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Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants
Preprint
in English
| bioRxiv
| ID: ppbiorxiv-516989
ABSTRACT
The never-ending emergence of SARS-CoV-2 variations of concern (VOCs) has challenged the whole world for pandemic control. In order to develop effective drugs and vaccines, one needs to efficiently simulate SARS-CoV-2 spike receptor binding domain (RBD) mutations and identify high-risk variants. We pretrain a large protein language model on approximately 408 million protein sequences and construct a high-throughput screening for the prediction of binding affinity and antibody escape. As the first work on SARS-CoV-2 RBD mutation simulation, we successfully identify mutations in the RBD regions of 5 VOCs and can screen millions of potential variants in seconds. Our workflow scales to 4096 NPUs with 96.5% scalability and 493.9x speedup in mixed precision computing, while achieving a peak performance of 366.8 PFLOPS (reaching 34.9% theoretical peak) on Pengcheng Cloudbrain-II. Our method paves the way for simulating coronavirus evolution in order to prepare for a future pandemic that will inevitably take place. Our models are released at https//github.com/ZhiweiNiepku/SARS-CoV-2_mutation_simulation to facilitate future related work.
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Full text:
Available
Collection:
Preprints
Database:
bioRxiv
Type of study:
Prognostic study
Language:
English
Year:
2022
Document type:
Preprint