Executable network of SARS-CoV-2-host interaction predicts drug combination treatments

Howell, Rowan; Clarke, Malcolm; Reuschl, Ann-Kathrin; Chen, Tianyi; Abbott-Imboden, Sean; Singer, Mervyn; Lowe, David M.; Bennett, Clare; Chain, Benjamin M.; Jolly, Clare; Fisher, Jasmin

doi:10.1038/s41746-022-00561-5

Cited by 10 publications

(6 citation statements)

References 97 publications

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“…N-0385, is a small-molecule compound that inhibits TMPRSS2 in a nanomolar concentration; it was proven to inhibit SARS-CoV-2 infection in human lung cells [ 153 ]. Nafamostat is another TMPRRS2 inhibitor promising to prevent the cleavage of S proteins [ 154 ] and the combination of camostat (TMPRSS2 inhibitor) and apilimod (PIK inhibitor) reduced viral entry and prevented viral replication [ 155 ]. Other molecules were reported to downregulate TMPRSS2 expression, such as the enzalutamide, an anti-androgen receptor [ 156 ]; the homoharringtonine used against chronic myeloid leukemia; and halofuginone, which inhibits T helper cell development [ 157 ].…”

Section: Treatments For Coronavirus Infectionmentioning

confidence: 99%

Human Coronavirus Cell Receptors Provide Challenging Therapeutic Targets

et al. 2023

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Coronaviruses interact with protein or carbohydrate receptors through their spike proteins to infect cells. Even if the known protein receptors for these viruses have no evolutionary relationships, they do share ontological commonalities that the virus might leverage to exacerbate the pathophysiology. ANPEP/CD13, DPP IV/CD26, and ACE2 are the three protein receptors that are known to be exploited by several human coronaviruses. These receptors are moonlighting enzymes involved in several physiological processes such as digestion, metabolism, and blood pressure regulation; moreover, the three proteins are expressed in kidney, intestine, endothelium, and other tissues/cell types. Here, we spot the commonalities between the three enzymes, the physiological functions of the enzymes are outlined, and how blocking either enzyme results in systemic deregulations and multi-organ failures via viral infection or therapeutic interventions is addressed. It can be difficult to pinpoint any coronavirus as the target when creating a medication to fight them, due to the multiple processes that receptors are linked to and their extensive expression.

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Section: Treatments For Coronavirus Infectionmentioning

confidence: 99%

Human Coronavirus Cell Receptors Provide Challenging Therapeutic Targets

et al. 2023

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“…Jin et al [ 31 ] presented a new deep learning architecture ComboNet for predicting synergistic drug combinations for COVID-19. Howell et al [ 33 ] developed a computational model of SARS-CoV-2-host interactions used to predict effective drug combinations.…”

Section: Introductionmentioning

confidence: 99%

A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

Lei

2023

PLoS Comput Biol

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Expressive molecular representation plays critical roles in researching drug design, while effective methods are beneficial to learning molecular representations and solving related problems in drug discovery, especially for drug-drug interactions (DDIs) prediction. Recently, a lot of work has been put forward using graph neural networks (GNNs) to forecast DDIs and learn molecular representations. However, under the current GNNs structure, the majority of approaches learn drug molecular representation from one-dimensional string or two-dimensional molecular graph structure, while the interaction information between chemical substructure remains rarely explored, and it is neglected to identify key substructures that contribute significantly to the DDIs prediction. Therefore, we proposed a dual graph neural network named DGNN-DDI to learn drug molecular features by using molecular structure and interactions. Specifically, we first designed a directed message passing neural network with substructure attention mechanism (SA-DMPNN) to adaptively extract substructures. Second, in order to improve the final features, we separated the drug-drug interactions into pairwise interactions between each drug’s unique substructures. Then, the features are adopted to predict interaction probability of a DDI tuple. We evaluated DGNN–DDI on real-world dataset. Compared to state-of-the-art methods, the model improved DDIs prediction performance. We also conducted case study on existing drugs aiming to predict drug combinations that may be effective for the novel coronavirus disease 2019 (COVID-19). Moreover, the visual interpretation results proved that the DGNN-DDI was sensitive to the structure information of drugs and able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method enhanced the performance and interpretation capability of DDI prediction modeling.

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“…Along with the technological advancement, bioinformatics approaches were shown to significantly benefit translational drug discovery research through the analysis of this vast body of knowledge (Wooller et al, 2017). Several computational approaches were reported to be implemented in SARS-CoV-2 drug repurposing studies, including network models (Li X. et al, 2021;Hamed et al, 2022;Howell et al, 2022;Siminea et al, 2022), text mining (Kuusisto et al, 2020;Tworowski et al, 2020;Muramatsu and Tanokura, 2021), molecular docking and molecular dynamics (MD) simulation (Wang, 2020;Egieyeh et al, 2021;Jalalvand et al, 2022), knowledge graph (KG) (Al-Saleem et al, 2021), weight regularization matrix factorization (WRMF) (Xu et al, 2022), and ensemble matrix completion model (Li W. et al, 2021). The application of artificial intelligence (AI) technologies was reported to hasten drug repurposing studies among the existing computational approaches Levin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

et al. 2023

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The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

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Executable network of SARS-CoV-2-host interaction predicts drug combination treatments

Cited by 10 publications

References 97 publications

Human Coronavirus Cell Receptors Provide Challenging Therapeutic Targets

Human Coronavirus Cell Receptors Provide Challenging Therapeutic Targets

A dual graph neural network for drug–drug interactions prediction based on molecular structure and interactions

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

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