Deep mutational engineering of broadly-neutralizing nanobodies accommodating SARS-CoV-1 and 2 antigenic drift

Laroche, Adrien; Delgado, María Lucía Orsini; Chalopin, Benjamin; Cuniasse, Philippe; DuBois, Steven G.; Sierocki, Raphaël; Gallais, Fabrice; Debroas, Stéphanie; Bellanger, Laurent; Simon, Stéphanie; Maillère, Bernard; Nozach, Hervé

doi:10.1080/19420862.2022.2076775

Cited by 8 publications

(9 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After a passage to an OD 600 of 0.25, cells were grown at 30°C until OD 600 0.5–1.0 and re-suspended in 50 mL of SG-CAA for induction and incubated at 20°C. 52 …”

Section: Methodsmentioning

confidence: 99%

Deciphering cross-species reactivity of LAMP-1 antibodies using deep mutational epitope mapping and AlphaFold

Pruvost

Mathieu

DuBois

et al. 2023

mAbs

Self Cite

View full text Add to dashboard Cite

Delineating the precise regions on an antigen that are targeted by antibodies has become a key step for the development of antibody therapeutics. X-ray crystallography and cryogenic electron microscopy are considered the gold standard for providing precise information about these binding sites at atomic resolution. However, they are labor-intensive and a successful outcome is not guaranteed. We used deep mutational scanning (DMS) of the human LAMP-1 antigen displayed on yeast surface and leveraged next-generation sequencing to observe the effect of individual mutants on the binding of two LAMP-1 antibodies and to determine their functional epitopes on LAMP-1. Fine-tuned epitope mapping by DMS approaches is augmented by knowledge of experimental antigen structure. As human LAMP-1 structure has not yet been solved, we used the AlphaFold predicted structure of the full-length protein to combine with DMS data and ultimately finely map antibody epitopes. The accuracy of this method was confirmed by comparing the results to the co-crystal structure of one of the two antibodies with a LAMP-1 luminal domain. Finally, we used AlphaFold models of non-human LAMP-1 to understand the lack of mAb cross-reactivity. While both epitopes in the murine form exhibit multiple mutations in comparison to human LAMP-1, only one and two mutations in the Macaca form suffice to hinder the recognition by mAb B and A, respectively. Altogether, this study promotes a new application of AlphaFold to speed up precision mapping of antibody–antigen interactions and consequently accelerate antibody engineering for optimization.

show abstract

“…After a passage to an OD 600 of 0.25, cells were grown at 30°C until OD 600 0.5–1.0 and re-suspended in 50 mL of SG-CAA for induction and incubated at 20°C. 52 …”

Section: Methodsmentioning

confidence: 99%

Deciphering cross-species reactivity of LAMP-1 antibodies using deep mutational epitope mapping and AlphaFold

Pruvost

Mathieu

DuBois

et al. 2023

mAbs

Self Cite

View full text Add to dashboard Cite

show abstract

“…In another application, DMS was combined with Deep learning to optimize the affinity, viscosity, clearance, solubility, and immunogenicity of trastuzumab ( Mason et al, 2021 ). Recently, by leveraging DMS technology, researchers engineered a nanobody initially specific for SARS-CoV-1 RBD in order to bind SARS-CoV-2 RBD ( Laroche et al, 2022 ). Our group contributed to the DMS-based engineering of an ACE2 decoy that could neutralize the SARS-Cov-2 Omicron variant and proved the decoy prevented escape for each single-residue mutation in the RBD of SARS-Cov-2 ( Ikemura et al, 2022 ).…”

Section: Experimental Validationmentioning

confidence: 99%

Advances in antibody discovery from human BCR repertoires

Ismanto

Zhou

et al. 2022

Front. Bioinform.

View full text Add to dashboard Cite

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

show abstract

“…Measurement of protein variant effects has provided valuable insight into mechanisms of protein allostery 1, 2 and regulation 3 , drug resistance and sensitivity 4, 5 , molecular evolution 6, 7 , and the effects of germline and somatic mutations 8-10 . Probing how protein sequence variation affects protein properties has also guided design and engineering efforts 11 as well as enabled the development and benchmarking of computational models of protein stability and structure. Multiplexed assays of variant effect (MAVEs) have emerged as powerful tools for characterizing the consequences of protein sequence variation because they leverage high-throughput DNA sequencing to enable the measurement of thousands of protein variants in a single experiment.…”

Section: Introductionmentioning

confidence: 99%

Multiplexed, multimodal profiling of the intracellular activity, interactions, and druggability of protein variants using LABEL-seq

Simon,

Fowler,

Maly

2024

Preprint

View full text Add to dashboard Cite

Multiplexed assays of variant effect are powerful tools for assessing the impact of protein sequence variation, but are limited to measuring a single protein property and often rely on indirect readouts of intracellular protein function. Here, we developed LAbeling with Barcodes and Enrichment for biochemicaL analysis by sequencing (LABEL-seq), a platform for the multimodal profiling of thousands of protein variants in cultured human cells. Multimodal measurement of ~20,000 variant effects for ~1,600 BRaf variants using LABEL-seq revealed that variation at positions that are frequently mutated in cancer had minimal effects on folding and intracellular abundance but could dramatically alter activity, protein-protein interactions, and druggability. Integrative analysis of our multimodal measurements identified networks of positions with similar roles in regulating BRaf signaling properties and enabled predictive modeling of variant effects on complex processes such as cell proliferation and small molecule-promoted degradation. LABEL-seq provides a scalable approach for the direct measurement of multiple biochemical effects of protein variants in their native cellular context, yielding insight into protein function, disease mechanisms, and druggability.

show abstract

Deep mutational engineering of broadly-neutralizing nanobodies accommodating SARS-CoV-1 and 2 antigenic drift

Cited by 8 publications

References 56 publications

Deciphering cross-species reactivity of LAMP-1 antibodies using deep mutational epitope mapping and AlphaFold

Deciphering cross-species reactivity of LAMP-1 antibodies using deep mutational epitope mapping and AlphaFold

Advances in antibody discovery from human BCR repertoires

Multiplexed, multimodal profiling of the intracellular activity, interactions, and druggability of protein variants using LABEL-seq

Contact Info

Product

Resources

About