bioRxivBioinformaticsPreprint
Alphavirus nsP2 protease structure and cleavage prediction: Possible relevance to the pathogenesis of viral arthritis
Abstract
Alphaviruses are a diverse genus of arboviruses capable of infecting many vertebrates including humans. Human infection is common in equatorial and subtropical regions and is often accompanied by arthralgia or encephalitis depending on viral lineage. No antivirals or vaccines have been approved, and many alphavirus lineages have only recently been discovered and classified. Alphavirus nsP2 protease is an important virulence factor yet is commonly thought to be a simple papain-like protease which only cleaves viral polyproteins. Here, I reveal novel molecular mechanisms of these proteases via sequence and predicted structure alignment and propose novel cellular mechanisms for the pathogenesis of viral arthritis by predicting which human proteins are likely cleaved by these proteases. In addition to the known primary cysteine mechanism in all alphaviruses and a secondary serine mechanism documented in chikungunya virus (CHIKV), I discovered secondary cysteine and threonine mechanisms exist in many other alphaviruses and that these secondary mechanisms coevolve with their viral polyprotein cleavages. As for cleavage prediction, neural networks trained on 93 different putative viral polyprotein cleavages achieved a Matthews correlation coefficient of 0.965, and, when applied to the human proteome, predicted that hundreds of proteins may be vulnerable. Notable pathways likely affected by cleavages include the cytoskeleton and extracellular matrix, antiproteases, protein translation/folding/glycosylation/ubiquitination, cellular differentiation, inflammation, and vesicle trafficking, hinting that this viral protease is a more important virulence factor than previously believed.
- assay: protein structure
- assay: protein sequence
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 1
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