- Open Access
Tumor necrosis factor receptor superfamily 10B (TNFRSF10B): an insight from structure modeling to virtual screening for designing drug against head and neck cancer
- Rana Adnan Tahir†1,
- Sheikh Arslan Sehgal†2,
- Naureen Aslam Khattak3,
- Jabar Zaman Khan Khattak1 and
- Asif Mir1Email author
© Tahir et al.; licensee BioMed Central Ltd. 2013
- Received: 24 February 2013
- Accepted: 28 May 2013
- Published: 1 June 2013
Head and neck cancer (HNC) belongs to a group of heterogeneous disease with distinct patterns of behavior and presentation. TNFRSF10B, a tumor suppressor gene mapped on chromosome 8. Mutation in candidate gene is responsible for the loss of chromosome p arm which is frequently observed in head and neck tumors. TNFRSF10B inhibits tumor formation through apoptosis but deregulation encourages metastasis, migration and invasion of tumor cell tissues.
Structural modeling was performed by employing MODELLER (9v10). A suitable template [2ZB9] was retrieved from protein databank with query coverage and sequence identity of 84% and 30% respectively. Predicted Model evaluation form Rampage revealed 93.2% residues in favoured region, 5.7% in allowed region while only 1 residue is in outlier region. ERRAT and ProSA demonstrated 51.85% overall quality with a −1.08 Z-score of predicted model. Molecular Evolutionary Genetics Analysis (MEGA 5) tool was executed to infer an evolutionary history of TNFRSF10B candidate gene. Orthologs and paralogs [TNFRSF10A & TNFRSF10D] protein sequences of TNFRSF10B gene were retrieved for developed ancestral relationship. Topology of tree presenting TNFRSF10A gene considered as outgroup. Human and gorilla shared more than 90% similarities with conserved amino acid sequence. Virtual screening approach was appliedfor identification of novel inhibitors. Library (Mcule) was screened for novel inhibitors and utilized the scrutinized lead compounds for protein ligand docking. Screened lead compounds were further investigated for molecular docking studies. STRING server was employed to explore protein-protein interactions of TNFRSF10B target protein. TNFSF10 protein showed highest 0.999 confidence score and selected protein-protein docking by utilizing GRAMM-X server. In-silico docking results revealed I-58, S-90 and A-62 as most active interacting residues of TNFRSF10B receptor protein with R-130, S-156 and R-130 of TNFSF10B ligand protein.
Current research may provide a backbone for understanding structural and functional insights of TNFRSF10B protein. The designed novel inhibitors and predicted interactions might serve to inhibit the disease. Effective in-vitro potent ligands are required which will be helpful in future to design a drug to against Head and neck cancer disease. There is an urgent need for affective drug designing of head and neck cancer and computational tools for examining candidate genes more efficiently and accurately are required.
- Head and neck cancer
- Tumor necrosis factor
- Virtual screening
HNC is the sixth most occurring cancers worldwide  whereas in Pakistan, the second most prevalent cancer affecting the pharynx, larynx and oral cavity . Multigenic nature and environmental agents made heterogeneous and complex epidemiology of disease . Different genetic polymorphisms are reported in enzymes involved in the metabolization of alcohol and tobacco greatly increases the risk of Squamous Cell Carcinoma of Head and Neck cancer (SCCHN) [4, 5]. Morbidity and prognosis differ from patient to patient depending on causative agents, anatomical site and the stage of disease.
DNA modifications and structural variations in the genomic content of cell controlling gene expression are responsible for cancers. Deletions and duplications of chromosomal segments or even whole chromosome lead to the genomic instability causing genetic alterations . DNA modifications are greatly responsible for change in expression level of HNC [6, 7]. Genetic events result in the activation of proto-oncogenes and inactivation of tumor suppressor genes or both, leading to the development of SCCHN [7, 8].
Tumor Necrosis Factor (TNF) is a mediator pro-inflammatory cytokine involved in the progression and development of cancer. The family of TNF inhibits tumor formation through apoptosis but TNFs deregulation encourages metastasis, migration and invasion of tumor cell tissues . TNFRSF10B gene consists of 10 gene coding exons. Sequence analysis of all exons suggested allelic loss of 8p was in 20 primary HNCs. A number of putative tumors suppressor genes are located on 8p region which is a frequent site of translocations in head and neck tumors. In 1998, 2-bp insertion in this gene at residue 1065 was found that introduces a premature stop codon lead to truncated protein in SCCHN. Sequence comparison between patient and normal tissues confirmed that germ line contains truncating mutation in the absence of p53 mutation .
In-silico analysis of TNFRSF10B gene was conducted to elucidate the novel molecules, interacting partners, their binding interactions and to find a most plausible functions. The main objective of our study was to design novel inhibitors. The aim of research was to elucidate the interactions of TNFRSF10B protein with novel inhibitors and to identify the relation of gene with disease.
