ArticlemSystems
Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework.
Article in mSystems. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05683353 (Mechanism and Immune Function Analysis of SARS-CoV-2 Infection in Hematologic Tumors and Discovery of Potential Drug Targets), which is not on this map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Mechanism and Immune Function Analysis of SARS-CoV-2 Infection in Hematologic Tumors and Discovery of Potential Drug Targets
Who cites it
29 citing papers in PubMed, 61 citations in OpenAlex.
- Unraveling the Network Signatures of Oncogenicity in Virus-Human Protein-Protein Interactions.Entropy (Basel, Switzerland) · 2025Article
- Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes.Genomics, proteomics & bioinformatics · 2024Review
- HBFormer: a single-stream framework based on hybrid attention mechanism for identification of human-virus protein-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations.mSystems · 2024Article
- Machine learning-based models for the prediction of breast cancer recurrence risk.BMC medical informatics and decision making · 2023Article
- Determining human-coronavirus protein-protein interaction using machine intelligence.Medicine in novel technology and devices · 2023Article
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- The impact of the suppression of highly connected protein interactions on the corona virus infection.Scientific reports · 2022Article
- MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.Frontiers in medicine · 2022Article
- LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor.PloS one · 2022Article
- LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.Briefings in bioinformatics · 2021Article
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
- Molecular scaffolds from mother nature as possible lead compounds in drug design and discovery against coronaviruses: A landscape analysis of published literature and molecular docking studies.Microbial pathogenesis · 2021Review
- Rabies Infection: An Overview of Lyssavirus-Host Protein Interactions.Iranian biomedical journal · 2021Review
- Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding.International journal of environmental research and public health · 2021Article
- SARSCOVIDB-A New Platform for the Analysis of the Molecular Impact of SARS-CoV-2 Viral Infection.ACS omega · 2021Article
- Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19.Aging and disease · 2021Article
- Prediction of the Effects of Variants and Differential Expression of Key Host GenesBioinformatics and biology insights · 2021Article
- Targeting hub genes and pathways of innate immune response in COVID-19: A network biology perspective.International journal of biological macromolecules · 2020Article
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational analysis of human-virus protein-protein interaction (PPI) data is an effective way toward systems understanding the molecular mechanism of viral infection. Previous work has mainly focused on characterizing the global properties of viral targets within the entire human PPI network. In comparison, how viruses manipulate host local networks (e.g., human protein complexes) has been rarely addressed from a computational perspective. By mainly integrating information about human-virus PPIs, human protein complexes, and gene expression profiles, we performed a large-scale analysis of virally targeted complexes (VTCs) related to five common human-pathogenic viruses, including influenza A virus subtype H1N1, human immunodeficiency virus type 1, Epstein-Barr virus, human papillomavirus, and hepatitis C virus. We found that viral targets are enriched within human protein complexes. We observed in the context of VTCs that viral targets tended to have a high within-complex degree and to be scaffold and housekeeping proteins. Complexes that are essential for viral propagation were simultaneously targeted by multiple viruses. We characterized the periodic expression patterns of VTCs and provided the corresponding candidates that may be involved in the manipulation of the host cell cycle. As a potential application of the current analysis, we proposed a VTC-based antiviral drug target discovery strategy. Finally, we developed an online VTC-related platform known as VTcomplex (http://zzdlab.com/vtcomplex/index.php or http://systbio.cau.edu.cn/vtcomplex/index.php). We hope that the current analysis can provide new insights into the global landscape of human-virus PPIs at the VTC level and that the developed VTcomplex will become a vital resource for the community.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.