Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach

References

• Abdulazeez, S., Sultana, S., Almandil, N. B., Almohazey, D., Bency, B. J., & Borgio, J. F. (2019). The rs61742690 (S783N) single nucleotide polymorphism is a suitable target for disrupting BCL11A-mediated foetal-to-adult globin switching. PLoS One, 14(2), e0212492. https://doi.org/10.1371/journal.pone.0212492
   PubMed Web of Science ®Google Scholar
• Adzhubei, I., Schmidt, S., Peshkin, L., Ramensky, V., Gerasimova, A., Bork, P., Kondrashov, A., & Sunyaev, S. (2010). A method and server for predicting damaging missense mutations. Nature Methods, 7(4), 248–249.
   PubMed Web of Science ®Google Scholar
• Ahmad, S. U., Khan, M. S., Jan, Z., Khan, N., Ali, A., Rehman, N., Haq, M., Khan, U., Bashir, Z., Tayyab, M., Haq, I., Bakht, S., & Zahir, F. (2021). Genome wide association study and phylogenetic analysis of novel SARS-COV-2 virus among different countries. Pakistan Journal of Pharmaceutical Sciences, 34(4), 1305–1313.
   PubMed Web of Science ®Google Scholar
• Ahmad, S. U., Qadus, A., Ahmad, B., Khan, J. A., Shah, Z. W., Saeed, A., & Mahmood, T. (2020). In-silico investigation of EGFR network in kidney cancer: A drug discovery approach. Pure and Applied Biology (PAB), 9(2), 1583–1595.
   Google Scholar
• Al-Shuhaib, M. B. S., Al-Kafajy, F. R., Badi, M. A., AbdulAzeez, S., Marimuthu, K., Al-Juhaishi, H. A. I., & Borgio, J. F. (2018). Highly deleterious variations in COX1, CYTB, SCG5, FK2, PRL and PGF genes are the potential adaptation of the immigrated African ostrich population. Computers in Biology and Medicine, 100, 17–26.
   PubMed Web of Science ®Google Scholar
• Ashkenazy, H., Erez, E., Martz, E., Pupko, T., & Ben-Tal, N. (2010). ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Research, 38(Web Server issue), W529–W533. https://doi.org/10.1093/nar/gkq399
   PubMed Web of Science ®Google Scholar
• Bashir, Z., Ahmad, S. U., Kiani, B. H., Jan, Z., Khan, N., Khan, U., Haq, I., Zahir, F.,Qadus, A., & Mahmood, T. (2021). Immunoinformatics approaches to explore B and T cell epitope-based vaccine designing for SARS-CoV-2 Virus. Pakistan Journal of Pharmaceutical Sciences, 34(1), 345–352.
   PubMed Web of Science ®Google Scholar
• Bendl, J., Stourac, J., Salanda, O., Pavelka, A., Wieben, E., Zendulka, J., Brezovsky, J., & Damborsky, J. (2014). PredictSNP: Robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Computational Biology, 10(1), e1003440. https://doi.org/10.1371/journal.pcbi.1003440
   PubMed Web of Science ®Google Scholar
• Bowers, K. J., Chow, D. E., Xu, H., Dror, R. O., Eastwood, M. P., Gregersen, B. A., … Shaw, D. E. (2006). Scalable algorithms for molecular dynamics simulations on commodity clusters. In SC'06: Proceedings of the 2006 ACM/IEEE conference on supercomputing (pp. 43–43). IEEE.
   Google Scholar
• Brunham, L. R., Singaraja, R. R., Pape, T. D., Kejariwal, A., Thomas, P. D., & Hayden, M. R. (2005). Accurate prediction of the functional significance of single nucleotide polymorphisms and mutations in the ABCA1 gene. PLoS Genetics, 1(6), e83. PMID: 16429166 https://doi.org/10.1371/journal.pgen.0010083
   PubMed Web of Science ®Google Scholar
• Calabrese, R., Capriotti, E., Fariselli, P., Martelli, P. L., & Casadio, R. (2009). Functional annotations improve the predictive score of human disease-related mutations in proteins. Human Mutation, 30(8), 1237–1244. PMID: 19514061 https://doi.org/10.1002/humu.21047
   PubMed Web of Science ®Google Scholar
• Capriotti, E., & Altman, R. B. (2011). A new disease-specific machine learning approach for the prediction of cancer-causing missense variants. Genomics, 98(4), 310–317.
