ArticleVirology journal2010
Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens.
Article in Virology journal, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- The association between HIV-1 Tat and Vif amino acid sequence variation, inflammation and Trp-Kyn metabolism: an exploratory investigation.BMC infectious diseases · 2024Article
- A predictive approach for host-pathogen interactions using deep learning and protein sequences.Virusdisease · 2024Article
- HIV-1 Tat amino acid residues that influence Tat-TAR binding affinity: a scoping review.BMC infectious diseases · 2023Article
- PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.Database : the journal of biological databases and curation · 2022Article
- In silico prediction of HIV-1-host molecular interactions and their directionality.PLoS computational biology · 2022Article
- Host-pathogen protein-nucleic acid interactions: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
- HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry.Journal of molecular biology · 2020Article
- Maintenance of hPSCs under Xeno-Free and Chemically Defined Culture Conditions.International journal of stem cells · 2019Article
- In silico evidence of de novo interactions between ribosomal and Epstein - Barr virus proteins.BMC molecular and cell biology · 2019Article
- A Bipartite Network Module-Based Project to Predict Pathogen-Host Association.Frontiers in genetics · 2019Article
- Interface-Based Structural Prediction of Novel Host-Pathogen Interactions.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions.Briefings in functional genomics · 2018Review
- Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed.Current bioinformatics · 2018Review
- Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer.Journal of molecular biology · 2017Article
- Preservation affinity in consensus modules among stages of HIV-1 progression.BMC bioinformatics · 2017Article
- A review on host-pathogen interactions: classification and prediction.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2016Review
- Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways.Scientific reports · 2016Article
- A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs.BMC bioinformatics · 2016Article
- A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.Scientific reports · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes.
resultsWe developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence.
conclusionsUnderstanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.
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