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
© e Zahra et al.; licensee BioMed Central Ltd. 2013
Received: 23 October 2012
Accepted: 11 December 2012
Published: 1 January 2013
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.
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.
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.
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) . 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 .
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.
Templates sorted by their overall quality Z-scores and E-values
Common functional partners between RB1 and P16-INK4A
Common functional partners
RB1 and P16-INK4A
Proteins and interactors for protein-protein docking
Binding interactions for Rb1 and P16-INK4A
Interactions (Receptor residue →Interacting protein residue)
Arg47:NH2 →Thr 104:OG1
Arg 80:NH2 → Trp 106: O
Met 1: N →Val 9: O
Glu 864:O →Gln 290:2HEZ
Ser 567:OG →Arg 22:1HH1
Asp 36: OD2 →Lys 89:1HZ
Arg 861:2HHZ →Thr 285:OG1
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.
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).
Parameters selected for YASARA comparative modeling
Alignment per template
Protein-protein docking of P16-INK4 and RB1 was carried out through GRAMM-X docking web server.
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  and its 3D structure was predicted using ab-initio approach through I-TASSER server. GRAMM-X docking server  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.
We are thankful to university institute of biochemistry and biotechnology (UIBB), PMAS- Arid Agriculture university for providing molecular modeling software, YASARA for insilico analysis.
- Herbst RS, Heymach JV, Lippman SM: Lung cancer. N Engl J Med. 2008, 359: 1367-1380. 10.1056/NEJMra0802714.View ArticlePubMedGoogle Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- Hooft RW, Vriend G, Sander C, Abola EE: Errors in protein structures. Nature. 1996, 6580: 272-View ArticleGoogle Scholar
- 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
- 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
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.