tetano
Editor, Senior Moderator
PLoS Comput Biol
. 2026 Jan 12;22(1):e1013582.
doi: 10.1371/journal.pcbi.1013582. Online ahead of print. Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning
Zengmiao Wang[SUP] 1 [/SUP], Ziqin Zhou[SUP] 1 [/SUP], Junfu Wang[SUP] 2 [/SUP], Lingyue Yang[SUP] 1 [/SUP], Zhirui Zhang[SUP] 3 [/SUP], Weina Xu[SUP] 2 [/SUP], Zeming Liu[SUP] 2 [/SUP], Yuxi Ge[SUP] 1 [/SUP], Liang Yang[SUP] 4 [/SUP], Xiaoli Wang[SUP] 5 6 7 [/SUP], Peng Yang[SUP] 5 6 7 [/SUP], Quanyi Wang[SUP] 5 6 7 [/SUP], Yunlong Cao[SUP] 8 9 10 [/SUP], Yuanfang Guo[SUP] 2 [/SUP], Huaiyu Tian[SUP] 1 11 [/SUP]
Affiliations
Understanding the heterogeneity of population-level viral fitness dynamics, which reflect the interplay between intrinsic viral properties and population immunity, is critical for pandemic preparedness. However, how these dynamics vary across diverse immune backgrounds and mutational landscapes remain poorly characterized. We present Geno-GNN, a graph representation learning approach for retrospectively characterizing the viral fitness dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Geno-GNN accurately predicts angiotensin-converting enzyme 2 (ACE2) binding affinity and immune escape potential across multiple external datasets. Using Geno-GNN, we identified temporal patterns in SARS-CoV-2 fitness and detected varying rates of fitness change associated with distinct immune backgrounds. Virtual mutation scanning revealed two fitness trajectories: broad immune evasion at the cost of ACE2 affinity and ACE2 affinity maintenance at or above the Wuhan-Hu-1 level along with moderate immune escape. Notably, real-world SARS-CoV-2 variants predominantly followed the latter trajectory, sustaining ACE2 affinity via fixed mutations. These findings underscore the heterogeneous, immune-contextualized nature of viral fitness dynamics and the complex evolutionary pathways of SARS-CoV-2.
. 2026 Jan 12;22(1):e1013582.
doi: 10.1371/journal.pcbi.1013582. Online ahead of print. Characterization of the heterogeneity in SARS-CoV-2 fitness dynamics via graph representation learning
Zengmiao Wang[SUP] 1 [/SUP], Ziqin Zhou[SUP] 1 [/SUP], Junfu Wang[SUP] 2 [/SUP], Lingyue Yang[SUP] 1 [/SUP], Zhirui Zhang[SUP] 3 [/SUP], Weina Xu[SUP] 2 [/SUP], Zeming Liu[SUP] 2 [/SUP], Yuxi Ge[SUP] 1 [/SUP], Liang Yang[SUP] 4 [/SUP], Xiaoli Wang[SUP] 5 6 7 [/SUP], Peng Yang[SUP] 5 6 7 [/SUP], Quanyi Wang[SUP] 5 6 7 [/SUP], Yunlong Cao[SUP] 8 9 10 [/SUP], Yuanfang Guo[SUP] 2 [/SUP], Huaiyu Tian[SUP] 1 11 [/SUP]
Affiliations
- PMID: 41525306
- DOI: 10.1371/journal.pcbi.1013582
Understanding the heterogeneity of population-level viral fitness dynamics, which reflect the interplay between intrinsic viral properties and population immunity, is critical for pandemic preparedness. However, how these dynamics vary across diverse immune backgrounds and mutational landscapes remain poorly characterized. We present Geno-GNN, a graph representation learning approach for retrospectively characterizing the viral fitness dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Geno-GNN accurately predicts angiotensin-converting enzyme 2 (ACE2) binding affinity and immune escape potential across multiple external datasets. Using Geno-GNN, we identified temporal patterns in SARS-CoV-2 fitness and detected varying rates of fitness change associated with distinct immune backgrounds. Virtual mutation scanning revealed two fitness trajectories: broad immune evasion at the cost of ACE2 affinity and ACE2 affinity maintenance at or above the Wuhan-Hu-1 level along with moderate immune escape. Notably, real-world SARS-CoV-2 variants predominantly followed the latter trajectory, sustaining ACE2 affinity via fixed mutations. These findings underscore the heterogeneous, immune-contextualized nature of viral fitness dynamics and the complex evolutionary pathways of SARS-CoV-2.