Identification of Anti-cancerous Drugs for The Mutated SNAP25 Protein Related to Brain Tumor through Structure-Based Virtual Screening Approach
Keywords:Anti-cancer, CADD, Drug Discovery, SBVS, Autodock
Brain tumor is a formidable challenge for drug development, and drugs derived from many advanced technologies are being tested in clinical trials. Several researchers have already discovered anti-cancerous drugs that can be used for the treatment of brain tumors in adults and as well as children. SNAP25 (synaptosomal-associated protein of 25 kDa) is a membrane-binding protein in neurons and it is critical in neurotransmission for the fusion of plasma membrane and synaptic vesicle making it a prime target to address brain tumors. The SNAP-25 gene is responsible for personality disorders, schizophrenia, attention deficit, and hyperactivity disorder in human beings. It is recently discovered, that this protein is responsible for brain cancer as well. In the present research, 17 investigational and experimental anticancer drugs were selected from the PubChem and DrugBank databases to identify potential inhibitors with high stability to treat mutated SNAP25 protein. For this purpose, we have used the structure-based virtual screening (SBVS) technique wherein, the candidate molecules are computationally docked into the 3D structure of the biological target that is derived from biophysical methods, such as X-ray crystallography, NMR spectroscopy, cryo-electron microscopy, homology modeling, or molecular dynamics simulations. The docking was achieved in PyRx 0.8 software and the drugs were then ranked based on their predicted binding affinity or complementarity to the binding site. Based on the ligand binding energy, the top six compounds having greater inhibitory effects towards SNAP25 were selected and then visualized with Pymol and Biovia visualizers. The compound Crenolanib has better pharmacological properties and demonstrated higher binding affinities with the target protein. Therefore this Crenolanib docked confirmations were appraised for molecular dynamic simulations. The study concluded that the anticancer drug Crenolanib exerted inhibitory potential against the mutated protein SNAP-25 and therefore it can be exploited as a cancer modulator to address brian tumors.
Gilbert, M., Dignam, J., Armstrong, T. et al. (2014). A randomized trial of bevacizumab for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 699–708.
Chinot, O. L., Wick, W., Mason, W. et al. (2014). Bevacizumab plus radiotherapy– temozolomide for newly diagnosed glioblastoma. N. Engl. J. Med. 370, 709–722.
Smith, M. A. & Reaman, G. H. (2015). Remaining challenges in childhood cancer and newer targeted therapeutics. Pediatr. Clin. North Am. 62, 301–312.
Phoenix, T. N., Patmore, D.M., Boop, S. et al. (2016). Medulloblastoma genotype dictates blood brain barrier phenotype. Cancer Cell. 29, 508–522.
Gerstner, E. R. & Fine, R. L. (2007). Increased permeability of the blood-brain barrier to chemotherapy in metastatic brain tumors: establishing a treatment paradigm. J. Clin. Oncol. 25, 2306–2312.
Mackay, A., Burford, A., Carvalho, D. et al. (2017). Integrated molecular meta-analysis of 1,000 pediatric high-grade and diffuse intrinsic pontine glioma. Cancer Cell. 32, 520–537.
Quail, D. F. & Joyce, J. A. (2017). The microenvironmental landscape of brain tumors. Cancer Cell. 31, 326–341.
Gilbertson, R. J. (2011). Mapping cancer origins. Cell. 145, 25–29.
Xia, L., Su, X., Shen, J., et al. (2018). ANLN functions as a key candidate gene in cervical cancer as determined by integrated bioinformatic analysis. Cancer Management and Res. 10, 663–670. https://doi.org/10.2147/CMAR.S162813.
Yuan, L., Zeng, G., Chen, L., et al. (2018). Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis. Int. J. Biol. Sci. 14, 266–279. https://doi.org/10.7150/ijbs.23574.
Aissouq, A. E., Toufik, H., Stitou, M., et al. (2020). In silico design of novel tetra-substituted pyridinylimidazoles derivatives as c-jun N-terminal kinase-3 inhibitors, using 2D/3D-QSAR studies, molecular docking and ADMET prediction. The International Journal of Peptide Research and therapeutics. 26, 1335–1351.
Song, C. M., Lim, S. J. and Tong, J. C. (2009). Recent advances in computer-aided drug design. Briefings in Bioinformatics. 10, 579–591.
