Predicted coronavirus Nsp5 protease cleavage sites in the human proteome

Abstract

Background The coronavirus nonstructural protein 5 (Nsp5) is a cysteine protease required for processing the viral polyprotein and is therefore crucial for viral replication. Nsp5 from several coronaviruses have also been found to cleave host proteins, disrupting molecular pathways involved in innate immunity. Nsp5 from the recently emerged SARS-CoV-2 virus interacts with and can cleave human proteins, which may be relevant to the pathogenesis of COVID-19. Based on the continuing global pandemic, and emerging understanding of coronavirus Nsp5-human protein interactions, we set out to predict what human proteins are cleaved by the coronavirus Nsp5 protease using a bioinformatics approach. Results Using a previously developed neural network trained on coronavirus Nsp5 cleavage sites (NetCorona), we made predictions of Nsp5 cleavage sites in all human proteins. Structures of human proteins in the Protein Data Bank containing a predicted Nsp5 cleavage site were then examined, generating a list of 92 human proteins with a highly predicted and accessible cleavage site. Of those, 48 are expected to be found in the same cellular compartment as Nsp5. Analysis of this targeted list of proteins revealed molecular pathways susceptible to Nsp5 cleavage and therefore relevant to coronavirus infection, including pathways involved in mRNA processing, cytokine response, cytoskeleton organization, and apoptosis. Conclusions This study combines predictions of Nsp5 cleavage sites in human proteins with protein structure information and protein network analysis. We predicted cleavage sites in proteins recently shown to be cleaved in vitro by SARS-CoV-2 Nsp5, and we discuss how other potentially cleaved proteins may be relevant to coronavirus mediated immune dysregulation. The data presented here will assist in the design of more targeted experiments, to determine the role of coronavirus Nsp5 cleavage of host proteins, which is relevant to understanding the molecular pathology of coronavirus infection.

Identification of motif-based interactions between SARS-CoV-2 protein domains and human peptide ligands pinpoint antiviral targets

“…Based on the peptides identified by ProP-PD selections, consensus motifs could be established for two proteins (Nsp5 [FLM][HQ][AS] and Nsp9 G[FL]xL[GDP]; Figure 2 ). For Nsp5 (Mpro), the ligands may serve as potential substrates because the recognition motif resembles the preferred proteolytic site of the protease (LQ↓[GAS]) 26 .…”

“…(B) The motifs obtained from the selections with the SARS-CoV-2 proteins Nsp5 and Nsp9 are highlighted in bold in the alignments of the representative peptides. The consensus motif for the Nsp5 (Mpro) protease cleavage site is shown below the alignment 26 . (C) Subset of identified interactions of SARS-CoV-2 Nsp5 and Nsp9.…”

“…; Figure 2). For Nsp5 (Mpro), the ligands may serve as potential substrates because the recognition motif resembles the preferred proteolytic site of the protease (LQ↓[GAS]) 26 . The enrichment analysis of the Gene Ontology (GO) terms for the combined dataset revealed enrichment of ligands associated with biological processes related to transcriptional regulation (Table S3; q < 0.001; PepTools).…”

Integrating knowledge of protein sequence with protein function for the prediction and validation of new MALT1 substrates

“…Knowledge of the substrate repertoire of proteases is essential for understanding their biological roles [1] . With recent exceptions [2] , [3] , [4] , protease cleavage site prediction algorithms are typically designed for predictions within one or a small number of targeted query proteins. Indeed, most predictor algorithms are neither designed nor scalable for proteome-wide ranking of candidate substrates.…”

“…To identify new substrates and test the precision of our workflow, we selected the paracaspase mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1). To our knowledge, no existing algorithm [10] , [2] , [3] , [4] , [12] , [13] , [14] , [15] , [16] , [17] can be readily implemented to predict MALT1 substrates. As MALT1 has just 14 known cleavage sites in 12 substrates, this limits algorithm training, necessitating new approaches to predict MALT1 substrates accurately.…”