In Silico Analyses of Immune System Protein Interactome Network, Single-Cell RNA Sequencing of Human Tissues, and Artificial Neural Networks Reveal Potential Therapeutic Targets for Drug Repurposing Against COVID-19

Abstract

There is pressing urgency to better understand the immunological underpinnings of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in order to identify potential therapeutic targets and drugs that allow treating patients effectively. To fill in this gap, we performed in silico analyses of immune system protein interactome network, single-cell RNA sequencing of human tissues, and artificial neural networks to reveal potential therapeutic targets for drug repurposing against COVID-19. As results, the high-confidence protein interactome network was conformed by 1,588 nodes between immune system proteins and human proteins physically associated with SARS-CoV-2. Subsequently, we screened all these nodes in ACE2 and TMPRSS2 co-expressing cells according to the Alexandria Project, finding 75 potential therapeutic targets significantly overexpressed (Z score > 2) in nasal goblet secretory cells, lung type II pneumocytes, and ileal absorptive enterocytes of patients with several immunopathologies. Then, we performed fully connected deep neural networks to find the best multitask classification model to predict the activity of 10,672 drugs for 25 of the 75 aforementioned proteins. On one hand, we obtained 45 approved drugs, 16 compounds under investigation, and 35 experimental compounds with the highest area under the receiver operating characteristic (AUROCs) for 15 immune system proteins. On the other hand, we obtained 4 approved drugs, 9 compounds under investigation, and 16 experimental compounds with the highest multi-target affinities for 9 immune system proteins. In conclusion, computational structure-based drug discovery focused on immune system proteins is imperative to select potential drugs that, after being effectively analyzed in cell lines and clinical trials, these can be considered for treatment of complex symptoms of COVID-19 patients, and for co-therapies with drugs directly targeting SARS-CoV-2.

Can proteomics-based approaches further help COVID-19 prevention and therapy?

“…The DeepVacPred computational framework has predicted several potential vaccine subunits from the available SARS-CoV-2 spike protein sequence [ 27 ]. Moreover, in silico analysis of the immune system protein interactome associated with SARS-CoV-2 infection and severity has revealed several novel therapeutic targets for drug repurposing against COVID-19 [ 28 ]. Hence, proteomics combined with bioinformatics may serve as a novel tool for understanding COVID-19 pathogenesis and designing therapeutics and vaccines.…”

Modeling COVID-19 disease biology to identify drug treatment candidates

“…This methodology is based on in silico disease biology modelling which aims to recapitulate the molecular environment of diseased target cells to identify candidate biomarkers and/or drugs that can be proposed as options for disease treatment. Others studying SARS-CoV-2 infection and COVID-19 have used protein-protein interaction (PPI) networks to analyze host-virus protein-protein interaction 8–10 , describe the interactome of the coronavirus S-glycoprotein and host proteins 11 , investigate viral-viral and virus-host PPI networks in peripheral blood mononuclear cells 12 , analyze the immune system PPI network and review potential therapeutic targets for drug repurposing against COVID-19 13 , and identify potentially repurposable drugs to treat COVID-19 14–16 .…”