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SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters

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Genotype Phenotype Coupling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2681))

Abstract

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

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References

  1. Rabia LA, Desai AA, Jhajj HS, Tessier PM (2018) Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility. Biochem Eng J 137:365–374. https://doi.org/10.1016/j.bej.2018.06.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Bailly M, Mieczkowski C, Juan V et al (2020) Predicting antibody developability profiles through early stage discovery screening. mAbs 12:1743053. https://doi.org/10.1080/19420862.2020.1743053

    Article  PubMed  PubMed Central  Google Scholar 

  3. Jain T, Sun T, Durand S et al (2017) Biophysical properties of the clinical-stage antibody landscape. Proc Natl Acad Sci U S A 114:944–949. https://doi.org/10.1073/pnas.1616408114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jarasch A, Koll H, Regula JT et al (2015) Developability assessment during the selection of novel therapeutic antibodies. J Pharm Sci 104:1885–1898. https://doi.org/10.1002/jps.24430

    Article  CAS  PubMed  Google Scholar 

  5. Kingsbury JS, Saini A, Auclair SM et al (2020) A single molecular descriptor to predict solution behavior of therapeutic antibodies. Sci Adv 6:eabb0372. https://doi.org/10.1126/sciadv.abb0372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kraft TE, Richter WF, Emrich T et al (2020) Heparin chromatography as an in vitro predictor for antibody clearance rate through pinocytosis. mAbs 12:1683432. https://doi.org/10.1080/19420862.2019.1683432

    Article  CAS  PubMed  Google Scholar 

  7. Xu Y, Wang D, Mason B et al (2018) Structure, heterogeneity and developability assessment of therapeutic antibodies. mAbs 11:239–264. https://doi.org/10.1080/19420862.2018.1553476

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yang X, Xu W, Dukleska S et al (2013) Developability studies before initiation of process development. mAbs 5:787–794. https://doi.org/10.4161/mabs.25269

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25. https://doi.org/10.1016/S0169-409X(96)00423-1

    Article  CAS  Google Scholar 

  10. Blanco MA (2022) Computational models for studying physical instabilities in high concentration biotherapeutic formulations. mAbs 14:2044744. https://doi.org/10.1080/19420862.2022.2044744

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Khetan R, Curtis R, Deane CM et al (2022) Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. mAbs 14:2020082. https://doi.org/10.1080/19420862.2021.2020082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chennamsetty N, Voynov V, Kayser V et al (2010) Prediction of aggregation prone regions of therapeutic proteins. J Phys Chem B 114:6614–6624. https://doi.org/10.1021/jp911706q

    Article  CAS  PubMed  Google Scholar 

  13. Lauer TM, Agrawal NJ, Chennamsetty N et al (2012) Developability index: a rapid in silico tool for the screening of antibody aggregation propensity. J Pharm Sci 101:102–115. https://doi.org/10.1002/jps.22758

    Article  CAS  PubMed  Google Scholar 

  14. Sormanni P, Aprile FA, Vendruscolo M (2015) The CamSol method of rational design of protein mutants with enhanced solubility. J Mol Biol 427:478–490. https://doi.org/10.1016/j.jmb.2014.09.026

    Article  CAS  PubMed  Google Scholar 

  15. Pérez A-MW, Sormanni P, Andersen JS et al (2019) In vitro and in silico assessment of the developability of a designed monoclonal antibody library. mAbs 11:388–400. https://doi.org/10.1080/19420862.2018.1556082

    Article  Google Scholar 

  16. Ahmed L, Gupta P, Martin KP et al (2021) Intrinsic physicochemical profile of marketed antibody-based biotherapeutics. Proc Natl Acad Sci 118:e2020577118. https://doi.org/10.1073/pnas.2020577118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Negron C, Fang J, McPherson MJ et al (2022) Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment. mAbs 14:2080628. https://doi.org/10.1080/19420862.2022.2080628

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Raybould MIJ, Marks C, Krawczyk K et al (2019) Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci 116:4025–4030. https://doi.org/10.1073/pnas.1810576116

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Sankar K, Krystek SR Jr, Carl SM et al (2018) AggScore: prediction of aggregation-prone regions in proteins based on the distribution of surface patches. Proteins 86:1147–1156. https://doi.org/10.1002/prot.25594

    Article  CAS  PubMed  Google Scholar 

  20. Walsh G, Jefferis R (2006) Post-translational modifications in the context of therapeutic proteins. Nat Biotechnol 24:1241–1252. https://doi.org/10.1038/nbt1252

    Article  CAS  PubMed  Google Scholar 

  21. Chuang G-Y, Boyington JC, Joyce MG et al (2012) Computational prediction of N-linked glycosylation incorporating structural properties and patterns. Bioinformatics 28:2249–2255. https://doi.org/10.1093/bioinformatics/bts426

