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3 • Computational Protein Design Strategies 51
effect (e.g. blocking antibody, antibody–drug conjugate) is key, and computational meth‑ ods can enhance outcomes. This is illustrated in the following selected case studies.
In the rst example, a strategy was designed to isolate antibodies that acted to ‘staple’ a molecular heterodimer by binding a composite epitope of two‑receptor extra‑ cellular domains (Figure3.4b). Interleukin‑4 (IL‑4) signals through either a type I het‑ erodimeric receptor comprising IL‑4Rα and the common γ‑chain or a type II receptor composed of IL‑4Rα and IL ‑13Rα1. A crystal structure of IL‑4 in a ternary complex with the cytokine receptor extracellular domains showed that the two‑receptor mem‑ brane proximal domains were in contact forming a neoepitope. An engineered variant of IL‑4 was available (Super‑4) with 3,700‑fold higher afnity than IL‑4, which sta‑ bilized the IL‑4 receptor complex. With this information, an antigen preparation was made in which a receptor was tagged with a C‑terminal biotin‑acceptor peptide allow‑ ing biotinylation and expressed receptor subunits were complexed with Super‑4. This biotinylated complex was used with a yeast scFv library to isolate a scFv, which bound specically to the membrane–proximal receptor interface. This binding mode was con‑ rmed by solving a structure of the antibody (reformatted as a Fab) with the ternary complex, which showed the antibody binding to the receptor interface [98].
In a second case study, the concept of de novo design of antibody Complementarity determining regions (CDR) loops was explored based on targeting epitopes for proteins in which a structure is available either from an experimentally solved structure or a com‑ putational model [95]. This approach required the exploitation of large structural data‑ bases to use a fragment‑based procedure to design CDRs complementary to a selected target epitope. This approach was experimentally tested using single‑domain antibodies based on their monomer structures, ease of production, and small size. To develop this approach, a database of CDR‑like fragments and corresponding antigen‑like regions was compiled from the non‑redundant PDB to build the so‑called AbAg database. Given a known structural target epitope, the database is searched to locate antigen‑like regions similar to the epitope. To perform the search, the input epitope is fragmented into either linear or surface‑patch fragments. The search method identies CDR‑like fragments that may interact with the target epitope. These fragments are evaluated for favourable interactions and ranked. Top‑ranking designed CDR motifs are then grafted to an antibody scaffold that is tolerant to loop replacement. This method does require that the structure of the target antigen is known but is capable of working with both experimentally derived structures and models created by tools such as AlphaFold2 [95]. This technique was applied to three target antigens– human serum albumin, bovine trypsin, and SARS‑CoV‑2 spike protein receptor‑binding domain (RBD). The six sin‑ gle‑domain antibodies designed bound to the target proteins with afnities ranging from 120 to 1,800 nM, with two antibodies targeting the RBD exhibiting afnities of 130 and 210 nM.
A nal case study example is drawn from the eld of vaccine research targeting RSV, which is the leading cause of serious respiratory disease in infants. Although a prophylactic, humanized monoclonal antibody, palivizumab, which targets RSV F protein, is approved, vaccine design has lagged behind. RSV F protein has been the primary antigen target for vaccine development. This is a class I fusion glycoprotein and requires proteolytic cleavage for activation and formation of the mature trimer.
