Authors: Jie Chen (Peng Cheng Laboratory; School of Electronic and Computer Engineering, Peking University); Zhiwei Nie (School of Electronic and Computer Engineering, Peking University; Peng Cheng Laboratory); Yu Wang, Kai Wang, Fan Xu, Zhennan Wang, Guoli Song, Xiansong Huang, and Zhixiang Ren (Peng Cheng Laboratory); Bin Zhou (School of Information Science and Engineering, Shandong University); Chao Yang (School of Mathematical Sciences, Peking University); and Yonghong Tian (Peng Cheng Laboratory; School of Electronic and Computer Engineering, Peking University)
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 493.9× 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.
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