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Current Pharmaceutical Biotechnology

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ISSN (Print): 1389-2010
ISSN (Online): 1873-4316

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Research Article

In Silico Investigation of Signal Peptide Sequences to Enhance Secretion of CD44 Nanobodies Expressed in Escherichia coli

Author(s): Soudabeh Kavousipour, Shiva Mohammadi, Ebrahim Eftekhar, Mahdi Barazesh* and Mohammad H. Morowvat

Volume 22, Issue 9, 2021

Published on: 12 October, 2020

Page: [1192 - 1205] Pages: 14

DOI: 10.2174/1389201021666201012162904

Price: $65

Abstract

Background: The selection of a suitable signal peptide that can direct recombinant proteins from the cytoplasm to the extracellular space is an important criterion affecting the production of recombinant proteins in Escherichia coli, a widely used host. Nanobodies are currently attracting the attention of scientists as antibody alternatives due to their specific properties and feasibility of production in E. coli.

Objective: CD44 nanobodies constitute a potent therapeutic agent that can block CD44/HA interaction in cancer and inflammatory diseases. This molecule may also function as a drug against cancer cells and has been produced previously in E. coli without a signal peptide sequence. The goal of this project was to find a suitable signal peptide to direct CD44 nanobody extracellular secretion in E. coli that will potentially lead to optimization of experimental methods and facilitate downstream steps such as purification.

Methods: We analyzed 40 E. coli derived signal peptides retrieved from the Signal Peptide database and selected the best candidate signal peptides according to relevant criteria including signal peptide probability, stability, and physicochemical features, which were evaluated using signalP software version 4.1 and the ProtParam tool, respectively.

Results: In this in silico study, suitable candidate signal peptide(s) for CD44 nanobody secretory expression were identified. CSGA, TRBC, YTFQ, NIKA, and DGAL were selected as appropriate signal peptides with acceptable D-scores, and appropriate physicochemical and structural properties. Following further analysis, TRBC was selected as the best signal peptide to direct CD44 nanobody expression to the extracellular space of E. coli.

Conclusion: The selected signal peptide, TRBC is the most suitable to promote high-level secretory production of CD44 nanobodies in E. coli and potentially will be useful for scaling up CD44 nanobody production in experimental research as well as in other CD44 nanobody applications. However, experimental work is needed to confirm the data.

Keywords: CD44, E. coli, in silico cloning, physicochemical properties, secretory production, SignalP software.

