In Silico Analysis for Determination and Validation of Human CD20 Antigen 3D Structure

  • Zahra Payandeh
  • Masoumeh RajabibazlEmail author
  • Yousef MortazaviEmail author
  • Azam Rahimpour


CD20 has been known as an attractive therapeutic target for refractory diseases such as B-cell chronic lymphocytic leukemia, rheumatoid arthritis and multiple sclerosis. Determining the 3D structure of the CD20 antigen could help to achieve a better deduction of its functions and its interactions with ligands. In this regard, we have launched an in silico protein modeling strategy to unveil the probable 3D structure of CD20 molecule. Various protein modeling approaches including homology modeling, Fold recognition and ab initio method were employed to build a qualified mode Protein BLAST tool from NCBI database was used to find a suitable template and the selected template was fed as input structure of the modeling software. Thereafter, the quality of the obtained models was evaluated invoking the model quality assessment software. CD20 Topology prediction shows that 4 trans membrane helixes. The best model predicted by LOMETS was selected for analyses. Refinement of 3D structure as well as determination of its B-cell epitopes, clefts and ligand binding sites was carried out on the structure. In conclusion, CD20 antigen 3D prediction led to design and production of a new monoclonal antibody.


CD20 antigen 3D prediction Bioinformatics 



The authors thank Zanjan University of Medical Sciences and Shahid Beheshti University of Medical Sciences for support to conduct this work.

