptor and ligand to predict the structure of their complex.

Docking

A number of powerful software programs, e.g. AutoDock, HEX, GOLD, FlexX, DOCK, Glide, Surflex, LigandFit, have been developed over the past several decades to carry out docking calculations, and good success in both binding mode and binding affinity prediction has often been achieved in selected test cases. We used a new shape-based method,LigandFit, for accurately docking ligands into protein active sites. The method employs a cavity detection algorithm for detecting invaginations in the protein as candidate active site regions. A shape comparison filter is combined with a Monte Carlo conformational search for generating ligand poses consistent with the active site shape. Candidate poses are minimized in the context of the active site using a gridbased method for evaluating protein-ligand interaction energies. The method appears quite promising, reproducing the X-ray structure ligand pose within an RMSD of 2A. A high-throughput screening study applied to the thymidine kinase receptor is also presented in which LigandFit, when combined with LigScore, an internally developed scoring function, yields very good hit rates for a ligand pool seeded with known actives [Venkatachalam, C.M. , et al. , 2003].Thus docking analysis of Gingerol, kaempferol, luteolin, andrographolide and Quercetin with PI3 Kinase was carried out by Ligand Fit of Discovery studio (Version 1.7, Accelry’s Software Inc.). The software allows us to virtually screen a database of compounds and predict the strongest binders based on various scoring functions. It explores the ways in which these five molecules and the enzyme PI3 Kinase fit together and dock to each other well, like pieces of a three-dimensional jigsaw puzzle. The collection of Gingerol, kaempferol, luteolin, andrographolide and Quercetin and PI3 Kinase complexes was identified via docking and their relative stabilities were evaluated using their binding affinities.

Docking Protocol

Ligand Preparation

The three dimensional structures of anticancer compounds like Gingerol, kaempferol, luteolin, andrographolide and Quercetin were downloaded in .sdf format from Pubchem database. Hydrogen Bonds were added and the energy was minimized using CHARMm force field. Molecular weight, log P and number of Hydrogen-bond donors and acceptors for the active principles were noted (shown in Table III). All the five molecules were satisfied Lipinski’s drug properties and their two dimensional structures were shown in Figure 3.

S.No PDB ID Resolution Length Species
1 1E90 2.70 961 Sus scrofa
2 3CST 3.20 966 Homo sapiens
3 1E8Z 2.40 966 Homo sapiens
4 1E7V 2.40 961 Sus scrofa
5 1E8W 2.50 961 Sus scrofa
6 2CHX 2.50 966 Homo sapiens
7 3CSF 2.80 966 Homo sapiens
8 3DBS 2.80 960 Homo sapiens
9 3ENE 2.40 959 Homo sapiens
10 2CHZ 2.60 966 Homo sapiens
11 2CHW 2.60 966 Homo sapiens
12 2A5U 2.70 966 Homo sapiens
13 1E8X 2.20 961 Sus scrofa
14 3DPD 2.85 966 Homo sapiens
15 2V4L 2.50 966 Homo sapiens
16 2A4Z 2.90 966 Homo sapiens
17 1HE8 3.00 965 Homo sapiens
18 1E7U 2.00 961 Sus scrofa

Table II: Summary of three dimensional structures available for PI3-kinase in ProteinDataBank

S.No Molecules Molecular weight
(<=500)g/mol
XLog P (<=5) H-Donor H-acceptor
1 Andrographolide 350.4492 [g/mol] 2.9 3 5
2 Gingerol 294.38594 [g/mol] 3 2 4
3 Kaempferol 286.2363 [g/mol] 1.9 4 6
4 Luteolin 286.2363 [g/mol] 0.7 4 6
5 Quercetin 302.2357 [g/mol] 1.1 5 7

Table III: L ipinski properties of the five flavanoids (Values obtained from Pubchem) For each molecule, many orientations and conformations are sampled; based on these configurations, each molecule is scored for complementarity to the receptor and ranked relative to the other members of the database.