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lu X, Nobrega RP, Lynaugh H et al (2019) Deamidation and isomerization liability analysis of 131 clinical-stage antibodies. mAbs 11:45–57. https://doi.org/10.1080/19420862.2018.1548233

    Article  CAS  PubMed  Google Scholar 

  23. Services. https://www.healthtech.dtu.dk; https://services.healthtech.dtu.dk. Accessed 19 Sept 2022

  24. Sydow JF, Lipsmeier F, Larraillet V et al (2014) Structure-based prediction of asparagine and aspartate degradation sites in antibody variable regions. PLoS One 9:e100736. https://doi.org/10.1371/journal.pone.0100736

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Vatsa S (2022) In silico prediction of post-translational modifications in therapeutic antibodies. mAbs 14:2023938. https://doi.org/10.1080/19420862.2021.2023938

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Xu A, Kim HS, Estee S et al (2018) Susceptibility of antibody CDR residues to chemical modifications can be revealed prior to antibody humanization and aid in the lead selection process. Mol Pharm 15:4529–4537. https://doi.org/10.1021/acs.molpharmaceut.8b00536

    Article  CAS  PubMed  Google Scholar 

  27. Harding FA, Stickler MM, Razo J, DuBridge RB (2010) The immunogenicity of humanized and fully human antibodies. mAbs 2:256–265

    Article  PubMed  PubMed Central  Google Scholar 

  28. Safdari Y, Farajnia S, Asgharzadeh M, Khalili M (2013) Antibody humanization methods – a review and update. Biotechnol Genet Eng Rev 29:175–186. https://doi.org/10.1080/02648725.2013.801235

    Article  CAS  PubMed  Google Scholar 

  29. De Groot AS, Moise L (2007) Prediction of immunogenicity for therapeutic proteins: state of the art. Curr Opin Drug Discov Devel 10:332–340

    PubMed  Google Scholar 

  30. Jawa V, Maamary J, Swanson M et al (2022) Implementing a clinical immunogenicity strategy using preclinical risk assessment outputs. J Pharm Sci 111:960–969. https://doi.org/10.1016/j.xphs.2022.01.032

    Article  CAS  PubMed  Google Scholar 

  31. Gupta P, Makowski EK, Kumar S et al (2022) Antibodies with weakly basic isoelectric points minimize trade-offs between formulation and physiological colloidal properties. Mol Pharm 19:775–787. https://doi.org/10.1021/acs.molpharmaceut.1c00373

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Grinshpun B, Thorsteinson N, Pereira JN et al (2021) Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies. mAbs 13:1932230. https://doi.org/10.1080/19420862.2021.1932230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Avery LB, Wade J, Wang M et al (2018) Establishing in vitro in vivo correlations to screen monoclonal antibodies for physicochemical properties related to favorable human pharmacokinetics. mAbs 10:244–255. https://doi.org/10.1080/19420862.2017.1417718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Bumbaca Yadav D, Sharma VK, Boswell CA et al (2015) Evaluating the use of antibody variable region (Fv) charge as a risk assessment tool for predicting typical cynomolgus monkey pharmacokinetics*. J Biol Chem 290:29732–29741. https://doi.org/10.1074/jbc.M115.692434

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Crowell SR, Wang K, Famili A et al (2019) Influence of charge, hydrophobicity, and size on vitreous pharmacokinetics of large molecules. Transl Vis Sci Technol 8:1. https://doi.org/10.1167/tvst.8.6.1

    Article  PubMed  PubMed Central  Google Scholar 

  36. Datta-Mannan A, Estwick S, Zhou C et al (2020) Influence of physiochemical properties on the subcutaneous absorption and bioavailability of monoclonal antibodies. mAbs 12:1770028. https://doi.org/10.1080/19420862.2020.1770028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Datta-Mannan A, Lu J, Witcher DR et al (2015) The interplay of non-specific binding, target-mediated clearance and FcRn interactions on the pharmacokinetics of humanized antibodies. mAbs 7:1084–1093. https://doi.org/10.1080/19420862.2015.1075109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Grevys A, Frick R, Mester S et al (2022) Antibody variable sequences have a pronounced effect on cellular transport and plasma half-life. iScience 25:103746. https://doi.org/10.1016/j.isci.2022.103746

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hötzel I, Theil F-P, Bernstein LJ et al (2012) A strategy for risk mitigation of antibodies with fast clearance. mAbs 4:753–760. https://doi.org/10.4161/mabs.22189

    Article  PubMed  PubMed Central  Google Scholar 

  40. Li B, Tesar D, Boswell CA et al (2014) Framework selection can influence pharmacokinetics of a humanized therapeutic antibody through differences in molecule charge. mAbs 6:1255–1264. https://doi.org/10.4161/mabs.29809