Compliance with Ethical Standards

Conflict of interest

The Authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Barun B, Bar-Or A (2012) Treatment of multiple sclerosis with anti-CD20 antibodies. Clin Immunol 142(1):31–37Google Scholar
  2. Beers SA, Chan CH, James S, French RR, Attfield KE, Brennan CM, Ahuja A, Shlomchik MJ, Cragg MS, Glennie MJ (2008) Type II (tositumomab) anti-CD20 monoclonal antibody out performs type I (rituximab-like) reagents in B-cell depletion regardless of complement activation. Blood 112(10):4170–4177Google Scholar
  3. Berezin C, Glaser F, Rosenberg J, Paz I, Pupko T, Fariselli P, Casadio R, Ben-Tal N (2004) ConSeq: the identification of functionally and structurally important residues in protein sequences. Bioinformatics 20(8):1322–1324Google Scholar
  4. Blundell T, Carney D, Gardner S, Hayes F, Howlin B, Hubbard T, Overington J, Singh DA, Sibanda BL, Sutcliffe M (1988) Knowledge-based protein modelling and design. FEBS J 172(3):513–520Google Scholar
  5. Brændstrup P, Bjerrum OW, Nielsen OJ, Jensen BA, Clausen NT, Hansen PB, Andersen I, Schmidt K, Andersen TM, Peterslund NA (2005) Rituximab chimeric anti-CD20 monoclonal antibody treatment for adult refractory idiopathic thrombocytopenic purpura. Am J Hematol 78(4):275–280Google Scholar
  6. Carugo O, Djinović-Carugo K (2013) Half a century of Ramachandran plots. Acta Crystallogr Sect D 69(8):1333–1341Google Scholar
  7. Chen J, Liu H, Yang J, Chou K-C (2007) Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33(3):423–428Google Scholar
  8. Chen C-C, Hwang J-K, Yang J-M (2009) 2-v2: template-based protein structure prediction server. BMC Bioinform 10(1):366Google Scholar
  9. Cragg MS, Walshe CA, Ivanov AO, Glennie MJ (2004) The biology of CD20 and its potential as a target for mAb therapy. B cell trophic factors and B cell antagonism in autoimmune disease. Karger Publ 8:140–174Google Scholar
  10. Deng X, Liu Q, Hu Y, Deng Y (2013) TOPPER: topology prediction of transmembrane protein based on evidential reasoning. Sci World J 2013:8Google Scholar
  11. Du J, Yang H, Guo Y, Ding J (2009) Structure of the Fab fragment of therapeutic antibody Ofatumumab provides insights into the recognition mechanism with CD20. Mol Immunol 46(11):2419–2423Google Scholar
  12. EL-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting linear B-cell epitopes using string kernels. J Mol Recogn 21(4):243–255Google Scholar
  13. Ernst JA, Li H, Kim HS, Nakamura GR, Yansura DG, Vandlen RL (2005) Isolation and characterization of the B-cell marker CD20. Biochemistry 44(46):15150–15158Google Scholar
  14. Farag SS, Flinn IW, Modali R, Lehman TA, Young D, Byrd JC (2004) FcγRIIIa and FcγRIIa polymorphisms do not predict response to rituximab in B-cell chronic lymphocytic leukemia. Blood 103(4):1472–1474Google Scholar
  15. Fiser A (2004) Protein structure modeling in the proteomics era. Expert Rev Proteomics 1(1):97–110Google Scholar
  16. Floudas C, Fung H, McAllister S, Mönnigmann M, Rajgaria R (2006) Advances in protein structure prediction and de novo protein design: a review. Chem Eng Sci 61(3):966–988Google Scholar
  17. Gasteiger E, Hoogland C, Gattiker A, Duvaud Se, Wilkins MR, Appel RD, Bairoch A (2005) Protein identification and analysis tools on the ExPASy server. Springer, New YorkGoogle Scholar
  18. Geourjon C, Deleage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 11(6):681–684Google Scholar
  19. Goede V, Fischer K, Busch R, Engelke A, Eichhorst B, Wendtner CM, Chagorova T, de la Serna J, Dilhuydy M-S, Illmer T (2014) Obinutuzumab plus chlorambucil in patients with CLL and coexisting conditions. N Engl J Med 370(12):1101–1110Google Scholar
  20. Golay J, Semenzato G, Rambaldi A, Foà R, Gaidano G, Gamba E, Pane F, Pinto A, Specchia G, Zaja F (2013) Lessons for the clinic from rituximab pharmacokinetics and pharmacodynamics. MAbs 5(6):826–837Google Scholar
  21. Haste Andersen P, Nielsen M, Lund O (2006) Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci 15(11):2558–2567Google Scholar
  22. Jaglowski SM, Alinari L, Lapalombella R, Muthusamy N, Byrd JC (2010) The clinical application of monoclonal antibodies in chronic lymphocytic leukemia. Blood 116(19):3705–3714Google Scholar
  23. Jahangiri A, Rasooli I, Owlia P, Fooladi AAI, Salimian J (2017) In silico design of an immunogen against Acinetobacter baumannii based on a novel model for native structure of outer membrane protein A. Microb Pathog 105:201–210Google Scholar
  24. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858Google Scholar
