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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Research Article

Gallocin-derived Engineered Peptides Targeting EGFR and VEGFR in Colorectal Cancer: A Bioinformatic Approach

Author(s): Batoul Kavyani, Fereshteh Saffari*, Ali Afgar, Sajjad Kavyani, Masoud Rezaie, Fatemeh Sharifi and Roya Ahmadrajabi

Volume 24, Issue 18, 2024

Published on: 04 June, 2024

Page: [1599 - 1614] Pages: 16

DOI: 10.2174/0115680266295587240522050712

Price: $65

Abstract

Background: Colorectal cancer (CRC) treatment using time-saving and cost-effective targeted therapies with high selectivity and low toxicity drugs, is a great challenge. In primary investigations on Gallocin, as the most proposed factor in CRC pathogenesis caused by Streptococcus gallolyticus, it was surprisingly found that this bacteriocin has four α-helix structures and some anti-cancer sequences.

Objective: The aim of this study was to determine the ability of Gallocin-based anticancer peptides (ACPs) against epidermal growth factor receptor (EGFR) and vascular epidermal growth factor receptor (VEGFR) and the evaluation of their pharmacokinetic properties using bioinformatic approaches.

Methods: Support vector machine algorithm web-based tools were used for predicting ACPs. The physicochemical characteristics and the potential of anti-cancer activity of Gallocin-derived ACPs were determined by in silico tools. The 3D structure of predicted ACPs was modeled using modeling tools. The interactions between predicted ACPs and targets were investigated by molecular docking exercises. Then, the stability of ligand-receptor interactions was determined by molecular dynamic simulation. Finally, ADMET analysis was carried out to check the pharmacokinetic properties and toxicity of ACPs.

Results: Four amino acid sequences with anti-cancer potential were selected. Through molecular docking, Pep2, and Pep3 gained the best scores, more binding affinity, and strong attachments by the formation of reasonable H-bonds with both EGFR and VEGFR. Molecular simulation confirmed the stability of Pep3- EGFR. According to pharmacokinetic analysis, the ACPs were safe and truthful.

Conclusion: Designed peptides can be nominated as drugs for CRC treatment. However, different in-vitro and in-vivo assessments are required to approve this claim.

