ArticleMolecular bioSystems2009
Towards inferring time dimensionality in protein-protein interaction networks by integrating structures: the p53 example.
Article in Molecular bioSystems, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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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.
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Who cites it
42 citing papers in PubMed.
- Pioneer in Molecular Biology: Conformational Ensembles in Molecular Recognition, Allostery, and Cell Function.Journal of molecular biology · 2025Review
- p53 Genetics and Biology in Lung Carcinomas: Insights, Implications and Clinical Applications.Biomedicines · 2024Review
- Structural coverage of the human interactome.Briefings in bioinformatics · 2023Article
- Integrating protein copy numbers with interaction networks to quantify stoichiometry in clathrin-mediated endocytosis.Scientific reports · 2022Article
- Cryptochrome deletion in p53 mutant mice enhances apoptotic and anti-tumorigenic responses to UV damage at the transcriptome level.Functional & integrative genomics · 2019Article
- 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients.PLoS computational biology · 2019Article
- Roles of computational modelling in understanding p53 structure, biology, and its therapeutic targeting.Journal of molecular cell biology · 2019Review
- Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.PLoS computational biology · 2019Review
- Precision medicine review: rare driver mutations and their biophysical classification.Biophysical reviews · 2019Review
- Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing.Methods in molecular biology (Clifton, N.J.) · 2019Article
- Enriching Traditional Protein-protein Interaction Networks with Alternative Conformations of Proteins.Scientific reports · 2017Article
- Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information.Methods in molecular biology (Clifton, N.J.) · 2017Article
- DNA-binding protects p53 from interactions with cofactors involved in transcription-independent functions.Nucleic acids research · 2016Article
- Great interactions: How binding incorrect partners can teach us about protein recognition and function.Proteins · 2016Article
- QSLiMFinder: improved short linear motif prediction using specific query protein data.Bioinformatics (Oxford, England) · 2015Article
- Time dynamics of protein complexes in the AD11 transgenic mouse model for Alzheimer's disease like pathology.BMC neuroscience · 2015Article
- Reconstruction of the temporal signaling network in Salmonella-infected human cells.Frontiers in microbiology · 2015Article
- Identifying transcription factor complexes and their roles.Bioinformatics (Oxford, England) · 2014Article
- Modeling protein assemblies in the proteome.Molecular & cellular proteomics : MCP · 2014Article
- Structural bioinformatics of the interactome.Annual review of biophysics · 2014Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Inspection of protein-protein interaction maps illustrates that a hub protein can interact with a very large number of proteins, reaching tens and even hundreds. Since a single protein cannot interact with such a large number of partners at the same time, this presents a challenge: can we figure out which interactions can occur simultaneously and which are mutually excluded? Addressing this question adds a fourth dimension into interaction maps: that of time. Including the time dimension in structural networks is an immense asset; time dimensionality transforms network node-and-edge maps into cellular processes, assisting in the comprehension of cellular pathways and their regulation. While the time dimensionality can be further enhanced by linking protein complexes to time series of mRNA expression data, current robust, network experimental data are lacking. Here we outline how, using structural data, efficient structural comparison algorithms and appropriate datasets and filters can assist in getting an insight into time dimensionality in interaction networks; in predicting which interactions can and cannot co-exist; and in obtaining concrete predictions consistent with experiment. As an example, we present p53-linked processes.
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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.