   PubMed Web of Science ®Google Scholar
• Capriotti, E., Calabrese, R., Fariselli, P., Martelli, P. L., Altman, R. B., & Casadio, R. (2013). WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics, 14(Suppl 3), S6–S7. https://doi.org/10.1186/1471-2164-14-S3-S6
   PubMed Web of Science ®Google Scholar
• Capriotti, E., Fariselli, P., & Casadio, R. (2005). I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Research, 33(Web Server issue), W306–W310. https://doi.org/10.1093/nar/gki375
   PubMed Web of Science ®Google Scholar
• Capriotti, E., Fariselli, P., Calabrese, R., & Casadio, R. (2005). Predicting protein stability changes from sequences using support vector machines. Bioinformatics, 21(Suppl 2), ii54–ii58. https://doi.org/10.1093/bioinformatics/bti1109
   PubMedGoogle Scholar
• Cheng, G., Zhang, L., Lv, W., Dong, C., Wang, Y., & Zhang, J. (2015). Overexpression of cyclin D1 in meningioma is associated with malignancy grade and causes abnormalities in apoptosis, invasion and cell cycle progression. Medical Oncology (Northwood, London, England), 32(1), 439. https://doi.org/10.1007/s12032-014-0439-0
   PubMed Web of Science ®Google Scholar
• Cheng, J., Randall, A., & Baldi, P. (2006). Prediction of protein stability changes for single-site mutations using support vector machines. Proteins, 62(4), 1125–1132.
   PubMed Web of Science ®Google Scholar
• Choi, Y., & Chan, A. P. (2015). PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics (Oxford, England), 31(16), 2745–2747.
   PubMed Web of Science ®Google Scholar
• Datta, A., Mazumder, M., Hasan, H., Chowdhury, A. S., Hasan, M., Capriotti, E., Calabrese, R., & Casadio, R. (2006). Functional and structural consequences of damaging single nucleotide polymorphisms in human prostate cancer predisposition gene RNASEL. BioMed Research International, 2015, 1–15. Mismatch] https://doi.org/10.1155/2015/271458
   Google Scholar
• Divaris, K., Monda, K. L., North, K. E., Olshan, A. F., Reynolds, L. M., Hsueh, W.-C., Lange, E. M., Moss, K., Barros, S. P., Weyant, R. J., Liu, Y., Newman, A. B., Beck, J. D., & Offenbacher, S. (2013). Exploring the genetic basis of chronic periodontitis: a genome-wide association study. Human Molecular Genetics, 22(11), 2312–2324. https://doi.org/10.1093/hmg/ddt065
   PubMed Web of Science ®Google Scholar
• Doss, C. G. P., Nagasundaram, N., Chakraborty, C., Chen, L., & Zhu, H. (2013). Extrapolating the effect of deleterious nsSNPs in the binding adaptability of flavopiridol with CDK7 protein: a molecular dynamics approach. Human Genomics, 7(1), 10. https://doi.org/10.1186/1479-7364-7-10
   PubMedGoogle Scholar
• Emadi, E., Akhoundi, F., Kalantar, S. M., & Emadi-Baygi, M. (2020). Predicting the most deleterious missense nsSNPs of the protein isoforms of the human HLA-G gene and in silico evaluation of their structural and functional consequences. BMC Genetics, 21(1), 1–27. https://doi.org/10.1186/s12863-020-00890-y
   PubMed Web of Science ®Google Scholar
• Fan, J., Wennmann, J. T., Wang, D., & Jehle, J. A. (2020). Single nucleotide polymorphism (SNP) frequencies and distribution reveal complex genetic composition of seven novel natural isolates of Cydia pomonella granulovirus. Virology, 541, 32–40. https://doi.org/10.1016/j.virol.2019.11.016
   PubMed Web of Science ®Google Scholar
• Feng, J., Liu, T., Qin, B., Zhang, Y., & Liu, X. S. (2012). Identifying ChIP-seq enrichment using MACS. Nature Protocols, 7(9), 1728–1740.
   PubMed Web of Science ®Google Scholar
• Guo, S., Jian, L., Tao, K., Chen, C., Yu, H., & Liu, S. (2019). Novel breast-specific long non-coding RNA LINC00993 acts as a tumor suppressor in triple-negative breast cancer. Frontiers in Oncology, 9, 1325.