Protein Selection

Sequences of Phosphoinositide 3-kinases catalytic subunit alpha isoform were retrieved from swissprot for various species in FASTA Format for multiple sequence alignment and for phylogenetic analysis using ClustalW. Phylogenetic analysis revealed that Mus musculus and Bovine were closely related to Human (Shown in Fig 1), but the three dimensional structures were available only for Human and Sus scrofa. Hence their structures were retrieved and compared for further analysis. There are several PDB structures available for the same protein and they are listed in table II along with their resolution and length. The PDB structure which was chosen for our study has a good resolution of 2.00 when compared to other structures. To predict the binding mechanism accurately, PDB structure (PDB ID: 1E7U) of Sus scrofa PI3 Kinase was chosen for the interaction analysis which is of 961 aminoacids. The PDB structure was also compared using the DALI server to find the structural alignment using the RMSD value as shown in Fig 2. As the RMSD score for the three dimensional structures of human PI3 kinase and Sus scrofa were below 2.00A0, the structure from Sus scrofa could be taken for further analysis.

proteomics-bioinformatics-phylogentic-analysis

Figure 1: Phylogentic analysis of Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha isoform PI3 kinase sequences.

proteomics-bioinformatics-structural-alignment

Figure 2: Structural alignment results using DALI.

proteomics-bioinformatics-molecular-structures

Figure 3: Molecular structures of the Flavanoids
Two dimensional structures of A)Andrographolide, B) Gingerol, C) Kaempferol, D) Luteolin E) Quercetin (retrieved from NCBI-Pubchem Compound Database).

Protein Preparation

The ligands and crystallographic water molecules were removed from the protein; and the chemistry of the protein was corrected for missing hydrogen. Crystallographic disorders and unfilled valence atoms were corrected using alternate conformations and valence monitor options. Following the above steps of preparation, the protein was subjected to energy minimization using the CHARMm force field.

Docking Studies

The active site of the protein was first identified and it is defined as the binding site resulted in a cavity size of 3475 point units. There is evidence that wortmannin alkylates a lysine residue at the putative ATP binding site of p110α (Wymann et al. 1996). LY294002, in contrast, is a pure competitive inhibitor of ATP. The X-ray structure of wortmannin, LY294002 and several broad-spectrum kinase inhibitors, including quercetin in complex with p110, confirms the mechanism of inhibition and offers a basis for designing more specific compounds (Walker et al. 2000). Thus Binding sites were defined based on the ligands already present in the PDB file (i.e. ATP binding site region) which were followed by site sphere definition. Here site 1 was chosen as the binding site and the site sphere size was set to (434.375 A° ^ 3, Partition level 1).The determination of the ligand binding affinity was calculated using LigScore and PLP1,JAIN and Dock score were used to estimate the ligand-binding energies. Apart from these, other input parameters for docking were set as default options.

Results and Discussion

Molecular Docking continues to holds great promise in the field of Computer based drug design which screens small molecules by orienting and scoring them in the binding site of a protein. As a result novel ligands for receptors of known structure were designed and their interaction energies were calculated using the scoring functions (Irwin et al., 2002). Number of reports citing successful application of CADD in developing specific drugs in different therapeutic areas is expanding rapidly. A very interesting example which can also serve as a proof of principle of the in silico approach involves a type I TGF β receptor kinase inhibitor. The same molecule (HTS-466284/LY-364947), a 27 nM inhibitor, was discovered independently using virtual screening by Biogen IDEC (Singh J et al., 2003) and traditional enzyme and cellbased high-throughput screening (Sawyer JS et al., 2003). Another in silico modeling drug development program led to clinical trials of a novel, potent, and selective anti-anxiety,anti-depression 5-HT1A agonist in less than 2 years from the start and requiring less than 6 months of lead optimization and synthesis of only 31 compounds (Becker OM et al., 2006). It is estimated that docking programs currently dock 70 – 80% of ligands correctly (Congreve M et al., 2005).