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[1]
Ponta, H.; Sherman, L.; Herrlich, P.A. CD44: From adhesion molecules to signalling regulators. Nat. Rev. Mol. Cell Biol., 2003, 4(1), 33-45.
[http://dx.doi.org/10.1038/nrm1004] [PMID: 12511867]
[2]
Wang, L.; Zuo, X.; Xie, K.; Wei, D. The role of CD44 and cancer stem cells. Methods Mol. Biol., 2018, 1692, 31-42.
[3]
Misra, S.; Heldin, P.; Hascall, V.C.; Karamanos, N.K.; Skandalis, S.S.; Markwald, R.R.; Ghatak, S. Hyaluronan-CD44 interactions as potential targets for cancer therapy. FEBS J., 2011, 278(9), 1429-1443.
[http://dx.doi.org/10.1111/j.1742-4658.2011.08071.x] [PMID: 21362138]
[4]
De Bree, R.; Tijink, B.; Buter, J.; Giaccone, G.; Lang, M.; Staab, A. A phase I dose escalation study with anti-CD44V6 bivatuzumab mertansine in patients with incurable squamous cell carcinoma of the head and neck or esophagus. Radiother. Oncol., 2007, (82), S29-S30.
[http://dx.doi.org/10.1016/S0167-8140(07)80090-9]
[5]
Kavousipour, S.; Mokarram, P.; Gargari, S.L.M.; Mostafavi-Pour, Z.; Barazesh, M.; Ramezani, A.; Ashktorab, H.; Mohammadi, S.; Ghavami, S. A comparison between cell, protein and peptide-based approaches for selection of nanobodies against CD44 from a synthetic library. Protein Pept. Lett., 2018, 25(6), 580-588.
[http://dx.doi.org/10.2174/0929866525666180530122159] [PMID: 29848261]
[6]
Mir, M.A.; Mehraj, U.; Sheikh, B.A.; Hamdani, S.S. Nanobodies: The “magic bullets” in therapeutics, drug delivery and diagnostics. Hum. Antibodies, 2020, 28(1), 29-51.
[7]
Henry, K.A.; MacKenzie, C.R., Eds.; Antigen recognition by single-domain antibodies: structural latitudes and constraints. MAbs; Taylor & Francis, 2018.
[8]
Hayat, S.M.G.; Farahani, N.; Golichenari, B.; Sahebkar, A. Recombinant protein expression in Escherichia coli (E. coli): What we need to know. Curr. Pharm. Des., 2018, 24(6), 718-725.
[http://dx.doi.org/10.2174/1381612824666180131121940] [PMID: 29384059]
[9]
Freudl, R. Signal peptides for recombinant protein secretion in bacterial expression systems. Microb. Cell Fact., 2018, 17(1), 52.
[http://dx.doi.org/10.1186/s12934-018-0901-3] [PMID: 29598818]
[10]
Green, E.R.; Mecsas, J. Bacterial secretion systems: An overview; Virulence mechanisms of bacterial pathogens. Microbiol. Spectr., 2016, 4(1)
[http://dx.doi.org/10.1128/microbiolspec]
[11]
Choi, J.H.; Lee, S.Y. Secretory and extracellular production of recombinant proteins using Escherichia coli. Appl. Microbiol. Biotechnol., 2004, 64(5), 625-635.
[http://dx.doi.org/10.1007/s00253-004-1559-9] [PMID: 14966662]
[12]
de Marco, A. Strategies for successful recombinant expression of disulfide bond-dependent proteins in Escherichia coli. Microb. Cell Fact., 2009, 8(1), 26.
[http://dx.doi.org/10.1186/1475-2859-8-26] [PMID: 19442264]
[13]
Gardy, J.L.; Brinkman, F.S. Methods for predicting bacterial protein subcellular localization. Nat. Rev. Microbiol., 2006, 4(10), 741-751.
[http://dx.doi.org/10.1038/nrmicro1494] [PMID: 16964270]
[14]
Choo, K.H.; Tan, T.W.; Ranganathan, S., Eds.; A comprehensive assessment of N-terminal signal peptides prediction methods. BMC, Bioinformatics; Springer, 2009.
[15]
Zarei, M.; Nezafat, N.; Morowvat, M.H.; Ektefaie, M.; Ghasemi, Y. In silico analysis of different signal peptides for secretory production of arginine deiminase in Escherichia coli. Recent Pat. Biotechnol., 2019, 13(3), 217-227.
[http://dx.doi.org/10.2174/1872208313666190101114602] [PMID: 30621572]
[16]