  25. Khalili S, Rahbar MR, Dezfulian MH, Jahangiri A (2015) In silico analyses of Wilms׳ tumor protein to designing a novel multi-epitope DNA vaccine against cancer. J Theor Biol 379:66–78Google Scholar
  26. Khalili S, Mohammadpour H, BAROUGH MS, Kokhaei P (2016) ILP-2 modeling and virtual screening of an FDA-approved library: a possible anticancer therapy. Turkish J Med Sci 46(4):1135–1143Google Scholar
  27. Khalili S, Jahangiri A, Hashemi ZS, Khalesi B, Mardsoltani M, Amani J (2017a) Structural pierce into molecular mechanism underlying Clostridium perfringens Epsilon toxin function. Toxicon 127:90–99Google Scholar
  28. Khalili S, Rasaee M, Bamdad T (2017b) 3D structure of DKK1 indicates its involvement in both canonical and non-canonical Wnt pathways. Mol Biol 51(1):155–166Google Scholar
  29. Khalili S, Zakeri A, Hashemi ZS, Masoumikarimi M, Manesh MRR, Shariatifar N, Sani MJ (2017c) Structural analyses of the interactions between the thyme active ingredients and human serum albumin. Turkish J Biochem 42:459–467Google Scholar
  30. Krogh A, Larsson B, Von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305(3):567–580Google Scholar
  31. Lassmann H, Brück W, Lucchinetti C (2001) Heterogeneity of multiple sclerosis pathogenesis: implications for diagnosis and therapy. Trends Mol Med 7(3):115–121Google Scholar
  32. Lin H-N, Sung T-Y, Ho S-Y, Hsu W-L (2010) Improving protein secondary structure prediction based on short subsequences with local structure similarity. BMC Genom 11(4):S4Google Scholar
  33. Liu W, Chen Y (2005) High epitope density in a single protein molecule significantly enhances antigenicity as well as immunogenicity: a novel strategy for modern vaccine development and a preliminary investigation about B cell discrimination of monomeric proteins. Eur J Immunol 35(2):505–514Google Scholar
  34. Lomize MA, Pogozheva ID, Joo H, Mosberg HI, Lomize AL (2012) OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res 40(D1):D370–D376Google Scholar
  35. Lunning MA, Vose JM, Schreeder MT, Fowler N, Nastoupil LJ, Siddiqi T, Blumel S, Pauli EK, Cutter K, Tse W (2014) Ublituximab, a novel glycoengineered anti-CD20 monoclonal antibody (mAb), in combination with TGR-1202, a next generation once daily PI3kδ inhibitor, demonstrates activity in heavily pre-treated and high-risk chronic lymphocytic leukemia (CLL) and B-cell lymphoma. Am Soc Hematology 124(21):801Google Scholar
  36. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404–405Google Scholar
  37. McLaughlin P, Grillo-López AJ, Link BK, Levy R, Czuczman MS, Williams ME, Heyman MR, Bence-Bruckler I, White CA, Cabanillas F (1998) Rituximab chimeric anti-CD20 monoclonal antibody therapy for relapsed indolent lymphoma: half of patients respond to a four-dose treatment program. J Clin Oncol 16(8):2825–2833Google Scholar
  38. Mihăşan M (2010) Basic protein structure prediction for the biologist: a review. Arch Biol Sci 62(4):857–871Google Scholar
  39. Mohammadpour H, Khalili S, Hashemi ZS (2015) Kremen is beyond a subsidiary co-receptor of Wnt signaling: an in silico validation. Turk J Biol 39(3):501–510Google Scholar
  40. Mohammadpour H, Pourfathollah AA, Zarif MN, Khalili S (2016) Key role of Dkk3 protein in inhibition of cancer cell proliferation: an in silico identification. J Theor Biol 393:98–104Google Scholar
  41. Pawluczkowycz AW, Beurskens FJ, Beum PV, Lindorfer MA, van de Winkel JG, Parren PW, Taylor RP (2009) Binding of submaximal C1q promotes complement-dependent cytotoxicity (CDC) of B cells opsonized with anti-CD20 mAbs ofatumumab (OFA) or rituximab (RTX): considerably higher levels of CDC are induced by OFA than by RTX. J Immunol 183(1):749–758Google Scholar
  42. Payandeh Z, Rajabibazl M, Mortazavi Y, Rahim-Pour A, Taromchi AH (2017) Ofatumumab monoclonal antibody affinity maturation through in silico modeling. Iran Biomed J 0(0):0–0Google Scholar
  43. Petrey D, Honig B (2005) Protein structure prediction: inroads to biology. Mol Cell 20(6):811–819Google Scholar
  44. Polyak MJ, Tailor SH, Deans JP (1998) Identification of a cytoplasmic region of CD20 required for its redistribution to a detergent-insoluble membrane compartment. J Immunol 161(7):3242–3248Google Scholar
  45. Ponomarenko J, Bui H-H, Li W, Fusseder N, Bourne PE, Sette A, Peters B (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinform 9(1):514Google Scholar
