SARS-CoV-2 has plagued the world since 2019 with continuously emergence of new variants, resulting in repeated waves of outbreak. Although the countermeasures like vaccination campaign has taken worldwide, the sophisticated virus mutated to escape immune system, threatening the public health. To win the race with the virus and ultimately end the pandemic, we have to take one step ahead to predict how the SARSCoV-2 might evolve and defeat it at the beginning of a new wave. Hence, we proposed a deep learning based framework to first build a deep learning model to shape the fitness landscape of the virus and then use genetic algorithm to predict the high-risk variants that might appear in the future. By combining pre-trained protein language model and structure modeling, the model is trained in a supervised way, predicting the viral transmissibility and antibodies escape ability to eight antibodies simultaneously. The prevenient virus evolution trajectory can be largely recovered by our model with high correlation to their sampling time. Novel mutations predicted by our model show high antibody escape through in silico simulation and overlapped with the mutations developed in prevenient infected patients. Overall, our scheme can provide insights into the evolution of SARS-CoV-2 and hopefully guide the development of vaccination and increase the preparedness.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/679636 |
| Date | 04 July 2022 |
| Creators | Chen, NingNing |
| Contributors | Gao, Xin, Biological and Environmental Science and Engineering (BESE) Division, Gojobori, Takashi, Wang, Di |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | 2023-07-06, At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2023-07-06. |
Page generated in 0.0015 seconds