Petersen, T.N.; Brunak, S.; von Heijne, G.; Nielsen, H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods, 2011, 8(10), 785-786.
[http://dx.doi.org/10.1038/nmeth.1701] [PMID: 21959131]
[17]
Bagos, P.G.; Nikolaou, E.P.; Liakopoulos, T.D.; Tsirigos, K.D. Combined prediction of Tat and Sec signal peptides with hidden Markov models. Bioinformatics, 2010, 26(22), 2811-2817.
[http://dx.doi.org/10.1093/bioinformatics/btq530] [PMID: 20847219]
[18]
Zeng, R.; Gao, S.; Xu, L.; Liu, X.; Dai, F. Prediction of pathogenesis-related secreted proteins from Stemphylium lycopersici. BMC Microbiol., 2018, 18(1), 191.
[http://dx.doi.org/10.1186/s12866-018-1329-y] [PMID: 30458731]
[19]
Forouharmehr, A.; Nassiri, M.; Ghovvati, S.; Javadmanesh, A. Evaluation of different signal peptides for secretory production of recombinant bovine pancreatic ribonuclease A in gram negative bacterial system: An in silico study. Curr. Proteomics, 2018, 15(1), 24-33.
[http://dx.doi.org/10.2174/1570164614666170725144424]
[20]
Harmsen, M.M.; De Haard, H.J. Properties, production, and applications of camelid single-domain antibody fragments. Appl. Microbiol. Biotechnol., 2007, 77(1), 13-22.
[http://dx.doi.org/10.1007/s00253-007-1142-2] [PMID: 17704915]
[21]
Karyolaimos, A.; Ampah-Korsah, H.; Hillenaar, T.; Mestre Borras, A.; Dolata, K.M.; Sievers, S.; Riedel, K.; Daniels, R.; de Gier, J.W. Enhancing recombinant protein yields in the E. coli periplasm by combining signal peptide and production rate screening. Front. Microbiol., 2019, 10, 1511.
[http://dx.doi.org/10.3389/fmicb.2019.01511] [PMID: 31396164]
[22]
Brockmeier, U.; Caspers, M.; Freudl, R.; Jockwer, A.; Noll, T.; Eggert, T. Systematic screening of all signal peptides from Bacillus subtilis: A powerful strategy in optimizing heterologous protein secretion in Gram-positive bacteria. J. Mol. Biol., 2006, 362(3), 393-402.
[http://dx.doi.org/10.1016/j.jmb.2006.07.034] [PMID: 16930615]
[23]
Nielsen, H. Predicting secretory proteins with SignalP. Protein function prediction; Springer, 2017, pp. 59-73.
[http://dx.doi.org/10.1007/978-1-4939-7015-5_6]
[24]
Melhem, H.; Min, X.J.; Butler, G. Eds.; In: The impact of SignalP 4.0 on the prediction of secreted proteins, IEEE Symposium Series on Computational Intelligence (IIEEE SSCI 2013): The 10th annual IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Singapore, April 16-19, 2013; pp.16-22.
[25]
Frank, K.; Sippl, M.J. High-performance signal peptide prediction based on sequence alignment techniques. Bioinformatics, 2008, 24(19), 2172-2176.
[http://dx.doi.org/10.1093/bioinformatics/btn422] [PMID: 18697773]
[26]
Liang, S.; Li, C.; Ye, Y.; Lin, Y. Endogenous signal peptides efficiently mediate the secretion of recombinant proteins in Pichia pastoris. Biotechnol. Lett., 2013, 35(1), 97-105.
[http://dx.doi.org/10.1007/s10529-012-1055-8] [PMID: 23160737]
[27]
Mohammadi, S.; Mostafavi-Pour, Z.; Ghasemi, Y.; Barazesh, M.; Pour, S.K.; Atapour, A. In silico analysis of different signal peptides for the excretory production of recombinant NS3-GP96 fusion protein in Escherichia coli. Int. J. Pept. Res. Ther., 2019, 25(4), 1279-1290.
[http://dx.doi.org/10.1007/s10989-018-9775-9]
[28]
Käll, L.; Krogh, A.; Sonnhammer, E.L. A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol., 2004, 338(5), 1027-1036.
[http://dx.doi.org/10.1016/j.jmb.2004.03.016] [PMID: 15111065]
[29]
Low, K.O.; Muhammad Mahadi, N.; Md Illias, R. Optimisation of signal peptide for recombinant protein secretion in bacterial hosts. Appl. Microbiol. Biotechnol., 2013, 97(9), 3811-3826.
