Advertisement

Optimisation of human VH domain antibodies specific to Mycobacterium tuberculosis heat shock protein (HSP16.3)

  • Jia Xin Soong
  • Soo Khim Chan
  • Theam Soon Lim
  • Yee Siew ChoongEmail author
Article
  • 31 Downloads

Abstract

Mycobacterium tuberculosis (Mtb) 16.3 kDa heat shock protein 16.3 (HSP16.3) is a latency-associated antigen that can be targeted for latent tuberculosis (TB) diagnostic and therapeutic development. We have previously developed human VH domain antibodies (dAbs; clone E3 and F1) specific against HSP16.3. In this work, we applied computational methods to optimise and design the antibodies in order to improve the binding affinity with HSP16.3. The VH domain antibodies were first docked to the dimer form of HSP16.3 and further sampled using molecular dynamics simulation. The calculated binding free energy of the HSP16.3-dAb complexes showed non-polar interactions were responsible for the antigen–antibody association. Per-residue free energy decomposition and computational alanine scanning have identified one hotspot residue for E3 (Y391) and 4 hotspot residues for F1 (M394, Y396, R397 and M398). These hotspot residues were then mutated and evaluated by binding free energy calculations. Phage ELISA assay was carried out on the potential mutants (E3Y391W, F1M394E, F1R397N and F1M398Y). The experimental assay showed improved binding affinities of E3Y391W and F1M394E against HSP16.3 compared with the wild type E3 and F1. This case study has thus showed in silico methods are able to assist in optimisation or improvement of antibody-antigen binding.

Keywords

Mycobacterium tuberculosis 16.3 kDa heat shock protein (HSP16.3) Human VH domain antibodies Antibody optimisation and design Per-residue energy decomposition Computational alanine scanning 

Notes

Author contributions

Computational work was carried out by JX Soong. Experimental work was performed by SK Chan. TS Lim and YS Choong planned the workflow. All authors contributed to the writing of the paper.

Funding

This work was supported by Bridging Grant (Grant No. 304/CIPPM/6316018) and Research University Grant (Grant No. RUi; 1001/CIPPM/811051) from Universiti Sains Malaysia. The computational resources were supported by Fundamental Research Grant Scheme (Grant No. FRGS; FRGS/1/2018/STG05/USM/02/01). T.S. Lim would like to acknowledge Higher Institution Centre of Excellence Grant (Grant No. HICoE; 311/CIPPM/44001005) from Malaysia Ministry of Education. Appreciation also extended to MyBrain15 from Ministry of Education Malaysia for the scholarship for J.X. Soong.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10822_2019_186_MOESM1_ESM.docx (1.3 mb)
Supplementary material 1 (DOCX 1295 KB)