    Article  PubMed  PubMed Central  Google Scholar 

  41. Piche-Nicholas NM, Avery LB, King AC et al (2018) Changes in complementarity-determining regions significantly alter IgG binding to the neonatal Fc receptor (FcRn) and pharmacokinetics. mAbs 10:81–94. https://doi.org/10.1080/19420862.2017.1389355

    Article  CAS  PubMed  Google Scholar 

  42. Rabia LA, Zhang Y, Ludwig SD et al (2018) Net charge of antibody complementarity-determining regions is a key predictor of specificity. Protein Eng Des Sel 31:409–418. https://doi.org/10.1093/protein/gzz002

    Article  CAS  PubMed  Google Scholar 

  43. Chemical Computing Group (CCG) | Research. https://www.chemcomp.com/Research-Citing_MOE.htm. Accessed 25 Aug 2022

  44. Ye J, Ma N, Madden TL, Ostell JM (2013) IgBLAST: an immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res 41:W34–W40. https://doi.org/10.1093/nar/gkt382

    Article  PubMed  PubMed Central  Google Scholar 

  45. Schrödinger Release 2021–3 (2021) BioLuminate. Schrödinger, LLC, New York. https://www.schrodinger.com/products/bioluminate. Accessed 25 Aug 2022

    Google Scholar 

  46. MHC-II Binding. http://tools.iedb.org/mhcii/. Accessed 24 Aug 2022

  47. Brochet X, Lefranc M-P, Giudicelli V (2008) IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis. Nucleic Acids Res 36:W503–W508. https://doi.org/10.1093/nar/gkn316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Giudicelli V, Brochet X, Lefranc M-P (2011) IMGT/V-QUEST: IMGT standardized analysis of the immunoglobulin (IG) and T cell receptor (TR) nucleotide sequences. Cold Spring Harb Protoc 2011:695–715. https://doi.org/10.1101/pdb.prot5633

    Article  PubMed  Google Scholar 

  49. IMGT Reference sequences page. https://www.imgt.org/vquest/refseqh.html. Accessed 24 Aug 2022

  50. Geneious Biologics | Antibody Discovery Software. In: Antibody Discovery Software | Geneious Biologics. https://www.geneious.com/biopharma/. Accessed 24 Aug 2022

  51. Schrödinger. The PyMOL molecular graphics system, version 2.3. Schrödinger, LLC. https://www.schrodinger.com/products/pymol. Accessed 25 Aug 2022

  52. Baker NA, Sept D, Joseph S et al (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci 98:10037–10041. https://doi.org/10.1073/pnas.181342398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Fernández-Quintero ML, Georges G, Varga JM, Liedl KR (2021) Ensembles in solution as a new paradigm for antibody structure prediction and design. mAbs 13:1923122. https://doi.org/10.1080/19420862.2021.1923122

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The presented approach is the result of a truly collaborative effort including many colleagues from different disciplines of our R&D organization. We would like to thank all these colleagues for continuous and constructive discussions, and (experimental and in silico) data acquisition that help to improve the predictivity of in silico predictions, and finally to establish SUMO as our standard workflow for early in silico sequence assessment for biologics.

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Authors and Affiliations

  1. Computational Chemistry & Biologics (CCB), Merck Healthcare KGaA, Darmstadt, Germany

    Andreas Evers, Daniel Kuhn & Friedrich Rippmann

  2. Computational Chemistry & Biologics (CCB), EMD Serono, Billerica, MA, USA

    Shipra Malhotra, Ahmad Najafian, Maria Borisovska, Yves Fomekong Nanfack, Alejandro Crespo & Vanita Sood

  3. Ginkgo Analytics GmbH, Hamburg, Germany

    Wolf-Guido Bolick

  4. Computational Chemistry & Biologics, Merck KGaA, Yavne, Israel

    Shira Warszawski

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  1. Andreas Evers
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  2. Shipra Malhotra
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  3. Wolf-Guido Bolick
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  4. Ahmad Najafian
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  5. Maria Borisovska
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  6. Shira Warszawski
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Correspondence to Andreas Evers .

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Editors and Affiliations

  1. Protein Engineering and Antibody Technologies (PEAT), Merck Healthcare KGaA, Darmstadt, Germany

    Stefan Zielonka

  2. Protein Engineering and Antibody Technologies (PEAT), Merck Healthcare KGaA, Darmstadt, Germany

    Simon Krah

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© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Evers, A. et al. (2023). SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters. In: Zielonka, S., Krah, S. (eds) Genotype Phenotype Coupling. Methods in Molecular Biology, vol 2681. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3279-6_22

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  • DOI: https://doi.org/10.1007/978-1-0716-3279-6_22

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3278-9

  • Online ISBN: 978-1-0716-3279-6

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