1,953
Views
0
CrossRef citations to date
0
Altmetric
Report

Monte Carlo Thompson sampling-guided design for antibody engineering

, , , , , , , & show all
Article: 2244214 | Received 22 Dec 2022, Accepted 29 Jul 2023, Published online: 21 Aug 2023

References

  • Haraya K, Tachibana T, Igawa T. Improvement of pharmacokinetic properties of therapeutic antibodies by antibody engineering. Drug Metab Pharmacokinet. 2019;34:25–10. doi:10.1016/j.dmpk.2018.10.003.
  • Igawa T, Ishii S, Tachibana T, Maeda A, Higuchi Y, Shimaoka S, Moriyama C, Watanabe T, Takubo R, Doi Y, et al. Antibody recycling by engineered Ph-dependent antigen binding improves the duration of antigen neutralization. Nat Biotechnol. 2010;28(11):1203–07. doi: 10.1038/nbt.1691.
  • Sampei Z, Haraya K, Tachibana T, Fukuzawa T, Shida-Kawazoe M, Gan SW, Shimizu Y, Ruike Y, Feng S, Kuramochi T, et al. Antibody engineering to generate SKY59, a long-acting anti-C5 recycling antibody. PLoS One. 2018;13(12):e0209509. doi: 10.1371/journal.pone.0209509.
  • Muramatsu H, Kuramochi T, Katada H, Ueyama A, Ruike Y, Ohmine K, Shida-Kawazoe M, Miyano-Nishizawa R, Shimizu Y, Okuda M, et al. Novel myostatin-specific antibody enhances muscle strength in muscle disease models. Sci Rep. 2021;11(1):2160. doi: 10.1038/s41598-021-81669-8.
  • Sulea T, Rohani N, Baardsnes J, Corbeil CR, Deprez C, Cepero-Donates Y, Robert A, Schrag JD, Parat M, Duchesne M, et al. Structure-based engineering of Ph-dependent antibody binding for selective targeting of solid-tumor microenvironment. MAbs. 2020;12(1):1682866. doi: 10.1080/19420862.2019.1682866.
  • Ruffolo JA, Guerra C, Mahajan SP, Sulam J, Gray JJ. Geometric potentials from deep learning improve prediction of CDR H3 loop structures. Bioinformatics. 2020;36(Supplement_1):i268–i75. doi:10.1093/bioinformatics/btaa457.
  • Abanades B, Georges G, Bujotzek A, Deane CM, Xu J. Ablooper: fast accurate antibody CDR loop structure prediction with accuracy estimation. Bioinformatics. 2022;38(7):1877–80. doi:10.1093/bioinformatics/btac016.
  • Khan A, Cowen-Rivers AI, Grosnit A, Deik DG, Robert PA, Greiff V, Smorodina E, Rawat P, Akbar R, Dreczkowski K, et al. Toward real-world automated antibody design with combinatorial Bayesian optimization. Cell Rep Methods. 2023;3(1):100374. doi: 10.1016/j.crmeth.2022.100374.
  • Romero PA, Krause A, Arnold FH. Navigating the protein fitness landscape with gaussian processes. Proc Natl Acad Sci U S A. 2013;110(3):E193–201. doi:10.1073/pnas.1215251110.
  • Saito Y, Oikawa M, Nakazawa H, Niide T, Kameda T, Tsuda K, Umetsu M. Machine-learning-guided mutagenesis for directed evolution of fluorescent Proteins. ACS Synth Biol. 2018;7(9):2014–22. doi:10.1021/acssynbio.8b00155.
  • Saito Y, Oikawa M, Sato T, Nakazawa H, Ito T, Kameda, T., Tsuda, K., Umetsu, M. Machine-learning-guided library design cycle for directed evolution of enzymes: the effects of training data composition on sequence space exploration. ACS Catal. 2021;11(23):14615–24. doi:10.1021/acscatal.1c03753.
  • Phan M, Abbasi-Yadkori Y, Domke J. Thompson sampling and approximate inference. arXiv 2019;abs/1908.04970. 10.48550/arXiv.1908.04970.
  • Balandat M, Karrer B, Jiang DR, Daulton S, Letham B, Wilson AG, Bakshy E. BoTorch: A framework for efficient Monte-Carlo Bayesian optimization. arXiv. 2019;abs/1910.06403. 10.48550/arXiv.1910.06403
  • Traxlmayr MW, Lobner E, Hasenhindl C, Stadlmayr G, Oostenbrink C, Rüker F, Obinger C. Construction of Ph-sensitive Her2-binding IgG1-Fc by directed evolution. Biotechnol J. 2014;9(8):1013–22. doi:10.1002/biot.201300483.
  • Gera N, Hill AB, White DP, Carbonell RG, Rao BM, Karnik S. Design of pH sensitive binding Proteins from the hyperthermophilic Sso7d scaffold. PLoS One. 2012;7(11):e48928. doi:10.1371/journal.pone.0048928.
  • Rasmussen CE, Williams CKI. Gaussian processes for machine learning. MIT Press; 2006. doi:10.7551/mitpress/3206.001.0001.
  • Chapelle O, Li L. An empirical evaluation of Thompson sampling. Adv Neural Inf Process Syst. 2011;24:2249–57.
  • Le Q, Mikolov T Distributed representations of sentences and documents. In, International conference on machine learning. arXiv2014;32:1188-1196. doi: 10.48550/arXiv.1405.4053.
  • The UniProt Consortium. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 2017;45(D1):D158–D69. doi:10.1093/nar/gkw1099.
  • Yang KK, Wu Z, Bedbrook CN, Arnold FH, Wren J. Learned protein embeddings for machine learning. Bioinformatics. 2018;34(15):2642–48. doi:10.1093/bioinformatics/bty178.
  • Řehůřek R, Sojka P. Software framework for topic modelling with large corpora. ELRA. 2010;45–50. doi:10.13140/2.1.2393.1847.
  • Lévy Flights PI. Lévy flights, non-local search and simulated annealing. Journal Of Computational Physics. 2007;226(2):1830–44. doi:10.1016/j.jcp.2007.06.008.