52 Biopharmaceutical Informatics
The RSV F protein is responsible for fusing the viral and host cell membranes during virus cell entry and exists in two antigenically unique forms–a pre‑fusion form and a post‑fusion form, which undergoes a dramatic conformational change. The pre‑fusion form is the target of most neutralizing antibodies [99]. A key issue in targeting the pre‑fusion form of RSV F is that the protein is metastable and can spontaneously change conformation when extracted from membranes with detergent or if subjected to vari‑ ous stresses (e.g. physical, chemical, or temperature stresses). To tackle this issue, sta‑ bilization of the pre‑fusion protein is an attractive strategy. To achieve stabilization, pre‑fusion stabilizing mutations were computationally designed using a combination of methods–introduction of cysteine residue pairs to form stabilizing disulphide bonds; introduction of non‑polar amino acids to ll cavities in the pre‑fusion protein that might otherwise allow for movement; and introduction of charged residue mutations to decrease ionic repulsion or increase ionic interaction between residues that are close in the pre‑fusion state [90]. These residue mutations were computationally assessed using a combination of Schrodinger BioLuminate, Molecular Operating Environment [100], and Rosetta‑based stability prediction protocols [101]. Using computational design, 398 hypothetical F domain constructs with combinations of disulphide, cavity lling, and charge mutations were expressed as ectodomain constructs, including a britin foldon trimerization domain at the C‑terminus. In general, the disulphide mutations had the greater stabilizing effect on the pre‑fusion conformation. Pre‑fusion F constructs that exhibited greater stabilization were tested and elicited a 10‑fold higher serum‑neutral‑ izing titre than a prototype vaccine F protein candidate. Introduction of the stabilizing mutations onto F‑proteins of two major RSV subgroups, followed by immunization led to complete protection against RSV challenge.
These selected case studies illustrate how a combination of protein struc‑ ture‑informed design and computational techniques can enhance antibody generation in both a therapeutic antibody generation context and in vaccine immunogen design.
3.6 CONCLUSIONS: COMPUTATIONAL ANTIGEN DESIGN AND FUTURE
DEVELOPMENTS
In this chapter, the topic of protein antigen and immunogen design has been explored, and the potential impact of computational tools in facilitating aspects of antigen generation from protein expression to protein structure has been examined. Advances in computing power, increasing capture of data from existing experimental approaches, and enhancements to the speed of determining protein structures, coupled with MI approaches, are leading to rapid breakthroughs and opportunities in biologics drug discovery. The clear, long‑term ambition of structure‑based and computational antigen and antibody design is the ability to design, entirely in silico, antibodies, which bind to a selected target. This breakthrough is imminent, although it seems certain to bring further challenges. Designing antibodies de novo presents several scientic and technical challenges due to the complexity of antibody
3 • Computational Protein Design Strategies 53
structures and their interactions with antigens. Assuming we can computationally design an antibody which binds to a target antigen with high afnity there are several other chal‑ lenges to consider. This includes, but is not limited to, issues such as the antibodies devel‑ opability prole and its pharmacokinetic, pharmacodynamic and toxicity prole. We are also experiencing an era where novel formats are being explored such as bi‑ and multi‑spe‑ cics, antibody–drug conjugates, and alternative scaffolds, each of which brings its own challenges. At the core of this challenge is the requirement to generate target antigen to drive biologics drug discovery and validate the performance of designed biologics. Given the diversity and complexity of potential protein targets, there remains much to be learned, and computational techniques must be central to advances in this area.

REFERENCES

1. Lyu, X., Zhao, Q. and Hui, J. etal., The global landscape of approved antibody therapies. Antib Ther, 2022. 5(4): pp.233–57.
2. Goulet, D.R. and W.M. Atkins, Considerations for the design of antibody‑based therapeu‑ tics. J Pharm Sci, 2020. 109(1): pp.74–103.
3. Shi, L., Lehto, S.G., Zhu, D.X.D. et al., Pharmacologic characterization of AMG 334, a potent and selective human monoclonal antibody against the calcitonin gene‑related pep‑ tide receptor. J Pharmacol Exp Ther, 2015. 356(1): pp.223–31.
4. Rouge, L., Chiang, N., Steffek, M. etal., Structure of CD20 in complex with the therapeutic monoclonal antibody rituximab. Science, 2020. 367(6483): pp.1224–30.
5. Scapin, G., Yang, X., Prosise, W.W. etal., Structure of full‑length human anti‑PD1 thera‑ peutic IgG4 antibody pembrolizumab. Nat Struct Mol Biol, 2015. 22(12): pp.953–8.
6. Catley, M.C., Coote, J., Bari, M. etal., Monoclonal antibodies for the treatment of asthma. Pharmacol Ther, 2011. 132(3): pp.333–51.
7. Mullen, T.E., Abdullah, R., Boucher, J. etal., Accelerated antibody discovery targeting the SARS‑CoV‑2 spike protein for COVID‑19 therapeutic potential. Antib Ther, 2021. 4(3): pp.185–96.
