ArticlePLoS computational biology2007
Characterization of protein hubs by inferring interacting motifs from protein interactions.
Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 53 citations in OpenAlex.
- Deep Learning in Protein Structural Modeling and Design.Patterns (New York, N.Y.) · 2020Review
- Interaction of Isocitrate Lyase with Proteins Involved in the Energetic Metabolism inJournal of fungi (Basel, Switzerland) · 2020Article
- Analyzing IDPs in Interactomes.Methods in molecular biology (Clifton, N.J.) · 2020Article
- Identifying miltefosine-resistant key genes in protein-protein interactions network and experimental verification in Iranian Leishmania major.Molecular biology reports · 2019Article
- Rosetta FunFolDes - A general framework for the computational design of functional proteins.PLoS computational biology · 2018Article
- Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.Molecules (Basel, Switzerland) · 2018Review
- Hub Protein Controversy: Taking a Closer Look at Plant Stress Response Hubs.Frontiers in plant science · 2018Review
- Functional Analysis of Human Hub Proteins and Their Interactors Involved in the Intrinsic Disorder-Enriched Interactions.International journal of molecular sciences · 2017Article
- Review
- Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies.Malaria journal · 2015Article
- Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.PloS one · 2015Article
- Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.PloS one · 2013Article
- Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB.Bioinformatics (Oxford, England) · 2012Article
- On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking.Protein science : a publication of the Protein Society · 2011Article
- Structure collisions between interacting proteins.PloS one · 2011Article
- Understanding protein evolutionary rate by integrating gene co-expression with protein interactions.BMC systems biology · 2010Article
- Estimation and efficient computation of the true probability of recurrence of short linear protein sequence motifs in unrelated proteins.BMC bioinformatics · 2010Article
- Protein-protein interaction networks: how can a hub protein bind so many different partners?Trends in biochemical sciences · 2009Review
- Human cancer protein-protein interaction network: a structural perspective.PLoS computational biology · 2009Article
- Triangle network motifs predict complexes by complementing high-error interactomes with structural information.BMC bioinformatics · 2009Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
The characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks.
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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.