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[1]
Morgan, E.; Arnold, M.; Gini, A.; Lorenzoni, V.; Cabasag, C.J.; Laversanne, M.; Vignat, J.; Ferlay, J.; Murphy, N.; Bray, F. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut, 2023, 72(2), 338-344.
[http://dx.doi.org/10.1136/gutjnl-2022-327736] [PMID: 36604116]
[2]
Xie, Y.H.; Chen, Y.X.; Fang, J.Y. Comprehensive review of targeted therapy for colorectal cancer. Signal Transduct. Target. Ther., 2020, 5(1), 22.
[http://dx.doi.org/10.1038/s41392-020-0116-z] [PMID: 32296018]
[3]
Zhang, H.; Berezov, A.; Wang, Q.; Zhang, G.; Drebin, J.; Murali, R.; Greene, M.I. ErbB receptors: from oncogenes to targeted cancer therapies. J. Clin. Invest., 2007, 117(8), 2051-2058.
[http://dx.doi.org/10.1172/JCI32278] [PMID: 17671639]
[4]
Manzi, J.; Hoff, C.O.; Ferreira, R.; Pimentel, A.; Datta, J.; Livingstone, A.S.; Vianna, R.; Abreu, P. Targeted Therapies in Colorectal Cancer: Recent Advances in Biomarkers, Landmark Trials, and Future Perspectives. Cancers (Basel), 2023, 15(11), 3023.
[http://dx.doi.org/10.3390/cancers15113023] [PMID: 37296986]
[5]
Tiwari, A.; Saraf, S.; Verma, A.; Panda, P.K.; Jain, S.K. Novel targeting approaches and signaling pathways of colorectal cancer: An insight. World J. Gastroenterol., 2018, 24(39), 4428-4435.
[http://dx.doi.org/10.3748/wjg.v24.i39.4428] [PMID: 30357011]
[6]
Scaltriti, M.; Baselga, J. The epidermal growth factor receptor pathway: a model for targeted therapy. Clin. Cancer Res., 2006, 12(18), 5268-5272.
[http://dx.doi.org/10.1158/1078-0432.CCR-05-1554] [PMID: 17000658]
[7]
Lopez, A.; Harada, K.; Vasilakopoulou, M.; Shanbhag, N.; Ajani, J.A. Targeting Angiogenesis in Colorectal Carcinoma. Drugs, 2019, 79(1), 63-74.
[http://dx.doi.org/10.1007/s40265-018-1037-9] [PMID: 30617958]
[8]
Le, X.; Nilsson, M.; Goldman, J.; Reck, M.; Nakagawa, K.; Kato, T.; Ares, L.P.; Frimodt-Moller, B.; Wolff, K.; Visseren-Grul, C.; Heymach, J.V.; Garon, E.B. Dual EGFR-VEGF Pathway Inhibition: A Promising Strategy for Patients With EGFR-Mutant NSCLC. J. Thorac. Oncol., 2021, 16(2), 205-215.
[http://dx.doi.org/10.1016/j.jtho.2020.10.006] [PMID: 33096270]
[9]
El Bali, M; Bakkach, J; Bennani Mechita, M. Colorectal cancer: From genetic landscape to targeted therapy. J Oncol., 2021, 2021, 9918116.
[10]
Seow, H.; Yip, W.K.; Fifis, T. Advances in targeted and immunobased therapies for colorectal cancer in the genomic era. OncoTargets Ther., 2016, 9, 1899-1920.
[http://dx.doi.org/10.2147/OTT.S95101] [PMID: 27099521]
[11]
Khan, K.; Valeri, N.; Dearman, C.; Rao, S.; Watkins, D.; Starling, N.; Chau, I.; Cunningham, D. Targeting EGFR pathway in metastatic colorectal cancer-tumour heterogeniety and convergent evolution. Crit. Rev. Oncol. Hematol., 2019, 143, 153-163.
[http://dx.doi.org/10.1016/j.critrevonc.2019.09.001] [PMID: 31678702]
[12]
Mishra, D.; Mishra, A. in silico Insight to Identify Potential Inhibitors of BUB1B from Mushroom Bioactive Compounds to Prevent Breast Cancer Metastasis. FBL., 2023, 28(7), 151.
[13]
Kumar, V.; Kumar, R.; Parate, S.; Yoon, S.; Lee, G.; Kim, D.; Lee, K.W. Identification of ACK1 inhibitors as anticancer agents by using computer-aided drug designing. J. Mol. Struct., 2021, 1235, 130200.
[http://dx.doi.org/10.1016/j.molstruc.2021.130200]
[14]
