A machine learning strategy for the identification of key in silico descriptors and prediction models for IgG monoclonal antibody developability properties

References

• Bailly M, Mieczkowski C, Juan V, Metwally E, Tomazela D, Baker J, Uchida M, Kofman E, Raoufi F, Motlagh S, et al. Predicting antibody developability Profiles through early stage Discovery screening. MAbs. 2020;12(1):1743053. doi:10.1080/19420862.2020.1743053.
   PubMed Web of Science ®Google Scholar
• Raybould MIJ, Marks C, Krawczyk K, Taddese B, Nowak J, Lewis AP, Bujotzek A, Shi J, Deane CM. Five computational developability guidelines for therapeutic antibody profiling. Proc Natl Acad Sci USA. 2019;116(10):4025–12. doi:10.1073/pnas.1810576116.
   PubMed Web of Science ®Google Scholar
• Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp Clin Trials Commun. 2018;11:156–64. doi:10.1016/j.conctc.2018.08.001.
   PubMed Web of Science ®Google Scholar
• Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov. 2004;3(8):711–16. doi:10.1038/nrd1470.
   PubMed Web of Science ®Google Scholar
• Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–89. doi:10.1038/s41586-021-03819-2.
   PubMed Web of Science ®Google Scholar
• Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Schaeffer RD, et al. Accurate prediction of protein structures and interactions using a 3-track neural network. Sci. 2021;373(6557):871–76. doi:10.1126/science.abj8754.
   PubMed Web of Science ®Google Scholar
• Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, Smetanin N, Verkuil R, Kabeli O, Shmueli Y, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Sci. 2023;379(6637):1123–30. doi:10.1126/science.ade2574.
   PubMed Web of Science ®Google Scholar
• Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, et al. RCSB protein data Bank (Rcsb.Org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res. 2023;51(D1):D488–508. doi:10.1093/nar/gkac1077.
   PubMed Web of Science ®Google Scholar
• UniProt Consortium T, Bateman A, Martin M-J, Orchard S, Magrane M, Agivetova R, Ahmad S, Alpi E, Bowler-Barnett EH, Britto R, et al. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49(D1):D480–9. doi:10.1093/nar/gkaa1100.
   PubMed Web of Science ®Google Scholar
• Olsen TH, Boyles F, Deane CM. Observed antibody space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences. Protein Sci. 2022;31(1):141–46. doi:10.1002/pro.4205.
   PubMed Web of Science ®Google Scholar
• Schneider C, Raybould MIJ, Deane CM. SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker. Nucleic Acids Res. 2022;50(D1):D1368–72. doi:10.1093/nar/gkab1050.
   PubMed Web of Science ®Google Scholar
• Leem J, Mitchell LS, Farmery JHR, Barton J, Galson JD. Deciphering the language of antibodies using self-supervised learning. Patterns. 2022;3(7):100513. doi:10.1016/j.patter.2022.100513.
   PubMedGoogle Scholar
• Khass M, Vale AM, Burrows PD, Schroeder HW. The sequences encoded by immunoglobulin diversity (DH) gene segments play key roles in controlling B-cell development, antigen-binding site diversity, and antibody production. Immunol Rev. 2018;284(1):106–19. doi:10.1111/imr.12669.
   PubMed Web of Science ®Google Scholar
• Prihoda D, Maamary J, Waight A, Juan V, Fayadat-Dilman L, Svozil D, Bitton DA. BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning. MAbs. 2022;14:2020203. doi:10.1080/19420862.2021.2020203.
   PubMed Web of Science ®Google Scholar
• Olsen TH, Moal IH, Deane CM. AbLang: an antibody language model for completing antibody sequences. Bioinforma Adv. 2022;2(1):vbac046. doi:10.1093/bioadv/vbac046.
   PubMedGoogle Scholar
• Ruffolo JA, Gray JJ. Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Biophys J. 2022;121(3):155a–56. doi:10.1016/j.bpj.2021.11.1942.