Validation of Docking Results

To ensure that the ligand orientation obtained from the docking studies were likely to represent valid and reasonable binding modes of the inhibitors, the LigandFit program docking parameters had to be first validated for the crystal structure’s Active site (PDBid 1E7U). Protein Utilities and Health protocol of Discovery’s studio was used to find out the active sites in the structure and it was found that the active site contains amino acids such as ASP950 TYR867 MET804 GLU880 LYS808 VAL882 SER806 ILE831 ILE879 ASP964 LYS833 TRP812. Results of docking showed that LigandFit determined the optimal orientation of the docked inhibitor, exactly to these active sites.The low RMS deviation of between the docked and crystal ligand coordinates indicate very good alignment of the experimental and calculated positions especially considering the resolution of the crystal structure (2.00Å) shown in table IX.

Here top ranked ligands were taken for binding affinity studies. The validation process consisted of two parts: (i) Hydrogen bond details of the top-ranked docked pose and (ii) prediction of Binding energy between the docked ligand and the enzyme using various score calculated using Discovery studio (DJD, 2 LigScore2, 3 LigScore1, 3 PLP, 45 PMF, 46 and JAIN47 scores were taken for the analysis.

A close view of the binding interactions of PI3 kinase with the flavanoids Andrographolide, Kaempferol, Quercetin Luteolin and Gingerol were shown in Fig. 4. Ligand is coloured in Yellow (in balland-stick drawing) where as amino acids involved in Hydrogen bonding where shown in blue colour.As shown in Fig. 4A, there are five hydrogen bonds (shown as green dotted lines) formed between the compound Andrographolide and PI3 Kinase. Those residues involved in forming hydrogen bonds with the enzyme were: (2 hydrogen bonds) Lys 802, Lys 807, Asp-950, Asp-964. Quercetin forms two hydrogen bonds (shown as green dotted lines in Fig.4B) and the residues involved in forming the hydrogen bonds from the enzyme were: Lys-807 and ASP- 950. Kaempferol forms two hydrogen bonds (shown as green dotted lines in Fig.4C) and here the residues involved in forming the hydrogen bonds from the enzyme were: Asp- 950 and Asp 964. Table 4 showed the detailed information of the hydrogen bonds. Luteolin and Gingerol forms three hydrogen bonds (shown as green dotted lines in Fig.4D and Fig 4E) and the residues involved in forming the hydrogen bonds from the enzyme were: Lys-807 and Asp-950. The detailed atoms in forming the hydrogen bonds are given in Table IV, V,VI,VII and VIII for each flavanoids separately, which may provide useful information for in-depth understanding binding mechanism of the compound to the active site of the protein.

Andrographolide:

Amino acid Atom in amino acid Position Position Atom in Ligand Hydrogen Bond length(A0)
LYS HZ1 802 O5 2.228000
LYS HZ1 802 O3 1.675000
LYS HZ3 807 O1 2.474000
ASP OD2 964 H51 2.474000
ASP OD2 950 H47 1.278000

Table IV: Hydrogen Bond interactions between the enzyme PI3 Kinase and the ligand Andrographolide (Results were analysed using Hbond Monitor of Discovery studio.2.1). Second and third column represents the atoms of amino acid and ligand involved in hydrogen bond formation. Position represents the position of aminoacid in the enzyme.

Kaempferol

Amino acid Atom in amino acid Position Atom in Ligand Hydrogen Bond length(A0)
ASP OD2 950 H29 1.099000
ASP OD2 964 H30 1.042000

Table V: Hydrogen Bond interactions between the enzyme PI3 Kinase and the ligand Kaempferol (Results were analysed using Hbond Monitor of Discovery studio.2.1). Second and third column represents the atoms of amino acid and ligand involved in hydrogen bond formation. Position represents the position of aminoacid in the enzyme.

