Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5387_Библиотеки_им_академика_М_И_Перельмана.pdf
7 • Antibody Structure-Function 191
Hu, B., Xia, J., Zheng, J., Tan, C., Huang, Y., Xu, Y., & Li, S. Z. (2022). Protein language mod‑
els and structure prediction: connection and progression. arXiv [q‑bio.QM], arXiv. https://
arxiv.org/abs/2211.16742
Hummer, A. M., Abanades, B., & Deane, C. M. (2022). Advances in computational structure‑based
antibody design. Current Opinion in Structural Biology, 74, 102379.
Ieong, P., Amaro, R. E., & Li, W. W. (2015). Molecular dynamics analysis of antibody recogni‑
tion and escape by human H1N1 inuenza hemagglutinin. Biophysical Journal, 108(11),
2704–2712.
Igawa, T., Maeda, A., Haraya, K., Tachibana, T., Iwayanagi, Y., Mimoto, F., Higuchi, Y., Ishii, S.,
Tamba, S., Hironiwa, N., Nagano, K., Wakabayashi, T., Tsunoda, H., & Hattori, K. (2013).
Engineered monoclonal antibody with novel antigen‑sweeping activity in vivo. PloS One,
8(5), e63236.
Jain, T., Sun, T., Durand, S., Hall, A., Houston, N. R., Nett, J. H., Sharkey, B., Bobrowicz, B.,
Caffry, I., Yu, Y., Cao, Y., Lynaugh, H., Brown, M., Baruah, H., Gray, L. T., Krauland, E. M.,
Xu, Y., Vásquez, M., & Wittrup, K. D. (2017). Biophysical properties of the clinical‑stage
antibody landscape. Proceedings of the National Academy of Sciences of the United States
of America, 114(5), 944–949.
Jankauskaitė, J., Jiménez‑García, B., Dapkūnas, J., Fernández‑Recio, J., & Moal, I. H. (2018).
SKEMPI 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics
and thermodynamics upon mutation. Bioinformatics, 35(3), 462–469.
Jarmoskaite, I., AlSadhan, I., Vaidyanathan, P. P., & Herschlag, D. (2020). How to measure and
evaluate binding afnities. eLife, 9. https://doi.org/10.7554/elife.57264
Jefferis, R. (2016). Posttranslational modications and the immunogenicity of biotherapeutics.
Journal of Immunology Research, 2016, 5358272.
Jemimah, S., Sekijima, M., & Gromiha, M. M. (2019). ProAfMuSeq: sequence‑based method to
predict the binding free energy change of protein–protein complexes upon mutation using
functional classication. Bioinformatics, 36(6), 1725–1730.
Jemimah, S., Yugandhar, K., & Michael Gromiha, M. (2017). PROXiMATE: a database of
mutant protein–protein complex thermodynamics and kinetics. Bioinformatics, 33(17),
2787–2788.
Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M., & Marcatili, P. (2019). Antibody specic
B‑Cell epitope predictions: leveraging information from antibody‑antigen protein com‑
plexes. Frontiers in Immunology, 10. https://doi.org/10.3389/mmu.2019.00298
Jiménez‑García, B., Roel‑Touris, J., Romero‑Durana, M., Vidal, M., Jiménez‑González, D., &
Fernández‑Recio, J. (2018). LightDock: a new multi‑scale approach to protein‑protein
docking. Bioinformatics, 34(1), 49–55.
Jin, W., Barzilay, R., & Jaakkola, T. (2022). Antibody–Antigen docking and design via hierarchi‑
cal equivariant renement. arXiv [q‑bio.BM], arXiv. https://arxiv.org/abs/2207.06616
Johnston, R. J., Su, L. J., Pinckney, J., Critton, D., Boyer, E., Krishnakumar, A., Corbett, M.,
Rankin, A. L., Dibella, R., Campbell, L., Martin, G. H., Lemar, H., Cayton, T., Huang, R.
Y.‑C., Deng, X., Nayeem, A., Chen, H., Ergel, B., Rizzo, J. M., … Korman, A. J. (2019).
VISTA is an acidic pH‑selective ligand for PSGL‑1. Nature, 574(7779), 565–570.
Jones, S., & Thornton, J. M. (1996). Principles of protein‑protein interactions. Proceedings of the
National Academy of Sciences, 93(1), 13–20. https://doi.org/10.1073/pnas.93.1.13
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K.,
Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A.
J., Cowie, A., Romera‑Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021).
Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.
Kastritis, P. L., & Bonvin, A. M. J. (2011). Are scoring functions in protein−protein docking
ready to predict interactomes? Clues from a novel binding afnity benchmark. Journal of
Proteome Research, 10(2), 921–922. https://doi.org/10.1021/pr101118t
192 Biopharmaceutical Informatics
Kastritis, P. L., & Bonvin, A. M. J. J. (2013). On the binding afnity of macromolecular interac‑
tions: daring to ask why proteins interact. Journal of the Royal Society, Interface/the Royal
Society, 10(79), 20120835.
Kastritis, P. L., Moal, I. H., Hwang, H., Weng, Z., Bates, P. A., Bonvin, A. M. J., & Janin, J. (2011).
A structure‑based benchmark for protein‑protein binding afnity. Protein Science, 20(3),
482–491. https://doi.org/10.1002/pro.580
Kawashima, S. (2000). AAindex: amino acid index database. Nucleic Acids Research, 28(1), 374–
374. https://doi.org/10.1093/nar/28.1.374
Kayser, V., Chennamsetty, N., Voynov, V., Forrer, K., Helk, B., & Trout, B. L. (2011).
