ArticleBMC pharmacology2007
An integrative in silico approach for discovering candidates for drug-targetable protein-protein interactions in interactome data.
Article in BMC pharmacology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective.Frontiers in molecular neuroscience · 2017Article
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- IRView: a database and viewer for protein interacting regions.Bioinformatics (Oxford, England) · 2012Article
- Dr. PIAS 2.0: an update of a database of predicted druggable protein-protein interactions.Database : the journal of biological databases and curation · 2012Article
- Disruption of cell wall fatty acid biosynthesis in Mycobacterium tuberculosis using a graph theoretic approach.Theoretical biology & medical modelling · 2011Article
- Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions.BMC bioinformatics · 2011Article
- Assessing the druggability of protein-protein interactions by a supervised machine-learning method.BMC bioinformatics · 2009Article
- Candidate gene identification approach: progress and challenges.International journal of biological sciences · 2007Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein-protein interactions (PPIs) are challenging but attractive targets for small chemical drugs. Whole PPIs, called the 'interactome', have been emerged in several organisms, including human, based on the recent development of high-throughput screening (HTS) technologies. Individual PPIs have been targeted by small drug-like chemicals (SDCs), however, interactome data have not been fully utilized for exploring drug targets due to the lack of comprehensive methodology for utilizing these data. Here we propose an integrative in silico approach for discovering candidates for drug-targetable PPIs in interactome data.
resultsOur novel in silico screening system comprises three independent assessment procedures: i) detection of protein domains responsible for PPIs, ii) finding SDC-binding pockets on protein surfaces, and iii) evaluating similarities in the assignment of Gene Ontology (GO) terms between specific partner proteins. We discovered six candidates for drug-targetable PPIs by applying our in silico approach to original human PPI data composed of 770 binary interactions produced by our HTS yeast two-hybrid (HTS-Y2H) assays. Among them, we further examined two candidates, RXRA/NRIP1 and CDK2/CDKN1A, with respect to their biological roles, PPI network around each candidate, and tertiary structures of the interacting domains.
conclusionAn integrative in silico approach for discovering candidates for drug-targetable PPIs was applied to original human PPIs data. The system excludes false positive interactions and selects reliable PPIs as drug targets. Its effectiveness was demonstrated by the discovery of the six promising candidate target PPIs. Inhibition or stabilization of the two interactions may have potential therapeutic effects against human diseases.
Indexed as
Identifiers
What OpenQuestion holds
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.