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iELM--a web server to explore short linear motif-mediated interactions.
Robert J Weatheritt1, Peter Jehl, Holger Dinkel
1Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
Nucleic Acids Research
|May 29, 2012
Summary
The iELM web server predicts the function and molecular interfaces of protein-protein interactions (PPIs) mediated by short linear motifs (SLiMs). This resource aids in understanding PPIs for proteomic analysis and network interpretation.
Area of Science:
- Bioinformatics
- Computational Biology
- Molecular Biology
Background:
- Protein-protein interactions (PPIs) are crucial for cellular functions, yet their functional roles are often unknown.
- Short linear motifs (SLiMs) mediate a significant subset of PPIs, but their functional implications require further elucidation.
Purpose of the Study:
- To introduce the interactions of Eukaryotic Linear Motif (iELM) web server, a resource for predicting the function and interface of SLiM-mediated PPIs.
- To provide a tool for exploring annotated and user-generated PPI networks, focusing on SLiM-driven interactions.
Main Methods:
- The iELM prediction algorithm utilizes annotated SLiM classes from the Eukaryotic Linear Motif (ELM) resource.
- Integration of data from UniProt and Phospho.ELM resources to display protein modular architecture.
- Graphical and tabular presentation of predicted PPI networks and molecular interfaces.
Main Results:
- iELM predicts the function and positional interface of SLiM-mediated PPIs.
- Functional details can infer roles in complex formation or degradation, aiding proteomic analysis.
- Predicted molecular interfaces are visualized graphically and in searchable tables.
Conclusions:
- The iELM server enhances the understanding of SLiM-mediated PPIs by providing functional and interface predictions.
- It serves as a valuable tool for researchers in proteomics and systems biology.
- The server supports both single protein queries and large-scale proteomic data analysis.
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