Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning
Fig 3
SARS-CoV-2 fitness trajectory, as revealed by Geno-GNN.
(A) Distribution of RBD mutations across major epidemic variants, including BA.1, BA.2, BA.4/5, BQ.1, XBB.1.5, XBB.1.9, and EG.5.1. Fixed mutations, shared by multiple variants, are highlighted. The x-axis represents mutation types; for loci with multiple mutations, specific types are displayed in the corresponding cell. (B) Fitness trajectories of SARS-CoV-2 identified through virtual mutation scanning. ACE2 binding affinities are grouped by the number of mutations and the number of immune types that escaped. Major real-world variants are denoted by white rhombuses. Color gradient represents the number of escaped immune types. Black dashed line indicates the ACE2 affinity of the wild type. “All mutations” refers to the complete set of RBD mutations in major variants relative to the wild-type strain, defining the combinatorial mutation space. Fixed mutations are regarded as a single mutation and assumed to co-occur or co-disappear in virtual mutation scanning. (C) Differentiation of two distinct trajectories based on fixed mutations. ACE2 binding affinities are categorized by the number of immune types escaped (IT: immune type). Median ACE2 affinities for intermediates with and without fixed mutations are indicated by blue and red dashed lines, respectively. Bar chart presents the number of intermediates in each immune type. Boxplots compare ACE2 affinities for intermediates with and without fixed mutations; statistical significance is annotated. Boxes represent the interquartile range (25th–75th percentiles), and whiskers extend to 1.5 times the interquartile range. Fixed mutations are shown in color.