Tornesello, A.L.; Borrelli, A.; Buonaguro, L.; Buonaguro, F.M.; Tornesello, M.L. Antimicrobial Peptides as Anticancer Agents: Functional Properties and Biological Activities. Molecules, 2020, 25(12), 2850.
[http://dx.doi.org/10.3390/molecules25122850] [PMID: 32575664]
[15]
Parchebafi, A.; Tamanaee, F.; Ehteram, H.; Ahmad, E.; Nikzad, H.; Haddad Kashani, H. The dual interaction of antimicrobial peptides on bacteria and cancer cells; mechanism of action and therapeutic strategies of nanostructures. Microb. Cell Fact., 2022, 21(1), 118.
[http://dx.doi.org/10.1186/s12934-022-01848-8] [PMID: 35717207]
[16]
Aymeric, L.; Donnadieu, F.; Mulet, C.; du Merle, L.; Nigro, G.; Saffarian, A.; Bérard, M.; Poyart, C.; Robine, S.; Regnault, B.; Trieu-Cuot, P.; Sansonetti, P.J.; Dramsi, S. Colorectal cancer specific conditions promote Streptococcus gallolyticus gut colonization. Proc. Natl. Acad. Sci. USA, 2018, 115(2), E283-E291.
[http://dx.doi.org/10.1073/pnas.1715112115] [PMID: 29279402]
[17]
Ocvirk, S.; O’Keefe, S.J.D. Influence of Bile Acids on Colorectal Cancer Risk: Potential Mechanisms Mediated by Diet-Gut Microbiota Interactions. Curr. Nutr. Rep., 2017, 6(4), 315-322.
[http://dx.doi.org/10.1007/s13668-017-0219-5] [PMID: 29430336]
[18]
Kumar, S.; Li, H. in silico design of anticancer peptides. Methods Mol. Biol., 2017, 1647, 245-254.
[http://dx.doi.org/10.1007/978-1-4939-7201-2_17] [PMID: 28809008]
[19]
Schaduangrat, N.; Nantasenamat, C.; Prachayasittikul, V.; Shoombuatong, W. ACPred: A computational tool for the prediction and analysis of anticancer peptides. Molecules, 2019, 24(10), 1973.
[http://dx.doi.org/10.3390/molecules24101973] [PMID: 31121946]
[20]
Chen, W.; Ding, H.; Feng, P.; Lin, H.; Chou, K.C. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget, 2016, 7(13), 16895-16909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID: 26942877]
[21]
Timmons, P.B.; Hewage, C.M. ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides. Biomed. Pharmacother., 2021, 133, 111051.
[http://dx.doi.org/10.1016/j.biopha.2020.111051] [PMID: 33254015]
[22]
Manavalan, B.; Subramaniyam, S.; Shin, T.H.; Kim, M.O.; Lee, G. Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy. J. Proteome Res., 2018, 17, 2715-2726.
[http://dx.doi.org/10.1021/acs.jproteome.8b00148]
[23]
Yang, J.; Zhang, Y. I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res., 2015, 43(W1), W174-W181.
[http://dx.doi.org/10.1093/nar/gkv342] [PMID: 25883148]
[24]
Mirdita, M.; Schütze, K.; Moriwaki, Y.; Heo, L.; Ovchinnikov, S.; Steinegger, M. ColabFold: making protein folding accessible to all. Nat. Methods, 2022, 19(6), 679-682.
[http://dx.doi.org/10.1038/s41592-022-01488-1] [PMID: 35637307]
[25]
Gasteiger, E.; Hoogland, C.; Gattiker, A.; Duvaud, S.; Wilkins, M.R.; Appel, R.D. The Proteomics Protocols Handbook; Springer: Humana Totowa, NJ, 2005, pp. 571-608.
[http://dx.doi.org/10.1385/1-59259-890-0:571]
[26]
Wiederstein, M.; Sippl, M.J. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res., 2007, 35(Web Server)(Suppl. 2), W407-W410.
[http://dx.doi.org/10.1093/nar/gkm290] [PMID: 17517781]
[27]
Benkert, P.; Biasini, M.; Schwede, T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics, 2011, 27(3), 343-350.
[http://dx.doi.org/10.1093/bioinformatics/btq662] [PMID: 21134891]
[28]
Tian, W.; Chen, C.; Lei, X.; Zhao, J.; Liang, J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res., 2018, 46(W1), W363-W367.
