ArticleBMC systems biology2009
Protein-protein interaction as a predictor of subcellular location.
Article in BMC systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 79 citations in OpenAlex.
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- Prediction of Protein-Protein Interactions with Local Weight-Sharing Mechanism in Deep Learning.BioMed research international · 2020Article
- Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019Article
- Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.International journal of molecular sciences · 2017Article
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- Protonation and pK changes in protein-ligand binding.Quarterly reviews of biophysics · 2013Review
- An ensemble method for predicting subnuclear localizations from primary protein structures.PloS one · 2013Article
- Minimalist ensemble algorithms for genome-wide protein localization prediction.BMC bioinformatics · 2012Article
- Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study.BMC bioinformatics · 2012Article
- Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system.Microbiology and molecular biology reviews : MMBR · 2012Review
- The what, where, how and why of gene ontology--a primer for bioinformaticians.Briefings in bioinformatics · 2011Article
- Removing bias against membrane proteins in interaction networks.BMC systems biology · 2011Article
- In silico modeling of pH-optimum of protein-protein binding.Proteins · 2011Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany biological processes are mediated by dynamic interactions between and among proteins. In order to interact, two proteins must co-occur spatially and temporally. As protein-protein interactions (PPIs) and subcellular location (SCL) are discovered via separate empirical approaches, PPI and SCL annotations are independent and might complement each other in helping us to understand the role of individual proteins in cellular networks. We expect reliable PPI annotations to show that proteins interacting in vivo are co-located in the same cellular compartment. Our goal here is to evaluate the potential of using PPI annotation in determining SCL of proteins in human, mouse, fly and yeast, and to identify and quantify the factors that contribute to this complementarity.
resultsUsing publicly available data, we evaluate the hypothesis that interacting proteins must be co-located within the same subcellular compartment. Based on a large, manually curated PPI dataset, we demonstrate that a substantial proportion of interacting proteins are in fact co-located. We develop an approach to predict the SCL of a protein based on the SCL of its interaction partners, given sufficient confidence in the interaction itself. The frequency of false positive PPIs can be reduced by use of six lines of supporting evidence, three based on type of recorded evidence (empirical approach, multiplicity of databases, and multiplicity of literature citations) and three based on type of biological evidence (inferred biological process, domain-domain interactions, and orthology relationships), with biological evidence more-effective than recorded evidence. Our approach performs better than four existing prediction methods in identifying the SCL of membrane proteins, and as well as or better for soluble proteins.
conclusionUnderstanding cellular systems requires knowledge of the SCL of interacting proteins. We show how PPI data can be used more effectively to yield reliable SCL predictions for both soluble and membrane proteins. Scope exists for further improvement in our understanding of cellular function through consideration of the biological context of molecular interactions.
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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.