# Mutational Analysis of Tradeoffs between Receptor Affinity and Antibody Escape for SARS-CoV-2 Variants of Concern

> **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $218,873

## Abstract

Emerging SARS-CoV-2 variants are of broad interest because they may be more resistant to current vaccines
and associated immune responses. Toward the long-term goal of understanding how receptor-binding domain
(RBD) mutations impact transmissibility, it is critical to elucidate the impacts of RBD mutations on ACE2 affinity
and antibody escape. This information is important because RBD mutations can strongly modulate ACE2 affinity,
which is linked to changes in viral infectivity, and antibody escape, which is linked to changes in antibody
neutralization potency. Moreover, this information is also important because of the inherent tradeoffs between
ACE2 affinity and antibody escape, as many RBD mutations that strongly increase one property also strongly
decrease the other property, suggesting that evaluating either property in isolation is unlikely to explain how RBD
mutations impact SARS-CoV-2 transmissibility. Therefore, the Tessier lab has developed machine learning
models to describe the impact of single and multisite RBD mutations on ACE2 affinity and antibody escape. This
approach uses large but sparsely sampled experimental datasets that measure the impact of single and multisite
RBD mutations on ACE2 affinity and antibody escape to train machine learning models. Next, the models are
used to predict the impact of vast numbers of additional RBD mutations that are absent in the experimental
datasets. The goal of this proposal is to use machine learning models and multiple experimental techniques to
predict and experimentally evaluate the impacts of additional RBD mutations in Variants of Concern, such as the
Delta variant, on ACE2 affinity and antibody escape. The hypothesis is that the models will be able to identify
additional single and multisite mutations in the RBDs of key Variants of Concern that strongly modulate ACE2
affinity and/or antibody escape. To test this hypothesis, in Aim 1, predictions of the impact of additional single
and multisite mutations in the RBDs of Variants of Concern on ACE2 affinity and infectivity will be tested. This
Aim will involve testing these predictions using i) yeast surface display of RBDs and flow cytometry to measure
ACE2 affinity, and ii) pseudovirus assays to measure infectivity. No live viruses will be generated or tested in
this work. Next, in Aim 2, predictions of the impact of additional single and multisite mutations in the RBDs of
Variants of Concern on antibody escape and neutralization will be tested. The human serum samples that will
be used are from donors that were either infected, vaccinated, or infected and subsequently vaccinated. This
Aim will involve testing the model predictions using i) yeast surface display of RBDs and flow cytometry to
measure antibody binding, and ii) pseudovirus assays to measure antibody neutralization. A key expected
outcome will be the optimization and validation of models that can be used to aid in the rapid identification of the
most threatening emerging SA...

## Key facts

- **NIH application ID:** 10510890
- **Project number:** 1R21AI171844-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Peter M Tessier
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $218,873
- **Award type:** 1
- **Project period:** 2022-06-16 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10510890

## Citation

> US National Institutes of Health, RePORTER application 10510890, Mutational Analysis of Tradeoffs between Receptor Affinity and Antibody Escape for SARS-CoV-2 Variants of Concern (1R21AI171844-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10510890. Licensed CC0.

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