   PubMed Web of Science ®Google Scholar
• Guo, Z., Wang, Y., Zhao, Y., Jin, Y., An, L., Xu, H., Liu, Z., Chen, X., Zhou, H., Wang, H., & Zhang, W. (2019). A Functional 5'-UTR polymorphism of myc contributes to nasopharyngeal carcinoma susceptibility and chemoradiotherapy induced toxicities. Journal of Cancer, 10(1), 147–155.
   PubMed Web of Science ®Google Scholar
• Guyot, A., Duchesne, M., Robert, S., Lia, A.-S., Derouault, P., Scaon, E., Lemnos, L., Salle, H., Durand, K., & Labrousse, F. (2019). Analysis of CDKN2A gene alterations in recurrent and non-recurrent meningioma. Journal of Neuro-Oncology, 145(3), 449–459.
   PubMed Web of Science ®Google Scholar
• Harder, E., Damm, W., Maple, J., Wu, C., Reboul, M., Xiang, J. Y., Wang, L., Lupyan, D., Dahlgren, M., Knight, J., Kaus, J., Cerutti, D., Krilov, G., Jorgensen, W., Abel, R., & Friesner, R. (2016). OPLS3: A force field providing broad coverage of drug-like small molecules and proteins. Journal of Chemical Theory and Computation, 12(1), 281–296.
   PubMed Web of Science ®Google Scholar
• Hess, B., Bekker, H., Berendsen, H. J., & Fraaije, J. G. (1997). LINCS: A linear constraint solver for molecular simulations. Journal of Computational Chemistry, 18(12), 1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H
   Web of Science ®Google Scholar
• Ijaz, A., Shah, K., Aziz, A., Rehman, F., Ali, Y., Tareen, A., Khan, K., Ayub, M., & Wali, A. (2021). Novel frameshift mutations in XPC gene underlie xeroderma pigmentosum in Pakistani families. Indian Journal of Dermatology, 66(2), 220–222.
   PubMed Web of Science ®Google Scholar
• Jan, Z., Ahmad, S. U., Amara, Qadus, Y. A., Sajjad, W., Rais, F., Tanveer, S., Haq, I. (2021). 19. Insilico structural and functional assessment of hypothetical protein L345_13461 from Ophiophagus hannah. Pure and Applied Biology (PAB), 10(4), 1109–1118.
   Google Scholar
• Johnson, M. M., Houck, J., & Chen, C. (2005). Screening for deleterious nonsynonymous single-nucleotide polymorphisms in genes involved in steroid hormone metabolism and response. Cancer Epidemiology Biomarkers & Prevention, 14(5), 1326–1329. https://doi.org/10.1158/1055-9965.EPI-04-0815
   PubMed Web of Science ®Google Scholar
• Kumar, P., Henikoff, S., & Ng, P. C. (2009). Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature Protocols, 4(7), 1073–1081.
   PubMed Web of Science ®Google Scholar
• Landau, M., Mayrose, I., Rosenberg, Y., Glaser, F., Martz, E., Pupko, T., & Ben-Tal, N. (2005). ConSurf 2005: The projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Research, 33(Web Server issue), W299–W302. https://doi.org/10.1093/nar/gki370
   PubMed Web of Science ®Google Scholar
• Lesseur, C., Ferreiro-Iglesias, A., McKay, J., Bossé, Y., Johansson, M., Gaborieau, V., Landi, M. T., Christiani, D., Caporaso, N., Bojesen, S., Amos, C., Shete, S., Liu, G., Rennert, G., Albanes, D., Aldrich, M., Tardon, A., Chen, C., Triantafillos, L., … Brennan, P. (2021). Genome-wide association meta-analysis identifies pleiotropic risk loci for aerodigestive squamous cell cancers. PLoS Genetics, 17(3), e1009254. https://doi.org/10.1371/journal.pgen.1009254
   PubMed Web of Science ®Google Scholar
• Li, J., Shi, L., Zhang, K., Zhang, Y., Hu, S., Zhao, T., Teng, H., Li, X., Jiang, Y., Ji, L., & Sun, Z. (2018). VarCards: An integrated genetic and clinical database for coding variants in the human genome. Nucleic Acids Research, 46(D1), D1039–D1048.
   PubMed Web of Science ®Google Scholar
• Liang, B., Ding, H., Huang, L., Luo, H., & Zhu, X. (2020). GWAS in cancer: progress and challenges. Molecular Genetics and Genomics, 295(3), 537–561.