Glycosylation inuences on the aggregation propensity of therapeutic monoclonal antibod‑
ies. Biotechnology Journal, 6(1), 38–44.
Kelow, S. P., Adolf‑Bryfogle, J., & Dunbrack, R. L. (2020). Hiding in plain sight: structure and
sequence analysis reveals the importance of the antibody DE loop for antibody‑antigen
binding. mAbs, 12(1), 1840005.
Keskin, O., Ma, B., Rogale, K., Gunasekaran, K., & Nussinov, R. (2005). Protein–protein interac‑
tions: organization, cooperativity and mapping in a bottom‑up systems biology approach.
Physical Biology, 2(2), S24–S35. https://doi.org/10.1088/1478‑3975/2/2/s03
Khetan, R., Curtis, R., Deane, C. M., Hadsund, J. T., Kar, U., Krawczyk, K., Kuroda, D., Robinson,
S. A., Sormanni, P., Tsumoto, K., Warwicker, J., & Martin, A. C. R. (2022). Current advances
in biopharmaceutical informatics: guidelines, impact and challenges in the computational
developability assessment of antibody therapeutics. mAbs, 14(1), 2020082.
Kim, D. Y., Kandalaft, H., Ding, W., Ryan, S., van Faassen, H., Hirama, T., Foote, S. J., MacKenzie,
R., & Tanha, J. (2012). Disulde linkage engineering for improving biophysical properties
of human VH domains. Protein Engineering, Design & Selection: PEDS, 25(10), 581–589.
Kortemme, T., Joachimiak, L. A., Bullock, A. N., Schuler, A. D., Stoddard, B. L., & Baker, D.
(2004). Computational redesign of protein‑protein interaction specicity. Nature Structural
& Molecular Biology, 11(4), 371–379.
Kralj, S., Hodošček, M., Podobnik, B., Kunej, T., Bren, U., Janežič, D., & Konc, J. (2021).
Molecular dynamics simulations reveal interactions of an IgG1 antibody with selected Fc
receptors. Frontiers in Chemistry, 9, 705931.
Krawczyk, K., Baker, T., Shi, J., & Deane, C. M. (2013). Antibody i‑Patch prediction of the
antibody binding site improves rigid local antibody‑antigen docking. Protein Engineering,
Design & Selection: PEDS, 26(10), 621–629.
Krawczyk, K., Liu, X., Baker, T., Shi, J., & Deane, C. M. (2014). Improving B‑cell epitope predic‑
tion and its application to global antibody‑antigen docking. Bioinformatics, 30(16), 2288–
2294. https://doi.org/10.1093/bioinformatics/btu190
Kulkarni‑Kale, U., Raskar‑Renuse, S., Natekar‑Kalantre, G., & Saxena, S. A. (2014). Antigen–
antibody interaction database (AgAbDb): a compendium of antigen–antibody interactions.
In R. K. De & N. Tomar (Eds.), Immunoinformatics (pp.149–164). Springer New York.
Lan, W., Valente, J. J., Ilott, A., Chennamsetty, N., Liu, Z., Rizzo, J. M., Yamniuk, A. P., Qiu,
D., Shackman, H. M., & Bolgar, M. S. (2020). Investigation of anomalous charge variant
prole reveals discrete pH‑dependent conformations and conformation‑dependent charge
states within the CDR3loop of a therapeutic mAb. mAbs, 12(1). https://doi.org/10.1080/19
420862.2020.1763138
Lazim, R., Suh, D., & Choi, S. (2020). Advances in molecular dynamics simulations and enhanced
sampling methods for the study of protein systems. International Journal of Molecular
Sciences, 21(17). https://doi.org/10.3390/ijms21176339
Leem, J., Mitchell, L. S., Farmery, J. H. R., Barton, J., & Galson, J. D. (2022). Deciphering
the language of antibodies using self‑supervised learning. Patterns (New York, N.Y.), 3(7),
100513.
7 • Antibody Structure-Function 193
Lees, W. D., Stejskal, L., Moss, D. S., & Shepherd, A. J. (2017). Investigating substitutions in anti‑
body–antigen complexes using molecular dynamics: a case study with broad‑spectrum, inu‑
enza A antibodies. Frontiers in Immunology, 8. https://doi.org/10.3389/mmu.2017.00143
Lima, W. C., Gasteiger, E., Marcatili, P., Duek, P., Bairoch, A., & Cosson, P. (2020). The ABCD
database: a repository for chemically dened antibodies. Nucleic Acids Research, 48(D1),
D261–D264.
Li, M., Simonetti, F. L., Goncearenco, A., & Panchenko, A. R. (2016). MutaBind estimates and
interprets the effects of sequence variants on protein–protein interactions. Nucleic Acids
Research, 44(W1), W494–W501. https://doi.org/10.1093/nar/gkw374
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., … Rives, A. (2023). Evolutionary‑scale pre‑
diction of atomic‑level protein structure with a language model. Science (New York, N.Y.),
379(6637), 1123–1130.
Li, Pahari, S., Murthy, A. K., Liang, S., Fragoza, R., Yu, H., & Alexov, E. (2021). SAAMBE‑SEQ:
a sequence‑based method for predicting mutation effect on protein‑protein binding afnity.
Bioinformatics, 37(7), 992–999.
Lippow, S. M., Dane Wittrup, K., & Tidor, B. (2007). Computational design of antibody‑afnity
improvement beyond in vivo maturation. Nature Biotechnology, 25(10), 1171–1176. https://
doi.org/10.1038/nbt1336
Liu, Luo, Y., Li, P., Song, S., & Peng, J. (2021). Deep geometric representations for modeling
effects of mutations on protein‑protein binding afnity. PLoS Computational Biology,
17(8), e1009284.