[http://dx.doi.org/10.1093/nar/gky473] [PMID: 29860391]
[29]
Yan, Y.; Tao, H.; He, J.; Huang, S.Y. The HDOCK server for integrated protein–protein docking. Nat. Protoc., 2020, 15(5), 1829-1852.
[http://dx.doi.org/10.1038/s41596-020-0312-x] [PMID: 32269383]
[30]
Zhou, P.; Jin, B.; Li, H.; Huang, S.Y. HPEPDOCK: a web server for blind peptide–protein docking based on a hierarchical algorithm. Nucleic Acids Res., 2018, 46(W1), W443-W450.
[http://dx.doi.org/10.1093/nar/gky357] [PMID: 29746661]
[31]
Kozakov, D.; Hall, D.R.; Xia, B.; Porter, K.A.; Padhorny, D.; Yueh, C.; Beglov, D.; Vajda, S. The ClusPro web server for protein–protein docking. Nat. Protoc., 2017, 12(2), 255-278.
[http://dx.doi.org/10.1038/nprot.2016.169] [PMID: 28079879]
[32]
Laskowski, R.A.; Jabłońska, J.; Pravda, L.; Vařeková, R.S.; Thornton, J.M. PDBsum: Structural summaries of PDB entries. Protein Sci., 2018, 27(1), 129-134.
[http://dx.doi.org/10.1002/pro.3289] [PMID: 28875543]
[33]
Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J.C. GROMACS: Fast, flexible, and free. J. Comput. Chem., 2005, 26(16), 1701-1718.
[http://dx.doi.org/10.1002/jcc.20291] [PMID: 16211538]
[34]
Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys., 2007, 126(1), 014101.
[http://dx.doi.org/10.1063/1.2408420] [PMID: 17212484]
[35]
Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys., 1981, 52(12), 7182-7190.
[http://dx.doi.org/10.1063/1.328693]
[36]
Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A.; Chen, X.; Hou, T.; Cao, D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res., 2021, 49(W1), W5-W14.
[http://dx.doi.org/10.1093/nar/gkab255] [PMID: 33893803]
[37]
Gabernet, G.; Müller, A.T.; Hiss, J.A.; Schneider, G. Membranolytic anticancer peptides. MedChemComm, 2016, 7(12), 2232-2245.
[http://dx.doi.org/10.1039/C6MD00376A]
[38]
Ghahremanian, S.; Rashidi, M.M.; Raeisi, K.; Toghraie, D. Molecular dynamics simulation approach for discovering potential inhibitors against SARS-CoV-2: A structural review. J. Mol. Liq., 2022, 354, 118901.https://pubmed.ncbi.nlm.nih.gov/33276180/
[http://dx.doi.org/10.1016/j.molliq.2022.118901] [PMID: 35309259]
[39]
Ali, S.; Khan, F.; Mohammad, T.; Lan, D.; Hassan, M.; Wang, Y. Identification and evaluation of inhibitors of lipase from Malassezia restricta using virtual high-throughput screening and molecular dynamics studies. Int. J. Mol. Sci., 2019, 20(4), 884.
[http://dx.doi.org/10.3390/ijms20040884] [PMID: 30781686]
[40]
Mitchell, S.M.; Heise, R.M.; Murray, M.E.; Lambo, D.J.; Daso, R.E.; Banerjee, I.A. An investigation of binding interactions of tumor-targeted peptide conjugated polyphenols with the kinase domain of ephrin B4 and B2 receptors. Molecular Diversity; Springer International Publishing, 2023.
[http://dx.doi.org/10.1007/s11030-023-10621-x]
[41]
Vemula, D.; Maddi, D.R.; Bhandari, V. Homology modeling, virtual screening, molecular docking, and dynamics studies for discovering Staphylococcus epidermidis FtsZ inhibitors. Front. Mol. Biosci., 2023, 10(March), 1087676.
[http://dx.doi.org/10.3389/fmolb.2023.1087676] [PMID: 36936991]
[42]
Jiang, Z.; You, L.; Dou, W.; Sun, T.; Xu, P. Effects of an electric field on the conformational transition of the protein: A molecular dynamics simulation study. Polymers (Basel), 2019, 11(2), 282.
[http://dx.doi.org/10.3390/polym11020282] [PMID: 30960266]
[43]
