Computational identification of human biological processes and protein sequence motifs putatively targeted by SARS-CoV-2 proteins using protein-protein interaction networks

While the COVID-19 pandemic is causing important loss of life, knowledge of the effects of the causative SARS-CoV-2 virus on human cells is currently limited. Investigating protein-protein interactions (PPIs) between viral and host proteins can provide a better understanding of the mechanisms exploited by the virus and enable the identification of potential drug targets. We therefore performed an in-depth computational analysis of the interactome of SARS-CoV-2 and human proteins in infected HEK293 cells published by Gordon et al. to reveal processes that are potentially affected by the virus and putative protein binding sites. Specifically, we performed a set of network-based functional and sequence motif enrichment analyses on SARS-CoV-2-interacting human proteins and on a PPI network generated by supplementing viral-host PPIs with known interactions. Using a novel implementation of our GoNet algorithm, we identified 329 Gene Ontology terms for which the SARS-CoV-2-interacting human proteins are significantly clustered in the network. Furthermore, we present a novel protein sequence motif discovery approach, LESMoN-Pro, that identified 9 amino acid motifs for which the associated proteins are clustered in the network. Together, these results provide insights into the processes and sequence motifs that are putatively implicated in SARS-CoV-2 infection and could lead to potential therapeutic targets.

Nanopore sensing of single-biomolecules: a new procedure to identify protein sequence motifs from molecular dynamics

MoS2 nanopores have emerged as one of the most promising solid-state nanopores for protein sequence motifs detection.

A customized program for the identification of conserved protein sequence motifs

We searched for viral protein sequences that could be important for tissue tropism. To achieve this goal, human pathogenic viruses were classified according to the tissue they infect (e.g., pulmonary), irrespective of whether they were enveloped or non-enveloped RNA or DNA viruses. Next, we developed an amino acid sequence alignment program and identified the conserved amino acid motif, VAIVLGG, in alphaviruses. The VAIVLGG sequence is located on the structural capsid protein of the chikungunya virus, a mosquito-borne arthrogenic member of the alphaviruses. Capsid protein translocation onto the host cell membrane is a required step for virion budding. Our identified VAIVLGG consensus sequence might potentially be used for developing a pan-vaccine effective against alphaviruses.

Quantification and discovery of sequence determinants of protein per mRNA amount in 29 human tissues

Despite their importance in determining protein abundance, a comprehensive catalogue of sequence features controlling protein-to-mRNA (PTR) ratios and a quantification of their effects is still lacking. Here we quantified PTR ratios for 11,575 proteins across 29 human tissues using matched transcriptomes and proteomes. We analyzed the contribution of known sequence determinants of protein synthesis and degradation and 15 novel mRNA and protein sequence motifs that we found by association testing. While the dynamic range of PTR ratios spans more than 2 orders of magnitude, our integrative model predicts PTR ratios at a median precision of 3.2-fold. A reporter assay provided significant functional support for two novel UTR motifs and a proteome-wide competition-binding assay identified motif-specific bound proteins for one motif. Moreover, our direct comparison of protein to RNA levels led to a new metrics of codon optimality. Altogether, this study shows that a large fraction of PTR ratio variance across genes can be predicted from sequence and identified many new candidate post-transcriptional regulatory elements in the human genome.