Computationally restoring the potency of a clinical antibody against Omicron
成果类型：
Article
署名作者：
Desautels, Thomas A.; Arrildt, Kathryn T.; Zemla, Adam T.; Lau, Edmond Y.; Zhu, Fangqiang; Ricci, Dante; Cronin, Stephanie; Zost, Seth J.; Binshtein, Elad; Scheaffer, Suzanne M.; Dadonaite, Bernadeta; Petersen, Brenden K.; Engdahl, Taylor B.; Chen, Elaine; Handal, Laura S.; Hall, Lynn; Goforth, John W.; Vashchenko, Denis; Nguyen, Sam; Weilhammer, Dina R.; Lo, Jacky Kai-Yin; Rubinfeld, Bonnee; Saada, Edwin A.; Weisenberger, Tracy; Lee, Tek-Hyung; Whitener, Bradley; Case, James B.; Ladd, Alexander; Silva, Mary S.; Haluska, Rebecca M.; Grzesiak, Emilia A.; Earnhart, Christopher G.; Hopkins, Svetlana; Bates, Thomas W.; Thackray, Larissa B.; Segelke, Brent W.; Lillo, Antonietta Maria; Sundaram, Shivshankar; Bloom, Jesse D.; Diamond, Michael S.; Crowe, James E., Jr.; Carnahan, Robert H.; Faissol, Daniel M.; Lyon, Emily Z. Alipio; Anderson, Penelope S.; Avila-Herrera, Aram; Bennett, William F.; Bourguet, Feliza A.; Chen, Julian C.; Coleman, Matthew A.; Collette, Nicole M.; Davis, Anastasiia; Vannest, Byron D.; Fong, Erika J.; Gilmore, Sean; Goncalves, Andre R.; Hall, Sara B.; Harmon, Brooke; He, Wei; Hoang-Phou, Steven A.; Landajuela, Mikel; Laurence, Ted A.; Lee, Tek Hyung; Da Silva, Felipe Leno; Liu, Chao; Mundhenk, Terrel N.; Mohagheghi, Mariam V.; McIlroy, Peter R.; Le Thanh Mai Pham; Sanchez, Joseph C.; Sinha, Anupama; Solomon, Emilia A.; Watkins, Nicholas; Yang, Jiachen; Ye, Congwang; Zhang, Boya
署名单位：
United States Department of Energy (DOE); Lawrence Livermore National Laboratory; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; Vanderbilt University; Washington University (WUSTL); Fred Hutchinson Cancer Center; Fred Hutchinson Cancer Center; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; United States Department of Defense; United States Department of Energy (DOE); Los Alamos National Laboratory; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; Howard Hughes Medical Institute; Washington University (WUSTL); Washington University (WUSTL); Vanderbilt University; Alphabet Inc.; Google Incorporated; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; United States Department of Energy (DOE); Sandia National Laboratories; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; United States Department of Energy (DOE); Sandia National Laboratories
刊物名称：
Nature
ISSN/ISSBN：
0028-5592
DOI：
10.1038/s41586-024-07385-1
发表日期：
2024-05-23
关键词：
摘要：
The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs(1-3) and revealed how quickly viral escape can curtail effective options(4,5). When the SARS-CoV-2 Omicron variant emerged in 2021, many antibody drug products lost potency, including Evusheld and its constituent, cilgavimab(4-6). Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination(4) and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign and renew the efficacy of COV2-2130 against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and subsequent variants of concern, and provides protection in vivo against the strains tested: WA1/2020, BA.1.1 and BA.5. Deep mutational scanning of tens of thousands of pseudovirus variants reveals that 2130-1-0114-112 improves broad potency without increasing escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Our computational approach does not require experimental iterations or pre-existing binding data, thus enabling rapid response strategies to address escape variants or lessen escape vulnerabilities.