Predicting the antigenic evolution of SARS-COV-2 with deep learning.
Wenkai HanNingning ChenXinzhou XuAdil SahilJuexiao ZhouZhongxiao LiHuawen ZhongElva GaoRuochi ZhangYu WangShiwei SunPeter Pak-Hang CheungXin GaoPublished in: Nature communications (2023)
The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infections. Gaining insights into potential antigenic changes is critical but challenging due to the vast sequence space. Here, we introduce the Machine Learning-guided Antigenic Evolution Prediction (MLAEP), which combines structure modeling, multi-task learning, and genetic algorithms to predict the viral fitness landscape and explore antigenic evolution via in silico directed evolution. By analyzing existing SARS-CoV-2 variants, MLAEP accurately infers variant order along antigenic evolutionary trajectories, correlating with corresponding sampling time. Our approach identified novel mutations in immunocompromised COVID-19 patients and emerging variants like XBB1.5. Additionally, MLAEP predictions were validated through in vitro neutralizing antibody binding assays, demonstrating that the predicted variants exhibited enhanced immune evasion. By profiling existing variants and predicting potential antigenic changes, MLAEP aids in vaccine development and enhances preparedness against future SARS-CoV-2 variants.
Keyphrases
- sars cov
- copy number
- machine learning
- public health
- respiratory syndrome coronavirus
- deep learning
- genome wide
- single cell
- big data
- risk assessment
- depressive symptoms
- molecular docking
- dna methylation
- zika virus
- gene expression
- high throughput
- transcription factor
- convolutional neural network
- respiratory failure
- climate change
- mechanical ventilation