8. Jakobovits, A., Amado, R.G., Yang, X. et al., From XenoMouse technology to panitu‑ mumab, the rst fully human antibody product from transgenic mice. Nat Biotechnol,
2007. 25(10): pp.1134–43.
9. Laustsen, A.H., Greiff, V., Karatt‑Vellatt, A. et al., Animal immunization, in vitro dis‑ play technologies, and machine learning for antibody discovery. Trends Biotechnol, 2021. 39(12): pp.1263–73.
10. Banik, S.S.R., Kushnir, N., Doranz, B.J. etal., Breaking barriers in antibody discovery: harnessing divergent species for accessing difcult and conserved drug targets. MAbs,
2023. 15(1): pp.2273018.
11. Pedrioli, A. and A. Oxenius, Single B cell technologies for monoclonal antibody discovery. Trends Immunol, 2021. 42(12): pp.1143–58.
12. Abdiche, Y.N., Harriman, R., Deng, X. etal., Assessing kinetic and epitopic diversity across orthogonal monoclonal antibody generation platforms. MAbs, 2016. 8(2): pp.264–77.
13. Alfaleh, M.A., Alsaab, H.O., Mahmoud, A.B. etal., Phage display derived monoclonal antibodies: from bench to bedside. Front Immunol, 2020. 11: p.1986.
14. Moraes, J.Z., Hamaguchi, B., Braggion, C. etal., Hybridoma technology: is it still useful? Curr Res Immunol, 2021. 2: pp.32–40.
54 Biopharmaceutical Informatics
15. Winters, A., McFadden, K., Bergen, J. etal., Rapid single B cell antibody discovery using nanopens and structured light. MAbs, 2019. 11(6): pp.1025–35.
16. Minter, R.R., A.M. Sandercock, and S.J. Rust, Phenotypic screening‑the fast track to novel antibody discovery. Drug Discov Today Technol, 2017. 23: pp.83–90.
17. Pun, F.W., I.V. Ozerov, and A. Zhavoronkov, AI‑powered therapeutic target discovery. Trends Pharmacol Sci, 2023. 44(9): pp.561–72.
18. UniProt, C., UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res,
2023. 51(D1): pp. D523–31.
19. Cunningham, F., Achuthan, P., Akanni, W. etal., Ensembl 2019. Nucleic Acids Res, 2019. 47(D1): pp. D745–51.
20. Goodsell, D.S., Zardecki, C., Di Costanzo, L. et al., RCSB protein data bank: enabling biomedical research and drug discovery. Protein Sci, 2020. 29(1): pp.52–65.
21. Wang, Y., Zhang, H., Zhong, H. etal., Protein domain identication methods and online resources. Comput Struct Biotechnol J, 2021. 19: pp.1145–53.
22. Wang, L., Zhong, H., Xue, Z. et al., Res‑Dom: predicting protein domain boundary from sequence using deep residual network and Bi‑LSTM. Bioinform Adv, 2022. 2(1): p. vbac060.
23. Spooner, W., McLaren, W., Slidel, T. etal., Haplosaurus computes protein haplotypes for use in precision drug design. Nat Commun, 2018. 9(1): p.4128.
24. Paiva, V.A., Nagarajan, R., and Selvaraj, S Protein structural bioinformatics: an overview. Comput Biol Med, 2022. 147: p.105695.
25. Lomize, M.A., Pogozheva, I.D., Joo, H. et al., OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res, 2012. 40(Database issue): pp. D370–6.
26. Lomize, A.L., S.C. Todd, and I.D. Pogozheva, Spatial arrangement of proteins in planar and curved membranes by PPM 3.0. Protein Sci, 2022. 31(1): pp.209–20.
27. Spackova, A., Vávra, O., Raček, T. etal., ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era. Nucleic Acids Res, 2024. 52(D1): pp. D413–8.
28. Munk, C., Mutt, E., Isberg, V. etal., An online resource for GPCR structure determination and analysis. Nat Methods, 2019. 16(2): pp.151–62.