Molecular functions, biological processes and cellular locations of TNFRSF10B gene
Extrinsic apoptotic signalling pathway via death domain receptors.
Integral to membrane
Regulation of apoptotic process.
Activation of cysteine-type endopeptidase activity involved in the apoptotic process.
Template aligned by high score and query coverage
Virtual screening technique
Drug related properties of the designed molecules
ARG-23, GLU-24, ALA-25, ARG-26, GLY-27, ALA-28, VAL-39, VAL-41, LEU-46
ARG-23, GLU-24, VAL-39, LEU-40, VAL-41, ALA-43, LEU-46
ILE-58, ALA-59, SER-60, ALA-62, MET-73, ILE-85, GLN-86, TRP-89, SER-90
PRO-9, ALA-10, SER-12, GLY-13, LYS-16, ARG-17 PRO-30, GLN-53, LYS-54, GLU-57
High quality lead structures are the requirement for the successful drug discovery and structures of drug properties are more acceptable than common . In the early steps of drug discovery, poor pharmacokinetics and toxicity should be eradicated. Toxicity and drug score characteristics were further used to screen the hits .
Interacting residues between TNFRSF10B and TNFSF10 proteins
Interactions (Receptor residue → Interacting protein residue
Bond distance (Å)
Ionic bonding (N-O)
ILE-58/O → ARG-130/NH1
SER-90/O → SER-156/N
ALA-62/N → ARG-130/O
Head and neck cancer remains a disfiguring disease associated with a high mortality rate . Progressive accumulation of genetic aberrations leads to SCCHN but exact nature is still unknown. Candidate gene identification approach may provide key factors to pinpoint candidate genes involved in different carcinomas, which leads to explore the receptor-ligand or protein-protein interactions recognize these carcinomas that might lead to the development of effective therapeutic strategies .
For head and neck candidate gene TNFRSF10B, 2ZB9, 3NKE and 3NKD templates were retrieved from PDB. Out of these three templates, 2ZB9 showed optimal alignment and query coverage. Rampage showed the 93.2% residues in favored region and 5.7% in the allowing region whereas only 1 residue was in outlier region. 51.852% quality factor and −1.08 Z-score were shown by ERRAT and ProSA evaluation tools respectively.
In literature, TNFRSF10B gene and its paralogs genes are predicted in primates and human. MEGA 5 was employed using neighbor-joining method to determine evolutionary relationship of genes among teleosts, rodents, birds, primates and mammals. TNFRSF10D and TNFRSF10A are the paralogs of TNFRSF10B gene that are used in the construction of a phylogenetic tree. TNFRSF10A gene is outgroup in TNFRSF10B tree that presents TNFRSF10A gene as an origin of other genes. TNFRSF10A gene gave rise to TNFRSF10B gene in Ciona Intestinalis and other cluster is further diverged into TNFRSF10D and TNFRSF10B genes. Human and gorilla are closely related in TNFRSF10B and TNFRSF10A genes while in TNFRSF10D, human is closely related with chimpanzee. Bootstrap replication values >50 are presented in phylogenetic tree representing the reliability of topology.
Novel designed molecules have drug related properties and served as inhibitors for candidate gene. Interactions were observed in binding pocket of TNFRSF10B showing polar nature of binding domain. The designed compounds fulfill the properties of a competent drug and have no toxicity, mutagenic, irritants and carcinogenic property.
TNFSF10 protein showed highest interacting score 0.999 with TNFRSF10B target protein belonging to the same protein family. TNFSF10 protein was used as a ligand for protein-protein docking with TNFRSF10B receptor protein. Post docking analysis was performed by PyMol to analyze the hydrogen, ionic and hydrophobic interactions. Only ionic interactions were found in docked complex. Isoleucine-58 of receptor protein TNFRSF10B showed ionic interactions with Arginine-130 of ligand protein TNFSF10 with the distance of 2.9 Å. The nitrogen atom of arginine showed interaction with oxygen atom of isoleucine. Serine-90 of TNFRSF10B receptor protein showed ionic interactions with Serine-156 of ligand protein with a bond distance of 3.2 Å. Nitrogen of serine of ligand protein TNFSF10 interacted with the oxygen of serine of receptor protein. Alanine-62 nitrogen of receptor protein TNFRSF10B interacted with arginine-130 oxygen of ligand protein TNFSF10 with 3.2 Å bond distance.
For protein-protein interaction and novel designed molecules, both functional and expressional studies of TNFRSF10B showed significant interaction. In-vivo experimentation could be performed in animal model to check the effect of selected protein interactions which may leads to the approved drug of head and neck cancer. More than 80% homology between human and primates are strong evidence to build ancestral relationship which will help in prediction of protein functions and family. Current research suggested a baseline for novel ligand screening, docking and ancestral hierarchy for development and validation of novel drugs in particular function prediction of candidate gene TNFRSF10B.
