2022
DOI: 10.1073/pnas.2122954119
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Deep learning guided optimization of human antibody against SARS-CoV-2 variants with broad neutralization

Abstract: Significance SARS-CoV-2 continues to evolve through emerging variants, more frequently observed with higher transmissibility. Despite the wide application of vaccines and antibodies, the selection pressure on the Spike protein may lead to further evolution of variants that include mutations that can evade immune response. To catch up with the virus’s evolution, we introduced a deep learning approach to redesign the complementarity-determining regions (CDRs) to target multiple virus variants and obtai… Show more

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Cited by 96 publications
(96 citation statements)
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“…To the end user, guiding evolution via pretrained, unsupervised models is less resource-intensive than collecting enough task-specific data to train a supervised model [33]. Our techniques can also be used in conjunction with supervised approaches [8], [31]- [34], [52]- [55], and supervising a model over multiple experimental rounds might ultimately lead to higher fitness. However, in many practical settings (for example, the rapid development of sotrovimab in response to the COVID-19 pandemic [35]), the efficiency of an unsupervised, single-round approach is preferable to a protracted, multi-round (machinelearning-guided) directed evolution campaign.…”
Section: Discussionmentioning
confidence: 99%
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“…To the end user, guiding evolution via pretrained, unsupervised models is less resource-intensive than collecting enough task-specific data to train a supervised model [33]. Our techniques can also be used in conjunction with supervised approaches [8], [31]- [34], [52]- [55], and supervising a model over multiple experimental rounds might ultimately lead to higher fitness. However, in many practical settings (for example, the rapid development of sotrovimab in response to the COVID-19 pandemic [35]), the efficiency of an unsupervised, single-round approach is preferable to a protracted, multi-round (machinelearning-guided) directed evolution campaign.…”
Section: Discussionmentioning
confidence: 99%
“…In particular, we hypothesized that the predictive capabilities of protein language models might enable a researcher to provide only a single, wildtype antibody sequence to the algorithm and receive a small, manageable set (~10 1 ) of high-likelihood variants to experimentally measure for desirable properties. This is a very general setting that does not assume knowledge of protein structure or task-specific training data, thereby avoiding the resource-intensive processes associated with structure determination [34] or high-throughput screens [33]. A major question, however, is if higher evolutionary likelihood would efficiently translate to higher fitness.…”
Section: Efficient Affinity Maturation With General Protein Language ...mentioning
confidence: 99%
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“…Due to the rapid mutation rate, vaccines may lose effectiveness against COVID-19 variants. In fact, it has been reported that recombinant trimeric RBD [5] and neutralizing antibodies [6] have neutralizing effects against the Beta and Delta variants, not Omicron, and broad protection against the Omicron variant has not yet been reported. Therefore, there is an urgent need to develop a broad-spectrum vaccine against the different COVID-19 variants.…”
Section: Introductionmentioning
confidence: 99%
“…Protein engineering is a growing area of research in which scientists use a variety of methods to design new proteins that can perform certain functions. For instance, enzymes that can biodegrade plastics, materials inspired by spider silk, or antibodies to neutralize viruses ( Lu et al, 2022 ; Shan et al, 2022 ).…”
mentioning
confidence: 99%