Luteolin

Amino acid Atom in amino acid Position Atom in Ligand Hydrogen Bond length(A0)
LYS HZ2 807 O6 2.484000
LYS HZ3 807 O5 2.02000
ASP OD2 950 H31 1.098000

Table VI: Hydrogen Bond interactions between the enzyme PI3 Kinase and the ligand Luteolin (Results were analysed using Hbond Monitor of Discovery studio.2.1). Second and third column represents the atoms of amino acid and ligand involved in hydrogen bond formation. Position represents the position of aminoacid in the enzyme.

Quercetin

Amino acid Atom in amino acid Position Atom in Ligand Hydrogen Bond length(A0)
LYS HZ3 807 O6 1.958000
ASP OD2 950 H32 1.056000

Table VII: Hydrogen Bond interactions between the enzyme PI3 Kinase and the ligand Andrographolide (Results were analysed using Hbond Monitor of Discovery studio.2.1). Second and third column represents the atoms of amino acid and ligand involved in hydrogen bond formation. Position represents the position of aminoacid in the enzyme.

Gingerol

Amino acid Atom in amino acid Position Atom in Ligand Hydrogen Bond length(A0)
LYS O3 807 HZ3 2.459000
LYS O3 807 HZ1 1.888000
ASP OD2 950 H44 1.108000

Table VIII: Hydrogen Bond interactions between the enzyme PI3 Kinase and the ligand Andrographolide (Results were analysed using Hbond Monitor of Discovery studio.2.1). Second and third column represents the atoms of amino acid and ligand involved in hydrogen bond formation. Position represents the position of aminoacid in the enzyme.

Name Ligscore1 Ligscore2 -PLP1 -PLP2 JAIN -PMF Dock Score
Andrographolide 4.58 4.33 42.84 47.06 2.4 111.9 62.735
Kaempferol 3.15 2.24 23.43 29.62 -0.89 66.36 65.058
Luteolin 4 3.7 30.73 37.11 1.23 56.66 69.14
Quercetin 3.76 3.81 35.11 42.99 0.74 57.5 71.407
Gingerol 3.64 4.07 41.25 44.27 -0.69 78.3 62.952

Table IX: Summary of docking information of the Top ranked poses of each flavanoids (values copied from the tablebrowser window of Discovery studio2.1).

proteomics-bioinformatics-summary-docked

Figure 4 : Summary of Docked Pose of the five anticancer compounds
Docking models of (A) Andrographolide (B) Kaempferol (C) Luteolin and (D) Quercetin and (E) Gingerol with PI3- kinase. The green dot lines denoted the hydrogen bonds. All the amino acid residues which involved in molecular interactions were shown in Blue color and the Ligands were shown in yellow color.

Docking Score and RMSD Values

As a result of docking there were 10 different conformations were generated for andrographolide, Quercetin, Kaempferol, luteolin and for gingerol. But only for top ranked docked complex the scores were copied from the table browser view of Discovery studio for binding affinity analysis. Table IX shown the different score values of top ranked ligands. The score values include Ligscore1&2 (Protein- Ligand Affinity Energy)( Krammer et al ., 2005), PLP1, PLP2 (Steric and H-bonding intermolecular function, Higher PLP scores indicate stronger receptor-ligand binding (larger pKi values)) (Gehlhaar et al., 1995,1999), JAIN(sum of five interaction terms namely Lipophilic interactions,Polar attractive interactions ,Polar repulsive interactions ,Solvation of the protein and ligand ,An entropy term for the ligand)( Jain 1996), PMF(developed based on statistical analysis of the 3D structures of protein-ligand complexes, scores are calculated by summing pairwise interaction terms over all interatomic pairs of the receptor-ligand complex, A higher score indicates a stronger receptor-ligand binding affinity)( Muegge 2006; Muegge et al., 1999) and Dockscore(Candidate ligand poses are evaluated and prioritized according to the DockScore function) . The determination of the ligandbinding affinity was calculated using the shape-based interaction energies of the ligand with the protein. The two scoring methodologies namely LigScore and PLP1 were used to estimate the ligand-binding energies. Larger score value indicates better ligand-binding affinity).