Liu, Z., Valente, J., Lin, S., Chennamsetty, N., Qiu, D., & Bolgar, M. (2019). Cyclization of
N‑terminal glutamic acid to pyro‑Glutamic acid impacts monoclonal antibody charge het‑
erogeneity despite its appearance as a neutral transformation. Journal of Pharmaceutical
Sciences, 108(10), 3194–3200.
Li, S., Wu, S., Wang, L., Li, F., Jiang, H., & Bai, F. (2022). Recent advances in predicting pro‑
tein‑protein interactions with the aid of articial intelligence algorithms. Current Opinion
in Structural Biology, 73, 102344.
Löhr, T., Sormanni, P., & Vendruscolo, M. (2022). Conformational entropy as a potential liabil‑
ity of computationally designed antibodies. Biomolecules, 12(5). https://doi.org/10.3390/
biom12050718
Lopes, P. E. M., Guvench, O., & MacKerell, A. D., Jr.(2015). Current status of protein force elds
for molecular dynamics simulations. Methods in Molecular Biology, 1215, 47–71.
Lo, Y.‑T., Shih, T.‑C., Pai, T.‑W., Ho, L.‑P., Wu, J.‑L., & Chou, H.‑Y. (2021). Conformational
epitope matching and prediction based on protein surface spiral features. BMC Genomics,
22(Suppl 2), 116.
Lu, R.‑M., Hwang, Y.‑C., Liu, I.‑J., Lee, C.‑C., Tsai, H.‑Z., Li, H.‑J., & Wu, H.‑C. (2020).
Development of therapeutic antibodies for the treatment of diseases. Journal of Biomedical
Science, 27(1), 1.
Lu, L. L, Suscovich, T. J., Fortune, S. M., & Alter, G. (2018). Beyond binding: antibody effector
functions in infectious diseases. Nature Reviews. Immunology, 18(1), 46–61.
Lu, X., Nobrega, R. P., Lynaugh, H., Jain, T., Barlow, K., Boland, T., Sivasubramanian, A.,
Vásquez, M., & Xu, Y. (2019). Deamidation and isomerization liability analysis of 131
clinical‑stage antibodies. mAbs, 11(1), 45–57.
Makowski, E. K., Kinnunen, P. C., Huang, J., Wu, L., Smith, M. D., Wang, T., Desai, A. A., Streu,
C. N., Zhang, Y., Zupancic, J. M., Schardt, J. S., Linderman, J. J., & Tessier, P. M. (2022).
Co‑optimization of therapeutic antibody afnity and specicity using machine learning
models that generalize to novel mutational space. Nature Communications, 13(1), 3788.
Marillet, S., Boudinot, P., & Cazals, F. (2016). High‑resolution crystal structures leverage protein
binding afnity predictions. Proteins, 84(1), 9–20.
194 Biopharmaceutical Informatics
Masiero, A., Nelly, L., Marianne, G., Christophe, S., Florian, L., Ronan, C., Claire, B., Cornelia,
Z., Grégoire, B., Eric, L., Ludovic, L., Dominique, B., Sylvie, A., Marie, G., Francis, D.,
Fabienne, S., Cécile, C., Isabelle, A., Jacques, D., … Catherine, P. (2020). The impact of
proline isomerization on antigen binding and the analytical prole of a trispecic anti‑HIV
antibody. mAbs, 12(1), 1698128.
Mason, D. M., Friedensohn, S., Weber, C. R., Jordi, C., Wagner, B., Meng, S. M., Ehling, R. A.,
Bonati, L., Dahinden, J., Gainza, P., Correia, B. E., & Reddy, S. T. (2021). Optimization of
therapeutic antibodies by predicting antigen specicity from antibody sequence via deep
learning. Nature Biomedical Engineering. https://doi.org/10.1038/s41551‑021‑00699‑9
Młokosiewicz, J., Deszyński, P., Wilman, W., Jaszczyszyn, I., Ganesan, R., Kovaltsuk, A., Leem,
J., Galson, J. D., & Krawczyk, K. (2022). AbDiver: a tool to explore the natural anti‑
body landscape to aid therapeutic design. Bioinformatics, 38(9), 2628–2630. https://doi.
org/10.1093/bioinformatics/btac151
Mullard, A. (2021). FDA approves 100th monoclonal antibody product. Nature Reviews. Drug
Discovery, 20(7), 491–495.
Myung, Y., Pires, D. E. V., & Ascher, D. B. (2022). CSM‑AB: graph‑based antibody–antigen
binding afnity prediction and docking scoring function. Bioinformatics, 38(4), 1141–1143.
https://doi.org/10.1093/bioinformatics/btab762
Nakamura, H., Yoshikawa, M., Oda‑Ueda, N., Ueda, T., & Ohkuri, T. (2021). A comprehensive
analysis of novel disulde bond introduction site into the constant domain of human Fab.
Scientic Reports, 11(1), 12937.
Narayanan, H., Dingfelder, F., Condado Morales, I., Patel, B., Heding, K. E., Bjelke, J. R.,
Egebjerg, T., Butté, A., Sokolov, M., Lorenzen, N., & Arosio, P. (2021a). Design of bio‑
pharmaceutical formulations accelerated by machine learning. Molecular Pharmaceutics,
18(10), 3843–3853.