Macalino, S. J. Y., Gosu, V., Hong, S. et al. (2015). Role of computer-aided drug design in modern drug discovery. Archives of Pharmacal Research. 38, 1686–1701.
Gangwal, R. P., Dhoke, G. V., Damre, M. V., et al. (2013). Structure-based virtual screening and molecular dynamic simulation studies to identify novel cytochrome bc1 inhibitors as antimalarial agents. Journal of Computational Medicine. 2013, Article ID 637901, 9 pages.
Srinivasan, P., Perumal, P. C. and Sudha, A. (2014). Discovery of novel inhibitors for Nek6 protein through homology model assisted structure based virtual screening and molecular docking approaches. The Scientific World Journal, vol. 2014, Article ID 967873, 9 pages.
Wang, Y., Han, R., Zhang, H., et al. (2017). Combined ligand/structure- based virtual screening and molecular dynamics simulations of steroidal androgen receptor antagonists. BioMed Research International. 2017, Article ID 3572394, 18 pages.
Wang, W., Wan, D., Liao, D., et al. (2017). Identification of potent chloride intracellular channel protein 1 inhibitors from traditional Chinese medicine through structure-based virtual screening and molecular dynamics analysis. BioMed Research International. 2017, Article ID 4751780, 18 pages.
Liu, Z., Zhao, J., Li, W., et al. (2015). Molecular docking of potential inhibitors for influenza H7N9. Computational and Mathematical Methods in Medicine. 2015, Article ID 480764, 8 pages.
Dubey, R., Tewari, A. K., Singh, V. P. et al. (2013). Molecular docking study of conformational polymorph: building block of crystal chemistry. The Scientific World Journal. 2013, Article ID 309710, 6 pages.
Zhang, D., Hu, Y., Sun, Q. et al. (2013). Inhibition of transforming growth factor beta-activated kinase 1 confers neuroprotection after traumatic brain injury in rats. Neuroscience. 238, 209–217.
Guo, C., Zhuang, Y., Chen, Y. et al. (2020). Significance of tumor protein p53 mutation in cellular process and drug selection in brain lower grade (WHO grades II and III) glioma. Biomark.Med. 14(12), 1139-1150.
Huang, Q., Lian, C., ong, Y., et al. (2021). SNAP25 Inhibits Glioma Progression by Regulating Synapse Plasticity via GLS-Mediated Glutaminolysis. Front. Oncol. https://doi.org/10.3389/fonc.2021.698835
The UniProt Consortium, UniProt: a worldwide hub of protein knowledge.(2019). Nucleic Acids Research. 47, D506–D515. https://doi.org/10.1093/nar/gky1049.
Roy, A., Kucukural, A. and Zhang, Y. (2010). I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols. 5, 725–738.
Bendl, J., Stourac, J., Salanda, O. et al. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations. PLoS Comput Biol. 10(1): e1003440.
Yuan, S., Chan, H.C., Hu, Z. (2017). Using PyMOL as a platform for computational drug design. WIREs. 7.
Wishart, D. S., Feunang, Y. D., Guo, A. C. et al. (2018). DrugBank 5.0: a major update to the drug bank database for 2018. Nucleic Acids Research. 46, D1074–D1082.
Kim, S., Chen, J., Cheng, T. et al. (2021). PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Research. 49, D1388–D1395.
Tian, S., Wang, J., Li, Y. et al. (2015). The application of in silico drug-likeness predictions in pharmaceutical research. Adv Drug Deliv Rev. 86, 2–10.
Lipinski, C.A., Lombardo, F., Dominy, B.W. et al. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Deliv. Rev. 46, 3–26.
Dallakyan, S. and Arthur, J.O. (2015). Small-molecule library screening by docking with PyRx. Methods Molecular Biology, 1263, 243–250.
BIOVIA - Scientific Enterprise Software for Chemical Research. (2020). Material Science R&D. https://www.3dsbiovia.com.
The PyMOL Molecular Graphics System, Version 2.2.0. (2015). Schrodinger.
Hollingsworth, S.A. and Dror, R.O. (2018). Molecular Dynamics Simulations for all. Neuron, 99(6), 1129-1143.
Ramachandran, G.N.,Ramakrishnan, C., Sasisekharan, V. (1963). Stereochemistry of polypeptide chain configurations. Journal of Molecular Biology. 7, 95–9.
How to Cite
Copyright (c) 2023 SAMEER SHARMA
This work is licensed under a Creative Commons Attribution 4.0 International License.