Abstract
Ovarian cancer is one of the most lethal gynecologic cancers. The high mortality rate is due to lack of early symptoms and developing drug resistance. Immunotherapy has emerged as a promising approach to treatment of cancer. Bevacizumab is a recombinant humanized monoclonal IgG antibody against VEGF A that used in cancer immunotherapy, especially in ovarian cancer therapy. For a successful cancer immunotherapy the different features of therapeutic antibodies such as binding affinity should be improved. Increased affinity enhances the biological action of the antibody, which in turn improves the therapeutic effects. Furthermore, the increased antibody affinity can reduce the therapeutic dose of the antibody, resulting in lower toxicity and handling cost. Considering the importance of this issue, using in silico analysis, we aimed to find the important amino acids of the Bevacizumab antibody, and then replaced these amino acids with others to improve antibody binding affinity. Finally, we examined the binding affinity of antibody variants to antigens. In conclusion, the designed antibodies could be potential candidates for binding to antigens with improved affinity.
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References
Badgwell D, Bast RC Jr (2007) Early detection of ovarian cancer. Dis Markers 23(5):6):397–410
Bandehpour M, Yarian F, Ahangarzadeh S (2017) Bioinformatics evaluation of novel ribosome display-selected single chain variable fragment (scFv) structure with factor H binding protein through docking. J Theor Comput Chem 16(03):1750021
Berezin C, Glaser F, Rosenberg J, Paz I, Pupko T, Fariselli P et al (2004) ConSeq: the identification of functionally and structurally important residues in protein sequences. Bioinformatics 20(8):1322–1324
Burger RA, Brady MF, Bookman MA, Fleming GF, Monk BJ, Huang H et al (2011) Incorporation of bevacizumab in the primary treatment of ovarian cancer. N Engl J Med 365(26):2473–2483
Dillman RO (1989) Monoclonal antibodies for treating cancer. Ann Intern Med 111(7):592–603
Eyvazi S, Kazemi B, Bandehpour M, Dastmalchi S (2017) Identification of a novel single chain fragment variable antibody targeting CD24-expressing cancer cells. Immunol Lett 190:240–246
Eyvazi S, Farajnia S, Dastmalchi S, Kanipour F, Zarredar H, Bandehpour M. Antibody based EpCAM targeted therapy of cancer, review and update. Curr Cancer Drug Targets. 2018. https://doi.org/10.2174/1568009618666180102102311
Ferrara N, Hillan KJ, Gerber H-P, Novotny W (2004) Discovery and development of bevacizumab, an anti-VEGF antibody for treating cancer. Nat Rev Drug Discov 3(5):391
Garcia A, Singh H (2013) Bevacizumab and ovarian cancer. Ther Adv Med Oncol 5(2):133–141
Hoogenboom HR (1997) Designing and optimizing library selection strategies for generating high-affinity antibodies. Trends Biotechnol 15(2):62–70
Jahangiri A, Amani J, Halabian R (2017) In silico analyses of staphylococcal enterotoxin B as a DNA vaccine for cancer therapy. Int J Pept Res Ther 24:131–142
Jahangiri A, Rasooli I, Owlia P, Fooladi AAI, Salimian J (2018a) An integrative in silico approach to the structure of Omp33-36 in Acinetobacter baumannii. Comput Biol Chem 72:77–86
Jahangiri A, Rasooli I, Owlia P, Fooladi AAI, Salimian J (2018b) Highly conserved exposed immunogenic peptides of Omp34 against Acinetobacter baumannii: an innovative approach. J Microbiol Methods 144:79–85
Kawabata T (2010) Detection of multiscale pockets on protein surfaces using mathematical morphology. Proteins Struct Funct Bioinform 78(5):1195–1211
Khalili S, Mohammadpour H, Barough MS, Kokhaei P (2016) ILP-2 modeling and virtual screening of an FDA-approved library: a possible anticancer therapy. Turk J Med Sci 46(4):1135–1143
Khalili S, Rasaee MJ, Mousavi SL, Amani J, Jahangiri A, Borna H (2017a) In silico prediction and in vitro verification of a novel multi-epitope antigen for HBV detection. Mol Genet Microbiol Virol 32(4):230–240
