Open Access

Comparative modeling and docking studies of p16ink4/Cyclin D1/Rb pathway genes in lung cancer revealed functionally interactive residue of RB1 and its functional partner E2F1

  • Syeda Naqsh e Zahra1,
  • Naureen Aslam Khattak2 and
  • Asif Mir1Email author
Theoretical Biology and Medical Modelling201310:1

DOI: 10.1186/1742-4682-10-1

Received: 23 October 2012

Accepted: 11 December 2012

Published: 1 January 2013

Abstract

Background

Lung cancer is the major cause of mortality worldwide. Major signalling pathways that could play significant role in lung cancer therapy include (1) Growth promoting pathways (Epidermal Growth Factor Receptor/Ras/ PhosphatidylInositol 3-Kinase) (2) Growth inhibitory pathways (p53/Rb/P14ARF, STK11) (3) Apoptotic pathways (Bcl-2/Bax/Fas/FasL). Insilico strategy was implemented to solve the mystery behind selected lung cancer pathway by applying comparative modeling and molecular docking studies.

Results

YASARA [v 12.4.1] was utilized to predict structural models of P16-INK4 and RB1 genes using template 4ELJ-A and 1MX6-B respectively. WHAT CHECK evaluation tool demonstrated overall quality of predicted P16-INK4 and RB1 with Z-score of −0.132 and −0.007 respectively which showed a strong indication of reliable structure prediction. Protein-protein interactions were explored by utilizing STRING server, illustrated that CDK4 and E2F1 showed strong interaction with P16-INK4 and RB1 based on confidence score of 0.999 and 0.999 respectively. In order to facilitate a comprehensive understanding of the complex interactions between candidate genes with their functional interactors, GRAMM-X server was used. Protein-protein docking investigation of P16-INK4 revealed four ionic bonds illustrating Arg47, Arg80,Cys72 and Met1 residues as actively participating in interactions with CDK4 while docking results of RB1 showed four hydrogen bonds involving Glu864, Ser567, Asp36 and Arg861 residues which interact strongly with its respective functional interactor E2F1.

Conclusion

This research may provide a basis for understanding biological insights of P16-INK4 and RB1 proteins which will be helpful in future to design a suitable drug to inhibit the disease pathogenesis as we have determined the interacting amino acids which can be targeted in order to design a ligand in-vitro to propose a drug for clinical trials. Protein -protein docking of candidate genes and their important interacting residues likely to be provide a gateway for developing computer aided drug designing.

Background

Lung cancer is the most prevalent type of cancer which causes greater than millions worldwide cancer-related death [1, 2]. About 85−90% of lung cancer is caused due to tobacco smoking resulting in bronchogenic carcinoma [3, 4].

It has been classified into four distinct histological types, namely, small cell lung carcinoma (SCLC) and three non-small cell lung carcinoma (NSCLC) types; adenocarcinoma (ADC), squamous cell carcinoma (SQC), and large cell carcinoma (LCC) [5]. This type of cancer develops its proliferation through alterations in oncogenes, such as EGFR and tumor suppressor genes, such as TP53, RB1, CDKN2A/p16[1, 6]. Smoking is the most important root of all lung cancer types but small-cell lung cancer and squamous-cell carcinoma are more strongly caused by tobacco smoke. However, in patients who have never smoked in their life, adenocarcinoma is the most frequent type.

Epigenetic changes have also a profound impact in development of lung cancer. In the DNA promoter sequence of protein-coding genes, hypermethylation of cytosine in clusters of CpG dinucleotides can cause loss of gene expression. Research indicated that more than 80 genes are hypermethylated including tumour suppressor genes, e.g. p16INK4a in this type of cancer. Early detection of methylated DNA in sputum or blood of a patient can be an effective biomarker for diagnosis of lung cancer at initial stages. DNA promotor methylation and histone deacetylation are reversible processes; therefore, pharmacological inhibition can be used as therapeutic strategy to cure this disorder as this strategy may reverse gene silencing which will be beneficial in curing lung cancer [7].