   PubMed Web of Science ®Google Scholar
• Liu, Y., Shete, S., Hosking, F., Robertson, L., Houlston, R., & Bondy, M. (2010). Genetic advances in glioma: Susceptibility genes and networks. Current Opinion in Genetics & Development, 20(3), 239–244. https://doi.org/10.1016/j.gde.2010.02.001
   PubMed Web of Science ®Google Scholar
• López-Ferrando, V., Gazzo, A., De La Cruz, X., Orozco, M., & Gelpí, J. L. (2017). PMut: A web-based tool for the annotation of pathological variants on proteins, 2017 update. Nucleic Acids Research, 45(W1), W222–W228. https://doi.org/10.1093/nar/gkx313
   PubMed Web of Science ®Google Scholar
• MacArthur, M. W., Laskowski, R. A., & Thornton, J. M. (1994). Knowledge-based validation of protein structure coordinates derived by X-ray crystallography and NMR spectroscopy. Current Opinion in Structural Biology, 4(5), 731–737. https://doi.org/10.1016/S0959-440X(94)90172-4
   Web of Science ®Google Scholar
• Nadeem, A., Javed, M., Hassan, F. U., Luo, X., Khalid, R. B., & Liu, Q. (2020). Genomic identification, evolution and sequence analysis of the heat-shock protein gene family in buffalo. Genes, 11(11), 1388.
   PubMed Web of Science ®Google Scholar
• Ng, P. C., & Henikoff, S. (2003). SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Research, 31(13), 3812–3814. PMID: 12824425
   PubMed Web of Science ®Google Scholar
• OriginPro. (2016). OriginLab corporation. Northampton.
   Google Scholar
• Padh, H. (2021). Sequencing and comparative genome analysis of three Indians. Mammalian Genome, 32(5), 401–412.
   PubMed Web of Science ®Google Scholar
• Patel, S. M., Koringa, P. G., Reddy, B. B., Nathani, N. M., & Joshi, C. G. (2015). In silico analysis of consequences of non-synonymous SNPs of Slc11a2 gene in Indian bovines. Genomics Data, 5, 72–79.
   PubMedGoogle Scholar
• Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics, 22(22), 2734.
   Web of Science ®Google Scholar
• Rehman, A. U., Khan, M. T., Liu, H., Wadood, A., Malik, S. I., & Chen, H. F. (2019). Exploring the pyrazinamide drug resistance mechanism of clinical mutants T370P and W403G in ribosomal protein S1 of Mycobacterium tuberculosis. Journal of Chemical Information and Modeling, 59(4), 1584–1597.
   PubMed Web of Science ®Google Scholar
• Rehman, A. U., Lu, S., Khan, A. A., Khurshid, B., Rasheed, S., Wadood, A., & Zhang, J. (2021). Hidden allosteric sites and De-Novo drug design. Expert Opinion on Drug Discovery, 17(3), 283–295.
   PubMed Web of Science ®Google Scholar
• Rehman, A. U., Rafiq, H., Rahman, M. U., Li, J., Liu, H., Luo, S., Arshad, T., Wadood, A., & Chen, H.-F. (2019). Gain-of-function SHP2 E76Q mutant rescuing autoinhibition mechanism associated with juvenile myelomonocytic leukemia. Journal of Chemical Information and Modeling, 59(7), 3229–3239.
   PubMed Web of Science ®Google Scholar
• Rehman, A. U., Rahman, M. U., Lu, S., Liu, H., Li, J.-Y., Arshad, T., Wadood, A., Ng, H. L., & Chen, H.-F. (2020). Decoding allosteric communication pathways in protein lysine acetyltransferase. International Journal of Biological Macromolecules, 149, 70–80.
   PubMed Web of Science ®Google Scholar
• Repenning, A., Happel, D., Bouchard, C., Meixner, M., Verel‐Yilmaz, Y., Raifer, H., Holembowski, L., Krause, E., Kremmer, E., Feederle, R., Keber, C. U., Lohoff, M., Slater, E. P., Bartsch, D. K., & Bauer, U. -T. (2021). PRMT1 promotes the tumor suppressor function of p14ARF and is indicative for pancreatic cancer prognosis. The EMBO Journal, 40(13), e106777.
   PubMed Web of Science ®Google Scholar
• Riaz, M., Rehman, A. U., Shah, S. A., Rafiq, H., Lu, S., Qiu, Y., & Wadood, A. (2021). Predicting multi-interfacial binding mechanisms of NLRP3 and ASC pyrin domains in inflammasome activation. ACS Chemical Neuroscience, 12(4), 603–612.