[http://dx.doi.org/10.1007/s00253-013-4831-z] [PMID: 23529680]
[30]
Desvaux, M.; Hébraud, M.; Talon, R.; Henderson, I.R. Secretion and subcellular localizations of bacterial proteins: A semantic awareness issue. Trends Microbiol., 2009, 17(4), 139-145.
[http://dx.doi.org/10.1016/j.tim.2009.01.004] [PMID: 19299134]
[31]
Desvaux, M.; Parham, N.J.; Scott-Tucker, A.; Henderson, I.R. The general secretory pathway: A general misnomer? Trends Microbiol., 2004, 12(7), 306-309.
[http://dx.doi.org/10.1016/j.tim.2004.05.002] [PMID: 15223057]
[32]
De Bona, P.; Deshmukh, L.; Gorbatyuk, V.; Vinogradova, O.; Kendall, D.A. Structural studies of a signal peptide in complex with signal peptidase I cytoplasmic domain: The stabilizing effect of membrane-mimetics on the acquired fold. Proteins, 2012, 80(3), 807-817.
[http://dx.doi.org/10.1002/prot.23238] [PMID: 22113858]
[33]
Ghovvati, S.; Pezeshkian, Z.; Mirhoseini, S.Z. In silico analysis of different signal peptides to discover a panel of appropriate signal peptides for secretory production of Interferon-beta 1b in Escherichia coli. Acta Biochim. Pol., 2018, 65(4), 521-534.
[http://dx.doi.org/10.18388/abp.2018_2351] [PMID: 30378597]
[34]
Dastjerdeh, M.S.; Marashiyan, M.; Boroujeni, M.B.; Golkar, M.; Shokrgozar, M.A.; Rahimi, H. In silico analysis of different signal peptides for the secretory production of recombinant human keratinocyte growth factor in Escherichia coli. Comput. Biol. Chem., 2019, 80, 225-233.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.03.003] [PMID: 30999249]
[35]
Jeiranikhameneh, M.; Moshiri, F.; Keyhan Falasafi, S.; Zomorodipour, A. Designing signal peptides for efficient periplasmic expression of human growth hormone in Escherichia coli. J. Microbiol. Biotechnol., 2017, 27(11), 1999-2009.
[http://dx.doi.org/10.4014/jmb.1703.03080] [PMID: 28851205]
[36]
Massahi, A.; Çalık, P. In-silico determination of Pichia pastoris signal peptides for extracellular recombinant protein production. J. Theor. Biol., 2015, 364, 179-188.
[PMID: 25218497]
[37]
Bagherinejad, M.R.; Sadeghi, H.M-M.; Abedi, D.; Chou, C.P.; Moazen, F.; Rabbani, M. Twin arginine translocation system in secretory expression of recombinant human growth hormone. Res. Pharm. Sci., 2016, 11(6), 461-469.
[PMID: 28003839]
[38]
Darvishi, F.; Zarei, A.; Madzak, C. In silico and in vivo analysis of signal peptides effect on recombinant glucose oxidase production in nonconventional yeast Yarrowia lipolytica. World J. Microbiol. Biotechnol., 2018, 34(9), 128.
[PMID: 30083963]
[39]
Gimenez, M.R.; Chandra, G.; Van Overvelt, P.; Voulhoux, R.; Bleves, S.; Ize, B. Genome wide identification and experimental validation of Pseudomonas aeruginosa Tat substrates. Sci. Rep., 2018, 8(1), 11950.
[PMID: 30093651]
[40]
Gomez, H.L.R.; Peralta, J.P.; Tejano, L.A.; Chang, Y-W. In silico and in vitro assessment of portuguese oyster (Crassostrea angulata) proteins as precursor of bioactive peptides. Int. J. Mol. Sci., 2019, 20(20), 5191.
[http://dx.doi.org/10.3390/ijms20205191] [PMID: 31635140]
[41]
Klatt, S.; Konthur, Z. Secretory signal peptide modification for optimized antibody-fragment expression-secretion in Leishmania tarentolae. Microb. Cell Fact., 2012, 11(1), 97.
[http://dx.doi.org/10.1186/1475-2859-11-97] [PMID: 22830363]
[42]
Molino, J.V.D.; de Carvalho, J.C.M.; Mayfield, S.P. Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS One, 2018, 13(2), e0192433.
[http://dx.doi.org/10.1371/journal.pone.0192433] [PMID: 29408937]

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