   Web of Science ®Google Scholar
• Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: making protein folding accessible to all. Nat Methods. 2022;19(6):679–82. doi:10.1038/s41592-022-01488-1.
   PubMed Web of Science ®Google Scholar
• Almagro JC, Teplyakov A, Luo J, Sweet RW, Kodangattil S, Hernandez-Guzman F, Gilliland GL. Second antibody modeling assessment (AMA-II). Proteins Struct Funct Bioinforma. 2014;82(8):1553–62. doi:10.1002/prot.24567.
   PubMed Web of Science ®Google Scholar
• Abanades B, Georges G, Bujotzek A, Deane CM. Ablooper: fast accurate antibody CDR loop structure prediction with accuracy estimation. Bioinformatics. 2022;38(7):4.
   Web of Science ®Google Scholar
• 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;6:1–8. doi:10.1038/s42003-023-04927-7.
   PubMed Web of Science ®Google Scholar
• Marks C, Deane CM. Antibody H3 structure prediction. Comput Struct Biotechnol J. 2017;15:222–31. doi:10.1016/j.csbj.2017.01.010.
   PubMed Web of Science ®Google Scholar
• Fernández-Quintero ML, Kokot J, Waibl F, Fischer A-L, Quoika PK, Deane CM, Liedl KR. Challenges in antibody structure prediction. MAbs. 2023;15(1):2175319. doi:10.1080/19420862.2023.2175319.
   PubMed Web of Science ®Google Scholar
• Molecular Operating Environment (MOE), 2022. 02 Chemical Computing Group ULC, 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2023.
   Google Scholar
• Schrödinger Release 2022-3: BioLuminate, Schrödinger, LLC, New York, NY, 2021.
   Google Scholar
• 1. BIOVIA, Dassault Systèmes, Discovery Studio, 2021, San Diego: Dassault Systèmes, 2021.
   Google Scholar
• Chen T, Guestrin C. Xgboost: A Scalable Tree boosting System [Internet]. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016 [cited 2023 Jan 4]. page 785–94. http://arxiv.org/abs/1603.02754
   Google Scholar
• Introduction to Boosted trees — xgboost 2.0.0-dev documentation [Internet]. [accessed 2023 Feb 13]: https://xgboost.readthedocs.io/en/latest/tutorials/model.html
   Google Scholar
• Moez A. PyCaret: An open source, low-code machine learning library in Python [Internet]. 2020 [cited 2023 Jan 4]; Available from: https://www.pycaret.org
   Google Scholar
• Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25(11):1422–23. doi:10.1093/bioinformatics/btp163.
   PubMed Web of Science ®Google Scholar
• Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Söding J, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7(1):539. doi:10.1038/msb.2011.75.
   PubMed Web of Science ®Google Scholar
• Black SD, Mould DR. Development of hydrophobicity parameters to analyze proteins which bear post- or cotranslational modifications. Anal Biochem. 1991;193(1):72–82. doi:10.1016/0003-2697(91)90045-U.
   PubMed Web of Science ®Google Scholar
• Ferri FJ, Pudil P, Hatef M, Kittler J. Comparative study of techniques for large-scale feature selection* *This work was suported by a SERC grant GR/E 97549. The first author was also supported by a FPI grant from the Spanish MEC, PF92 73546684. In: Gelsema E Kanal L editors. Machine intelligence and pattern recognition. North-Holland; 1994. pp. 403–13. [accessed 2023 Jul 6]. https://www.sciencedirect.com/science/article/pii/B9780444818928500407.
   Google Scholar
• Jetha A, Thorsteinson N, Jmeian Y, Jeganathan A, Giblin P, Fransson J. Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies. MAbs. 2018;10(6):890–900. doi:10.1080/19420862.2018.1475871.
   PubMed Web of Science ®Google Scholar
• Salgado JC, Rapaport I, Asenjo JA. Predicting the behaviour of proteins in hydrophobic interaction chromatography. 1: Using the hydrophobic imbalance (HI) to describe their surface amino acid distribution. J Chromatogr A. 2006;1107(1–2):110–19. doi:10.1016/j.chroma.2005.12.032.
   PubMed Web of Science ®Google Scholar
• de Groot NS, Pallarés I, Avilés FX, Vendrell J, Ventura S. Prediction of “hot spots” of aggregation in disease-linked polypeptides. BMC Struct Biol. 2005;5(1):18. doi:10.1186/1472-6807-5-18.
   PubMedGoogle Scholar
• Conchillo-Solé O, de Groot NS, Avilés FX, Vendrell J, Daura X, Ventura S. AGGRESCAN: a server for the prediction and evaluation of “hot spots” of aggregation in polypeptides. BMC Bioinform. 2007;8(1):65. doi:10.1186/1471-2105-8-65.
   PubMed Web of Science ®Google Scholar
• Zhang C, Vasmatzis G, Cornette JL, DeLisi C. Determination of atomic desolvation energies from the structures of crystallized proteins. J Mol Biol. 1997;267(3):707–26. doi:10.1006/jmbi.1996.0859.
   PubMed Web of Science ®Google Scholar
• Hopp TP, Woods KR. Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci U S A. 1981;78(6):3824–28. doi:10.1073/pnas.78.6.3824.
   PubMed Web of Science ®Google Scholar
• Vanommeslaeghe K, MacKerell AD. CHARMM additive and polarizable force fields for biophysics and computer-aided drug design. Biochim Biophys Acta. 2015;1850(5):861–71. doi:10.1016/j.bbagen.2014.08.004.
   PubMed Web of Science ®Google Scholar
• Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. Prediction of aggregation prone regions of therapeutic proteins. J Phys Chem B. 2010;114(19):6614–24. doi:10.1021/jp911706q.
   PubMed Web of Science ®Google Scholar
• Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. Design of therapeutic proteins with enhanced stability. Proc Natl Acad Sci USA. 2009;106(29):11937–42. doi:10.1073/pnas.0904191106.
   PubMed Web of Science ®Google Scholar
• Tjong H, Zhou H-X. Prediction of protein solubility from calculation of transfer free energy. Biophys J. 2008;95(6):2601–09. doi:10.1529/biophysj.107.127746.
   PubMed Web of Science ®Google Scholar
• Spassov VZ, Kemmish H, Yan L. Two physics-based models for pH-dependent calculations of protein solubility. Protein Sci. 2022;31(5):e4299. doi:10.1002/pro.4299.
   PubMed Web of Science ®Google Scholar
• Eisenberg D, Schwarz E, Komaromy M, Wall R. Analysis of membrane and surface protein sequences with the hydrophobic moment plot. J Mol Biol. 1984;179(1):125–42. doi:10.1016/0022-2836(84)90309-7.
   PubMed Web of Science ®Google Scholar
• Sankar K, Krystek SR Jr, Carl SM, Day T, Maier JKX. AggScore: Prediction of aggregation-prone regions in proteins based on the distribution of surface patches. Proteins Struct Funct Bioinforma. 2018;86(11):1147–56. doi:10.1002/prot.25594.
   PubMed Web of Science ®Google Scholar
• Negron C, Fang J, McPherson MJ, Stine WB, McCluskey AJ. Separating clinical antibodies from repertoire antibodies, a path to in silico developability assessment. MAbs. 2022;14(1):2080628. doi:10.1080/19420862.2022.2080628.
   PubMed Web of Science ®Google Scholar
• Sankar K, Trainor K, Blazer LL, Adams JJ, Sidhu SS, Day T, Meiering E, Maier JKX. A descriptor set for quantitative structure-property relationship prediction in Biologics. Mol Inform. 2022;41(9):2100240. doi:10.1002/minf.202100240.
   Web of Science ®Google Scholar
• Trainor K, Gingras Z, Shillingford C, Malakian H, Gosselin M, Lipovšek D, Meiering EM. Ensemble modeling and Intracellular aggregation of an engineered immunoglobulin-like Domain. J Mol Biol. 2016;428(6):1365–74. doi:10.1016/j.jmb.2016.02.016.
   PubMed Web of Science ®Google Scholar
• Jain T, Sun T, Durand S, Hall A, Houston NR, Nett JH, Sharkey B, Bobrowicz B, Caffry I, Yu Y, et al. Biophysical properties of the clinical-stage antibody landscape. Proc Natl Acad Sci U S A. 2017;114(5):944–49. doi:10.1073/pnas.1616408114.
   PubMed Web of Science ®Google Scholar
• Shehata L, Maurer DP, Wec AZ, Lilov A, Champney E, Sun T, Archambault K, Burnina I, Lynaugh H, Zhi X, et al. Affinity maturation enhances antibody specificity but compromises conformational stability. Cell Rep. 2019;28(13):3300–8.e4. doi:10.1016/j.celrep.2019.08.056.
   PubMed Web of Science ®Google Scholar
• Cai Z, Zafferani M, Akande OM, Hargrove AE. Quantitative Structure–Activity Relationship (QSAR) study Predicts Small-molecule binding to RNA structure. J Med Chem. 2022;65(10):7262–77. doi:10.1021/acs.jmedchem.2c00254.
   PubMed Web of Science ®Google Scholar
• Platts JA. Theoretical prediction of hydrogen bond donor capacity. Phys Chem Chem Phys. 2000;2(5):973–80. doi:10.1039/a908853i.
   Web of Science ®Google Scholar
• Spassov VZ, Yan L. A pH-dependent computational approach to the effect of mutations on protein stability. J Comput Chem. 2016;37(29):2573–87. doi:10.1002/jcc.24482.
   PubMed Web of Science ®Google Scholar
• Spassov VZ, Yan L. A fast and accurate computational approach to protein ionization. Protein Sci. 2008;17(11):1955–70. doi:10.1110/ps.036335.108.
   PubMed Web of Science ®Google Scholar
• Agrawal NJ, Helk B, Kumar S, Mody N, Sathish HA, Samra HS, Buck PM, Li L, Trout BL. Computational tool for the early screening of monoclonal antibodies for their viscosities. MAbs. 2016;8(1):43–48. doi:10.1080/19420862.2015.1099773.
   PubMed Web of Science ®Google Scholar
• Thorsteinson N, Gunn JR, Kelly K, Long W, Labute P. Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics. MAbs. 2021;13(1):1981805. doi:10.1080/19420862.2021.1981805.
   PubMed Web of Science ®Google Scholar
• Gainza P, Sverrisson F, Monti F, Rodolà E, Boscaini D, Bronstein MM, Correia BE. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat Methods. 2020;17(2):184–92. doi:10.1038/s41592-019-0666-6.
   PubMed Web of Science ®Google Scholar
• Gainza P, Wehrle S, Van Hall-Beauvais A, Marchand A, Scheck A, Harteveld Z, Buckley S, Ni D, Tan S, Sverrisson F, et al. De Novo design of protein interactions with learned surface fingerprints. Nature. 2023;617(7959):176–84. doi:10.1038/s41586-023-05993-x.
   PubMed Web of Science ®Google Scholar
• Roney JP, Ovchinnikov S. State-of-the-Art estimation of protein model accuracy using AlphaFold. Phys Rev Lett. 2022;129(23):238101. doi:10.1103/PhysRevLett.129.238101.
   PubMed Web of Science ®Google Scholar
• Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–72. doi:10.1038/s41592-019-0686-2.
   PubMed Web of Science ®Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Cikit-learn: Machine learning in Python. Mach learn PYTHON. J Mach Learn Res. 12:2825–30.
   Google Scholar
• Xu Y, Roach W, Sun T, Jain T, Prinz B, Yu T-Y, Torrey J, Thomas J, Bobrowicz P, Vasquez M, et al. Addressing polyspecificity of antibodies selected from an in vitro yeast presentation system: a FACS-based, high-throughput selection and analytical tool. Protein Eng Des Sel. 2013;26(10):663–70. doi:10.1093/protein/gzt047.
   PubMed Web of Science ®Google Scholar