Conclusion

The Protein-Ligand interaction plays a significant role in structural based drug designing. In the present work we have taken the enzyme PI3 Kinase and the drugs to explore the binding mechanism of flavanoids to the PI3 kinase enzyme. They are Andrographolide, Gingerol, Kaempferol, luteolin and Quercetin. When the enzyme docked to the five anticancer compounds the scores obtained were shown in Table…:

Andrographolide (Dock score = 62.735), Quercetin (Dock score= 71.407), Kaempferol (Dock score= 65.058), Luteolin (Dock score= 69.14) and gingerol (Dock score=62. 952). Based on all the Dock score values it was predicted that the ligands Quercetin and Luteolin were have similar and good binding affinities towards the protein. It was also predicted that the compound gingerol showed good binding affinities towards the protein when compared to others. For all the four compounds like kaempferol, Quercetin, luteolin and Andrographolide literature proofs were available to indicate that they inhibit PI3-Kinase but for gingerol there is no such a proof is available. Here through in silico approach it was predicted that the compound gingerol also shown to inhibit PI3-Kinase as it had good Ligscore and PLP1 when compared to Quercetin and Luteolin. Hydrogen bond formation also makes important contributions to the interactions between ligand and the enzyme. Here a maximum of four hydrogen bonds were formed between the protein and the ligand Andrographolide followed by three hydrogen bonds were formed between the enzyme and the ligand Gingerol and luteolin. Thus the concept of protein-Ligand interaction helps in analyzing the binding properties of the protein PI3-Kinase with its inhibitors. The study report also concluded that the residues Lys 802, Lys-807, Asp-950, Asp 964 plays an important role in binding mechanism. Hence drugs such as Luteolin and Gingerol which were shown similar binding mechanism and good docking score to quercetin could be the lead one to target the PI3 Kinase. Our results provide insight into the structural requirement for the activity of the inhibitor and the most favorable binding mode of the top ranking compounds will be useful in designing new derivatives of Luteolin and gingerol as PI3 kinase inhibitors similar to the quercetin derivative of LYS2002.

Future Perspectives

Understanding the interactions between proteins and ligands is crucial for the pharmaceutical and functional food industries. The experimental structures of these protein/ligand complexes are usually obtained, under highly expert control, by time-consuming techniques such as X-ray crystallography or NMR. Molecular modeling and molecular docking methods still have a long way to run before producing completely reliable results. This could be achieved by NMR screening remains a ultifaceted and unique technique that is sensitive to both structure and dynamics and that can monitor the binding of low molecular weight ligands to biological macromolecules in the early stages of drug discovery due to its ability to detect even very weak binders.

Acknowledgement

This project is supported by DBT (Department of Biotechnology), Government of India, New Delhi