The sequence of TNFRSF10B protein in FASTA format was retrieved from Uniprot Knowledge base (http://www.uniprot.org/) of accession number E9PBT3. The retrieved amino acid sequence of TNFRSF10B was subjected to a protein-protein BLAST (BLASTp) search against the Protein Data Bank (PDB) (http://www.rcsb.org/)  to recognize a suitable template structure. Suitable template [PDB ID: 2ZB9] having 84% query coverage, 30% sequence identity and 5.1 E-value was used in comparative modeling of TNFRSF10B protein. The homology modeling program MODELLER 9v10 was applied to generate 3D models. TNFRSF10B 3D model having lowest objective function was further assessed by Rampage , ERRAT  and ProSA  evaluation tools for the reliability of predicted structure.
Molecular Evolutionary Genetic Algorithm (MEGA 5) was used to infer ancestral history and species relationship of TNFRSF10B gene. Distance based approach through neighbor-joining technique was applied to construct the phylogenetic tree by using 1000 bootstrap replicates.
The compounds obtained through virtual screening were used in docking analysis by AutoDock Vina . The ligand molecules of target protein were not reported in earlier studies and also not found in biological databases, hence virtual screening technique was used to screen drug like lead compounds for TNFRSF10B docking calculations. Four novel lead compounds (as A, B, C and D) were screened. Mcule suit was employed for virtual screening and to predict the bioactivity and molecular properties of lead compounds.
Parameters of AutoDock used in docking analysis
Size (X-axis* Y-axis* Z-axis)
Rate of gene mutation
Rate of crossover
Binding affinity (Kcal/mol)
The STRING and STITCH3 servers were employed to identify the functional partners of TNFRSF10B. These databases are online database of known and predicted protein interactions including direct (physical) and indirect (functional) relationships. Gramm-X and Hex online servers were applied in protein docking of TNFRSF10B protein with its interactive partner TNFSF10 protein. Post docking analysis of docked complex was performed by PyMol tool.
We are thankful to Babar Ashraf Sheikh, Sajjad Ahmad Larra, Syed Babar Jamal and Muhammad Waqar Arshad for assistance.
- Devasena A, Pranay MC, Sadhana K, Rajani AB, Manoj BM: Susceptibility to oral cancer by genetic polymorphisms at CYP1A1, GSTM1 and GSTT1 loci among Indians: tobacco exposure as a risk modulator. Carcinogenesis. 2007, 28: 1455-1462.View ArticleGoogle Scholar
- Hanif M, Zaidi P, Kamal S, Hameed A: Institution-based cancer incidence in a local population in Pakistan: nine year data analysis. Asia Pac J Cancer Prev. 2009, 10: 227-230.Google Scholar
- Vokes EE, Weichselbaum RR, Lippman SM, Hong WK: Head and neck cancer. N Eng J Med. 1993, 328: 184-194.View ArticleGoogle Scholar
- Sturgis EM, Wei Q: Genetic susceptibility—molecular epidemiology of head and neck cancer. Curr Opin Oncol. 2002, 14: 310-317.View ArticlePubMedGoogle Scholar
- Hashibe M, Boffetta P, Zaridze D: Evidence for an important role of alcohol- and aldehyde-metabolizing genes in cancers of the upper aerodigestive tract. Cancer Epidemiol Biomarkers Prev. 2006, 15: 696-703.View ArticlePubMedGoogle Scholar
- Hittelman WN: Genetic instability in epithelial tissues at risk for cancer. Ann N Y Acad Sci. 2001, 952: 1-12.View ArticlePubMedGoogle Scholar
- Ha PK, Califano JA: Promoter methylation and inactivation of tumour-suppressor genes in oral squamous-cell carcinoma. Lancet Oncol. 2006, 7: 77-82.View ArticlePubMedGoogle Scholar
- Perez-Ordonez B, Beauchemin M, Jordan RC: Molecular biology of squamous cell carcinoma of the head and neck. J Clin Pathol. 2006, 59: 445-453.PubMed CentralView ArticlePubMedGoogle Scholar
- Schabath MB, Giuliano AR, Thompson Z, Fenstermacher D, Jonathan K, Sellers TA, Haura E: TNFRSF10B polymorphisms and haplotypes predicts survival in non-small cell lung cancer patients. Cancer Res. 2012, 72: 4506-4520.View ArticleGoogle Scholar
- Pai SI, Wu GS, Ozoren N, Wu L, Jen J, Sidransky D, El-Deiry WS: Rare loss-of-function mutation of a death receptor gene in head and neck cancer. Cancer Res. 1998, 58: 3513-3518.PubMedGoogle Scholar
