ArticlePeerJ2014
DoReMi: context-based prioritization of linear motif matches.
Article in PeerJ, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- PSSMSearch: a server for modeling, visualization, proteome-wide discovery and annotation of protein motif specificity determinants.Nucleic acids research · 2018Article
- Novel linear motif filtering protocol reveals the role of the LC8 dynein light chain in the Hippo pathway.PLoS computational biology · 2017Article
- SLiMSearch: a framework for proteome-wide discovery and annotation of functional modules in intrinsically disordered regions.Nucleic acids research · 2017Article
- A bioinformatics pipeline to search functional motifs within whole-proteome data: a case study of poxviruses.Virus genes · 2017Article
- High-throughput methods for identification of protein-protein interactions involving short linear motifs.Cell communication and signaling : CCS · 2015Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many protein domains bind to short peptide sequences, called linear motifs. Data on their sequence specificities is sparse, which is why biologists usually resort to basic pattern searches to identify new putative binding sites for experimental follow-up. Most motifs have poor specificity and prioritization of the matches is thus crucial when scanning a full proteome with a pattern. Here we present a generic method to prioritize motif occurrence predictions by using cellular contextual information. We take 2 parameters as input: the motif occurrences and one or more of the interacting domains. The potential hits are ranked based on how strongly the context network associates them with a protein containing one of the specified domains, which leads to an increased predictive performance. The method is available through a web interface at doremi.jensenlab.org, which allows for an easy application of the method. We show that this approach leads to improved predictions of binding partners for PDZ domains and the SUMO binding domain. This is consistent with the earlier observation that coupling sequence motifs with network information improves kinase-specific substrate predictions.
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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.