Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization

Patten, J.J.; Keiser, Patrick T.; Morselli-Gysi, Deisy; Menichetti, Giulia; Mori, Hiroyuki; Donahue, Callie J.; Gan, Xiao; Valle, Ítalo Faria do; Geoghegan-Barek, Kathleen; Anantpadma, Manu; Boytz, RuthMabel; Berrigan, Jacob; Stubbs, Sarah H.; Ayazika, Kirabo Tess; O’Leary, Colin; Jalloh, Sallieu; Wagner, Florence F.; Ayehunie, Seyoum; Elledge, Stephen J.; Anderson, Deborah; Loscalzo, Joseph; Žitnik, Marinka; Gummuluru, Suryaram; Namchuk, Mark; Barabási, Albert‐László; Davey, Robert A.

doi:10.1016/j.isci.2022.104925

Cited by 16 publications

(17 citation statements)

References 64 publications

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“…We evaluated in this assay the antiviral activity against SARS-CoV-2 replication of a selection of SARS-CoV-2 reference inhibitors. The results obtained were concordant to reports from other publications ( Agostini et al, 2019 )– ( Vangeel et al, 2022 ), ( Chang et al, 2022 ), ( Patten et al, 2022 ), ( Softic et al, 2020 ). An additional benefit of our assay is that it allows simultaneous read-out of both the antiviral potential and cytotoxicity caused by the compound.…”

Section: Discussionsupporting

confidence: 91%

Development of a robust and convenient dual-reporter high-throughput screening assay for SARS-CoV-2 antiviral drug discovery

et al. 2023

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Section: Discussionsupporting

confidence: 91%

Development of a robust and convenient dual-reporter high-throughput screening assay for SARS-CoV-2 antiviral drug discovery

et al. 2023

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“…Docking simulations offer a reliable but computationally intensive method to predict (or validate) binding between proteins and ligands 34 . Motivated by the need to model rapid response to sudden health crises, we chose as our validation set the 26 SARS-CoV-2 viral proteins and the 332 human proteins targeted by the SARS-CoV-2 viral proteins 35 – 37 . These proteins are missing from the training data of AI-Bind, hence represent novel targets and allow us to rely on recent efforts to understand the biology of COVID-19 to validate the AI-Bind predictions.…”

Section: Resultsmentioning

confidence: 99%

Improving the generalizability of protein-ligand binding predictions with AI-Bind

et al. 2023

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Identifying novel drug-target interactions is a critical and rate-limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, here we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Here we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training to improve binding predictions for novel proteins and ligands. We validate AI-Bind predictions via docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. AI-Bind is a high-throughput approach to identify drug-target combinations with the potential of becoming a powerful tool in drug discovery.

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“…Given the comorbitity of COVID‐19 and PAH, we next aimed to identify which repositioned drugs for COVID‐19 could be particulary useful for COVID‐19 patients with PAH as a comorbidity. A previous study evaluated 6710 clinical and preclinical drugs by immunocytofluorescence‐based screening to identify SARS‐CoV‐2 infection inhibitors 53 and characterized those with strong effects, weak effects, very weak effects, and no effects on COVID‐19. We collected 631 drugs that have been experimentally demonstrated to have strong, weak, or very weak effects on COVID‐19 from this study and another set of 77 drugs that have experimental strong or weak effects on COVID‐19, 34 and examined which of them could be potentially repurposed for patients with COVID‐19 who also had PAH.…”

Section: Resultsmentioning

confidence: 99%

Uncovering common pathobiological processes between COVID‐19 and pulmonary arterial hypertension by integrating Omics data

Wang

Loscalzo

2023

Pulm. circ.

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Coronavirus disease 2019 (COVID‐19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which led to the current pandemic. Many factors, including age and comorbidities, influence the severity and mortality of COVID‐19. SARS‐CoV‐2 infection can cause pulmonary vascular dysfunction. The COVID‐19 case‐fatality rate in patients with pulmonary arterial hypertension (PAH) is higher in comparison with the general population. In this study, we aimed to identify pathobiological processes common to COVID‐19 and PAH by utilizing the human protein–protein interactome and whole‐genome transcription data from peripheral blood mononuclear cells (PBMCs) and from lung tissue. We found that there are significantly more interactions between SARS‐CoV‐2 targets and PAH disease proteins than expected by chance, suggesting that the PAH disease module is in the neighborhood of SARS‐CoV‐2 targets in the human interactome. In addition, SARS‐CoV‐2 infection‐induced changes in gene expression significantly overlap with PAH‐induced gene expression changes in both tissues, indicating SARS‐CoV‐2 and PAH may share common transcriptional regulators. We identified many upregulated genes and downregulated genes common to COVID‐19 and PAH. Interestingly, we observed different co‐regulation patterns and dysfunctional signaling pathways in PBMCs versus lung tissue. Endophenotype enrichment analysis revealed that genes regulating fibrosis, inflammation, hypoxia, oxidative stress, immune response, and thromboembolism are significantly enriched in the COVID‐19‐PAH co‐expression modules. We examined the network proximity of the targets of repositioned drugs for COVID‐19 to the co‐expression modules in PBMCs and lung tissue, and identified 42 drugs that can be potentially used for COVID‐19 patients with PAH as a comorbidity. The uncovered common pathobiological pathways are crucial for discovering therapeutic targets and designing tailored treatments for COVID‐19 patients who also have PAH.

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Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization

Cited by 16 publications

References 64 publications

Development of a robust and convenient dual-reporter high-throughput screening assay for SARS-CoV-2 antiviral drug discovery

Development of a robust and convenient dual-reporter high-throughput screening assay for SARS-CoV-2 antiviral drug discovery

Improving the generalizability of protein-ligand binding predictions with AI-Bind

Uncovering common pathobiological processes between COVID‐19 and pulmonary arterial hypertension by integrating Omics data

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