29. Pandy‑Szekeres, G., Caroli, J., Mamyrbekov, A. etal., GPCRdb in 2023: state‑specic structure models using AlphaFold2 and new ligand resources. Nucleic Acids Res, 2023. 51(D1): pp. D395–402.
30. Screnci, B., Stafford, L.J., Barnes, T. etal., Antibody specicity against highly conserved membrane protein Claudin 6 driven by single atomic contact point. iScience, 2022. 25(12): p.105665.
31. Bowley, D.R., Labrijn, A.F., Zwick, M.B. etal., Antigen selection from an HIV‑1 immune antibody library displayed on yeast yields many novel antibodies compared to selection from the same library displayed on phage. Protein Eng Des Sel, 2007. 20(2): pp.81–90.
32. Robertson, N., Lopez‑Anton, N., Gurjar, S.A. etal., Development of a novel mammalian display system for selection of antibodies against membrane proteins. J Biol Chem, 2020. 295(52): pp.18436–48.
33. Beerli, R.R. and C. Rader, Mining human antibody repertoires. MAbs, 2010. 2(4): pp.365–78.
34. Ching, K.H., Berg, K., Morales, J., etal., Expression of human lambda expands the reper‑ toire of OmniChickens. PLoS One, 2020. 15(1): p. e0228164.
35. Cox, J.H., Hussell, S., Søndergaard, H. etal., Antibody‑mediated targeting of the Orai1 calcium channel inhibits T cell function. PLoS One, 2013. 8(12): p. e82944.
36. Niwa, R., Shoji‑Hosaka, E., Sakurada, M. et al., Defucosylated chimeric anti‑CC che‑ mokine receptor 4 IgG1 with enhanced antibody‑dependent cellular cytotoxicity shows potent therapeutic activity to T‑cell leukemia and lymphoma. Cancer Res, 2004. 64(6): pp.2127–33.
3 • Computational Protein Design Strategies 55
37. Sasaki, Y., Kosaka, H., Usami, K. etal., Establishment of a novel monoclonal antibody against LGR5. Biochem Biophys Res Commun, 2010. 394(3): pp.498–502.
38. Lee, J.H., Park, C.K., Chen, G. etal., A monoclonal antibody that targets a NaV1.7 channel voltage sensor for pain and itch relief. Cell, 2014. 157(6): pp.1393–404.
39. Kesidis, A., Depping, P., Lodé, A. etal., Expression of eukaryotic membrane proteins in eukaryotic and prokaryotic hosts. Methods, 2020. 180: pp.3–18.
40. Assenberg, R., Wan, P.T., Geisse, S. etal., Advances in recombinant protein expression for use in pharmaceutical research. Curr Opin Struct Biol, 2013. 23(3): pp.393–402.
41. Jain, N.K., Barkowski‑Clark, S., Altman, R. etal., A high density CHO‑S transient trans‑ fection system: comparison of ExpiCHO and Expi293. Protein Expr Purif, 2017. 134: pp.38–46.
42. Jamshad, M.,, Lin, Y.P., Knowles, T.J. etal., Surfactant‑free purication of membrane pro‑ teins with intact native membrane environment. Biochem. Soc. Trans., 2011. 39: pp.813–18.
43. Zeltins, A., Construction and characterization of virus‑like particles: a review. Mol Biotechnol, 2013. 53(1): pp.92–107.
44. Schmidpeter, P.A.M., N. Sukomon, and C.M. Nimigean, Reconstitution of membrane pro‑ teins into platforms suitable for biophysical and structural analyses. Methods Mol Biol,
2020. 2127: pp.191–205.
45. Ravn, P., Madhurantakam, C., Kunze, S. etal., Structural and pharmacological character‑ ization of novel potent and selective monoclonal antibody antagonists of glucose‑depen‑ dent insulinotropic polypeptide receptor. J Biol Chem, 2013. 288(27): pp.19760–72.
46. Birch, J. and A. Quigley, The high‑throughput production of membrane proteins. Emerg Top Life Sci, 2021. 5(5): pp.655–63.