Khalili S, Zakeri A, Hashemi ZS, Masoumikarimi M, Manesh MRR, Shariatifar N et al (2017b) Structural analyses of the interactions between the thyme active ingredients and human serum albumin. Turk J Biochem 42(4):459–467
Khalili S, Rasaee M, Bamdad T (2017c) 3D structure of DKK1 indicates its involvement in both canonical and non-canonical Wnt pathways. Mol Biol 51(1):155–166
Kim SJ, Park Y, Hong HJ (2005) Antibody engineering for the development of therapeutic antibodies. Mol Cells 20(1):17–29
Kufareva I, Budagyan L, Raush E, Totrov M, Abagyan R (2007) PIER: protein interface recognition for structural proteomics. Proteins Struct Funct Bioinform 67(2):400–417
Kunik V, Ashkenazi S, Ofran Y (2012) Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure. Nucleic Acids Res 40(W1):W521–W524
Li T, Pantazes RJ, Maranas CD (2014) OptMAVEn—a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes. PLoS ONE 9(8):e105954
Lippow SM, Tidor B (2007) Progress in computational protein design. Curr Opin Biotechnol 18(4):305–311
Luvero D, Milani A, Ledermann JA (2014) Treatment options in recurrent ovarian cancer: latest evidence and clinical potential. Ther Adv Med Oncol 6(5):229–239
Mard-Soltani M, Rasaee MJ, Khalili S, Sheikhi A, Hedayati M, Ghaderi-Zefrehi H et al (2018) The effect of differentially designed fusion proteins to elicit efficient anti-human thyroid stimulating hormone immune responses. Iran J Allergy Asthma Immunol 17(2):158–170
Mellman I, Coukos G, Dranoff G (2011) Cancer immunotherapy comes of age. Nature 480(7378):480
Negi SS, Schein CH, Oezguen N, Power TD, Braun W (2007) InterProSurf: a web server for predicting interacting sites on protein surfaces. Bioinformatics 23(24):3397–3399
Olimpieri PP, Chailyan A, Tramontano A, Marcatili P (2013) Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server. Bioinformatics 29(18):2285–2291
Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A, Taromchi AH, Dastmalchi S (2018a) Affinity maturation and characterization of the ofatumumab monoclonal antibody. J Cell Biochem. https://doi.org/10.1002/jcb.27457
Payandeh Z, Rajabibazl M, Mortazavi Y, Rahimpour A, Taromchi AH (2018b) Ofatumumab monoclonal antibody affinity maturation through in silico modeling. Iran Biomed J 22(3):180
Pedotti M, Simonelli L, Livoti E, Varani L (2011) Computational docking of antibody-antigen complexes, opportunities and pitfalls illustrated by influenza hemagglutinin. Int J Mol Sci 12(1):226–251
Perren TJ, Swart AM, Pfisterer J, Ledermann JA, Pujade-Lauraine E, Kristensen G et al (2011) A phase 3 trial of bevacizumab in ovarian cancer. N Engl J Med 365(26):2484–2496
Poveda A, Ray-Coquard I, Romero I, Lopez-Guerrero JA, Colombo N (2014) Emerging treatment strategies in recurrent platinum-sensitive ovarian cancer: focus on trabectedin. Cancer Treat Rev 40(3):366–375
Presta LG (2005) Selection, design, and engineering of therapeutic antibodies. J Allergy Clin Immunol 116(4):731–736
Presta LG (2008) Molecular engineering and design of therapeutic antibodies. Curr Opin Immunol 20(4):460–470
Pujade-Lauraine E, Hilpert F, Weber B, Reuss A, Poveda A, Kristensen G et al (2014) Bevacizumab combined with chemotherapy for platinum-resistant recurrent ovarian cancer: the AURELIA open-label randomized phase III trial. Obstet Gynecol Surv 69(7):402–404
Roberts SA, Cheetham J, Rees A (1987) Generation of an antibody with enhanced affinity and specificity for its antigen by protein engineering. Nature 328(6132):731
Tan KP, Nguyen TB, Patel S, Varadarajan R, Madhusudhan MS (2013) Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins. Nucleic Acids Res 41(W1):W314–W321
Tiller KE, Tessier PM (2015) Advances in antibody design. Annu Rev Biomed Eng 17:191–216
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Eyvazi, S., Payandeh, Z., Khalili, S. et al. Bevacizumab Antibody Affinity Maturation to Improve Ovarian Cancer Immunotherapy: In Silico Approach. Int J Pept Res Ther 25, 1417–1430 (2019). https://doi.org/10.1007/s10989-018-9787-5
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DOI: https://doi.org/10.1007/s10989-018-9787-5