- Eswar N, Eramian D, Webb B, Shen MY, Sali A: Protein structure modeling with MODELLER. Methods Mol Biol. 2008, 426: 145-159.View ArticlePubMedGoogle Scholar
- Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE: UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem. 2004, 25: 1605-1612.View ArticlePubMedGoogle Scholar
- Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011, 28: 2731-2739.PubMed CentralView ArticlePubMedGoogle Scholar
- Tondi D, Slomczynska U, Costi MP, Watterson DM, Ghelli S, Shoichet BK: Structure-based discovery and in-parallel optimization of novel competitive inhibitors of thymidylate synthase. Chem Biol. 1999, 6: 319-331.View ArticlePubMedGoogle Scholar
- Ertl P, Rohde B, Selzer P: Fast calculation of molecular polar surface area as a sum of fragment based contributions and its application to the prediction of drug transport properties. J Med Chem. 2000, 43: 3714-3717.View ArticlePubMedGoogle Scholar
- Lipinski CA, Lombardo F, Dominy BW, Feeney PJ: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 1997, 23: 3-25.View ArticleGoogle Scholar
- Muegge I: Selection criteria for drug-like compounds. Med Res Rev. 2003, 23: 302-321.View ArticlePubMedGoogle Scholar
- Kiss R, Sandor M, Szalai FA: http://Mcule.com: a public web service for drug discovery. J Chem Inform. 2012, 4: 17-Google Scholar
- Proudfoot JR: Drugs, leads, and drug-likeness: an analysis of some recently launched drugs. Bioorg Med Chem Lett. 2002, 12: 1647-1650.View ArticlePubMedGoogle Scholar
- OSIRIS: 2001,http://www.organic-chemistry.org/prog/peo/,
- Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von-Mering C, Jensen LJ: STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013, 41: 808-815.View ArticleGoogle Scholar
- Kuhn M, Szklarczyk D, Franceschini A, Mering CV, Jensen LJ, Bork P: STITCH 3: zooming in on protein–chemical interactions. Nucl Acids Res. 2012, 40: 876-880.View ArticleGoogle Scholar
- Tovchigrechko A, Vakser IA: GRAMM-X public web server for protein-protein docking. Nucleic Acids Res. 2006, 34: 310-314.View ArticleGoogle Scholar
- Macindoe G, Mavridis L, Venkatraman V, Devignes MD, Ritchie DW: HexServer: an FFT-based protein docking server powered by graphics processors. Nucleic Acids Res. 2010, 38: 445-449.View ArticleGoogle Scholar
- Forastiere A, Koch W, Trotti A, Sidransky D: Head and neck cancer. N Engl J Med. 2001, 345: 1890-1900.View ArticlePubMedGoogle Scholar
- Scully C, Field JK, Tanzawa H: Genetic aberrations in oral or head and neck squamous cell carcinoma (SCCHN): 1. Carcinogen metabolism, DNA repair and cell cycle control. Oral Oncol. 2000, 36: 256-263.View ArticlePubMedGoogle Scholar
- Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The protein data bank. Nucleic Acids Res. 2000, 28: 235-242.PubMed CentralView ArticlePubMedGoogle Scholar
- Lovell SC, Davis IW, Arendall WB, de Bakker PIW, Word JM, Prisant MG, Richardson JS, Richardson DC: Structure validation by Cαgeometry: φ/ψ and Cβ deviation. Proteins: Structure, Function Genetics. 2002, 50: 437-450.View ArticleGoogle Scholar
- Colovos C, Yeates TO: Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci. 1993, 2: 1511-1519.PubMed CentralView ArticlePubMedGoogle Scholar
- Wiederstein & Sippl: ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007, 35: 407-410.View ArticleGoogle Scholar
- Trott O, Olson AJ: AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem. 2010, 31: 455-461.PubMed CentralPubMedGoogle Scholar
- Chang LCW, Spanjersberg RF, von Frijtag DK, Unzel JK, Mulder-Krieger T, van den Hout G: 2,4,6-Trisubstituted pyrimidines as a new class of selective adenosine A, receptor antagonists. J Med Chem. 2004, 47: 6529-6540.View ArticlePubMedGoogle Scholar
- Pedretti A, Villa L, Vistoli G: VEGA – An open platform to develop chemo-bio-informatics applications, using plug-in architecture and script ”programming“. J Comput Aided Mol Des. 2004, 18: 167-173.View ArticlePubMedGoogle Scholar
- Mendelsohn LD: ChemDraw 8 ultra: windows and Macintosh versions. J Chem Inf Comput Sci. 2004, 44: 2225-2226.View ArticleGoogle 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.