References

  1. Amit KT, Madhumita R, Bhattacharya RK “Natural products as inducers of apoptosis: Implication for cancer therapy and prevention”. » CrossRef » Google Scholar
  2. Ali IU, Schriml LM, Dean M (1999) Mutational spectra ofPTEN/ MMAC1 gene: a tumor suppressor with lipid phosphatase activity. J Natl Cancer Inst 91: 1922-1932. » CrossRef » PubMed » Google Scholar
  3. Bellacosa A, de Feo D, Godwin AK, Bell DW, Cheng JQ, (1995) Molecular alterations ofthe AKT2 oncogene in ovarian and breast carcinomas. Int J Cancer 64: 280-285. » CrossRef » PubMed » Google Scholar
  4. Becker OM, Dhanoa DS, Marantz Y, Chen D, Shacham S, (2006) An Integrated in Silico 3D Model-Driven Discovery of a Novel, Potent, and Selective Amidosulfonamide 5-HT1A Agonist (PRX-00023) for the Treatment of Anxiety and Depression. J Med Chem 49:3116-3135. » CrossRef » PubMed » Google Scholar
  5. Brooijmans N, Kuntz ID (2003) Molecular recognition and docking algorithms. Annu Rev Biophys Biomol Struct 32: 335-373. » CrossRef » PubMed » Google Scholar
  6. Carnero A, Blanco-Aparicio C, Renner O, Link W, Leal JF (2008) The PTEN/PI3K/AKT signalling pathway in cancer, therapeutic implications. Curr Cancer Drug Targets 8: 187-98. » CrossRef » PubMed » Google Scholar
  7. Chang RS, Ding L, Chen GQ, Pan QC, Zhao ZL, (1991) Dehydroandrographolide succinic acid monoester as an inhibitor against the human immunodeficiency virus. Proc Soc Exp Biol Med 197: 59-66. » PubMed » Google Scholar
  8. Congreve M, Murray CW, Blundell TL ( 2005) Structural biology and drug discovery. Drug Discov Today 10: 895-907. » CrossRef » PubMed » Google Scholar
  9. Fang XK, Gao J, Zhu DN (2008) Kaempferol and quercetin isolated from Euonymus alatus improve glucose uptake of 3T3-L1 cells without adipogenesis activity. Life Sci 82: 615-22. » CrossRef » PubMed » Google Scholar
  10. Gehlhaar DK, Verkhivker GM, Rejto PA, Sherman CJ, Fogel DB, ( 1995) Molecular Recognition of the Inhibitor AG-1343 by HIV-1 Protease: Conformationally Flexible Docking by Evolutionary Programming. Chemistry & Biology 2: 317. » CrossRef » PubMed » Google Scholar
  11. Gamet-Payrastre L, Manenti S, Gratacap MP, Tulliez J, Chap H, et al. (1999) Flavonoids and the inhibition of PKC and PI 3-kinase. General Pharmacology 32: 279- 286. » CrossRef » PubMed » Google Scholar
  12. Gehlhaar DK, Bouzida D, Rejto PA (1999) Rational Drug Design: Novel Methodology and Practical Applications. American Chemical Society 292-311.
  13. Irwin JJ, Lorber DM, McGovern SL, Wei B, Shoichet BK (2002 ) Computational Nanoscience and Nanotechnology ISBN 0-9708275-6-3.
  14. Jain AN (1996) Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities. J Comput Aided Mol Design 10: 427-440. » CrossRef » PubMed » Google Scholar
  15. Jeong JC, Kim MS, Kim TH, Kim YK (2009) Kaempferol induces cell death through ERK and Aktdependent down-regulation of XIAP and survivin in human glioma cells. Neurochem Res May 34: 991-1001. » CrossRef » PubMed » Google Scholar
  16. Krammer A, Kirchhoff PD, Jiang X, Venkatachalam CM, Waldman M (2005) LigScore: a novel scoring function for predicting binding affinities. J Mol Graph Model 23: 395-407. » CrossRef » PubMed » Google Scholar
  17. Kim EJ, Choi CH, Park JY, Kang SK, Kim YK (2008) Underlying mechanism of quercetin-induced cell death in human glioma cells. Neurochem Res 33: 971-9. » CrossRef » PubMed » Google Scholar