   PubMed Web of Science ®Google Scholar
• Siva Kumar, G., Singh, H., & Sobhia, E. M. (2021). Structure based insights into the association of fluoroquinolones with mycobacterial DNA-gyrase complexes. Journal of Bioinformatics and Systems Biology, 4, 103–116.
   Google Scholar
• Thomas, P. D., Kejariwal, A., Guo, N., Mi, H., Campbell, M. J., Muruganujan, A., & Lazareva-Ulitsky, B. (2006). Applications for protein sequence–function evolution data: mRNA/protein expression analysis and coding SNP scoring tools. Nucleic Acids Research, 34(Web Server issue), W645–W650.
   PubMedGoogle Scholar
• Thusberg, J., & Vihinen, M. (2009). Pathogenic or not? And if so, then how? Studying the effects of missense mutations using bioinformatics methods. Human Mutation, 30(5), 703–714. https://doi.org/10.1002/humu.20938
   PubMed Web of Science ®Google Scholar
• Varfolomeev, S. D., Uporov, I. V., & Fedorov, E. V. (2002). Bioinformatics and molecular modeling in chemical enzymology. Active sites of hydrolases. Biochemistry, 67(10), 1099–1108.
   PubMed Web of Science ®Google Scholar
• Vincent, E. E., & Yaghootkar, H. (2020). Using genetics to decipher the link between type 2 diabetes and cancer: shared aetiology or downstream consequence? Diabetologia, 63(9), 1706–1712. https://doi.org/10.1007/s00125-020-05228-y
   PubMed Web of Science ®Google Scholar
• Wadood, A., Shareef, A., Ur Rehman, A., Muhammad, S., Khurshid, B., Khan, R. S., Shams, S., & Afridi, S. G. (2022). In silico drug designing for ala438 deleted ribosomal protein S1 (RpsA) on the basis of the active compound Zrl 15. ACS Omega, 7(1), 397–408. https://doi.org/10.1021/acsomega.1c04764
   PubMed Web of Science ®Google Scholar
• Wakil, S., Ram, R., Muiya, N., Mehta, M., Andres, E., Mazhar, N., Baz, B., Hagos, S., Alshahid, M., Meyer, B., Morahan, G., & Dzimiri, N. (2016). A genome-wide association study reveals susceptibility loci for myocardial infarction/coronary artery disease in Saudi Arabs. Atherosclerosis, 245, 62–70. https://doi.org/10.1016/j.atherosclerosis.2015.11.019
   PubMed Web of Science ®Google Scholar
• Xu, J., Li, N., Deng, W., & Luo, S. (2021). Discovering the mechanism and involvement of the methylation of cyclin-dependent kinase inhibitor 2A (CDKN2A) gene and its special locus region in gastric cancer. Bioengineered, 12(1), 1286–1298. https://doi.org/10.1080/21655979.2021.1887646
   PubMed Web of Science ®Google Scholar
• Yang, J., & Zhang, Y. (2015). I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Research, 43(W1), W174–W181. PMID: 25883148 https://doi.org/10.1093/nar/gkv342
   PubMed Web of Science ®Google Scholar
• Yates, C. M., & Sternberg, M. J. (2013). The effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on protein-protein interactions. Journal of Molecular Biology, 425(21), 3949–3963. https://doi.org/10.1016/j.jmb.2013.07.012
   PubMed Web of Science ®Google Scholar
• Yue, P., & Moult, J. (2006). Identification and analysis of deleterious human SNPs. Journal of Molecular Biology, 356(5), 1263–1274.
   PubMed Web of Science ®Google Scholar
• Zhang, Q., Zheng, X., Li, X., Sun, D., Xue, P., Zhang, G., Xiao, M., Cai, Y., Jin, C., Yang, J., Wu, S., & Lu, X. (2018). The polymorphisms of mi RNA‐binding site in MLH 3 and ERCC 1 were linked to the risk of colorectal cancer in a case–control study. Cancer Medicine, 7(4), 1264–1274. https://doi.org/10.1002/cam4.1319
   PubMed Web of Science ®Google Scholar
• Zheng, W., Zhang, C., Li, Y., Pearce, R., Bell, E. W., & Zhang, Y. (2021). Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Reports Methods, 1(3), 100014. https://doi.org/10.1016/j.crmeth.2021.100014
   PubMedGoogle Scholar