  46. Reff ME, Carner K, Chambers K, Chinn P, Leonard J, Raab R, Newman R, Hanna N, Anderson D (1994) Depletion of B cells in vivo by a chimeric mouse human monoclonal antibody to CD20. Blood 83(2):435–445Google Scholar
  47. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5(4):725–738Google Scholar
  48. Roy A, Yang J, Zhang Y (2012) COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res 40(W1):W471–W477Google Scholar
  49. Rubinstein ND, Mayrose I, Martz E, Pupko T (2009) Epitopia: a web-server for predicting B-cell epitopes. BMC Bioinform 10(1):287Google Scholar
  50. Salles G, Morschhauser F, Lamy T, Milpied N, Thieblemont C, Tilly H, Bieska G, Asikanius E, Carlile D, Birkett J (2012) Phase 1 study results of the type II glycoengineered humanized anti-CD20 monoclonal antibody obinutuzumab (GA101) in B-cell lymphoma patients. Blood 119(22):5126–5132Google Scholar
  51. Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31(13):3381–3385Google Scholar
  52. Sefid F, Rasooli I, Jahangiri A, Bazmara H (2015) Functional exposed amino acids of BauA as potential immunogen against Acinetobacter baumannii. Acta Biotheor 63(2):129–149Google Scholar
  53. Sefid F, Rasooli I, Payandeh Z (2016) Homology modeling of a Camelid antibody fragment against a conserved region of Acinetobacter baumannii biofilm associated protein (Bap). J Theor Biol 397:43–51Google Scholar
  54. Sharman JP, Farber CM, Mahadevan D, Schreeder MT, Brooks HD, Kolibaba KS, Fanning SR, Klein LM, Sportelli P, Miskin HP (2014) Ublituximab (TG-1101), a novel glycoengineered anti-CD20 monoclonal antibody, in combination with ibrutinib is highly active in patients with relapsed and/or refractory CLL and MCL; results of a phase II trial. Am Soc Hematol 4679Google Scholar
  55. Somarowthu S, Ondrechen MJ (2012) POOL server: machine learning application for functional site prediction in proteins. Bioinformatics 28(15):2078–2079Google Scholar
  56. Stroopinsky D, Katz T, Rowe JM, Melamed D, Avivi I (2012) Rituximab-induced direct inhibition of T-cell activation. Cancer Immunol Immunother 61(8):1233–1241Google Scholar
  57. Tedder TF, Streuli M, Schlossman SF, Saito H (1988) Isolation and structure of a cDNA encoding the B1 (CD20) cell-surface antigen of human B lymphocytes. Proc Natl Acad Sci USA 85(1):208–212Google Scholar
  58. Tsirigos KD, Peters C, Shu N, Käll L, Elofsson A (2015) The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic acids Res 43(W1):W401–W407Google Scholar
  59. van Meerten T, Hagenbeek A (2010). CD20-targeted therapy: the next generation of antibodies. Seminars in hematology, Elsevier, AmsterdamGoogle Scholar
  60. Viklund H, Elofsson A (2008) OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics 24(15):1662–1668Google Scholar
  61. Volkamer A, Kuhn D, Rippmann F, Rarey M (2012) DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics 28(15):2074–2075Google Scholar
  62. Wierda WG, Kipps TJ, Mayer J, Stilgenbauer S, Williams CD, Hellmann A, Robak T, Furman RR, Hillmen P, Trneny M (2010) Ofatumumab as single-agent CD20 immunotherapy in fludarabine-refractory chronic lymphocytic leukemia. J Clin Oncol 28(10):1749–1755Google Scholar
  63. Wu S, Zhang Y (2007) LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res 35(10):3375–3382Google Scholar
  64. Wu S, Zhang Y (2008) MUSTER: improving protein sequence profile–profile alignments by using multiple sources of structure information. Proteins 72(2):547–556Google Scholar
  65. Xu D, Zhang Y (2011) Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J 101(10):2525–2534Google Scholar
  66. Yang J, Roy A, Zhang Y (2013) Protein–ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 29(20):2588–2595Google Scholar
  67. Yu CS, Chen YC, Lu CH, Hwang JK (2006) Prediction of protein subcellular localization. Proteins 64(3):643–651Google Scholar
  68. Yu C-S, Cheng C-W, Su W-C, Chang K-C, Huang S-W, Hwang J-K, Lu C-H (2014) CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation. PLoS ONE 9(6):e99368Google Scholar
  69. Zhang B (2009). Ofatumumab. MAbs, Taylor & Francis, RoutledgeGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Medical Biotechnology and Nanotechnology, Faculty of MedicineZanjan University of Medical SciencesZanjanIran
  2. 2.Department of Clinical Biochemistry, Faculty of MedicineShahid Beheshti University of Medical SciencesTehranIran
  3. 3.School of Advanced Technologies in MedicineShahid Beheshti University of Medical SciencesTehranIran
  4. 4.Cancer Gene Therapy Research Center, Faculty of MedicineZanjan University of Medical SciencesZanjanIran

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