STRUCTURAL PREDICTION AND ANTIGENIC ANALYSIS OF A HYPOTHETICAL PLASMODIUM FALCIPARUM PROTEIN USING BIOINFORMATICS TOOLS
Abstract
Malaria is caused by Plasmodium falciparum which remains a major global health problem and there is no effective vaccine with broad operational impact. The genome of P. falciparum has been sequenced by others indicating 5,600 genes in the genome. Presently, about 60 % of the genes encoding proteins in the parasite have no-known function. Thus, identifying potential drug/vaccine candidates and biomarkers for evaluating malaria transmission intensity such as the uncharacterized protein Q8IJ58 is a crucial step towards effective malaria intervention. Computer-based approach was used to analyze an uncharacterized Plasmodium falciparum protein - Q8IJ58 (Gene ID: PF10_0342, PlasmoDB ID: PF3D7_1035100), for its basic and theoretical information such as the physicochemical properties, probable B- and T- cell epitopes, secondary and tertiary structures, cellular localization, and other criteria important for further in vivo study, for an efficacious vaccine candidate against malaria. The evaluation of the antigenicity and allergenicity showed that this protein was immunogenic and non-allergenic, also, several potential B- and T-cell epitopes were detected. A total of 60 potential post-translational modification sites were found in the sequence, with 56 phosphorylation sites and 4 acylation sites. The secondary structure of Q8IJ58 is made up of 28.34% alpha-helix, 53.83% random coil, and 17.83% extended strand. Iterative Threading ASSEmbly Refinement (I-TASSER) was used for the three-dimensional structure prediction of Q8IJ58, the Ramachandran plot showed that 96.7% residues were in the most favored region, 2.1% in the allowed regions, and 0.2% residues in the allowed regions, with an overall quality factor of 98.47%...
References
Albrecht-Schgoer, K., Lackner, P., Schmutzhard, E. and Baier, G. (2022). Cerebral Malaria: Current Clinical and Immunological Aspects. Front Immunol, 13, 863568. https://doi.org/10.3389/fimmu.2022.863568
Amlabu, E., Mensah-Brown, H., Nyarko, P. B., Akuh, O.-a., Opoku, G., Ilani, P., Oyagbenro, R., Asiedu, K., Aniweh, Y. and Awandare, G. A. (2018). Functional characterization of Plasmodium falciparum surface-related antigen as a potential blood-stage vaccine target. The Journal of Infectious Diseases, 218(5), 778-790. doi.org/10.1093/infdis/jiy222
Arora, N., C Anbalagan, L. and Pannu, A. K. (2021). Towards eradication of malaria: is the WHO’s RTS, S/AS01 vaccination effective enough? Risk Management and Healthcare Policy, 1033-1039. doi: 10.2147/RMHP.S219294.
Aurrecoechea, C., Brestelli, J., Brunk, B. P., Dommer, J., Fischer, S., Gajria, B., Gao, X., Gingle, A., Grant, G., Harb, O. S., Heiges, M., Innamorato, F., Iodice, J., Kissinger, J. C., Kraemer, E., Li, W., Miller, J. A., Nayak, V., Pennington, C., . . . Wang, H. (2009). PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res, 37(Database issue), D539-543. https://doi.org/10.1093/nar/gkn814
Barber, B. E., Grigg, M. J., Piera, K. A., William, T., Cooper, D. J., Plewes, K., Dondorp, A. M., Yeo, T. W. and Anstey, N. M. (2018). Intravascular haemolysis in severe Plasmodium knowlesi malaria: association with endothelial activation, microvascular dysfunction, and acute kidney injury. Emerg Microbes Infect, 7(1), 106. https://doi.org/10.1038/s41426-018-0105-2
Bhasin, M., and Raghava, G. P. (2004). Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine, 22(23-24), 3195-3204. https://doi.org/10.1016/j.vaccine.2004.02.005
Birkett, A. J., Moorthy, V. S., Loucq, C., Chitnis, C. E. and Kaslow, D. C. (2013). Malaria vaccine R&D in the Decade of Vaccines: breakthroughs, challenges and opportunities. Vaccine, 31 Suppl 2, B233-243. https://doi.org/10.1016/j.vaccine.2013.02.040
Blom, N., Sicheritz-Ponten, T., Gupta, R., Gammeltoft, S. and Brunak, S. (2004). Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics, 4(6), 1633-1649. https://doi.org/10.1002/pmic.200300771
Burns, A. L., Dans, M. G., Balbin, J. M., de Koning-Ward, T. F., Gilson, P. R., Beeson, J. G., Boyle, M. J. and Wilson, D. W. (2019). Targeting malaria parasite invasion of red blood cells as an antimalarial strategy. FEMS microbiology reviews, 43(3), 223-238. doi: 10.1093/femsre/fuz005.