Narayanan, H., Dingfelder, F., Condado Morales, I., Patel, B., Heding, K. E., Bjelke, J. R.,
Egebjerg, T., Butté, A., Sokolov, M., Lorenzen, N., & Arosio, P. (2021b). Design of bio‑
pharmaceutical formulations accelerated by machine learning. Molecular Pharmaceutics,
18(10), 3843–3853.
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., & Madani, A. (2023). ProGen2: Exploring
the boundaries of protein language models. Cell Systems, 14(11), 968–978.e3.
Norman, R. A., Ambrosetti, F., Bonvin, A. M. J. J., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk,
K. (2020). Computational approaches to therapeutic antibody design: established methods
and emerging trends. Briengs in Bioinformatics, 21(5), 1549–1567.
Ofer, D., Brandes, N., & Linial, M. (2021). The language of proteins: NLP, machine learning &
protein sequences. Computational and Structural Biotechnology Journal, 19, 1750–1758.
Olsen, T. H., Boyles, F., & Deane, C. M. (2022a). Observed antibody space: a diverse database of
cleaned, annotated, and translated unpaired and paired antibody sequences. Protein Science:
A Publication of the Protein Society, 31(1), 141–146.
Olsen, T. H., Moal, I. H., & Deane, C. M. (2022). AbLang: an antibody language model for com‑
pleting antibody sequences. Bioinformatics Advances, 2(1), vbac046.
Omer, A., Shemesh, O., Peres, A., Polak, P., Shepherd, A. J., Watson, C. T., Boyd, S. D., Collins,
A. M., Lees, W., & Yaari, G. (2020). VDJbase: an adaptive immune receptor genotype and
haplotype database. Nucleic Acids Research, 48(D1), D1051–D1056.
Padlan, E. A., Abergel, C., & Tipper, J. P. (1995). Identication of specicity‑determining residues
in antibodies. FASEB Journal, 9(1), 133–139.
Pagadala, N. S., Syed, K., & Tuszynski, J. (2017). Software for molecular docking: a review.
Biophysical Reviews, 9(2), 91–102.
Parkinson, J., Hard, R., & Wang, W. (2023). The RESP AI model accelerates the identication of
tight‑binding antibodies. Nature Communications, 14(1), 454.
7 • Antibody Structure-Function 195
Pérez, A.‑M. W., Lorenzen, N., Vendruscolo, M., & Sormanni, P. (2022). Assessment of therapeu‑
tic antibody developability by combinations of in vitro and in silico methods. Therapeutic
Antibodies, 57–113. https://doi.org/10.1007/978‑1‑0716‑1450‑1_4
Perkins, J. R., Diboun, I., Dessailly, B. H., Lees, J. G., & Orengo, C. (2010). Transient pro‑
tein‑protein interactions: structural, functional, and network properties. Structure, 18(10),
1233–1243.
Petersen, B. M., Ulmer, S. A., Rhodes, E. R., Gutierrez‑Gonzalez, M. F., Dekosky, B. J., Sprenger, K.
G., & Whitehead, T. A. (2021). Regulatory approved monoclonal antibodies contain framework
mutations predicted from human antibody repertoires. Frontiers in Immunology, 12, 728694.
Petukh, M., Li, M., & Alexov, E. (2015). Predicting binding free energy change caused by point
mutations with knowledge‑modied MM/PBSA method. PLoS Computational Biology,
11(7), e1004276. https://doi.org/10.1371/journal.pcbi.1004276
Pierce, B. G., Wiehe, K., Hwang, H., Kim, B.‑H., Vreven, T., & Weng, Z. (2014). ZDOCK server:
interactive docking prediction of protein‑protein complexes and symmetric multimers.
Bioinformatics, 30(12), 1771–1773.
Pinzi, L., & Rastelli, G. (2019). Molecular docking: shifting paradigms in drug discovery.
International Journal of Molecular Sciences, 20(18). https://doi.org/10.3390/ijms20184331
Pires, D. E. V., & Ascher, D. B. (2016). mCSM‑AB: a web server for predicting antibody–anti‑
gen afnity changes upon mutation with graph‑based signatures. Nucleic Acids Research,
44(W1), W469–W473.
Pittala, S., & Bailey‑Kellogg, C. (2020). Learning context‑aware structural representations to pre‑
dict antigen and antibody binding interfaces. Bioinformatics, 36(13), 3996–4003.
Qasba, P. K. (2015). Glycans of antibodies as a specic site for drug conjugation using glycosyl‑
transferases. Bioconjugate Chemistry, 26(11), 2170–2175.
Qing, R., Hao, S., Smorodina, E., Jin, D., Zalevsky, A., & Zhang, S. (2022). Protein design: from
the aspect of water solubility and stability. Chemical Reviews, 122(18), 14085–14179.
Rabia, L. A., Zhang, Y., Ludwig, S. D., Julian, M. C., & Tessier, P. M. (2018). Net charge of
antibody complementarity‑determining regions is a key predictor of specicity. Protein
Engineering, Design & Selection, 31(11), 409–418.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are
Unsupervised Multitask Learners. https://life‑extension.github.io/2020/05/27/GPT%E6%8
A%80%E6%9C%AF%E5%88%9D%E6%8E%A2/language‑models.pdf
Ramazi, S., & Zahiri, J. (2021). Posttranslational modications in proteins: resources, tools and
prediction methods. Database: The Journal of Biological Databases and Curation, 2021.
https://doi.org/10.1093/database/baab012
Rangel, M. A., Bedwell, A., Costanzi, E., Taylor, R. J., Russo, R., Bernardes, G. J. L., … Sormanni,
P. (2022). Fragment‑based computational design of antibodies targeting structured epitopes.
Science Advances, 8(45), eabp9540.