Several different signalling pathways play significant roles in lung cancer therapy, for example, Growth promoting pathways (Epidermal Growth Factor Receptor/Ras/ PhosphatidylInositol 3-Kinase),Growth inhibitory pathways (p53/Rb/P14ARF, STK11), Apoptotic pathways (Bcl-2/Bax/Fas/FasL),DNA repair and immortalisation genes. Among these pathways, we have selected p16INK4/cyclin D1/Rb pathway for this particular study.

Expression profiling of eleven genes involved in this pathway was done by utilizing several databases like BioGPS, HPRD and GeneCards. Two candidate genes were short listed based on (i) Molecular Function, (ii) Biological process and (iii) Cellular location. Furthermore, common functional partners of selected pathway genes through STRING database were evaluated and it was found that three dimensional structures of these short listed proteins P16-INK4A and RB1 are not reported to have been resolved yet. Therefore, in current study, 3-D structures are predicted using a computational methodology i.e., homology modeling. Furthermore, Protein-protein docking was performed for proteins encoded by these genes.

Results

Templates selected for all proteins with optimal alignment of fist template and good alignment for remaining templates sorted by their overall quality Z-scores and E-values are listed in Table 1.
Table 1

Templates sorted by their overall quality Z-scores and E-values

Protein

Model ID

Z-Score

Alignment

BLAST E-value

RB1

4ELJ-A

−0.007

Good

0

P16-INK4A

1MX6-B

−0.289

Optimal

7e-032

 

1BLX-B

−0.528

Good

1e-034

 

1BD8-A

−0.620

Good

2e-036

 

1BI7-B

−1.084

Good

6e-042

 

2A5E-A

−2.110

Good

9e-050

Hybrid structure of RB1 protein was generated using best aligned parts of templates (Figure 1). Among the selected templates for RB1, 4ELJ was best scoring template used for modeling. Plot of its overall quality Z-score, shown per residue is displayed in Figure 2. For P16-INK4A protein, hybrid structure was generated using best aligned parts of all the five templates (Figure 3). The best scoring template used for modeling was 1MX6. Plot of its overall quality Z-score, shown per residue is displayed in Figure 4.
https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig1_HTML.jpg
Figure 1

Predicted Structure of RB1 using 4ELJ template with Z-Score of −0.007. Red color represent alpha helix, cyan color represents beta sheets and white represents loops.

https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig2_HTML.jpg
Figure 2

Overall quality of predicted RB1 model.

https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig3_HTML.jpg
Figure 3

Predicted Structure of P16-INK4A using 1MX6 template with Z-Score of −0.289. Red color represents alpha helix and loops are shown in white.

https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig4_HTML.jpg
Figure 4

Overall quality of predicted P16-INK4A model.

Protein-protein docking

GRAMM-X was utilized for protein-protein docking of two proteins RB1 and P16-INK4A for which no ligand was reported in literature/databases. Figure 5 and 6 shows the functional partners for these proteins obtained through STRING database. Table 2 shows the functional proteins which are found to be common between RB1 and P16-INK4A. Table 3 displays the protein and their functional interactors considered for docking. Table 4 shows the GRAMM-X docking results and Figure 7, 8, 9 and 10 displays the docked complexes and their interactions.
https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig5_HTML.jpg
Figure 5

Functional partners of P16-INK4A protein through STRING database.

https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig6_HTML.jpg
Figure 6

Functional partners of RB1 protein through STRING database.