Chou, P. Y., and Fasman, G. D. (1979). Prediction of beta-turns. Biophys J, 26(3), 367-383. https://doi.org/10.1016/S0006-3495(79)85259-5
Chuh, K. N., Batt, A. R. and Pratt, M. R. (2016). Chemical Methods for Encoding and Decoding of Posttranslational Modifications. Cell Chem Biol, 23(1), 86-107. https://doi.org/10.1016/j.chembiol.2015.11.006
Colovos, C., and Yeates, T. O. (1993). Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci, 2(9), 1511-1519. https://doi.org/10.1002/pro.5560020916
Cowman, A. F., Tonkin, C. J., Tham, W. H. and Duraisingh, M. T. (2017). The Molecular Basis of Erythrocyte Invasion by Malaria Parasites. Cell Host Microbe, 22(2), 232-245. https://doi.org/10.1016/j.chom.2017.07.003
Deléage, G. (2017). ALIGNSEC: viewing protein secondary structure predictions within large multiple sequence alignments. Bioinformatics, 33(24), 3991-3992. doi: 10.1093/bioinformatics/btx521. PMID: 28961944.
Dephoure, N., Gould, K. L., Gygi, S. P. and Kellogg, D. R. (2013). Mapping and analysis of phosphorylation sites: a quick guide for cell biologists. Mol Biol Cell, 24(5), 535-542. https://doi.org/10.1091/mbc.E12-09-0677
Dimitrov, I., Flower, D. R. and Doytchinova, I. (2013). AllerTOP-a server for in silico prediction of allergens. BMC Bioinformatics 14 (Suppl 6), S4 (2013). https://doi.org/10.1186/1471-2105-14-S6-S4
Doytchinova, I. A., and Flower, D. R. (2007). VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC bioinformatics, 8, 4. https://doi.org/10.1186/1471-2105-8-4
Egbewande, O. M. (2022). The RTS,S malaria vaccine: Journey from conception to recommendation. Public Health Pract (Oxf), 4, 100283. https://doi.org/10.1016/j.puhip.2022.100283
Emini, E. A., Hughes, J. V., Perlow, D. and Boger, J. (1985). Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. Journal of virology, 55(3), 836-839. doi: 10.1128/JVI.55.3.836-839.
Ferre, F., and Clote, P. (2005). DiANNA: a web server for disulfide connectivity prediction. Nucleic Acids Res, 33(Web Server issue), W230-232. https://doi.org/10.1093/nar/gki412
Ghaffari, A. D., Dalimi, A., Ghaffarifar, F. and Pirestani, M. (2020). Structural predication and antigenic analysis of ROP16 protein utilizing immunoinformatics methods in order to identification of a vaccine against Toxoplasma gondii: An in silico approach. Microb Pathog, 142, 104079. https://doi.org/10.1016/j.micpath.2020.104079
Gardner MJ, Hall N, Fung E, et al. Genome sequence of the human malaria parasite Plasmodium falciparum. Nature 2002; 419:498–511. doi: 10.1038/nature01097.
Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S. e., Wilkins, M. R., Appel, R. D. and Bairoch, A. (2005). Protein identification and analysis tools on the ExPASy server. Springer. doi.org/10.1385/1-59259-890-0:571
Heo, L., Park, H. and Seok, C. (2013). GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res, 41(Web Server issue), W384-388. https://doi.org/10.1093/nar/gkt458
Karplus, P., and Schulz, G. (1985). Prediction of chain flexibility in proteins: a tool for the selection of peptide antigens. Naturwissenschaften, 72(4), 212-213.