47. Bayburt, T.H. and S.G. Sligar, Membrane protein assembly into Nanodiscs. FEBS Lett, 20 09. 584: pp.1721–7.
48. Agharkar, A., Rzadkowolski, J., McBroom, M. etal., Detergent screening of the human voltage‑gated proton channel using uorescence‑detection size‑exclusion chromatography. Protein Sci, 2014. 23(8): pp.1136–47.
49. Wang, M., Wei, L., Xiang, H. etal., A megadiverse naive library derived from numerous camelids for efcient and rapid development of VHH antibodies. Anal Biochem, 2022. 657: p.114871.
50. Tucker, D.F., Sullivan, J.T., Mattia, K.A. et al., Isolation of state‑dependent monoclonal antibodies against the 12‑transmembrane domain glucose transporter 4 using virus‑like particles. Proc Natl Acad Sci U S A, 2018. 115(22): pp. E4990–9.
51. Elegheert, J., Behiels, E., Bishop, B. etal., Lentiviral transduction of mammalian cells for fast, scalable and high‑level production of soluble and membrane proteins. Nat Protoc,
2018. 13(12): pp.2991–3017.
52. Gulezian, E.,, Crivello, C., Bednenko, J. etal., Membrane protein production and formula‑ tion for drug discovery. Trends Pharmacol Sci, 2021. 42(8): pp.657–74.
53. Hutchings, C.J., P. Colussi, and T.G. Clark, Ion channels as therapeutic antibody targets. MAbs, 2019. 11(2): pp.265–96.
54. Dodd, R., Schoeld, D.J., Wilkinson, T. etal., Generating therapeutic monoclonal antibod‑ ies to complex multi‑spanning membrane targets: overcoming the antigen challenge and enabling discovery strategies. Methods, 2020. 180: pp.111–26.
55. Guarra, F. and G. Colombo, Computational methods in immunology and vaccinology: design and development of antibodies and immunogens. J Chem Theory Comput, 2023. 19(16): pp.5315–33.
56. Bauer, J., Rajagopal, N., Gupta, P. etal., How can we discover developable antibody‑based biotherapeutics? Front Mol Biosci, 2023. 10: p.1221626.
57. Schoeder, C.T., Schmitz, S., Adolf‑Bryfogle, J. etal., Modeling immunity with Rosetta: methods for antibody and antigen design. Biochemistry, 2021. 60(11): pp.825–46.
56 Biopharmaceutical Informatics
58. Zhao, J., Nussinov, R., Wu, W.J. etal., In dilico methods in sntibody design. Antibodies,
2018. 7(3): p. 22.
59. Bai, G., Sun, C., Guo, Z. etal., Accelerating antibody discovery and design with articial intelligence: recent advances and prospects. Semin Cancer Biol, 2023. 95: pp.13–24.
60. Wang, D., Discrepancy between mRNA and protein abundance: insight from information retrieval process in computers. Comput Biol Chem, 2008. 32(6): pp.462–8.
61. Nikolados, E.M. and D.A. Oyarzun, Deep learning for optimization of protein expression. Curr Opin Biotechnol, 2023. 81: pp.102941.
62. Sumida, K.H., Núñez‑Franco, R., Kalvet, I. etal., Improving protein expression, stability, and function with proteinMPNN. J Am Chem Soc, 2024. 14 6(3): pp.2054–61.
63. Ding, Z., Guan, F., Xu, G. etal., MPEPE, a predictive approach to improve protein expres‑ sion in E. coli based on deep learning. Comput Struct Biotechnol J, 2022. 20: pp.1142–53.
64. Bhandari, B.K., Lim, C.S., Remus, D.M. etal., Analysis of 11,430 recombinant protein production experiments reveals that protein yield is tunable by synonymous codon changes of translation initiation sites. PLoS Comput Biol, 2021. 17(10): p. e1009461.
65. Bhandari, B.K., C.S. Lim, and P.P. Gardner, TISIGNER.com: web services for improving recombinant protein production. Nucleic Acids Res, 2021. 49(W1): pp. W654–61.