Table 2

Common functional partners between RB1 and P16-INK4A

Proteins

Common functional partners

RB1 and P16-INK4A

CDK4

E2F1

MDM2

CCND1

Table 3

Proteins and interactors for protein-protein docking

Receptor protein

Functional interactors

P16-INK4A

CDK4

Rb1

E2F1

Table 4

Binding interactions for Rb1 and P16-INK4A

Receptor protein

Interacting protein

Interactions (Receptor residue →Interacting protein residue)

Bond distance

P16-INK4A

CDK4

Arg47:NH2 →Thr 104:OG1

3.6

Arg 80:NH2 → Trp 106: O

2.2

Met 1: N →Val 9: O

3.4

Cys72:N →Thr104:O

2.9

RB1

E2F1

Glu 864:O →Gln 290:2HEZ

2.5

Ser 567:OG →Arg 22:1HH1

2.7

Asp 36: OD2 →Lys 89:1HZ

2.2

Arg 861:2HHZ →Thr 285:OG1

1.9

https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig7_HTML.jpg
Figure 7

Docked complex of P16-INK4A and CDK4 showing P16-INK4A in blue and CDK4 in green.

https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig8_HTML.jpg
Figure 8

Interactions between P16-INK4A and CDK4. P16-INK4A is shown in lines and CDK4 in sticks format.

https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig9_HTML.jpg
Figure 9

Docked Complex of RB1 and E2F1showing RB1 in blue and E2F1 in green.

https://static-content.springer.com/image/art%3A10.1186%2F1742-4682-10-1/MediaObjects/12976_2012_Article_371_Fig10_HTML.jpg
Figure 10

Interactions between RB1 and E2F1 showing RB1 in lines and E2F1 in sticks format.

Discussion

In current study, 3D structures of the prioritized genes are predicted. RB1 and P16-INK4A are found to have expressions in lung tissue. Best docking complex of RB1 and E2F1 analysis suggested that , hydrogen bond interactions are found between O of Glu 864, Ser 567, Asp 36, Arg 861(of RB1) and H of Gln 290, Arg 22,Lys 89 and Thr 285 (of E2F1) with bond distances of 2.5,2.7,2.2 and 1.9 respectively. P16-INK4A and CDK4 protein-protein complex showed ionic bond interactions between Arg47, Arg80, Met 1, Cys72, Thr 104, Trp 106 and Val9 with 3.6, 2.2, 2.9 and 3.4 bond distances indicating the potential role of these residues in protein-protein interaction. Interactions of RB1 and E2F1 complexes will help in cell cycle arrest in G1 phase as RB1 acts as a transcription repressor of E2F1 target genes. The underphosphorylated, active form of RB1 interacts with E2F1 and represses its transcription activity, leading to cell cycle arrest. P16-INK4A and CDK4 interactions help to inhibit the proliferation of the cells . Results revealed through Protein-protein binding may provide a basis for designing a suitable drug for preventing this widely spreading disease by using the information retrieved about the amino acids involved in interactions with the respective proteins.

Conclusion

3-dimensional structure prediction of most plausible candidate genes proposed that it may be used further to understand the potential mechanism of lung cancer development and role of these proteins in causing abnormalities. By exploring protein- protein docking interaction with in wild type and mutant protein can open the new gate for computer aided drug designing for the better identification of potential drug inhibitor.

Materials & methods

Sequence retrieval and 3d model building

Sequences in FASTA format of P16-INK4 and RB1 were retrieved from NCBI (National Centre of Biotechnology Information) having accession numbers of P42771, P06400 and OMIM id’s of 614041 and 600160 respectively. Since the target sequence was the only available information, possible templates were identified by running 3 PSI-BLAST iterations to search the PDB for match (i.e. hits with an E-value below the homology modeling cutoff 0.5).

Comparative modeling approach was implemented to generate 3D structures of genes using YASARA software. YASARA generated a hybrid structure using 2–5 templates which are ranked on the basis of alignment score (PSI_BLAST) and structural quality (Z_Score) according to WHAT CHECK [8] obtained from the PDBFinder2 [8] database for all six candidate genes. Selected Parameters used by YASARA for structure prediction are mentioned in Table 5.
Table 5

Parameters selected for YASARA comparative modeling

Parameters

Value

PSI-Blast iteration

3

Psi-BLAST E-value

0.5

Oligomerization state

4

Templates

2–5

Alignment per template

5

Modeling Speed

Slow

Loop Samples

50

Model validation

YASARA softwares uses WHAT CHECK [8] obtained from the PDBFinder2 [8] database for generating plot of overall quality Z-score.

