In Silico Structural and Functional Annotation of Nine Essential Hypothetical Proteins from Streptococcus pneumoniae

https://doi.org/10.22146/ijc.41817

Khairiah Razali(1), Azzmer Azzar Abdul Hamid(2), Noor Hasniza Md Zin(3), Noraslinda Muhamad Bunnori(4), Hanani Ahmad Yusof(5), Kamarul Rahim Kamarudin(6), Aisyah Mohamed Rehan(7*)

(1) Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia, Jl. Sultan Ahmad Shah, 25200, Kuantan, Pahang, Malaysia
(2) Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia, Jl. Sultan Ahmad Shah, 25200, Kuantan, Pahang, Malaysia; Research Unit for Bioinformatics and Computational Biology (RUBIC), Kulliyyah of Science, International Islamic University Malaysia, Jl. Sultan Ahmad Shah, Kuantan, Pahang, 25200, Malaysia
(3) Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia, Jl. Sultan Ahmad Shah, 25200, Kuantan, Pahang, Malaysia
(4) Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia, Jl. Sultan Ahmad Shah, 25200, Kuantan, Pahang, Malaysia
(5) Department of Biomedical Sciences, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia, Jl. Sultan Ahmad Shah, Kuantan, Pahang, 25200, Malaysia
(6) Department of Technology and Natural Resources, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Campus, Pagoh Education Hub, Km 1, Jalan Panchor, Muar, Johor Darul Takzim, 84600, Malaysia
(7) Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia, Jl. Sultan Ahmad Shah, 25200, Kuantan, Pahang, Malaysia; Research Unit for Bioinformatics and Computational Biology (RUBIC), Kulliyyah of Science, International Islamic University Malaysia, Jl. Sultan Ahmad Shah, Kuantan, Pahang, 25200, Malaysia
(*) Corresponding Author

Abstract


The ability of Streptococcus pneumoniae to induce infections relies on its virulence factor machinery. A previous CRISPR interference (CRISPRi) study had identified 254 essential proteins that may be responsible towards the pathogenicity of S. pneumoniae serotype 2 strain D39. However, 39 of them were functionally and structurally uncharacterized. Hence, by using in silico approach, this study aimed to annotate the function and structure of these un-annotated proteins. Initially, all 39 proteins went through primary screening for template availability and pathogenicity. From there, 11 of them were selected and underwent further physicochemical, functional and structural categorization through integrated bioinformatics approach by means of amino acid sequence- and structure- based analyses. The obtained data revealed that 9 targeted proteins showed high possibility to be involved in either cell viability or cell pathogenicity mechanism of the bacterium, with SPD_1333 and SPD_1743 being the two most promising proteins to be further studied. Findings from this study can help in facilitating a better understanding of pathogenic ability of this microorganism and enhance drug development and target identification processes in the aim of improving pneumococcal disease control.

Keywords


hypothetical proteins; S. pneumoniae strain D39; in silico analysis of protein; bioinformatics tools



References

[1] World Health Organization, Pneumococcal Disease, https://www.who.int/biologicals/vaccines/pneumococcal/en/, accessed on February 19, 2019.

[2] Weiser, J.N., Ferreira, D.M., and Paton, J.C., 2018, Streptococcus pneumoniae: Transmission, colonization and invasion, Nat. Rev. Microbiol., 16 (6), 355–367.

[3] Henriques-Normark, B., and Tuomanen, E.I., 2013, The pneumococcus: Epidemiology, microbiology, and pathogenesis, Cold Spring Harb. Perspect. Med., 3 (7), a010215.

[4] Song, J.H., 2013, Advances in pneumococcal antibiotic resistance, Expert Rev. Respir. Med., 7 (5), 491–498.

[5] Cherazard, R., Epstein, M., Doan, T.L., Salim, T., Bharti, S., and Smith, M.A., 2017, Antimicrobial resistant Streptococcus pneumoniae, Am. J. Ther., 24 (3), e361–e369.

[6] Lipsitch, M., and Siber, G.R., 2016, How can vaccines contribute to solving the antimicrobial resistance problem?, MBio, 7 (3), 00428-16.

[7] Rodgers, G.L., and Klugman, K.P., 2016, Surveillance of the impact of pneumococcal conjugate vaccines in developing countries, Hum. Vaccin. Immunother., 12 (2), 417–420.

[8] Mitchell, A.M., and Mitchell, T.J., 2010, Streptococcus pneumoniae: Virulence factors and variation, Clin. Microbiol. Infect., 16 (5), 411–418.

[9] Hyams, C., Camberlein, E., Cohen, J.M., Bax, K., and Brown, J.S., 2010, The Streptococcus pneumoniae capsule inhibits complement activity and neutrophil phagocytosis by multiple mechanisms, Infect. Immun., 78 (2), 704–715.