Kolaskar, A. S., and Tongaonkar, P. C. (1990). A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett, 276(1-2), 172-174. https://doi.org/10.1016/0014-5793(90)80535-q
Krogh, A., Larsson, B., von Heijne, G. and Sonnhammer, E. L. (2001). Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol, 305(3), 567-580. https://doi.org/10.1006/jmbi.2000.4315
Larsen, J. E., Lund, O. and Nielsen, M. (2006). Improved method for predicting linear B-cell epitopes. Immunome Res, 2, 2. https://doi.org/10.1186/1745-7580-2-2
McGuffin, L. J., Bryson, K. and Jones, D. T. (2000). The PSIPRED protein structure prediction server. Bioinformatics, 16(4), 404-405. https://doi.org/10.1093/bioinformatics/16.4.404
Nakai, K., and Horton, P. (1999). PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends in Biochemical Sciences, 24(1), 34-35. doi: 10.1016/s0968-0004(98)01336-x.
Nezafat, N., Ghasemi, Y., Javadi, G., Khoshnoud, M. J. and Omidinia, E. (2014). A novel multi-epitope peptide vaccine against cancer: an in silico approach. J Theor Biol, 349, 121-134. https://doi.org/10.1016/j.jtbi.2014.01.018
Pannu, A. K. (2019). Malaria today: advances in management and control. Trop Doct, 49(3), 160-164. https://doi.org/10.1177/0049475519846382
Phillips, M., Burrows, J., Manyando, C., van Huijsduijnen, R. H., Van Voorhis, W. and Wells, T. (2017). Malaria Nat Rev Dis Primers, 3 (2017). 17050. doi.org/10.1038/nrdp.2017.50
Ren, J., Wen, L., Gao, X., Jin, C., Xue, Y. and Yao, X. (2008). CSS-Palm 2.0: an updated software for palmitoylation sites prediction. Protein Eng Des Sel, 21(11), 639-644. https://doi.org/10.1093/protein/gzn039
Reynisson, B., Alvarez, B., Paul, S., Peters, B. and Nielsen, M. (2020). NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res, 48(W1), W449-W454. https://doi.org/10.1093/nar/gkaa379
Romano, P., Giugno, R. and Pulvirenti, A. (2011). Tools and collaborative environments for bioinformatics research. Brief Bioinform, 12(6), 549-561. https://doi.org/10.1093/bib/bbr055
Roy, A., Kucukural, A. and Zhang, Y. (2010). I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc, 5(4), 725-738. https://doi.org/10.1038/nprot.2010.5
Saha, S., and Raghava, G. P. (2006). Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins, 65(1), 40-48. https://doi.org/10.1002/prot.21078
Saha, S., and Raghava, G. P. S. (2004). BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. International conference on artificial immune systems, ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_16
Talapko, J., Skrlec, I., Alebic, T., Jukic, M. and Vcev, A. (2019). Malaria: The Past and the Present. Microorganisms, 7(6), 179. https://doi.org/10.3390/microorganisms7060179
Varo, R., Chaccour, C. and Bassat, Q. (2020). Update on malaria. Med Clin (Barc), 155(9), 395-402. https://doi.org/10.1016/j.medcli.2020.05.010
Vita, R., Mahajan, S., Overton, J. A., Dhanda, S. K., Martini, S., Cantrell, J. R., Wheeler, D. K., Sette, A. and Peters, B. (2019). The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res, 47(D1), D339-D343. https://doi.org/10.1093/nar/gky1006
WHO. (2021). World malaria report 2021. In: World Health Organization.
Wiederstein, M., and Sippl, M. J. (2007). ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res, 35(Web Server issue), W407-410. https://doi.org/10.1093/nar/gkm290
Winther, J. R., and Thorpe, C. (2014). Quantification of thiols and disulfides. Biochim Biophys Acta, 1840(2), 838-846. https://doi.org/10.1016/j.bbagen.2013.03.031
Wu, S., and Zhang, Y. (2007). LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res, 35(10), 3375-3382. https://doi.org/10.1093/nar/gkm251
Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J. and Zhang, Y. (2015). The I-TASSER Suite: protein structure and function prediction. Nat Methods, 12(1), 7-8. https://doi.org/10.1038/nmeth.3213
Zheng, W., Zhang, C., Wuyun, Q., Pearce, R., Li, Y. and Zhang, Y. (2019). LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins. Nucleic Acids Res, 47(W1), W429-W436. https://doi.org/10.1093/nar/gkz384
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