Related Experiment Video
Updated: May 29, 2026
10:34
Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
Published on: December 9, 2022
A deep mutational scanning-informed protein language model predicts SARS-CoV-2 evolution dynamics with spatiotemporal
Sijie Yang1,2,3, Xiaowei Luo2, Jiejian Luo2
1Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, P. R. China.
Nature Microbiology
|May 27, 2026
Summary
DeepCoV, a novel deep-learning framework, accurately forecasts emerging SARS-CoV-2 variants a month in advance. This tool aids public health by identifying dominant strains and predicting their spread with reduced errors.
Area of Science:
- Virology
- Genomics
- Computational Biology
Background:
- Real-time surveillance of emerging pathogen variants like SARS-CoV-2 is crucial for public health.
- Current surveillance methods lack the feasibility for dynamic, real-time tracking of dominant strains.
Purpose of the Study:
- To introduce DeepCoV, a deep-learning framework for dynamic identification of emerging SARS-CoV-2 variants with high prevalence potential.
- To enable spatiotemporal resolution in variant surveillance and forecasting.
Main Methods:
- Integration of deep mutational scanning (DMS)-derived mutation phenotypes.
- Incorporation of evolutionary sequence data and epidemiological surveillance data reflecting immune pressures.
- Benchmarking against logistic regression and other deep-learning approaches in simulated scenarios.
Main Results:
- DeepCoV accurately forecasts lineage dominance a month in advance, reducing the false discovery rate by 90%.
- The framework captures temporal and geographic dynamics of variant spread and reconstructs regional prevalence.
- In silico identification of Omicron mutational hotspots revealed convergent evolution trends.
Conclusions:
- DeepCoV offers a scalable framework for timely identification of immune-evasive variants and critical mutations.
- Provides actionable insights for public health responses to evolving pathogens.
- Enhances early warning systems for future pandemic preparedness.
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