Arooj, M.; Shehadi, I.; Nassab, C.N.; Mohamed, A.A. Computational insights into binding mechanism of drugs as potential inhibitors against SARS-CoV-2 targets. Chem. Zvesti, 2022, 76(1), 111-121.
[http://dx.doi.org/10.1007/s11696-021-01843-0] [PMID: 34483461]
[44]
Kavyani, S.; Dadvar, M.; Modarress, H.; Amjad-Iranagh, S. Molecular Perspective Mechanism for Drug Loading on Carbon Nanotube–Dendrimer: A Coarse-Grained Molecular Dynamics Study. J. Phys. Chem. B, 2018, 122(33), 7956-7969.
[http://dx.doi.org/10.1021/acs.jpcb.8b04434] [PMID: 30067904]
[45]
Johnson, T.W.; Gallego, R.A.; Edwards, M.P. Lipophilic Efficiency as an Important Metric in Drug Design. J. Med. Chem., 2018, 61(15), 6401-6420.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00077] [PMID: 29589935]
[46]
Waring, M.J. Defining optimum lipophilicity and molecular weight ranges for drug candidates—Molecular weight dependent lower logD limits based on permeability. Bioorg. Med. Chem. Lett., 2009, 19(10), 2844-2851.
[http://dx.doi.org/10.1016/j.bmcl.2009.03.109] [PMID: 19361989]
[47]
Balimane, P.V.; Chong, S. Cell culture-based models for intestinal permeability: A critique. Drug Discov. Today, 2005, 10(5), 335-343.
[http://dx.doi.org/10.1016/S1359-6446(04)03354-9] [PMID: 15749282]
[48]
Artursson, P.; Ungell, A.L.; Löfroth, J.E. Selective paracellular permeability in two models of intestinal absorption: cultured monolayers of human intestinal epithelial cells and rat intestinal segments. Pharm. Res., 1993, 10(8), 1123-1129.
[http://dx.doi.org/10.1023/A:1018903931777] [PMID: 8415396]
[49]
Wu, D.; Chen, Q.; Chen, X.; Han, F.; Chen, Z.; Wang, Y. The blood–brain barrier: structure, regulation, and drug delivery. Signal Transduct. Target. Ther., 2023, 8(1), 217.
[http://dx.doi.org/10.1038/s41392-023-01481-w] [PMID: 37231000]
[50]
Ingelman-Sundberg, M.; Sim, S.C.; Gomez, A.; Rodriguez-Antona, C. Influence of cytochrome P450 polymorphisms on drug therapies: Pharmacogenetic, pharmacoepigenetic and clinical aspects. Pharmacol. Ther., 2007, 116(3), 496-526.
[http://dx.doi.org/10.1016/j.pharmthera.2007.09.004] [PMID: 18001838]
[51]
McDonnell, A.M.; Dang, C.H. Basic review of the cytochrome p450 system. J. Adv. Pract. Oncol., 2013, 4(4), 263-268.
[PMID: 25032007]
[52]
Johnson, T.W.; Dress, K.R.; Edwards, M. Using the Golden Triangle to optimize clearance and oral absorption. Bioorg. Med. Chem. Lett., 2009, 19(19), 5560-5564.
[http://dx.doi.org/10.1016/j.bmcl.2009.08.045] [PMID: 19720530]
[53]
Lamothe, S.M.; Guo, J.; Li, W.; Yang, T.; Zhang, S. The Human Ether-a-go-go-related Gene (hERG) potassium channel represents an unusual target for protease-mediated damage. J. Biol. Chem., 2016, 291(39), 20387-20401.
[http://dx.doi.org/10.1074/jbc.M116.743138] [PMID: 27502273]
[54]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 2012, 64(Suppl.), 4-17.
[http://dx.doi.org/10.1016/j.addr.2012.09.019] [PMID: 11259830]
[55]
Hann, M.M. Molecular obesity, potency and other addictions in drug discovery. MedChemComm, 2011, 2(5), 349-355.
[http://dx.doi.org/10.1039/C1MD00017A]
[56]
Liscano, Y.; Oñate-Garzón, J.; Delgado, J.P. Peptides with dual antimicrobial–anticancer activity: Strategies to overcome peptide limitations and rational design of anticancer peptides. Molecules, 2020, 25(18), 4245.
[http://dx.doi.org/10.3390/molecules25184245] [PMID: 32947811]

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