In silico analysis of the aggregation propensity of the SARS-CoV-2 proteome: Insight into possible cellular pathologies

Abstract

The coronavirus disease 19 emerged in 2020 is caused by the SARS-CoV-2 virus. 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.

Generation of a Soluble African Horse Sickness Virus VP7 Protein Capable of Forming Core-like Particles

“…The most recent example of which is SARS-CoV-2 and SARS-CoV. Using several computational tools, 10 aggregation-prone proteins in the SARS-CoV-2 reference strain were identified [ 10 ]. The aggregation of these predicted aggregation-prone SARS-CoV-2 proteins, including spike protein peptides, NSP6 protein peptide, ORF10, and NSP1, was confirmed in vitro, and it was also revealed that NSP11 aggregates were toxic to mammalian cells [ 11 ].…”

“…It is not clear how VP7 aggregation affects AHSV particle assembly or whether the presence of these crystals could contribute to the cellular pathogenesis caused by AHSV infection, which is more severe than that of BTV. Virus protein aggregation has been associated with a range of human diseases such as COVID-19 [ 10 , 11 , 12 , 13 ], hepatitis B [ 14 ], and influenza [ 15 ]. It is, therefore, necessary to identify which forces drive the formation of these crystalline particles to better understand the evolution, pathogenesis, and replication of AHSV.…”

Quantification of nuclear transport inhibition by SARS-CoV-2 ORF6 using a broadly applicable live-cell dose-response pipeline

“…For example, WGA forms a dimer with eight binding sites for O-GlcNAc, a glycosylation abundant in the FG domains (Li and Kohler, 2014; Schwefel et al, 2010). A recent computational analysis predicted that ORF6 is also aggregate-prone due to the hydrophobic N-terminal region (Flores-Leon et al, 2021). Therefore, we hypothesized that the mechanistic role of the N-terminal region of ORF6 in the nuclear transport inhibition is to mediate aggregation or homo-oligomerization, while its membrane binding is rather coincidental.…”

A3D database: structure-based predictions of protein aggregation for the human proteome

“…A3D can predict the effect of mutations on protein stability and aggregation propensity, as well as suggest solubility-enhancing mutations. This algorithm has been employed to study the constraints imposed by aggregation on protein evolution ( Carija et al , 2019 ), to diagnose the functional impact of genetic mutations ( Seaby and Ennis, 2020 ), to predict the aggregation of the SARS-CoV-2 proteome ( Flores-León et al , 2021 ), to assist the design of novel nanomaterials ( Gil-Garcia and Ventura, 2021 ) or to engineer the solubility of therapeutic proteins ( de Aguiar et al , 2021 ; Gil-Garcia et al , 2018 ) among many other applications.…”