Rawat, P., Prabakaran, R., Sakthivel, R., Mary Thangakani, A., Kumar, S., & Gromiha, M. M.
(2020). CPAD2.0: a repository of curated experimental data on aggregating proteins and
peptides. Amyloid: The International Journal of Experimental and Clinical Investigation:
The Ofcial Journal of the International Society of Amyloidosis, 27(2), 128–133.
Rawat, P., Sharma, D., Prabakaran, R., Ridha, F., Mohkhedkar, M., Janakiraman, V., & Gromiha,
M. M. (2022). Ab‑CoV: a curated database for binding afnity and neutralization proles
of coronavirus related antibodies. Bioinformatics. https://doi.org/10.1093/bioinformatics/
btac439
Rawat, P., Sharma, D., Srivastava, A., Janakiraman, V., & Gromiha, M. M. (2021). Exploring
antibody repurposing for COVID‑19: beyond presumed roles of therapeutic antibodies.
Scientic Reports, 11(1), 10220.
Raybould, M. I. J., Kovaltsuk, A., Marks, C., & Deane, C. M. (2021). CoV‑AbDab: the coronavi‑
rus antibody database. Bioinformatics, 37(5), 734–735.
196 Biopharmaceutical Informatics
Raybould, M. I. J., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A. P., Bujotzek, A.,
Shi, J., & Deane, C. M. (2019). Five computational developability guidelines for therapeutic
antibody proling. Proceedings of the National Academy of Sciences of the United States of
America, 116(10), 4025–4030.
Raybould, M. I. J., Marks, C., Lewis, A. P., Shi, J., Bujotzek, A., Taddese, B., & Deane, C.
M. (2020). Thera‑SAbDab: the therapeutic structural antibody database. Nucleic Acids
Research, 48(D1), D383–D388.
Reth, M. (2013). Matching cellular dimensions with molecular sizes. Nature Immunology, 14(8),
765–767.
Retter, I., Althaus, H. H., Münch, R., & Müller, W. (2005). VBASE2, an integrative V gene data‑
base. Nucleic Acids Research, 33(Database issue), D671–D674.
Reusch, D., & Tejada, M. L. (2015). Fc glycans of therapeutic antibodies as critical quality attri‑
butes. Glycobiology, 25(12), 1325–1334.
Robert, P. A., Akbar, R., Frank, R., Pavlović, M., Widrich, M., Snapkov, I., Slabodkin, A.,
Chernigovskaya, M., Scheffer, L., Smorodina, E., Rawat, P., Mehta, B. B., Vu, M. H., Mathisen,
I. F., Prósz, A., Abram, K., Olar, A., Miho, E., Haug, D. T. T., … Greiff, V. (2022). Unconstrained
generation of synthetic antibody–antigen structures to guide machine learning methodology for
antibody specicity prediction. Nature Computational Science, 2(12), 845–865.
Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2021). mmCSM‑PPI: predicting the effects
of multiple point mutations on protein–protein interactions. Nucleic Acids Research,
49(W1), W417–W424. https://doi.org/10.1093/nar/gkab273
Rodriguez‑Quijada, C., Gomez‑Marquez, J., & Hamad‑Schifferli, K. (2020). Repurposing old
antibodies for new diseases by exploiting cross‑reactivity and multicolored nanoparticles.
ACS Nano, 14(6), 6626–6635.
Romero‑Molina, S., Ruiz‑Blanco, Y. B., Mieres‑Perez, J., Harms, M., Münch, J., Ehrmann, M., &
Sanchez‑Garcia, E. (2022). PPI‑Afnity: a web tool for the prediction and optimization of
protein‑peptide and protein‑protein binding afnity. Journal of Proteome Research, 21(8),
1829–1841.
Rosato, A. (2010). Faculty opinions recommendation of are scoring functions in protein‑protein
docking ready to predict interactomes? Clues from a novel binding afnity benchmark.
Faculty Opinions–Post‑Publication Peer Review of the Biomedical Literature. https://doi.
org/10.3410/f.3437978.3131081
Rose, Y., Duarte, J. M., Lowe, R., Segura, J., Bi, C., Bhikadiya, C., Chen, L., Rose, A. S., Bittrich,
S., Burley, S. K., & Westbrook, J. D. (2021). RCSB protein data bank: architectural advances
towards integrated searching and efcient access to macromolecular structure data from the
PDB archive. Journal of Molecular Biology, 433(11), 166704.
Rudnick, S. I., & Adams, G. P. (2009). Afnity and avidity in antibody‑based tumor targeting.
Cancer Biotherapy & Radiopharmaceuticals, 24(2), 155–161.
Ruffolo, J. A., Chu, L.‑S., Mahajan, S. P., & Gray, J. J. (2023). Fast, accurate antibody structure
prediction from deep learning on massive set of natural antibodies. Nature Communications,
14(1), 2389.
Ruffolo, J. A., Sulam, J., & Gray, J. J. (2022b). Antibody structure prediction using interpretable
deep learning. Patterns (New York, N.Y.), 3(2), 100406.
Sacquin‑Mora, S., Carbone, A., & Lavery, R. (2008). Identication of protein interaction partners
and protein–protein interaction sites. Journal of Molecular Biology, 382(5), 1276–1289.
https://doi.org/10.1016/j.jmb.2008.08.002
Saerens, D., Conrath, K., Govaert, J., & Muyldermans, S. (2008). Disulde bond introduction
for general stabilization of immunoglobulin heavy‑chain variable domains. Journal of
Molecular Biology, 377(2), 478–488.
