M. Cyrus Maher et al. (ed.): Predicting the mutational drivers of future SARS-CoV-2 variants of concern, in: Science Translational Medicine 14, Nr. 633 (January 11, 2022) S. 1-10, online in: https://doi.org/10.1126/scitranslmed.abk3445.
Abstract
SARS-CoV-2 evolution threatens vaccine- and natural infection–derived immunity and the efficacy of therapeutic antibodies. To improve public health preparedness, the authors sought to predict which existing amino acid mutations in SARS-CoV-2 might contribute to future variants of concern. They tested the predictive value of features comprising epidemiology, evolution, immunology, and neural network–based protein sequence modeling and identified primary biological drivers of SARS-CoV-2 intrapandemic evolution. The authors found evidence that ACE2-mediated transmissibility and resistance to population-level host immunity has waxed and waned as a primary driver of SARS-CoV-2 evolution over time. They retroactively identified with high accuracy (area under the receiver operator characteristic curve = 0.92 to 0.97) mutations that will spread, at up to 4 months in advance, across different phases of the pandemic. The behavior of the model was consistent with a plausible causal structure where epidemiological covariates combine the effects of diverse and shifting drivers of viral fitness. They applied their model to forecast mutations that will spread in the future and characterize how these mutations affect the binding of therapeutic antibodies. These findings demonstrate that it is possible to forecast the driver mutations that could appear in emerging SARS-CoV-2 variants of concern. The authors validated this result against Omicron, showing elevated predictive scores for its component mutations before emergence and rapid score increase across daily forecasts during emergence. This modeling approach may be applied to any rapidly evolving pathogens with sufficiently dense genomic surveillance data, such as influenza, and unknown future pandemic viruses.