Review of computational methods for virus–host protein interaction prediction: a case study on novel Ebola–human interactions

Dutta, Pritha; Kundu, Mahantapas; Basu, Subhadip; Nasipuri, Mita

doi:10.1093/bfgp/elx026

Cited by 22 publications

(18 citation statements)

References 129 publications

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“…In addition, the collected virus-host interactions are far from completeness and the quality can be influenced by multiple factors, including different experimental assays and human cell line models. We may computationally predict a new virus-host interactome for 2019-nCoV/SARS-CoV-2 using sequence-based and structure-based approaches 77 . Drug targets representing nodes within cellular networks are often intrinsically coupled with both therapeutic and adverse profiles 78 , as drugs can inhibit or activate protein functions (including antagonists vs. agonists).…”

Section: Discussionmentioning

confidence: 99%

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

et al. 2020

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Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

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Section: Discussionmentioning

confidence: 99%

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

et al. 2020

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“…In addition, the collected virus-host interactions are far from complete and the quality can be influenced by multiple factors, including different experimental assays and human cell line models. We may computationally predict a new virus-host interactome for HCoVs using sequence-based and structure-based approaches [70] . The current systems pharmacology model cannot separate therapeutic antiviral effects from those predictions due to lack of detailed pharmacological effects of drug targets and unknown functional consequences of virus-host interactions.…”

Section: Discussionmentioning

confidence: 99%

Network-based Drug Repurposing for Human Coronavirus

Zhou

Hou

Shen

et al. 2020

Preprint

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Human Coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV), Middle east respiratory syndrome coronavirus (MERS-CoV), and 2019 novel coronavirus (2019-nCoV), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV has the highest nucleotide sequence identity with SARS-CoV (79.7%) among the six other known pathogenic HCoVs. Specifically, the envelope and nucleocapsid proteins of 2019-nCoV are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV.Using network proximity analyses of drug targets and known HCoV-host interactions in the human protein-protein interactome, we computationally identified 135 putative repurposable drugs for the potential prevention and treatment of HCoVs. In addition, we prioritized 16 potential anti-HCoV repurposable drugs (including melatonin, mercaptopurine, and sirolimus) that were further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines.Finally, we showcased three potential drug combinations (including sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.is the (which was not peer-reviewed) The copyright holder for this preprint .

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“…Recently, we have witnessed numerous applications focusing on domains containing an abundance of unknown data, which require hypothesis verification [5,13,58,59].…”

Section: Machine-learning-based Methods For Predictionmentioning

confidence: 99%

“…Although structure-based and domain-based information have some benefits for exploring HPIs [80,81], they can limit the scope of studied HP-PPIs to specific genres and species, such as HIV-1, HCV, Ebola viruses and so on [7,10,58,[82][83][84]. One dominant reason is the limited amount of available experimentally determined structures and domain information, particularly for bacteria.…”

Section: * Protein Informationmentioning

confidence: 99%

Systematic evaluation of machine learning methods for identifying human–pathogen protein–protein interactions

Chen¹,

Li²,

Wang³

et al. 2020

Briefings in Bioinformatics

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In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein–protein interaction identification, including human–pathogen protein–protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human–bacterium protein–protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on ‘host–pathogen interactions’ whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

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Review of computational methods for virus–host protein interaction prediction: a case study on novel Ebola–human interactions

Cited by 22 publications

References 129 publications

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2

Network-based Drug Repurposing for Human Coronavirus

Systematic evaluation of machine learning methods for identifying human–pathogen protein–protein interactions

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