Molecular docking

Protein-protein docking of P16-INK4 and RB1 was carried out through GRAMM-X docking web server.

Protein-protein docking

Protein to be used as a ligand in protein-protein docking was retrieved from STRING database, an online database for physical (direct) and functional (indirect) protein–protein interactions [9] and its 3D structure was predicted using ab-initio approach through I-TASSER server. GRAMM-X docking server [10] was used for Protein-protein docking which generated a docked complex. Post docking analysis was carried out using Pymol software which is a molecular visualization system for use in structural biology which provides a user with high quality 3D images of small molecules and biological macromolecules, such as proteins.

Declarations

Acknowledgements

We are thankful to university institute of biochemistry and biotechnology (UIBB), PMAS- Arid Agriculture university for providing molecular modeling software, YASARA for insilico analysis.

Authors’ Affiliations

(1)
Department of Bioinformatics and Biotechnology, International Islamic University
(2)
Institute of Biochemistry and Biotechnology, Department of Biochemistry, Arid Agriculture University Rawalpindi

References

  1. Herbst RS, Heymach JV, Lippman SM: Lung cancer. N Engl J Med. 2008, 359: 1367-1380. 10.1056/NEJMra0802714.View ArticlePubMedGoogle Scholar
  2. Brenner DR, McLaughlin JR, Rayjean J, Hung RJ: Previous lung diseases and lung cancer risk: a systematic review and meta-analysis. PLoS One. 2011, 3: e17479-View ArticleGoogle Scholar
  3. Hackshaw KA, Law RM, Wald JN: The accumulated evidence on lung cancer and environmental tobacco smoke. BMJ. 1997, 315: 980-10.1136/bmj.315.7114.980.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Blot WJ, McLaughlin JK: Passive smoking and lung cancer risk: what is the story now. J Natl Cancer Inst. 1998, 90: 1416-1417. 10.1093/jnci/90.19.1416.View ArticlePubMedGoogle Scholar
  5. Kohno T, Otsuka A, Girard L, Sato M, Iwakawa R, Ogiwara H, Cespedes MS, Minna JD, Yokota J: A Catalog of Genes Homozygously Deleted in Human Lung Cancer and the Candidacy of PTPRD as a Tumor Suppressor Gene. Genes Chromosomes Cancer. 2010, 4: 342-352.Google Scholar
  6. Minna JD, Roth JA, Gazdar AF: Focus on lung cancer. Cancer Cell. 2002, 1: 49-52. 10.1016/S1535-6108(02)00027-2.View ArticlePubMedGoogle Scholar
  7. Belinsky AS, Liechty CK, Gentry DF, Wolf JH, Rogers J, Vu K, Haney J, Kennedy CT, Hirsch RF, Miller Y, Franklin AW, Herman GJ, Baylin BS, Bunn AP, Byers T: Promoter Hypermethylation of Multiple Genes in Sputum Precedes Lung Cancer Incidence in a High-Risk Cohort. Cancer Res. 2006, 66: 3338-3344. 10.1158/0008-5472.CAN-05-3408.View ArticlePubMedGoogle Scholar
  8. Hooft RW, Vriend G, Sander C, Abola EE: Errors in protein structures. Nature. 1996, 6580: 272-View ArticleGoogle Scholar
  9. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen JL, Mering VCL: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Research. 2011, 39: D561-D568. 10.1093/nar/gkq973.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Tovchigrechko A, Vakser AI: GRAMM-X public web server for protein–protein docking. Nucleic Acids Res. 2006, 34: W310-W314. 10.1093/nar/gkl206.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© e Zahra et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement