Mutational analysis of SARS-CoV-2 variants of concern reveals key tradeoffs between receptor affinity and antibody escape

Abstract

SARS-CoV-2 variants with enhanced transmissibility represent a serious threat to global health. Here we report machine learning models that can predict the impact of receptor-binding domain (RBD) mutations on receptor (ACE2) affinity, which is linked to infectivity, and escape from human serum antibodies, which is linked to viral neutralization. Importantly, the models predict many of the known impacts of RBD mutations in current and former Variants of Concern on receptor affinity and antibody escape as well as novel sets of mutations that strongly modulate both properties. Moreover, these models reveal key opposing impacts of RBD mutations on transmissibility, as many sets of RBD mutations predicted to increase antibody escape are also predicted to reduce receptor affinity and vice versa. These models, when used in concert, capture the complex impacts of SARS-CoV-2 mutations on properties linked to transmissibility and are expected to improve the development of next-generation vaccines and biotherapeutics.

Fig 1. Overview of the development of machine learning models for predicting mutations in the receptor-binding domain of SARS-CoV-2 that increase transmissibility.

Machine-learning models were trained and tested on large but sparsely sampled experimental datasets that characterize the impact of single and multisite RBD mutations on ACE2 affinity and human serum antibody binding (>100,000 RBD variants with 1–10 mutations). The relative binding levels of human serum antibodies to RBD mutants were converted into % human serum antibody escape values as 100% minus the % antibody binding to mutant RBD relative to wild-type RBD. It is important to consider the impacts of RBD mutations on both properties because ACE2 affinity strongly impacts viral infectivity and human antibody binding strongly impacts viral neutralization. The two models were employed to predict mutations, and combinations thereof, that increase ACE2 affinity, human serum antibody escape or both for the vast mutational space that is much larger than what is possible to evaluate using experimental methods. Mutations were identified that enhance transmissibility for wild-type SARS-CoV-2 as well as additional mutations that further enhance transmissibility of several current and former CDC Variants of Concern.

Fig 2. Machine learning models predict the impact of RBD mutations on ACE2 affinity and % human serum antibody escape, including for current and former CDC Variants of Concern.

(A, B) Increasing numbers of RBD mutations generally (A) reduce ACE2 affinity and (B) increase % antibody escape. (C, D) Machine learning models predict the impact of RBD mutations on (C) ACE2 affinity and (D) % antibody escape for the training and test datasets with 1–10 RBD mutations. (E, F) The two models reveal natural tradeoffs between the impact of RBD mutations on ACE2 affinity and % antibody escape. The predictions for wild-type SARS-CoV-2 are highlighted in red, while the predicted values for VOCs are highlighted in different colors (see legend). In (A), (C) and (E-F), the ACE2 affinities are reported as log[K_A,app (M)] values and higher values reflect higher affinity. The affinities are apparent values because the experimental datasets were obtained using bivalent ACE2 (ACE2-Fc), which results in much higher apparent affinity than that observed for monovalent ACE2. In (A-D), Spearman’s ρ values are given. In (E), the Pareto frontier that corresponds to the maximal level of antibody escape at each ACE2 affinity is indicated by the hashed red line. In (F), the ‘Region of Concern’ is defined as ACE2 affinity predictions greater than that for the Beta variant and % human antibody escape predictions values >0%. In (C-D), model performance metrics are averages of tenfold cross-validation. In (C-F), each plot shows a randomly selected subset of 5,000 RDB mutants.

Fig 3. Identification of SARS-CoV-2 RBD double mutants at the Pareto frontier with co-maximal levels of ACE2 affinity and antibody escape.

(A) Twenty-nine RBD double mutants (single nucleotide exchanges, dark gray), located at the Pareto frontier, were isolated with the largest increases in ACE2 affinity or antibody escape relative to wild type (red point). The white points at the Pareto frontier required multiple nucleotide exchanges and were not considered further, and only a subset of the evaluated RBD variants (including those at the Pareto frontier) are shown for clarity. (B) Structural locations of eleven of the sixteen RBD sites mutated in the 29 RBD double mutants that resulted in the largest increases in either ACE2 affinity or antibody escape or both. (C) Structural locations of the other RBD sites that were also mutated in the 29 RBD double mutants located at the Pareto frontier. (D) Predicted values of (top) ACE2 affinity and (bottom) antibody escape for the 29 RBD double mutants located at the Pareto frontier. (E) Predicted values for the single RBD mutations observed in the RBD double mutants located at the Pareto frontier. ACE2 affinities are reported as the log[K_A,app(M)]. In (B) and (C), the wild-type residues are colored red (negative charge), blue (positive charge), green (hydrophobic), orange (tyrosine) and purple (polar).

Fig 4. Predictions of single mutations in wild-type SARS-CoV-2 and additional single mutations in current and former CDC Variants of Concern at the Pareto frontier with the largest increases in ACE2 affinity or antibody escape.

(A) Single RBD mutations that are predicted to increase ACE2 affinity or antibody escape without reducing the other property for wild-type SARS-CoV-2 and Variants of Concern. (B) Expanded view of the graphs in (A) with highlighted single RBD mutations that increase ACE2 affinity or antibody escape. (C) Location of key RBD sites that are commonly mutated to increase ACE2 affinity (L452 and N460) and antibody escape (K386 and E484). In (A) and (B), colored symbols generally correspond to single nucleotide exchange mutations (except for wild-type SARS-CoV-2 or the parental Variants of Concern), while white symbols correspond to multiple nucleotide exchanges.