7 • Antibody Structure-Function 197
Salmaso, V., & Moro, S. (2018). Bridging molecular docking to molecular dynamics in exploring
ligand‑protein recognition process: an overview. Frontiers in Pharmacology, 9. https://doi.
org/10.3389/fphar.2018.00923
Sankar, K., Trainor, K., Blazer, L. L., Adams, J. J., Sidhu, S. S., Day, T., Meiering, E., & Maier, J.
K. X. (2022). A descriptor set for quantitative structure‑property relationship prediction in
biologics. Molecular Informatics, 41, e2100240.
Schlander, M., Hernandez‑Villafuerte, K., Cheng, C.‑Y., Mestre‑Ferrandiz, J., & Baumann, M.
(2021). How much does it cost to research and develop a new drug? A systematic review
and assessment. PharmacoEconomics, 39(11), 1243–1269.
Schmitz, S., Schmitz, E. A., Crowe, J. E., Jr, & Meiler, J. (2022). The human antibody sequence space
and structural design of the V, J regions, and CDRH3 with Rosetta. mAbs, 14(1), 2068212.
Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022). DLAB: deep learning methods
for structure‑based virtual screening of antibodies. Bioinformatics, 38(2), 377–383. https://
doi.org/10.1093/bioinformatics/btab660
Schneidman‑Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2005). PatchDock and
SymmDock: servers for rigid and symmetric docking. Nucleic Acids Research, 33(Web
Server issue), W363–W367.
Schoeder, C. T., Schmitz, S., Adolf‑Bryfogle, J., Sevy, A. M., Finn, J. A., Sauer, M. F., Bozhanova,
N. G., Mueller, B. K., Sangha, A. K., Bonet, J., Sheehan, J. H., Kuenze, G., Marlow, B.,
Smith, S. T., Woods, H., Bender, B. J., Martina, C. E., Del Alamo, D., Kodali, P., … Moretti,
R. (2021). Modeling immunity with Rosetta: methods for antibody and antigen design.
Biochemistry, 60(11), 825–846.
Schröter, C., Günther, R., Rhiel, L., Becker, S., Toleikis, L., Doerner, A., Becker, J., Schönemann,
A., Nasu, D., Neuteboom, B., Kolmar, H., & Hock, B. (2015). A generic approach to engi‑
neer antibody pH‑switches using combinatorial histidine scanning libraries and yeast dis‑
play. mAbs, 7(1), 138–151.
Segal, D. M., Padlan, E. A., Cohen, G. H., Rudikoff, S., Potter, M., & Davies, D. R. (1974). The
three‑dimensional structure of a phosphorylcholine‑binding mouse immunoglobulin Fab
and the nature of the antigen binding site. Proceedings of the National Academy of Sciences
of the United States of America, 71(11), 4298–4302.
Sela‑Culang, I., Ashkenazi, S., Peters, B., & Ofran, Y. (2015). PEASE: predicting B‑cell epitopes
utilizing antibody sequence. Bioinformatics, 31(8), 1313–1315. https://doi.org/10.1093/
bioinformatics/btu790
Sela‑Culang, I., Benhnia, M. R.‑E.‑I., Matho, M. H., Kaever, T., Maybeno, M., Schlossman, A.,
Nimrod, G., Li, S., Xiang, Y., Zajonc, D., Crotty, S., Ofran, Y., & Peters, B. (2014). Using
a combined computational‑experimental approach to predict antibody‑specic B cell epit‑
opes. Structure, 22(4), 646–657.
Sela‑Culang, I., Kunik, V., & Ofran, Y. (2013). The structural basis of antibody‑antigen recogni‑
tion. Frontiers in Immunology, 4, 302.
Sharma, D., Rawat, P., Janakiraman, V., & Gromiha, M. M. (2022). Elucidating important struc‑
tural features for the binding afnity of spike ‑ SARS‑CoV‑2 neutralizing antibody com‑
plexes. Proteins, 90(3), 824–834.
Sharma, Patapoff, T. W., Kabakoff, B., Pai, S., Hilario, E., Zhang, B., Li, C., Borisov, O., Kelley, R.
F., Chorny, I., Zhou, J. Z., Dill, K. A., & Swartz, T. E. (2014). In silico selection of therapeu‑
tic antibodies for development: viscosity, clearance, and chemical stability. Proceedings of
the National Academy of Sciences of the United States of America, 111(52), 18601–18606.
Shuai, R. W., Ruffolo, J. A., & Gray, J. J. (2023). IgLM: Inlling language modeling for antibody
sequence design. Cell Systems, 14(11), 979–989.e4.
Sinclair, N. R., Lees, R. K., & Elliott, E. V. (1968). Role of the Fc fragment in the regulation of the
primary immune response. Nature, 220(5171), 1048–1049.
198 Biopharmaceutical Informatics
Sirin, S., Apgar, J. R., Bennett, E. M., & Keating, A. E. (2016). AB‑Bind: antibody binding muta‑
tional database for computational afnity predictions. Protein Science: A Publication of the
Protein Society, 25(2), 393–409.
Strickley, R. G., & Lambert, W. J. (2021). A review of formulations of commercially available
antibodies. Journal of Pharmaceutical Sciences, 110(7), 2590–2608.e56.
Sverrisson, F., Feydy, J., Correia, B. E., & Bronstein, M. M. (2021). Fast end‑to‑end learning on
protein surfaces. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), 15267–15276. IEEE.
Swindells, M. B., Porter, C. T., Couch, M., Hurst, J., Abhinandan, K. R., Nielsen, J. H., Macindoe,
G., Hetherington, J., & Martin, A. C. R. (2017). abYsis: integrated antibody sequence and
structure—management, analysis, and prediction. Journal of Molecular Biology, 429(3),
356–364.
Szymborski, J., & Emad, A. (2022). RAPPPID: towards generalizable protein interaction predic‑
tion with AWD‑LSTM twin networks. Bioinformatics, 38(16), 3958–3967.
Teng, S., Sobitan, A., Rhoades, R., Liu, D., & Tang, Q. (2021). Systemic effects of missense muta‑
tions on SARS‑CoV‑2 spike glycoprotein stability and receptor‑binding afnity. Briengs in
Bioinformatics, 22(2), 1239–1253.
Tiller, K. E., & Tessier, P. M. (2015). Advances in antibody design. Annual Review of Biomedical
Engineering, 17, 191–216.
Tucs, A., Tsuda, K., & Sljoka, A. (2023). Probing conformational dynamics of antibodies with
geometric simulations. Methods in Molecular Biology, 2552, 125–139.
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko,
A., & Müller, K.‑R. (2021). Machine learning force elds. Chemical Reviews, 121(16),
10142–10186.
Unsal, S., Atas, H., Albayrak, M., Turhan, K., Acar, A. C., & Doğan, T. (2022). Learning func‑
tional properties of proteins with language models. Nature Machine Intelligence, 4(3),
227–245.
van den Bremer, E. T. J., Beurskens, F. J., Voorhorst, M., Engelberts, P. J., de Jong, R. N., van der
Boom, B. G., Cook, E. M., Lindorfer, M. A., Taylor, R. P., van Berkel, P. H. C., & Parren,
P. W. (2015). Human IgG is produced in a pro‑form that requires clipping of C‑terminal
lysines for maximal complement activation. mAbs, 7(4), 672–680. https://doi.org/10.1080/
19420862.2015.1046665
Vangone, A., & Bonvin, A. M. (2015). Contacts‑based prediction of binding afnity in protein–
protein complexes. eLife, 4. https://doi.org/10.7554/elife.07454
Vatsa, S. (2022). In silico prediction of post‑translational modications in therapeutic antibodies.
mAbs, 14(1), 2023938.
Vita, R., Mahajan, S., Overton, J. A., Dhanda, S. K., Martini, S., Cantrell, J. R., Wheeler, D. K.,
Sette, A., & Peters, B. (2019). The immune epitope database (IEDB): 2018 update. Nucleic
Acids Research, 47(D1), D339–D343.
Vu, M. H., Akbar, R., Robert, P. A., Swiatczak, B., Sandve, G. K., Greiff, V., & Haug, D. T. T.
(2023). Linguistically inspired roadmap for building biologically reliable protein language
models. Nature Machine Intelligence, 5(5), 485–496.
Vu, M. H., Robert, P. A., Akbar, R., Swiatczak, B., Sandve, G. K., Haug, D. T. T., & Greiff,
V. (2022). ImmunoLingo: linguistics‑based formalization of the antibody language. arXiv
[q‑bio.QM], arXiv. https://arxiv.org/abs/2209.12635
Wang, Gallolu Kankanamalage, S., Dong, J., & Liu, Y. (2021). Optimization of therapeutic anti‑
bodies. Antibody Therapeutics, 4(1), 45–54.
Wang, Lu, Y., & Wang, S. (2003). Comparative evaluation of 11 scoring functions for molecular
docking. Journal of Medicinal Chemistry, 46(12), 2287–2303.
Wang, M., Cang, Z., & Wei, G.‑W. (2020). A topology‑based network tree for the prediction of
protein‑protein binding afnity changes following mutation. Nature Machine Intelligence,
2(2), 116–123.
7 • Antibody Structure‑Function 199
Wang, Su, Z., & Wu, Y. (2021). Computational assessment of protein‑protein binding afnity
by reversely engineering the energetics in protein complexes. Genomics, Proteomics &
Bioinformatics, 19(6), 1012–1022.
Wardemann, H., Yurasov, S., Schaefer, A., Young, J. W., Meffre, E., & Nussenzweig, M. C.
(2003). Predominant autoantibody production by early human B cell precursors. Science,
301(5638), 1374–1377.
Warszawski, S., Katz, A. B., Lipsh, R., Khmelnitsky, L., Ben Nissan, G., Javitt, G., Dym, O.,
Unger, T., Knop, O., Albeck, S., Diskin, R., Fass, D., Sharon, M., & Fleishman, S. J.
(2019). Optimizing antibody afnity and stability by the automated design of the variable
light‑heavy chain interfaces. PLoS Computational Biology, 15(8), e1007207. https://doi.
org/10.1371/journal.pcbi.1007207
Wee, J., & Xia, K. (2022). Persistent spectral based ensemble learning (PerSpect‑EL) for pro‑
tein‑protein binding afnity prediction. Briengs in Bioinformatics, 23(2). https://doi.
org/10.1093/bib/bbac024
Wilman, W., Wróbel, S., Bielska, W., Deszynski, P., Dudzic, P., Jaszczyszyn, I., Kaniewski, J.,
Młokosiewicz, J., Rouyan, A., Satława, T., Kumar, S., Greiff, V., & Krawczyk, K. (2022).
Machine‑designed biotherapeutics: opportunities, feasibility and advantages of deep learn‑
ing in computational antibody discovery. Briengs in Bioinformatics, 23(4). https://doi.
org/10.1093/bib/bbac267
Wong, M. T. Y., Kelm, S., Liu, X., Taylor, R. D., Baker, T., & Essex, J. W. (2022). Higher Afnity
antibodies bind with lower hydration and exibility in large scale simulations. Frontiers in
Immunology, 13. https://doi.org/10.3389/mmu.2022.884110
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., Ma,
J., & Peng, J. (2022). High‑resolution de novo structure prediction from primary sequence.
bioRxiv, 2022.07.21.500999. https://doi.org/10.1101/2022.07.21.500999
Xie, H., Gilar, M., & Gebler, J. C. (2009). Analysis of deamidation and oxidation in monoclonal
antibody using peptide mapping with UPLC/MSE. Waters Application Note. https://www.
waters.com/webassets/cms/library/docs/720002897en.pdf
Xiong, P., Zhang, C., Zheng, W., & Zhang, Y. (2017). BindProfX: assessing mutation‑induced
binding afnity change by protein interface proles with pseudo‑counts. Journal of
Molecular Biology, 429(3), 426–434.
Xu, & Davis, M. M. (2000). Diversity in the CDR3 region of VH is sufcient for most antibody
specicities. Immunity, 13(1), 37–45.
Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M., & Vangone, A. (2016). PRODIGY: a
web server for predicting the binding afnity of protein–protein complexes. Bioinformatics,
btw514. https://doi.org/10.1093/bioinformatics/btw514
Xue, Y., Liu, Z., Fang, X., & Wang, F. (2021). Multimodal pre‑training model for sequence‑based
prediction of protein‑protein interaction. ArXiv, https://arxiv.org/abs/2112.04814.
Xu, Y., Wang, D., Mason, B., Rossomando, T., Li, N., Liu, D., Cheung, J. K., Xu, W., Raghava,
S., Katiyar, A., Nowak, C., Xiang, T., Dong, D. D., Sun, J., Beck, A., & Liu, H. (2019).
Structure, heterogeneity and developability assessment of therapeutic antibodies. mAbs,
11(2), 239–264. https://doi.org/10.1080/19420862.2018.1553476
Yamashita, T. (2018). Toward rational antibody design: recent advancements in molecular dynam‑
ics simulations. International Immunology, 30(4), 133–140.
Yamashita, T., Mizohata, E., Nagatoishi, S., Watanabe, T., Nakakido, M., Iwanari, H., Mochizuki,
Y., Nakayama, T., Kado, Y., Yokota, Y., Matsumura, H., Kawamura, T., Kodama, T.,
Hamakubo, T., Inoue, T., Fujitani, H., & Tsumoto, K. (2019). Afnity improvement of a
cancer‑targeted antibody through alanine‑induced adjustment of antigen‑antibody interface.
Structure, 27(3), 519–527.e5.
Yang, Wang, P., & Zhu, B. T. (2023). Binding afnity prediction for antibody–protein antigen com‑
plexes: a machine learning analysis based on interface and surface areas. Journal of Molecular
Graphics and Modelling, 118, 108364. https://doi.org/10.1016/j.jmgm.2022.108364
200 Biopharmaceutical Informatics
Yang, Y., Stella, C., Wang, W., Schöneich, C., & Gennaro, L. (2014). Characterization of oxida‑
tive carbonylation on recombinant monoclonal antibodies. Analytical Chemistry, 86(10),
4799–4806.
Yin, V., Lai, S.‑H., Caniels, T. G., Brouwer, P. J. M., Brinkkemper, M., Aldon, Y., Liu, H., Yuan, M.,
Wilson, I. A., Sanders, R. W., van Gils, M. J., & Heck, A. J. R. (2021). Probing afnity, avid‑
ity, anticooperativity, and competition in antibody and receptor binding to the SARS‑CoV‑2
spike by single particle mass analyses. ACS Central Science, 7(11), 1863–1873.
Yugandhar, K., & Michael Gromiha, M. (2014). Protein–protein binding afnity prediction
from amino acid sequence. Bioinformatics, 30(24), 3583–3589. https://doi.org/10.1093/
bioinformatics/btu580
Yu, X., Orr, C. M., Chan, H. T. C., James, S., Penfold, C. A., Kim, J., Inzhelevskaya, T., Mockridge,
C. I., Cox, K. L., Essex, J. W., Tews, I., Glennie, M. J., & Cragg, M. S. (2023). Reducing
afnity as a strategy to boost immunomodulatory antibody agonism. Nature. https://doi.
org/10.1038/s41586‑022‑05673‑2
Zhang, W., Wang, L., Liu, K., Wei, X., Yang, K., Du, W., Wang, S., Guo, N., Ma, C., Luo, L., Wu,
J., Lin, L., Yang, F., Gao, F., Wang, X., Li, T., Zhang, R., Saksena, N. K., Yang, H., … Liu,
X. (2020). PIRD: pan immune repertoire database. Bioinformatics, 36(3), 897–903.
Zhao, L., & Li, J. (2010). Mining for the antibody‑antigen interacting associations that
predict the B cell epitopes. BMC Structural Biology, 10(Suppl 1), S6. https://doi.
org/10.1186/1472‑6807‑10‑s1‑s6
Zhou, P., Wang, C., Tian, F., Ren, Y., Yang, C., & Huang, J. (2013). Biomacromolecular quantita‑
tive structure‑activity relationship (BioQSAR): a proof‑of‑concept study on the modeling,
prediction and interpretation of protein‑protein binding afnity. Journal of Computer‑Aided
Molecular Design, 27(1), 67–78.