More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one's immune system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to improve vaccine design, this project proposes Optimus PPIme - a deep learning approach to predict future, more infectious variants from an existing virus (exemplified by SARS-CoV-2). The approach comprises an algorithm which acts as a "virus" attacking a host cell. To increase infectivity, the "virus" mutates to bind better to the host's receptor. 2 algorithms were attempted - greedy search and beam search. The strength of this variant-host binding was then assessed by a transformer network we developed, with a high accuracy of 90%. With both components, beam search eventually proposed more infectious variants. Therefore, this approach can potentially enable researchers to develop vaccines that provide protection against future infectious variants before they emerge, pre-empting outbreaks and saving lives.
翻译：更多的传染性病毒变异可能来自蛋白质的快速突变,产生新的感染波浪。 这些变异可能逃避免疫系统,感染接种疫苗的个人,降低疫苗的功效。 因此,为了改进疫苗的设计,该项目提出了“Optimus PPIme ” — — 一种深入的学习方法来预测未来,即现有病毒(SARS-COV-2)中更具传染性的变异(由SARS-COV-2)。这个方法包括一种算法,它起到攻击宿主细胞的“病毒”的作用。为了增加感染性,“病毒”变异法可以更好地与宿主的受体结合。 2种算法是尝试的 — 贪婪搜索和光束搜索。 然后,我们开发了一个变异主体网络来评估了这个变异体宿主的强度,其精度高达90%。在这两个构件中,星的搜索最终提出了更具感染性的变体变体。 因此,这个方法有可能使研究人员开发出疫苗,在出现之前保护未来的感染变体, 预防爆发和拯救生命。
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