  18. Labbé D, Provençal M, Lamy S, Boivin D, Gingras D, (2009) The flavonols quercetin, kaempferol, and myricetin inhibit hepatocyte growth factor-induced medulloblastoma cell migration. J Nutr 139: 646-52. » CrossRef » PubMed » Google Scholar
  19. Lee SH, Cekanova M, Baek SJ (2008) Multiple mechanisms are involved in 6-gingerol-induced cell growth arrest and apoptosis in human colorectal cancer cells. Mol Carcino 47: 197-208. » CrossRef » PubMed » Google Scholar
  20. López-Lázaro M (2009) Distribution and biological activities of the flavonoid luteolin. Mini Rev Med Chem 9: 31-59. » CrossRef » PubMed » Google Scholar
  21. Lin FL, Wu SJ, Lee SC, (2009) Antioxidant, antioedema and analgesic activities of Andrographis paniculata extracts and their active constituent andrographolide. Phytother Res. » CrossRef » PubMed » Google Scholar
  22. Masuda Y, Kikuzaki H, Hisamoto M, Nakatani N (2004) Antioxidant properties of gingerol related compounds from ginger. Biofactors 21: 293-6. » CrossRef » PubMed » Google Scholar
  23. Moore SM, Rintoul RC, Walker TR, Chilvers ER, Haslett C, (1998) The presence of a constitutively-active phosphoinositide 3-kinase in small cell lung cancer cells mediates anchorage-independent proliferation via a protein kinase B and p70s6k-dependent pathway. Cancer Res 58: 5239-5247. » CrossRef » PubMed » Google Scholar
  24. Muegge I and Martin YC (1999) A General and Fast Scoring Function for Protein-Ligand Interactions: A Simplified Potential Approach. J Med Chem 42: 791. » CrossRef » PubMed » Google Scholar
  25. Muegge I (2006) PMF Scoring Revisited. J Med Chem 49: 5895-5902. » CrossRef » PubMed » Google Scholar
  26. Nakatani K, Thompson DA, Barthel A, Sakaue H, Liu W, (1999) Up-regulation ofAkt3 in estrogen receptor- deficient breast cancers and androgen-independent prostate cancer lines. J Biol Chem 274: 21528- 21532. » CrossRef » PubMed » Google Scholar
  27. NCBI-PubChem Compound database. [http:// pubchem.ncbi.nlm.nih.gov/]
  28. Noguchi M and Kinowaki K (2008) PI3K-AKT network roles in infectious diseases Kansenshogaku Zasshi 82: 161-7. » PubMed » Google Scholar
  29. Park M, Bae J, Lee DS (2008) Nov Antibacterial activity of [10]-gingerol and [12]-gingerol isolated from ginger rhizome against periodontal bacteria. Phytother Res 22: 1446-9. » CrossRef » PubMed
  30. Pierre PM, Peter MT, Yu S, Rahman SMJ, Yildiz P, (2004) Early Involvement of the Phosphatidylinositol 3-Kinase/Akt Pathway in Lung Cancer Progression. Am J Respir Crit Care Med 170: 1088-1094. » CrossRef » PubMed » Google Scholar
  31. Powis G, Bonjouklian R, Berggren MM, Gallegos A, Abraham R, (1994) Wortmannin, a potent and selective inhibitor of phosphatidylinositol-3-kinase. Cancer Res 54: 2419-2423. » CrossRef » PubMed » Google Scholar
  32. Protein Data Bank. [http://www.rcsb.org/pdb/home/ home.do]
  33. Pubmed
  34. Rajagopal S, Kumar RA, Deevi DS, Satyanarayana C, Rajagopalan R (2003) Andrographolide, a potential cancer therapeutic agent isolated from Andrographis paniculata. J Exp Ther Oncol 147-58. » CrossRef » PubMed » Google Scholar
  35. Sawyer JS, Anderson BD, Beight DW, Campbell RM, Jones ML, (2003) Synthesis and activity of new aryl- and heteroaryl-substituted pyrazole inhibitors of the transforming growth factor-beta type I receptor kinase domain. J Med Chem 46: 3953-3956. » CrossRef » PubMed » Google Scholar