[10] Mostowy, R., Croucher, N.J., Hanage, W.P., Harris, S.R., Bentley, S., and Fraser, C., 2014, Heterogeneity in the frequency and characteristics of homologous recombination in pneumococcal evolution, PLoS Genet., 10 (5), 1004300.

[11] Jedrzejas, M.J., 2001, Pneumococcal virulence factors: Structure and function, Microbiol. Mol. Biol. Rev., 65 (2), 187–207.

[12] Dahlström, K.M., 2015, From Protein Structure to Function with Bioinformatics, Dissertation, Faculty of Science and Engineering, Åbo Akademi University, Turku, Finland.

[13] Wuchty, S., Rajagopala, S.V., Blazie, S.M., Parrish, J.R., Khuri, S., Finley, R.L., and Uetz, P., 2017, The protein interactome of Streptococcus pneumoniae and bacterial meta-interactomes improve function predictions, mSystems, 2 (3), 00019-17.

[14] Liu, X., Kjos, M., Sorg, R.A., Veening, J., van Kessel, S.P., Zhang, J., Knoops, K., Slager, J., Domenech, A., and Gallay, C., 2017, High‐throughput CRISPRi phenotyping identifies new essential genes in Streptococcus pneumoniae, Mol. Syst. Biol., 13 (5), 931.

[15] Gupta, A., Kapil, R., Dhakan, D.B., and Sharma, V.K., 2014, MP3: A software tool for the prediction of pathogenic proteins in genomic and metagenomic data, PLoS One, 9 (4), e93907.

[16] Pearson, W.R., 2013, An introduction to sequence similarity (“homology”) searching, Curr. Protoc. Bioinf., 42 (1), 3.1.1–3.1.8.

[17] Bairoch, A., Gattiker, A., Wilkins, M.R., Gasteiger, E., Duvaud, S., Appel, R.D., and Hoogland, C., 2009, “Protein Identification and Analysis Tools on the ExPASy Server”, in The Proteomics Protocols Handbook, Eds., Walker, J.M., Humana Press, 571–607.

[18] El-Gebali, S., Mistry, J., Bateman, A., Eddy, S.R., Luciani, A., Potter, S.C., Qureshi, M., Richardson, L.J., Salazar, G.A., Smart, A., Sonnhammer, E.L.L., Hirsh, L., Paladin, L., Piovesan, D., Tosatto, S.C.E., and Finn, R.D., 2019, The Pfam protein families database in 2019, Nucleic Acids Res., 47 (D1), D427–D432.

[19] Marchler-Bauer, A., Bo, Y., Han, L., He, J., Lanczycki, C.J., Lu, S., Chitsaz, F., Derbyshire, M.K., Geer, R.C., Gonzales, N.R., Gwadz, M., Hurwitz, D.I., Lu, F., Marchler, G.H., Song, J.S., Thanki, N., Wang, Z., Yamashita, R.A., Zhang, D., Zheng, C., Geer, L.Y., and Bryant, S.H., 2017, CDD/SPARCLE: Functional classification of proteins via subfamily domain architectures, Nucleic Acids Res., 45 (D1), D200–D203.

[20] Yu, N.Y., Wagner, J.R., Laird, M.R., Melli, G., Rey, S., Lo, R., Dao, P., Sahinalp, S.C., Ester, M., Foster, L.J., and Brinkman, F.S.L., 2010, PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes, Bioinformatics, 26 (13), 1608–1615.

[21] Tusnády, G.E., and Simon, I., 2001, The HMMTOP transmembrane topology prediction server, Bioinformatics, 17 (9), 849–850.

[22] Armenteros, J.J.A., Tsirigos, K.D., Sønderby, C.K., Petersen, T.N., Winther, O., Brunak, S., von Heijne, G., and Nielsen, H., 2019, SignalP 5.0 improves signal peptide predictions using deep neural networks, Nat. Biotechnol., 37, 420–423.

[23] Bendtsen, J.D., Jensen, L.J., Blom, N., von Heijne, G., and Brunak, S., 2004, Feature-based prediction of non-classical and leaderless protein secretion, Protein Eng. Des. Sel., 17 (4), 349–356.

[24] Szklarczyk, D., Gable, A.L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N.T., Morris, J.H., Bork, P., Jensen, L.J., and von Mering, C., 2019, STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res., 47 (D1), D607–D613.

[25] Buchan, D.W.A., Minneci, F., Nugent, T.C.O., Bryson, K., and Jones, D.T., 2013, Scalable web services for the PSIPRED Protein Analysis Workbench, Nucleic Acids Res., 41 (W1), W349–W357.

