This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants (preprint)
biorxiv; 2022.
Preprint
in English
| bioRxiv | ID: ppzbmed-10.1101.2022.11.17.516989
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 pro- tein language model on approximately 408 million pro- tein sequences and construct a high-throughput screen- ing for the prediction of binding affinity and antibody escape. As the first work on SARS-CoV-2 RBD mu- tation 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% theo- retical peak) on Pengcheng Cloudbrain-II. Our method paves the way for simulating coronavirus evolution in or- der to prepare for a future pandemic that will inevitably take place.
Full text:
Available
Collection:
Preprints
Database:
bioRxiv
Language:
English
Year:
2022
Document Type:
Preprint
Similar
MEDLINE
...
LILACS
LIS