References

  1. 1.
    Chowdhury PS, Wu H (2005) Tailor-made antibody therapeutics. Methods 36:11–24CrossRefGoogle Scholar
  2. 2.
    Carter PJ (2006) Potent antibody therapeutics by design. Nat Rev Immunol 6:343–357CrossRefGoogle Scholar
  3. 3.
    Sormanni P, Aprile FA, Vendruscolo M (2015) Rational design of antibodies targeting specific epitopes within intrinsically disordered proteins. Proc Natl Acad Sci USA 112:9902–9907CrossRefGoogle Scholar
  4. 4.
    Kuroda D, Shirai H, Jacobson MP, Nakamura H (2012) Computer-aided antibody design. Protein Eng Des Sel 25:507–522CrossRefGoogle Scholar
  5. 5.
    Clark LA, Boriack-Sjodin PA, Eldredge J, Fitch C, Friedman B, Hanf KJ, Jarpe M, Liparoto SF, Li Y, Lugovskoy A (2006) Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design. Protein Sci 15:949–960CrossRefGoogle Scholar
  6. 6.
    Kiyoshi M, Caaveiro JMM, Miura E, Nagatoishi S, Nakakido M, Soga S, Shirai H, Kawabata S, Tsumoto K (2014) Affinity improvement of a therapeutic antibody by structure-based computational design: Generation of electrostatic interactions in the transition state stabilizes the antibody-antigen complex. PLoOS ONE 9:e87099CrossRefGoogle Scholar
  7. 7.
    Kaushik A, Singh UB, Porwal C, Venugopal SJ, Mohan A, Krishnan A, Goyal V, Banavaliker JN (2012) Diagnostic potential of 16 kDa (HspX, alpha-crystalline) antigen for serodiagnosis of tuberculosis. Indian J Med Res 135:771–777Google Scholar
  8. 8.
    Ziegenbalg A, Prados-Rosales R, Jenny-Avital ER, Kim RS, Casadevall A, Achkar JM (2013) Immunogenicity of mycobacterial vesicles in humans: identification of a new tuberculosis antibody biomarker. Tuberculosis (Edinb) 93:448–455CrossRefGoogle Scholar
  9. 9.
    Li Q, Yu H, Zhang Y, Wang B, Jiang W, Da Z, Xian Q, Wang Y, Liu X, Zhu B (2011) Immunogenicity and protective efficacy of a fusion protein vaccine consisting of antigen Ag85B and HspX against Mycobacterium tuberculosis infection in mice. Scand J Immunol 73:568–576CrossRefGoogle Scholar
  10. 10.
    Bahara NHH, Chin ST, Choong YS, Lim TS (2016) Construction of a semisynthetic human VH single-domain antibody library and selection of domain antibodies against α-crystalline of Mycobacterium tuberculosis. J Biomol Screen 21:35–43CrossRefGoogle Scholar
  11. 11.
    Wu Y, Jiang S, Ying T (2017) Single-domain antibodies as therapeutics against human viral diseases. Front Immunol 8:1802CrossRefGoogle Scholar
  12. 12.
    Šali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815CrossRefGoogle Scholar
  13. 13.
    Bernstein FC, Koetzle TF, Williams GJ, Meyer EF Jr, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1977) The protein data bank: a computer-based archival file for macromolecular structures. J Mol Biol 112:535–542CrossRefGoogle Scholar
  14. 14.
    Shen M-y, Sali A (2006) Statistical potential for assessment and prediction of protein structures. Protein Sci 15:2507–2524CrossRefGoogle Scholar
  15. 15.
    Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 26:283–291CrossRefGoogle Scholar
  16. 16.
    Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170CrossRefGoogle Scholar
  17. 17.
    Luthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356:83–85CrossRefGoogle Scholar
  18. 18.
    Benkert P, Künzli M, Schwede T (2009) QMEAN server for protein model quality estimation. Nucleic Acids Res 37:W510–W514CrossRefGoogle Scholar
  19. 19.
    Soong JX, Lim TS, Choong YS (2018) The structural insights of 16.3 kDa heat shock protein (HSP16.3) from Mycobacterium tuberculosis via in silico study. Mol Simul 44:117–127CrossRefGoogle Scholar
  20. 20.
    Davydov YI, Tonevitsky AG (2009) Prediction of linear B-cell epitopes. Mol Biol 43:150–158CrossRefGoogle Scholar
  21. 21.
    Larsen JEP, Lund O, Nielsen M (2006) Improved method for predicting linear B-cell epitopes. Immunome Res 2:2–2CrossRefGoogle Scholar
  22. 22.
    Kringelum JV, Lundegaard C, Lund O, Nielsen M (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol 8:e1002829CrossRefGoogle Scholar
  23. 23.
    Ponomarenko J, Bui HH, 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 9:514CrossRefGoogle Scholar
  24. 24.
    Lian Y, Ge M, Pan X-M (2014) EPMLR: Sequence-based linear B-cell epitope prediction method using multiple linear regression. BMC Bioinform 15:1–6CrossRefGoogle Scholar
  25. 25.
    El-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting flexible length linear B-cell epitopes. Comput Syst Bioinform Conf 7:121–132Google Scholar
  26. 26.