[26] Zhang, Y., 2008, I-TASSER server for protein 3D structure prediction, BMC Bioinf., 9 (1), 40.

[27] Chen, C.C., Hwang, J.K., and Yang, J.M., 2006, (PS)2: Protein structure prediction server, Nucleic Acids Res., 34 (Web Server), W152–W157.

[28] Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F.T., de Beer, T.A.P., Rempfer, C., Bordoli, L., Lepore, R., and Schwede, T., 2018, SWISS-MODEL: Homology modelling of protein structures and complexes, Nucleic Acids Res., 46 (W1), W296–W303.

[29] Eisenberg, D., Lüthy, R., and Bowie, J.U., 1997, VERIFY3D: Assessment of protein models with three-dimensional profiles, Methods Enzymol., 277, 396–404.

[30] Benkert, P., Biasini, M., and Schwede, T., 2011, Toward the estimation of the absolute quality of individual protein structure models, Bioinformatics, 27 (3), 343–350.

[31] Abraham, M.J., Murtola, T., Schulz, R., Páll, S., Smith, J.C., Hess, B., and Lindahl, E., 2015, GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers, SoftwareX, 1-2, 19–25.

[32] Huang, B., 2009, MetaPocket: A meta approach to improve protein ligand binding site prediction, OMICS, 13 (4), 325–330.

[33] Yang, J., Roy, A., and Zhang, Y., 2013, Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment, Bioinformatics, 29 (20), 2588–2595.

[34] Wang, F., Xiao, J., Pan, L., Yang, M., Zhang, G., Jin, S., and Yu, J., 2008, A systematic survey of mini-proteins in bacteria and archaea, PLoS One, 3 (12), e4027.

[35] Wan, S., Duan, Y., and Zou, Q., 2017, HPSLPred: An ensemble multi-label classifier for human protein subcellular location prediction with imbalanced source, Proteomics, 17 (17-18), 1700262.

[36] Davis, M.J., Hanson, K.A., Clark, F., Fink, J.L., Zhang, F., Kasukawa, T., Kai, C., Kawai, J., Carninci, P., Hayashizaki, Y., and Teasdale, R.D., 2006, Differential use of signal peptides and membrane domains is a common occurrence in the protein output of transcriptional units, PLoS Genet., 2 (4), e46.

[37] Rydzewski, J., Jakubowski, R., and Nowak, W., 2015, Communication: Entropic measure to prevent energy over-minimization in molecular dynamics simulations, J. Chem. Phys., 143 (17), 171103.

[38] Chen, J., Almo, S.C., and Wu, Y., 2017, General principles of binding between cell surface receptors and multi-specific ligands: A computational study, PLOS Comput. Biol., 13 (10), e1005805.

[39] Miclet, E., Stoven, V., Michels, P.A.M., Opperdoes, F.R., Lallemand, J.Y., and Duffieux, F., 2001, NMR spectroscopic analysis of the first two steps of the pentose-phosphate pathway elucidates the role of 6-phosphogluconolactonase, J. Biol. Chem., 276 (37), 34840–34846.

[40] Andersson, H., Kappeler, F., and Hauri, H.P., 1999, Protein targeting to endoplasmic reticulum by dilysine signals involves direct retention in addition to retrieval, J. Biol. Chem., 274 (21), 15080–15084.

[41] Ruhanen, H., Hurley, D., Ghosh, A., O’Brien, K.T., Johnston, C.R., and Shields, D.C., 2014, Potential of known and short prokaryotic protein motifs as a basis for novel peptide-based antibacterial therapeutics: a computational survey, Front. Microbiol., 5, 4.

[42] Luthra, A., Swinehart, W., Bayooz, S., Phan, P., Stec, B., Iwata-Reuyl, D., and Swairjo, M.A., 2018, Structure and mechanism of a bacterial t6A biosynthesis system, Nucleic Acids Res., 46 (3), 1395–1411.

[43] Miyauchi, K., Kimura, S., and Suzuki, T., 2013, A cyclic form of N6-threonylcarbamoyladenosine as a widely distributed tRNA hypermodification, Nat. Chem. Biol., 9 (2), 105–111.

[44] Bugreev, D.V., and Mazin, A.V., 2004, Ca2+ activates human homologous recombination protein Rad51 by modulating its ATPase activity, Proc. Natl. Acad. Sci. U.S.A., 101 (27), 9988–9993.

[45] Teplyakov, A., Obmolova, G., Tordova, M., Thanki, N., Bonander, N., Eisenstein, E., Howard, A.J., and Gilliland, G.L., 2002, Crystal structure of the YjeE protein from Haemophilus influenzae: A putative ATPase involved in cell wall synthesis, Proteins Struct. Funct. Genet., 48 (2), 220–226.



DOI: https://doi.org/10.22146/ijc.41817

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