66. Fu, H., Liang, Y., Zhong, X. etal., Codon optimization with deep learning to enhance pro‑ tein expression. Sci Rep, 2020. 10(1): p.17617.
67. Gamage, N., Cheruvara, H., Harrison, P.J. etal., High‑throughput production and opti‑ mization of membrane proteins after expression in mammalian cells. Methods Mol Biol,
2023. 2652: pp.79–118.
68. Sun, J., Kulandaisamy, A., Liu, J. etal., Machine learning in computational modelling of membrane protein sequences and structures: from methodologies to applications. Comput Struct Biotechnol J, 2023. 21: pp.1205–26.
69. Li, H., Sun, X., Cui, W. etal., Computational drug development for membrane protein targets. Nat Biotechnol, 2024. 42(2): pp.229–42.
70. Tehan, B.G. and J.A. Christopher, The use of conformationally thermostabilised GPCRs in drug discovery: application to fragment, structure and biophysical techniques. Curr Opin Pharmacol, 2016. 30: pp.8–13.
71. Muk, S.,, Ghosh, S., Achuthan, S. etal., Machine learning for prioritization of thermosta‑ bilizing mutations for G‑protein coupled receptors. Biophys J, 2019. 117(11): pp.2228–39.
72. Bedbrook, C.N., Yang, K.K., Robinson, J.E. etal., Machine learning‑guided channelrho‑ dopsin engineering enables minimally invasive optogenetics. Nat Methods, 2019. 16(11): pp.1176–84.
73. Norman, R.A., Ambrosetti, F., Bonvin, A.M.J.J. etal., Computational approaches to thera‑ peutic antibody design: established methods and emerging trends. Brief Bioinform, 2020. 21(5): pp.1549–67.
74. Haddad, Y., V. Adam, and Z. Heger, Ten quick tips for homology modeling of high‑resolu‑ tion protein 3D structures. PLoS Comput Biol, 2020. 16(4): p. e1007449.
75. Hameduh, T., Haddad, Y., Adam, V. etal., Homology modeling in the time of collective and articial intelligence. Comput Struct Biotechnol J, 2020. 18: pp.3494–506.
76. Nimrod, G., Fischman, S., Austin, M. etal., Computational design of epitope‑specic func‑ tional antibodies. Cell Rep, 2018. 25(8): pp.2121–31 e5.
77. Cannon, D.A., Shan, L., Du, Q. et al., Experimentally guided computational antibody afnity maturation with de novo docking, modelling and rational design. PLoS Comput Biol, 2019. 15(5): p. e1006980.
78. Dunbar, J., Krawczyk, K., Leem, J. etal., SAbPred: a structure‑based antibody prediction server. Nucleic Acids Res, 2016. 44(W1): pp. W474–8.
79. Li, L., R. Chen, and Z. Weng, RDOCK: renement of rigid‑body protein docking predic‑ tions. Proteins, 2003. 53(3): pp.693–707.
3 • Computational Protein Design Strategies 57
80. Agarwal, D., Kumar, S., Ambatwar, R. etal., Lead identication through in silico studies: targeting acetylcholinesterase enzyme against Alzheimer’s disease. Cent Nerv Syst Agents Med Chem, 2024. 24(2): pp.219–42.
81. Verma, D.K., Kapoor, S., Das, S. etal., Potential inhibitors of SARS‑CoV‑2main protease (M(pro)) identied from the library of FDA‑approved drugs using molecular docking stud‑ ies. Biomedicines, 2022. 11(1): p.85.
82. Weitzner, B.D., Jeliazkov, J.R., Lyskov, S. etal., Modeling and docking of antibody struc‑ tures with Rosetta. Nat Protoc, 2017. 12(2): pp.401–16.
83. AlQuraishi, M., Protein‑structure prediction revolutionized. Nature, 2021. 596(7873): pp.487–8.
84. Tunyasuvunakool, K., Adler, J., Wu, Z. etal., Highly accurate protein structure prediction for the human proteome. Nature, 2021. 596(7873): pp.590–6.
85. Birch, J., Cheruvara, H., Gamage, N. etal., Changes in membrane protein structural biol‑ og y. Biology (Basel), 2020. 9(11): p.401.
86. Jumper, J., Evans, R., Pritzel, A. etal., Highly accurate protein structure prediction with AlphaFold. Nature, 2021. 596(7873): p.583–9.
87. Baek, M., DiMaio, F., Anishchenko, I. etal., Accurate prediction of protein structures and interactions using a three‑track neural network. Science, 2021. 373(6557): pp.871–6.
88. Sormanni, P., F.A. Aprile, and M. Vendruscolo, The CamSol method of rational design of protein mutants with enhanced solubility. J Mol Biol, 2015. 427(2): pp.478–90.
89. Rosace, A.,, Bennett, A., Oeller, M. etal., Automated optimisation of solubility and confor‑ mational stability of antibodies and proteins. Nat Commun, 2023. 14(1): p.1937.
90. Che, Y., Gribenko, A.V., Song, X. etal., Rational design of a highly immunogenic prefu‑ sion‑stabilized F glycoprotein antigen for a respiratory syncytial virus vaccine. Sci Transl Med, 2023. 15(693): p. eade6422.
91. Krawczyk, K., Liu, X., Baker, T. etal., Improving B‑cell epitope prediction and its applica‑ tion to global antibody‑antigen docking. Bioinformatics, 2014. 30(16): pp.2288–94.
92. Bourquard, T., Musnier, A., Puard, V. etal., MAbTope: a method for improved epitope mapping. J Immunol, 2018. 201(10): pp.3096–105.
93. Hoie, M.H., Gade, F.S., Johansen, J.M. etal., DiscoTope‑3.0: improved B‑cell epitope pre‑ diction using inverse folding latent representations. Front Immunol, 2024. 15: p.1322712.
94. Sela‑Culang, I., Y. Ofran, and B. Peters, Antibody specic epitope prediction‑emergence of a new paradigm. Curr Opin Virol, 2015. 11: pp.98–102.
95. Aguilar Rangel, M., Bedwell, A., Costanzi, E. etal., Fragment‑based computational design of antibodies targeting structured epitopes. Sci Adv, 2022. 8(45): p. eabp9540.
96. Cervenak, J., R. Kurrle, and I. Kacskovics, Accelerating antibody discovery using trans‑ genic animals overexpressing the neonatal Fc receptor as a result of augmented humoral immunity. Immunol Rev, 2015. 268(1): pp.269–87.
97. Caradonna, T.M. and A.G. Schmidt, Protein engineering strategies for rational immunogen design. NPJ Vaccines, 2021. 6(1): p.154.
98. Spangler, J.B., Moraga, I., Jude, K.M. etal., A strategy for the selection of monovalent antibodies that span protein dimer interfaces. J Biol Chem, 2019. 294(38): pp.13876–86.
99. Ngwuta, J.O., Chen, M., Modjarrad, K. etal., Prefusion F‑specic antibodies determine the magnitude of RSV neutralizing activity in human sera. Sci Transl Med, 2015. 7(309): p.309ra162.
100. Roy, U. and L.A. Luck, Molecular modeling of estrogen receptor using molecular operat‑ ing environment. Biochem Mol Biol Educ, 2007. 35(4): pp.238–43.
101. Kellogg, E.H., A. Leaver‑Fay, and D. Baker, Role of conformational sampling in com‑ puting mutation‑induced changes in protein structure and stability. Proteins, 2011. 79(3): pp.830–8.
58 Biopharmaceutical Informatics
102. Yang, J., Yan, R., Roy, A. etal., The I‑TASSER suite: protein structure and function predic‑ tion. Nat Methods, 2015. 12(1): pp.7–8.
103. Webb, B. and A. Sali, Comparative protein structure modeling using MODELLER. Curr Protoc Bioinform, 2016. 54: pp.5.6.1–5.6.37.
104. Kelley, L.A., Mezulis, S., Yates, C.M. etal., The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc, 2015. 10(6): pp.845–58.
105. Waterhouse, A., Bertoni, M., Bienert, S. etal., SWISS‑MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res, 2018. 46(W1): pp. W296–303.
106. Leaver‑Fay, A., Tyka, M., Lewis, S.M. etal., ROSETTA3: an object‑oriented software suite for the simulation and design of macromolecules. Methods Enzymol, 2011. 487: pp.545–74.
107. Mortuza, S.M., Zheng, W., Zhang, C. etal., Improving fragment‑based ab initio protein structure assembly using low‑accuracy contact‑map predictions. Nat Commun, 2021. 12(1): p.5011.

Bioinformatic Analyses of Antibody Repertoires and Their Roles in Modern Antibody Drug Discovery

Melody Shahsavarian, Thomas Watkins, Ponraj Prabakaran*, Adrian Carr, Maria Wendt, and Yu Qiu
4

4.1 INTRODUCTION

The eld of antibody discovery has evolved dramatically with the integration of next‑gen‑ eration sequencing (NGS), high‑throughput screening, and computational methods, reshaping the traditional paradigms of immunization and in vitro antibody discovery. This has allowed for an expanded understanding of antibody diversity as well as the selec‑ tion of highly specic antibodies with desirable therapeutic traits. Historically, antibody discovery relied heavily on animal immunization, a method with its own set of advan‑ tages, including the in vivo maturation of antibodies, which often results in high afnity
*
author for correspondence
1–3
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and specicity.4 The process of antibody discovery has been signicantly enhanced by the precision and scale afforded by NGS technologies, which facilitate a deeper exploration of the antibody repertoire.5 Technological limitations and logistical complexities associ‑ ated with animal use have driven the development of alternative methods. In vitro display technologies, such as phage, yeast, or mammalian display, represent a crucial shift toward non‑animal‑derived antibody discovery.6 These methods not only minimize animal use but also allow for precise control over the selection process, targeting specicities that are challenging to achieve in vivo. Thus, in vitro display technologies have become founda‑ tional in the transition from animal models to more controlled experimental setups, where libraries of antibodies can be screened against a myriad of antigens with high throughput and specicity. Integration of NGS with these display platforms enables the rapid screen‑ ing of vast combinatorial libraries that are directly cloned from a pool of B‑cells or syn‑ thetically designed, thus capturing a broader diversity of antibody sequences.
7
Deep sequencing of antibody repertoires with NGS in recent years has allowed an unprecedented quantitative understanding of immune repertoire dynamics applied to anti‑ body discovery,8 both from natural B‑cells isolated either from human donors9 or immu‑ nized animal models10 and from synthetic antibody display libraries.11 Paired sequencing strategies, more recently, can even provide information at the single‑cell level on the natu‑ ral pairing of antibody heavy and light chains, thus revealing essential information about the antigen specicity and function of the antibodies.
12–14
These technologies have had a
signicant impact on time, efciency, and throughput of antibody discovery platforms.
Recently, machine learning (ML) algorithms have become instrumental in creating innovative approaches that impact antibody discovery and development.
15–26
By lever‑ aging large datasets of antibody sequences generated from NGS, ML can help develop models that could predict antibody behavior, optimize binding afnities, and enhance the developability of therapeutic antibodies. This synergy between NGS and ML is set‑ ting new standards in the rapid identication and optimization of antibodies, pushing the boundaries of what can be achieved in therapeutic development.
27,28
In this chapter, we will briey overview the NGS technologies for antibody discovery. We will then explore NGS‑enabled in vivo, in vitro, and in silico methods, representing the integration of NGS technology with traditional immunization, display libraries, and articial intelligence/machine learning (AI/ML) for advanced antibody discovery. This integrated approach exemplies how cutting‑edge technologies are reshaping the eld of antibody dis‑ covery, leading to the development of more rapid and effective antibody therapeutics.
4.2 NGS TECHNOLOGIES, TOOLS,
AND DATA ANALYSIS FOR MODERN
ANTIBODY DISCOVERY
NGS technologies have greatly transformed the eld of antibody discovery, providing an unprecedented ability to explore antibody repertoires derived from immunizations and display libraries at the individual molecule level. methods, such as Sanger sequencing, which was useful for identifying dominant clones
3,29,30
The shift from traditional