The SARS-CoV-2 virus causes the coronavirus disease 19 emerged in 2020. The pandemic triggered a turmoil in public health and is having a tremendous social and economic impact around the globe. Upon entry into host cells, the SARS-CoV-2 virus hijacks cellular machineries to produce and maintain its own proteins, spreading the infection. Although the disease is known for prominent respiratory symptoms, accumulating evidence is also demonstrating the involvement of the central nervous system, with possible mid- and long-term neurological consequences. In this study, we conducted a detailed bioinformatic analysis of the SARS-CoV-2 proteome aggregation propensity by using several complementary computational tools. Our study identified 10 aggregation prone proteins in the reference SARS-CoV-2 strain: the non-structural proteins Nsp4, Nsp6 and Nsp7 as well as ORF3a, ORF6, ORF7a, ORF7b, ORF10, CovE and CovM. By searching for the available mutants of each protein, we have found that most proteins are conserved, while ORF3a and ORF7b are variable and characterized by the occurrence of a large number of mutants with increased aggregation propensity. The geographical distribution of the mutants revealed interesting differences in the localization of aggregation-prone mutants of each protein. Aggregation-prone mutants of ORF7b were found in 7 European countries, whereas those of ORF3a in only 2. Aggregation-prone sequences of ORF7b, but not of ORF3a, were identified in Australia, India, Nepal, China, and Thailand. Our results are important for future analysis of a possible correlation between higher transmissibility and infection, as well as the presence of neurological symptoms with aggregation propensity of SARS-CoV-2 proteins.
Keywords: Bioinformatics; Protein aggregation; Proteostasis; SARS-CoV-2.
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