  36. Stein RC (2001) Prospects for phosphoinositide 3-kinase inhibition as a cancer treatment. Endocr Relat Cancer 8: 237-248. » CrossRef » PubMed » Google Scholar
  37. Sekiya K, Ohtani A, Kusano S ( 2004) Enhancement of insulin sensitivity in adipocytes by ginger 22: 153-6. » CrossRef » PubMed » Google Scholar
  38. Shayesteh L, Lu Y, Kuo W, Baldocchi R, Godfrey T, (1999) PIK3CA is implicated as an oncogene in ovarian cancer. Nat Genet 21: 99-102. » CrossRef » PubMed » Google Scholar
  39. Singh J, Chuaqui CE, Boriack-Sjodin PA, Lee WC, Pontz T, (2003) Successful shape-based virtual screening: the discovery of a potent inhibitor of the type I TGFbeta receptor kinase (TbetaRI). Bioorg Med Chem Lett 13: 4355-4359. » CrossRef » PubMed » Google Scholar
  40. Tereschuk ML, Baigorí MD, De Figueroa LI, Abdala LR (2004) Flavonoids from Argentine Tagetes (Asteraceae) with antimicrobial activity. Methods Mol Biol 268: 317-30. » CrossRef » PubMed » Google Scholar
  41. Tsai HR, Yang LM, Tsai WJ, Chiou WF (2004) Andrographolide acts through inhibition of ERK1/2 and Akt phosphorylation to suppress chemotactic migration. Eur J Pharmacol 498: 45-52. » CrossRef » PubMed » Google Scholar
  42. Vanhaesebroeck B, Waterfield MD (1999) Signaling by distinct classes of phosphoinositide 3-kinases. Exp Cell Res 253: 239-254. » CrossRef » PubMed » Google Scholar
  43. Venkatachalam CM, Jiang X, Oldfield T, Waldman M (2003) LigandFit: A Novel Method for the Shape-Directed Rapid Docking of Ligands to Protein Active Sites. J Mol Graph Modell 21: 289-307. » CrossRef » PubMed » Google Scholar
  44. Verma AR, Vijayakumar M, Mathela CS, Rao CV (2009) In vitro and in vivo antioxidant properties of different fractions of Moringa oleifera leaves. Food Chem Toxicol. » CrossRef » PubMed
  45. Vlahos CJ, Matter WF, Hui KY, Brown RF (1994) A specific inhibitor of phosphatidylinositol 3-kinase, 2-(4- morpholinyl)- 8-phenyl-4H-1–benzopyran-4-one (LY294002). J Biol Chem 269: 5241-5248. » CrossRef » PubMed » Google Scholar
  46. Woscholski R, Kodaki T, McKinnona M, Waterfield MD, Parker PJ (1994) A comparison of demethoxyviridin and wortmannin as inhibitors of phosphatidylinositol 3-kinase. FEBS Letters 342: 109-114. » CrossRef » PubMed » Google Scholar
  47. Yu BC, Hung CR, Chen WC, Cheng JT (2003) Antihyperglycemic effect of andrographolide in streptozotocin-induced diabetic rats. Planta Med 69: 1075-9. » CrossRef » PubMed » Google Scholar
  48. Yuan ZQ, Sun M, Feldman RI, Wang G, Ma X, (2000) Frequent activation ofAKT2 and induction ofapoptosis by inhibition Of phosphoinositide- 3-OH kinase/ Akt pathway in human ovarian cancer. Oncogene 19: 2324-2330. » CrossRef » PubMed » Google Scholar
  49. Zhong YC (2006) Effect of three flavones on enzyme activity of recombinant human phosphoinositide 3-kinase p110beta catalytic subunit 29: 33-6. » PubMed » Google Scholar
Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

  • 9th International Conference on Bioinformatics
    October 23-24, 2017 Paris, France
  • 9th International Conference and Expo on Proteomics
    October 23-25, 2017 Paris, France

Article Usage

  • Total views: 11695
  • [From(publication date):
    June-2009 - Jul 23, 2017]
  • Breakdown by view type
  • HTML page views : 7913
  • PDF downloads :3782
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords