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6 • From Deep Generative Models to Structure-Based Simulations 141
experiments and DMS, followed by deep sequencing. Notably, these studies succeeded
in generating novel antibody sequences not found in the original libraries. However,
experimental validation remains essential for ML‑generated antibody sequences to
conrm their binding to target antigens and assess their developability. Although sev‑
eral other studies have also employed generative models and ML techniques, including
LSTM,
165
GAN,
166
CNN/GAN,
167
hallucination,
168
and Bayesian optimization,
169,17 0
for
optimizing antibody sequences, the evaluation of such ML‑generated sequences has
frequently been conned to computational analyses. Given the current technological
limitations and our partial understanding of sequence‑function relationships in antibod‑
ies, experimental validation remains an indispensable step in the optimization process.
6.3.6 Computer‑Aided Antibody Repositioning
Drug repositioning, also known as drug repurposing, is a strategic approach in pharma‑
ceutical development that seeks to identify new therapeutic uses for existing drugs.
179
This innovative strategy offers a promising pathway to accelerate the drug develop‑
ment process, signicantly reducing the time and costs associated with bringing a drug
to market. Unlike traditional drug discovery, drug repositioning leverages the existing
safety and pharmacokinetic proles of approved or investigational drugs and explores
their potential applications beyond their original intended use, thereby uncovering valu‑
able novel therapeutic benets. This approach not only enhances the efciency of drug
development but also opens new avenues for treating complex diseases, providing hope
for patients with conditions that currently lack effective therapies.
This concept of drug repositioning is equally applicable to antibody drug discovery,
where existing antibodies that bind to different antigens may be redesigned to target
specic antigens. This process can be seen as an expansion of traditional specicity
design. In this context, Nimrod etal. demonstrated the capability to redesign an exist‑
ing antibody to target a specic epitope.
ZDOCK
181
and HEX,
182
molecular dynamics simulations, and antibody sequence design
180
They utilized rigid‑body docking programs
calculations through the Discovery Studio suite, followed by screening with a yeast
surface display experiment. This study demonstrated the successful engineering of a
functional antibody targeting the cytokine interleukin‑17A through computational
“re‑epitoping” of an existing antibody.
180
The crystal structure of the antibody‑antigen
complex conrms the targeted epitope, validating the accuracy of the computational
design. A ML classier based on a random forest algorithm was also employed to pre‑
dict specic residue‑residue contacts between the antigen and antibody, contributing to
the rational design of the functional antibody.
The study by Nimrod etal.
180
utilized a yeast surface display experiment to mature
the initial hits of the repurposed antibody. In a more computationally focused study,
Tam etal. repurposed nanobodies found in the PDB to bind to ELMO1‑RBD, a non‑cog‑
nate antigen of the original nanobodies.
docking program PatchDock
184
to select initial binding poses for subsequent design cal‑
183
They employed the shape‑based, rigid‑body
culations. The initial docking poses were further ltered based on their resemblance
to the binding modes of known nanobodies and the quality of their docking energy
142 Biopharmaceutical Informatics
landscapes. Following this, rigid‑body docking and design calculations with Rosetta,
based on the selected poses, identied a weakly binding nanobody. This was followed
by a purely computational afnity maturation process, which involved rigid‑body dock‑
ing and design calculations with Rosetta using parameters different from those in the
initial design step. This process enabled further renement of the docking poses and
sequences. The nal selection was based on MM/PBSA calculations through MD simu‑
lations and Flex‑ddG calculations
identied, was experimentally conrmed to exhibit improved binding afnity toward
the target antigen and inhibit the target interaction effectively.
These results emphasize the innovative and impactful nature of the computational
repositioning approach, offering new possibilities for the development of targeted anti‑
body therapies. In a potentially related approach, Schneider et al. proposed a deep
learning framework, DLAB, for screening existing antibodies.
ture‑based deep learning approach, specically a CNN model trained on rigid‑body
docking decoys of antibody‑antigen complex structures. It includes components such as
DLAB‑Re (rescoring) and DLAB‑VS (virtual screening) to improve pose selection in
antibody‑antigen docking simulations and enable the classication of antibody‑antigen
pairings as binders or non‑binders. Demonstrated to enrich binders against non‑binding
sequences in realistic scenarios of predicting antibody escape in SARS‑CoV‑2 variants,
DLAB effectively discriminated binding antibodies from non‑binders, enhancing the
virtual screening process in antibody drug discovery. Although antibody‑non‑cognate
antigen pairs would exhibit poor scores, making it more difcult to discriminate poten‑
tial binders from non‑binders, computational screening approaches like DLAB may still
be useful for antibody repositioning studies.
185
in Rosetta. The best binder, as computationally
186
DLAB utilizes a struc‑
100
6.4 GEOMETRIC AND COMPUTATIONAL
CONSIDERATIONS IN THE DESIGN
OF MULTISPECIFIC BIOLOGICS
Multispecic antibodies represent a signicant advancement in antibody engineering,
designed to target two or more distinct antigens.
therapeutic potential than traditional monospecic therapies. This section explores vari‑
ous antibody formats used in creating multispecic biologics and highlights the role of
computational methods in their design.
6.4.1 Antibody Formats in Multispecic Biologics
The modular nature of antibodies enables the creation of various formats beyond tra‑
ditional IgG structures, illustrating the versatility and innovation in antibody engineer‑
ing. However, existing bispecic antibody (BsAb) formats encounter limitations, such
as altered antibody geometry and the need for extensive engineering. Formats like
187,188
This capability offers a broader
6 • From Deep Generative Models to Structure-Based Simulations 143
diabodies, IgG‑single‑chain FV (scFV), and dual‑variable domain (DVD)‑Ig modify the
native structure of antibodies to target multiple antigens, but this may affect stabil‑
ity and solubility. Furthermore, antibody fragments often require substantial modica‑
tion to stabilize variable domains outside their native context, adding complexity to the
design process.
Among various immunotherapeutic agents, bispecic T‑cell engagers (BiTEs) are
innovative formats that harness the immune system to target cancer.
189
They function
by linking two scFVs with a peptide linker: one targeting CD3 on T cells and the other
targeting tumor cell antigens. This dual binding facilitates an immunological synapse,
enabling direct cytotoxic signaling from T cells to tumor cells and resulting in targeted
cancer cell destruction. BiTEs, therefore, offer a potent and specic strategy to enhance
the immune system’s tumor cell recognition and elimination capabilities. A notable
example of BiTEs in clinical use is blinatumomab,
190
which targets CD3 on T cells and
CD19 on B‑cell malignancies. Future advancements of BiTEs may involve substituting
the scFv component targeting CD19 with one aimed at different tumor‑associated anti‑
gens, potentially even employing T‑cell receptor (TCR)‑like antibodies. While TCRs
and antibodies share structural similarities in antigen recognition through their CDRs,
the specics of their interaction mechanisms and domain orientations—Vα/Vβ in TCRs
and VL/VH in antibodies—are distinctively different.
191
This distinction illustrates the
complexity of computationally designing TCR‑like antibodies and further BiTEs, pre‑
senting a challenging yet promising frontier in antibody engineering.
While computational methods could offer insights, their application in multispe‑
cic antibody design is nascent. MD simulations with explicit solvent can trace the
dynamics of these modied formats, although their use is somewhat constrained by the
large sizes of multispecic antibody formats. A noteworthy application is the study by
Aertker etal., which utilized all‑atom MD simulations to elucidate the in vitro pharma‑
cokinetic proles of a BsAb.
192
In this study, the C‑terminal ends of an IgG1 were cova‑
lently linked to a single‑chain FV format targeting a distinct antigen via a peptide linker.
The computational analysis began with a hypothesized complex between the BsAb and
two neonatal Fc receptors (FcRn). This study unveiled interactions between the Fab and
scFV regions of the BsAb with FcRn, potentially inuencing its pharmacokinetic behav‑
ior. Notably, these atomic contacts were not apparent in the initial model; they emerged
only after 300 ns of simulation, underscoring the importance of extended simulations to
capture the dynamic interactions that inuence the mechanism of action of BsAbs. Such
interactions, revealed through all‑atom MD simulations, could serve as a foundation for
enhancing the pharmacokinetic proles of BsAbs. The computational design strategies
discussed in this chapter could then be applied to modify these interactions without
compromising the antibody’s antigen recognition capabilities.
Another bispecic format is IgG BsAbs, which provide several advantages over con‑
ventional BsAb formats. They maintain the biophysical behavior and pharmacokinetic
properties of native IgG, potentially enhancing stability and efcacy. Additionally, IgG
BsAbs can be produced by directly combining parental mAbs, simplifying production
compared to methods requiring separate expression and recombination. Consequently,
IgG BsAbs represent a promising strategy for creating therapeutic molecules with
enhanced properties and clinical potential.
144 Biopharmaceutical Informatics
However, the production of fully IgG BsAbs introduces its own set of challenges.
A primary concern is the highly heterogeneous pairing of heavy and light chains when
produced in mammalian cells, as achieving the desired quaternary structure requires
specic combinations of these chains. Moreover, fully IgG BsAbs often rely on het‑
erodimeric Fc designs to facilitate the heterodimerization of different antibody heavy
chains, further complicating the production process. Additionally, the design of fully
IgG BsAbs that maintain desired specicity and afnity for both targets, while also pre‑
serving IgG‑like pharmacokinetics and effector functions, necessitates precise optimi‑
zation and validation. Overcoming these obstacles through sophisticated computational
design and protein engineering is pivotal for the successful development of fully IgG
BsAbs with optimal therapeutic properties.
6.4.2 Computational Multistate Design of
Bispecic IgG Antibodies
The challenge of heterodimerization in IgG BsAb production, recognized nearly three
decades ago,
193
has been the persistent issue of light chain mispairing. Only recently
have signicant advancements been made toward resolving this. Within this framework,
multistate design (MSD) presents itself as a forward‑thinking approach.
129
It aims to
simultaneously minimize the free energy across multiple protein conformations, each
recognizing different targets, moving beyond the conventional focus on designing pro‑
teins for a single, static structure.
The primary goal in the design of IgG BsAb is to engineer an orthogonal Fab
interface that promotes heavy‑chain heterodimerization while minimizing undesired
by‑products. This involves modifying a constant domain to facilitate heavy‑chain het‑
erodimerization and creating an orthogonal heavy chain‑light chain interface to ensure
correct pairings. For example, Lewis etal. employed a MSD strategy to re‑engineer
the CH1‑CL interface of an IgG1 antibody, utilizing multistate design application in
Rosetta.
194
They identied sequences that prefer mutant‑mutant pairing over mutant‑wild
type, leading to parental monoclonal antibodies that, when co‑expressed, assemble into
IgG BsAb with enhanced heavy chain‑light chain pairing. The resulting IgG BsAb
retained the pharmacokinetic properties of native IgG while binding to target antigens
monovalently.
In another study, Leaver‑Fay etal. suggested the explicit inclusion of negative state
repertoires during antibody sequence optimization, employing an iterative method that
alternates between sequence design and protein docking.
195
Incorporating negative state
repertoires allows for the addition of xed‑backbone conformations that simulate real
docking trajectories, offering deeper insights into the structural impact of mutations on
negative states and leading to more specic and stable bispecic antibodies.
Froning etal. focused on developing novel solutions for the heavy chain‑light chain
pairing issue through computational and rational engineering methods.
196
Their work in
producing and characterizing multiple fully IgG BsAbs underscores the critical role of
specicity engineering in both the variable and constant domains for achieving robust
heavy chain‑light chain specicity across all BsAbs. The collective efforts in employing
6 • From Deep Generative Models to Structure-Based Simulations 145
these advanced computational strategies signify a pivotal shift in optimizing bispecic
antibodies, emphasizing the synergy between precision engineering and computational
antibody design.
6.5 CONCLUSIONS AND PERSPECTIVES
This chapter focused on antibody sequence generation and optimization, utilizing
structure‑based simulations and machine learning. While AlphaFold2 has made
signicant strides in structural biology and drug discovery, accurately predicting
antibody structures, particularly the highly variable CDR‑H3, remains challenging.
The extensive conformational and sequence space of CDR‑H3 is not fully repre‑
sented by existing experimental structures or B‑cell repertoire data, highlighting
the need for innovative computational or experimental methods to bridge this gap.
The current limitations in accurately modeling long CDR‑H3make structure‑based
simulations challenging.
In the absence of structures, how do we proceed with drug discovery? Advances
in B‑cell repertoire sequencing have signicantly enhanced our understanding of
the immune system, impacting research in infectious diseases, vaccine development,
and antibody therapeutics. High‑throughput technologies have rapidly expanded the
sequence data available, allowing the exploration of sequence‑function relationships
in antibodies. Consequently, optimizing antibody functions based solely on sequence
information is becoming a viable approach. However, computationally predicted model
structures still play a crucial role in rationalizing the sequences generated and optimized
through such sequence‑based methods. The concept of “developability” in antibody
drug discovery, akin to the “rule of ve” for small molecules,
importance of structural information. Structural features are invaluable in assessing the
developability of antibody therapeutics.
While machine learning models present a promising path for antibody sequence
generation and optimization, experimental validation is still vital to conrm their effec‑
tiveness. These models offer an alternative to structure‑based approaches, potentially
lowering costs and increasing efciency. Nevertheless, the functionality and develop‑
ability of computationally generated antibodies warrant further investigation.
With the evolution of computational antibody design and optimization, several
future directions emerge. Miniaturizing antibodies is a logical progression, enhancing
their therapeutic applicability. Single‑domain antibodies and peptide‑based drugs are
promising for their potential to penetrate tissues and treat respiratory diseases, with
computational technologies playing a crucial role in their development.
In conclusion, this chapter highlighted the synergy between machine learning,
structure‑based simulations, and high‑throughput experimental techniques in advanc‑
ing antibody engineering. It pointed toward a future where machine‑made therapeutics
could play a pivotal role in healthcare, demonstrating the evolving landscape of anti‑
body design and its implications for medical science.
197
further underscores the
146 Biopharmaceutical Informatics
ACKNOWLEDGMENTS
The author acknowledges support from the Japan Agency for Medical Research and
Development (JP23wm0325047), the Japan Society for the Promotion of Science
(JP19H04202 and JP21K18310), and the Okawa Foundation for Information and
Telecommunications (20‑10).
REFERENCES
1. Makowski EK, Chen H‑T, Tessier PM. Simplifying complex antibody engineering using
machine learning. Cell Syst 2023; 14:667–75. Available from: https://linkinghub.elsevier.
com/retrieve/pii/S2405471223001187
2. Mieczkowski C, Zhang X, Lee D, Nguyen K, Lv W, Wang Y, Zhang Y, Way J, Gries J‑M.
Blueprint for antibody biologics developability. MAbs 2023; 15:2185924. Available from:
https://www.tandfonline.com/doi/full/10.1080/19420862.2023.2185924
3. Marks C, Deane CM. How repertoire data are changing antibody science. J Biol Chem 2020;
295:9823–37. Ava ilable from: https:// link inghub.elsevier.com/retrieve/pii/S0021925817489265
4. Wilman W, Wróbel S, Bielska W, Deszynski P, Dudzic P, Jaszczyszyn I, Kaniewski J,
Młokosiewicz J, Rouyan A, Satława T, etal. Machine‑designed biotherapeutics: opportuni‑
ties, feasibility and advantages of deep learning in computational antibody discovery. Brief
Bioinform 2022; 23(4):bbac267. Available from: https://academic.oup.com/bib/article/
doi/10.1093/bib/bbac267/6643456
5. Chungyoun MF, Gray JJ. AI models for protein design are driving antibody engineering.
Curr Opin Biomed Eng 2023; 28:100473. Available from: https://linkinghub.elsevier.com/
retrieve/pii/S2468451123000296
6. Kumar S, Singh SK. Developability of Biotherapeutics. CRC Press; 2015. Available from:
https://www.taylorfrancis.com/books/9781482246155
7. Akbar R, Bashour H, Rawat P, Robert PA, Smorodina E, Cotet T‑S, Flem‑Karlsen K, Frank
R, Mehta BB, Vu MH, etal. Progress and challenges for the machine learning‑based design
of t‑for‑purpose monoclonal antibodies. MAbs 2022; 14(1):2008790. Available from:
https://www.tandfonline.com/doi/full/10.1080/19420862.2021.2008790
8. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classication with deep convolutional
neural networks. In: F. Pereira and C.J. Burges and L. Bottou and K.Q. Weinberger (eds.),
Advances in Neural Information Processing Systems 25(NIPS 2012).
9. Graves A, Mohamed A, Hinton G. Speech recognition with deep recurrent neural networks.
In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
IEEE; 2013. page 6645–9. Available from: https://ieeexplore.ieee.org/document/6638947/
10. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016
IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016. page
770–8.Available from: https://ieeexplore.ieee.org/document/7780459/
11. Hochreiter S, Schmidhuber J. Long short‑term memory. Neural Comput 1997; 9:1735–80.
Available from: https://direct.mit.edu/neco/article/9/8/1735‑1780/6109
6 • From Deep Generative Models to Structure-Based Simulations 147
12. Goodfellow IJ, Pouget‑Abadie J, Mirza M, Xu B, Warde‑Farley D, Ozair S, Courville A,
Bengio Y. Generative adversarial nets. In: Z. Ghahramani and M. Welling and C. Cortes
and N. Lawrence, K.Q. Weinberger (eds.), Advances in Neural Information Processing
Systems 27. (NIPS 2014).
13. Kingma DP, Welling M. Auto‑encoding variational bayes. In: International Conference
on Learning Representations (ICLR) 2014. 2013. Available from: https://doi.org/10.48550/
arXiv.1312.6114
14. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin
I. Attention is all you need. In: I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach
and R. Fergus and S. Vishwanathan and R. Garnett (eds.), Advances in Neural Information
Processing Systems 30. (NIPS 2017).
15. Kuroda D, Shirai H, Jacobson MP, Nakamura H. Computer‑aided antibody design. Protein
Eng Design Select 2012; 25:507–21. Available from: https://doi.org/10.1093/protein/gzs024
16. Childers MC, Daggett V. Molecular dynamics methods for antibody design. In:
Kouhei Tsumoto, Daisuke Kuroda (eds.), Methods in Molecular Biology. 2023.
page 109–24. New York: Humana New York. Available from: https://link.springer.
com/10.1007/978‑1‑0716‑2609‑2_5
17. Hummer AM, Abanades B, Deane CM. Advances in computational structure‑based
antibody design. Curr Opin Struct Biol 2022; 74:102379. Available from: https://doi.
org/10.1016/j.sbi.2022.102379
18. Robert PA, Akbar R, Frank R, Pavlović M, Widrich M, Snapkov I, Slabodkin A,
Chernigovskaya M, Scheffer L, Smorodina E, etal. Unconstrained generation of synthetic
antibody–antigen structures to guide machine learning methodology for antibody specic‑
ity prediction. Nat Comput Sci 2022; 2:845–65. Available from: https://www.nature.com/
articles/s43588‑022‑00372‑4
19. Weitzner BD, Jeliazkov JR, Lyskov S, Marze N, Kuroda D, Frick R, Adolf‑Bryfogle J, Biswas
N, Dunbrack RL, Gray JJ. Modeling and docking of antibody structures with Rosetta.
Nat Protoc 2017; 12:401–16. Available from: https://www.nature.com/doinder/10.1038/
nprot.2016.180
20. Schritt D, Li S, Rozewicki J, Katoh K, Yamashita K, Volkmuth W, Cavet G, Standley DM.
Repertoire Builder: high‑throughput structural modeling of B and T cell receptors. Mol
Syst Des Eng 2019; 4:761–8. Available from: https://xlink.rsc.org/?DOI=C9ME00020H
21. Ruffolo JA, Sulam J, Gray JJ. Antibody structure prediction using interpretable deep learn‑
ing. Patterns 2022; 3:100406. Available from: https://doi.org/10.1016/j.patter.2021.100406
22. Abanades B, Georges G, Bujotzek A, Deane CM. ABlooper: fast accurate antibody CDR
loop structure prediction with accuracy estimation. Bioinformatics 2022; 38:1877–80.
Available from: https://academic.oup.com/bioinformatics/article/38/7/1877/6517780
23. Ruffolo JA, Chu L‑S, Mahajan SP, Gray JJ. Fast, accurate antibody structure prediction
from deep learning on massive set of natural antibodies. Nat Commun 2023; 14:2389.
Available from: https://www.nature.com/articles/s41467‑023‑38063‑x
24. Abanades B, Wong WK, Boyles F, Georges G, Bujotzek A, Deane CM. ImmuneBuilder:
deep‑learning models for predicting the structures of immune proteins. Commun Biol
2023; 6:575. Available from: https://www.nature.com/articles/s42003‑023‑04927‑7
25. Jin W, Barzilay R, Jaakkola T. Antibody‑antigen docking and design via hierarchical
structure renement. In: Proceedings of the 39th International Conference on Machine
Learning. (PMLR) 2022; 162:10217‑27
26. Xu Z, Davila A, Wila mowski J, Teraguchi S, Sta ndley DM. Improved antibody‐specic epitope
prediction using AlphaFold and AbAdapt. ChemBioChem 2022; 23(18):e202200303. Available
from: https://chemistry‑europe.onlinelibrary.wiley.com/doi/10.1002/cbic.202200303
148 Biopharmaceutical Informatics
27. Ambrosetti F, Jandova Z, Bonvin AMJJ. Information‑driven antibody–antigen modelling
with HADDOCK. In: Kouhei Tsumoto, Daisuke Kuroda (eds.), Methods in Molecular
Biology. 2023. page 267–82. New York: Humana New York. Available from: https://link.
springer.com/10.1007/978‑1‑0716‑2609‑2_14
28. Gaudreault F, Corbeil CR, Sulea T. Enhanced antibody‑antigen structure prediction from
molecular docking using AlphaFold2. Sci Rep 2023; 13:15107. Available from: https://
www.nature.com/articles/s41598‑023‑42090‑5
29. L iberis E , Veličković P, Sorma nni P, Vendruscolo M, Liò P. Parapred: antibody pa ratope pre‑
diction using convolutional and recurrent neural networks. Bioinformatics 2018; 34:2944–
50. Available from: https://academic.oup.com/bioinformatics/article/34/17/2944/4972995
30. Leem J, Mitchell LS, Farmery JHR, Barton J, Galson JD. Deciphering the language of
antibodies using self‑supervised learning. Patterns 2022; 3:100513. Available from: https://
doi.org/10.1016/j.patter.2022.100513
31. Wang M, Cang Z, Wei G‑W. A topology‑based network tree for the prediction of pro‑
tein–protein binding afnity changes following mutation. Nat Mach Intell 2020; 2:116–23.
Available from: https://www.nature.com/articles/s42256‑020‑0149‑6
32. Myung Y, Pires DE V, Ascher DB. mmCSM‑AB: guiding rational antibody engineering
through multiple point mutations. Nucleic Acids Res 2020; 48:W125–31. Available from:
https://academic.oup.com/nar/article/48/W1/W125/5841134
33. Kuroda D, Tsumoto K. Engineering stability, viscosity, and immunogenicity of antibodies
by computational design. J Pharm Sci 2020; 109:1631–51. Available from: https://linking‑
hub.elsevier.com/retrieve/pii/S0022354920300162
34. Marks C, Hummer AM, Chin M, Deane CM. Humanization of antibodies using a machine
learning approach on large‑scale repertoire data. Bioinformatics 2021; 37:4041–7. Available
from: https://academic.oup.com/bioinformatics/article/37/22/4041/6295884
35. Liu X, Luo Y, Li P, Song S, Peng J. Deep geometric representations for modeling effects
of mutations on protein‑protein binding afnity. PLoS Comput Biol 2021; 17:e1009284.
Available from: https://dx.plos.org/10.1371/journal.pcbi.1009284
36. Feng J, Jiang M, Shih J, Chai Q. Antibody apparent solubility prediction from sequence
by transfer learning. iScience2022; 25:105173. Available from: https://linkinghub.elsevier.
com/retrieve/pii/S2589004222014456
37. Yuan Y, Chen Q, Mao J, Li G, Pan X. DG‑Afnity: predicting antigen‑antibody afn‑
ity with language models from sequences. BMC Bioinform 2023; 24:430. Available from:
https://www.ncbi.nlm.nih.gov/pubmed/37957563
38. Jia L, Sun Y. In silico prediction method for protein asparagine deamidation. In: Kouhei
Tsumoto, Daisuke Kuroda (eds.), Methods in Molecular Biology. 2023. page 199–217. New
York: Humana New York. Available from: https://link.springer.com/10.1007/978‑1‑0716‑2
609‑2_10
39. Liang S, Zhang C. PITHA: a webtool to predict immunogenicity for humanized and fully
human therapeutic antibodies. In: Kouhei Tsumoto, Daisuke Kuroda (eds.), Methods in
Molecular Biology. 2023. page 143–50. New York: Humana New York. Available from:
https://link.springer.com /10.1007/978‑1‑0716‑2609‑2_7
40. Ramon A, Ali M, Atkinson M, Saturnino A, Didi K, Visentin C, Ricagno S, Xu X, Greenig
M, Sormanni P. Assessing antibody and nanobody nativeness for hit selection and human‑
ization with AbNatiV. Nat Mach Intell 2024; 6:74–91. Available from: https://www.nature.
com/articles/s42256‑023‑00778‑3
41. Bozhanova NG, Sangha AK, Sevy AM, Gilchuk P, Huang K, Nargi RS, Reidy JX, Trivette
A, Carnahan RH, Bukreyev A, etal. Discovery of Marburg virus neutralizing antibodies
from virus‑naïve human antibody repertoires using large‑scale structural predictions. Proc
Natl Acad Sci U S A 2020; 117:31142–8. Available from: https://pnas.org/doi/full/10.1073/
pnas.1922654117
6 • From Deep Generative Models to Structure-Based Simulations 149
42. Magar R, Yadav P, Barati Farimani A. Potential neutralizing antibodies discovered for
novel corona virus using machine learning. Sci Rep2021; 11:5261. Available from: https://
www.nature.com/articles/s41598‑021‑84637‑4
43. Zhang J, Du Y, Zhou P, Ding J, Xia S, Wang Q, Chen F, Zhou M, Zhang X, Wang W,
etal. Predicting unseen antibodies’ neutralizability via adaptive graph neural networks.
Nat Mach Intell 2022; 4:964–76. Available from: https://www.nature.com/articles/
s42256‑022‑00553‑w
44. Bozhanova NG, Flyak AI, Brown BP, Ruiz SE, Salas J, Rho S, Bombardi RG, Myers
L, Soto C, Bailey JR, etal. Computational identication of HCV neutralizing antibod‑
ies with a common HCDR3 disulde bond motif in the antibody repertoires of infected
individuals. Nat Commun 2022; 13:3178. Available from: https://www.nature.com/articles/
s41467‑022‑30865‑9
45. Saksena SD, Liu G, Banholzer C, Horny G, Ewert S, Gifford DK. Computational
counterselection identies nonspecic therapeutic biologic candidates. Cell Rep
Methods 2022; 2:100254. Available from: https://linkinghub.elsevier.com/retrieve/pii/
S2667237522001278
46. Vu MH, Akbar R, Robert PA, Swiatczak B, Sandve GK, Greiff V, Haug DTT. Linguistically
inspired r oadmap for building biologically reliable protein la nguage models. Nat Mac h Intell
2023; 5:485–96. Available from: https://www.nature.com/articles/s42256‑023‑00637‑1
47. Gomes T, Teichmann SA, Talavera‑López C. Immunology driven by large‑scale single‑cell
sequencing. Trends Immunol 2019; 40:1011–21. Available from: https://linkinghub.elsevier.
com/retrieve/pii/S1471490619301929
48. Morgan D, Tergaonkar V. Unraveling B cell trajectories at single cell resolution. Tre n d s
Immunol 2022; 43:210–29. Available from: https://linkinghub.elsevier.com/retrieve/pii/
S1471490622000035
49. Rawlings DJ, Metzler G, Wray‑Dutra M, Jackson SW. Altered B cell signalling in auto‑
immunity. Nat Rev Immunol 2017; 17:421–36. Available from: https://www.nature.com/
articles/nri.2017.24
50. Parameswaran P, Liu Y, Roskin KM, Jackson KKL, Dixit VP, Lee J‑Y, Artiles KL, Zompi
S, Vargas MJ, Simen BB, etal. Convergent antibody signatures in human dengue. Cell
Host Microbe 2013; 13:691–700. Available from: https://linkinghub.elsevier.com/retrieve/
pii /S19313128130 01911
51. Jackson KJL, Liu Y, Roskin KM, Glanville J, Hoh RA, Seo K, Marshall EL, Gurley TC,
Moody MA, Haynes BF, etal. Human responses to inuenza vaccination show seroconver‑
sion signatures and convergent antibody rearrangements. Cell Host Microbe 2014; 16:105–
14. Available from: https://linkinghub.elsevier.com/retrieve/pii/S193131281400184X
52. Galson JD, Schaetzle S, Bashford‑Rogers RJM, Raybould MIJ, Kovaltsuk A, Kilpatrick
GJ, Minter R, Finch DK, Dias J, James LK, et al. Deep sequencing of B cell receptor
repertoires from COVID‑19 patients reveals strong convergent immune signatures.
Front Immunol 2020; 11. Available from: https://www.frontiersin.org/articles/10.3389/
mmu.2020.605170/full
53. Liu R‑X, Wen C, Ye W, Li Y, Chen J, Zhang Q, Li W, Liang W, Wei L, Zhang J, etal.
Altered B cell immunoglobulin signature exhibits potential diagnostic values in human
colorectal cancer. iScience 2023; 26:106140. Available from: https://linkinghub.elsevier.
com/retrieve/pii/S2589004223002171
54. Galson JD, Trück J, Fowler A, Clutterbuck EA, Münz M, Cerundolo V, Reinhard C, van
der Most R, Pollard AJ, Lunter G, etal. Analysis of B cell repertoire dynamics following
hepatitis B vaccination in humans, and enrichment of vaccine‑specic antibody sequences.
EBioMedicine 2015; 2:2070–9. Available from: https://linkinghub.elsevier.com/retrieve/
pii/S2352396415302176
150 Biopharmaceutical Informatics
55. Forgacs D, Abreu RB, Sautto GA, Kirchenbaum GA, Drabek E, Williamson KS, Kim D,
Emerling DE, Ross TM. Convergent antibody evolution and clonotype expansion following
inuenza virus vaccination. PLoS One 2021; 16:e0247253. Available from: https://dx.plos.
org/10.1371/journal.pone.0247253
56. Kwong PD, DeKosky BJ, Ulmer JB. Antibody‑guided structure‑based vaccines. Semin
Immunol 2020; 50:101428. Available from: https://linkinghub.elsevier.com/retrieve/pii/
S1044532320300440
57. Cao Y, Su B, Guo X, Sun W, Deng Y, Bao L, Zhu Q, Zhang X, Zheng Y, Geng C, etal.
Potent neutralizing antibodies against SARS‑CoV‑2 identied by high‑throughput sin‑
gle‑cell sequencing of convalescent patients’ B cells. Cell 2020; 182:73–84.e16. Available
from: https://linkinghub.elsevier.com/retrieve/pii/S0092867420306206
58. Yaari G, Kleinstein SH. Practical guidelines for B‑cell receptor repertoire sequencing
analysis. Genome Med 2015; 7:121. Available from: https://genomemedicine.biomedcen‑
tral.com/articles/10.1186/s13073‑015‑0243‑2
59. Hederman AP, Ackerman ME. Leveraging deep learning to improve vaccine design. Tre n d s
Immunol 2023; 44:333–44. Available from: https://linkinghub.elsevier.com/retrieve/pii/
S1471490623000467
60. Horst A, Smakaj E, Natali EN, Tosoni D, Babrak LM, Meier P, Miho E. Machine learning
detects anti‑DENV signatures in antibody repertoire sequences. Front Artif Intell 2021; 4.
Available from: https://www.frontiersin.org/articles/10.3389/frai.2021.715462/full
61. Ostmeyer J, Christley S, Rounds WH, Toby I, Greenberg BM, Monson NL, Cowell LG.
Statistical classiers for diagnosing disease from immune repertoires: a case study using
multiple sclerosis. BMC Bioinform 2017; 18:401. Available from: https://bmcbioinformat‑
ics.biomedcent ra l.com /articles/10.1186/s12859‑017‑1814‑6
62. Yang KK, Wu Z, Bedbrook CN, Arnold FH. Learned protein embeddings for machine
learning. Bioinformatics 2018; 34:2642–8. Available from: https://academic.oup.com/
bioinformatics/article/34/15/2642/4951834
63. Wang M, Patsenker J, Li H, Kluger Y, Kleinstein SH. Language model‑based B cell recep‑
tor sequence embeddings can effectively encode receptor specicity. Nucleic Acids Res
2024; 52:548–57. Available from: https://doi.org/10.1093/nar/gkad1128
64. Ostrovsky‑Berman M , Frankel B, Polak P, Yaari G. Im mune2vec: embeddi ng B/T cell re cep‑
tor sequences in RN using natural language processing. Front Immunol 2021; 12:680687.
Available from: https://www.frontiersin.org/articles/10.3389/mmu.2021.680687/full
65. Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli
Y, etal. Evolutionary‑scale prediction of atomic‑level protein structure with a language
model. Science (1979) 2023; 379:1123–30. Available from: https://www.science.org/
doi/10.1126/science.ade2574
66. Elnaggar A, Heinzinger M, Dallago C, Rehawi G, Wang Y, Jones L, Gibbs T, Feher T,
Angerer C, Steinegger M, et al. ProtTrans: toward understanding the language of life
through self‑supervised learning. IEEE Trans Pattern Anal Mach Intell 2022; 44:7112–27.
Available from: https://ieeexplore.ieee.org/document/9477085/
67. Ruffolo JA, Gray JJ, Sulam J. Deciphering antibody afnity maturation with language mod‑
els and weakly super vised learning. In: Machine Learning for Structural Biology Workshop
at NeurIPS 2021. 2021. Available from: https://doi.org/10.48550/arXiv.2112.07782
68. Wu R, Ding F, Wang R, Shen R, Zhang X, Luo S, Su C, Wu Z, Xie Q, Berger B, etal.
High‑resolution de novo structure prediction from primary sequence. bioRxiv 2022.
Available from: https://doi.org/10.1101/2022.07.21.500999
69. Wang W, Peng Z, Yang J. Single‑sequence protein structure prediction using supervised
transformer protein language models. Nat Comput Sci 2022; 2:804–14. Available from:
https://www.nature.com/articles/s43588‑022‑00373‑3