Dataset Information

Vir2Drug: a drug repurposing framework based on protein similarities between pathogens.

ABSTRACT: We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy distance between pathogens and (c) drugs targeting specific pathogen's and host proteins. The underlying pathogen networks are used to screen drugs by means of specific methodologies that account for either the host or pathogen's protein targets. Vir2Drug is a useful and yet informative tool for drug repurposing against known or unknown pathogens especially in periods where the emergence for repurposed drugs plays significant role in handling viral outbreaks, until reaching a vaccine. The web tool is available at: https://bioinformatics.cing.ac.cy/vir2drug, https://vir2drug.cing-big.hpcf.cyi.ac.cy.

SUBMITTER: Minadakis G

PROVIDER: S-EPMC9851336 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Vir2Drug: a drug repurposing framework based on protein similarities between pathogens.

Minadakis George G Tomazou Marios M Dietis Nikolas N Spyrou George M GM

Briefings in bioinformatics 20230101 1

We draw from the assumption that similarities between pathogens at both pathogen protein and host protein level, may provide the appropriate framework to identify and rank candidate drugs to be used against a specific pathogen. Vir2Drug is a drug repurposing tool that uses network-based approaches to identify and rank candidate drugs for a specific pathogen, combining information obtained from: (a) ranked pathogen-to-pathogen networks based on protein similarities between pathogens, (b) taxonomy ...[more]

PMID: 36513376

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Project description:Drug repurposing plays an important role in screening old drugs for new therapeutic efficacy. The existing methods commonly treat prediction of drug-target interaction as a problem of binary classification, in which a large number of randomly sampled drug-target pairs accounting for over 50% of the entire training dataset are necessarily required. Such a large number of negative examples that do not come from experimental observations inevitably decrease the credibility of predictions. In this study, we propose a multi-label learning framework to find new uses for old drugs and discover new drugs for known target genes. In the framework, each drug is treated as a class label and its target genes are treated as the class-specific training data to train a supervised learning model of l2-regularized logistic regression. As such, the inter-drug associations are explicitly modelled into the framework and all the class-specific training data come from experimental observations. In addition, the data constraint is less demanding, for instance, the chemical substructures of a drug are no longer needed and the novel target genes are inferred only from the underlying patterns of the known genes targeted by the drug. Stratified multi-label cross-validation shows that 84.9% of known target genes have at least one drug correctly recognized, and the proposed framework correctly recognizes 86.73% of the independent test drug-target interactions (DTIs) from DrugBank. These results show that the proposed framework could generalize well in the large drug/class space without the information of drug chemical structures and target protein structures. Furthermore, we use the trained model to predict new drugs for the known target genes, identify new genes for the old drugs, and infer new associations between old drugs and new disease phenotypes via the OMIM database. Gene ontology (GO) enrichment analyses and the disease associations reported in recent literature provide supporting evidences to the computational results, which potentially shed light on new clinical therapies for new and/or old disease phenotypes.

Dataset Information

Vir2Drug: a drug repurposing framework based on protein similarities between pathogens.

Publications

Vir2Drug: a drug repurposing framework based on protein similarities between pathogens.

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