    Srivastava SK, Ruigrok VJB, Thompson NJ, Trilling AK, Heck AJR, van Rijn C, Beekwilder J, Jongsma MA (2013) 16 kDa heat shock protein from heat-inactivated Mycobacterium tuberculosis is a homodimer—suitability for diagnostic applications with specific llama VHH monoclonals. PLoS ONE 8:e64040CrossRefGoogle Scholar
  27. 27.
    Pierce BG, Hourai Y, Weng Z (2011) Accelerating protein docking in ZDOCK using an advanced 3D convolution library. PLoS ONE 6:e24657CrossRefGoogle Scholar
  28. 28.
    Chermak E, Petta A, Serra L, Vangone A, Scarano V, Cavallo L, Oliva R (2015) CONSRANK: a server for the analysis, comparison and ranking of docking models based on inter-residue contacts. Bioinformatics 31:1481–1483CrossRefGoogle Scholar
  29. 29.
    Moal IH, Jiménez-García B, Fernández-Recio J (2015) CCharPPI web server: computational characterization of protein–protein interactions from structure. Bioinformatics 31:123–125CrossRefGoogle Scholar
  30. 30.
    Gray JJ, Moughon S, Wang C, Schueler-Furman O, Kuhlman B, Rohl CA, Baker D (2003) Protein–protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. J Mol Biol 331:281–299CrossRefGoogle Scholar
  31. 31.
    Hekkelman ML, te Beek TAH, Pettifer SR, Thorne D, Attwood TK, Vriend G (2010) WIWS: A protein structure bioinformatics web service collection. Nucleic Acids Res 38:W719–W723CrossRefGoogle Scholar
  32. 32.
    Case D, Darden T, Cheatham TE III, Simmerling C, Wang J, Duke R, Luo R, Walker R, Zhang W, Merz K (2012) AMBER 12Google Scholar
  33. 33.
    Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C (2015) FF14SB: improving the accuracy of protein side chain and backbone parameters from FF99SB. J Chem Theory Comput 11:3696–3713CrossRefGoogle Scholar
  34. 34.
    Ryckaert J-P, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341CrossRefGoogle Scholar
  35. 35.
    Izaguirre JA, Catarello DP, Wozniak JM, Skeel RD (2001) Langevin stabilization of molecular dynamics. J Chem Phys 114:2090–2098CrossRefGoogle Scholar
  36. 36.
    Miller BR, McGee TD, Swails JM, Homeyer N, Gohlke H, Roitberg AE (2012) MMPBSA.py: an efficient program for end-state free energy calculations. J Chem Theory Comput 8:3314–3321CrossRefGoogle Scholar
  37. 37.
    Onufriev A, Bashford D, Case DA (2004) Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins: Struct Funct Bioinf 55:383–394CrossRefGoogle Scholar
  38. 38.
    Pires DEV, Ascher DB (2016) mCSM-AB: a web server for predicting antibody–antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Res 44:W469–W473CrossRefGoogle Scholar
  39. 39.
    Liu Y, Kuhlman B (2006) RosettaDesign server for protein design. Nucleic Acids Res 34:W235–W238CrossRefGoogle Scholar
  40. 40.
    Kortemme T, Baker D (2002) A simple physical model for binding energy hot spots in protein–protein complexes. Proc Natl Acad Sci USA 99:14116–14121CrossRefGoogle Scholar
  41. 41.
    Eisenberg D, Schwarz E, Komaromy M, Wall R (1984) Analysis of membrane and surface protein sequences with the hydrophobic moment plot. J Mol Biol 179:125–142CrossRefGoogle Scholar
  42. 42.
    Gohlke H, Kiel C, Case DA (2003) Insights into protein–protein binding by binding free energy calculation and free energy decomposition for the Ras–Raf and Ras–RalGDS complexes. J Mol Biol 330:891–913CrossRefGoogle Scholar
  43. 43.
    Wang W, Kollman PA (2000) Free energy calculations on dimer stability of the HIV protease using molecular dynamics and a continuum solvent model. J Mol Biol 303:567–582CrossRefGoogle Scholar
  44. 44.
    Hou T, Wang J, Li Y, Wang W (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69–82CrossRefGoogle Scholar
  45. 45.
    Ieong P, Amaro Rommie E, Li Wilfred W (2015) Molecular dynamics analysis of antibody recognition and escape by human H1N1 influenza hemagglutinin. Biophys J 108:2704–2712CrossRefGoogle Scholar
  46. 46.
    Fellouse FA, Esaki K, Birtalan S, Raptis D, Cancasci VJ, Koide A, Jhurani P, Vasser M, Wiesmann C, Kossiakoff AA, Koide S, Sidhu SS (2007) High-throughput generation of synthetic antibodies from highly functional minimalist phage-displayed libraries. J Mol Biol 373:924–940CrossRefGoogle Scholar
  47. 47.
    Persson H, Kirik U, Thörnqvist L, Greiff L, Levander F, Ohlin M (2018) In vitro evolution of antibodies inspired by in vivo evolution. Front Immunol 9:1391CrossRefGoogle Scholar
  48. 48.
    Lippow SM, Wittrup KD, Tidor B (2007) Computational design of antibody affinity improvement beyond in vivo maturation. Nature Biotechnol 25:1171–1176CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for Research in Molecular Medicine (INFORMM)Universiti Sains MalaysiaPenangMalaysia
  2. 2.Analytical Biochemistry Research CentreUniversiti Sains MalaysiaPenangMalaysia

Personalised recommendations