ELM--the database of eukaryotic linear motifs

Area of Science:

• Molecular Biology
• Bioinformatics
• Cellular Regulation

Background:

• Linear motifs are short, adaptable protein sequences mediating crucial cellular functions like signaling and localization.
• Discovering these motifs is challenging due to experimental and computational difficulties.
• Understanding linear motifs is vital for comprehending cellular regulatory mechanisms.

Purpose of the Study:

• To enhance the Eukaryotic Linear Motif (ELM) resource for improved discovery and accessibility of linear motifs.
• To expand the ELM database with new motif instances and functional classes.
• To refine motif discovery tools by incorporating conservation and structural attributes.

Main Methods:

• Curating and updating the ELM database with experimentally validated motif instances and functional classes.
• Developing and integrating new features into the ELM resource, including full-text search and an enhanced user interface.
• Incorporating conservation and structural data into the motif discovery algorithms.

Main Results:

• The ELM database now contains 1800 annotated motif instances across 170 functional classes, including ~500 new instances and 24 new classes.
• Significant improvements in database accessibility through enhanced interface, search, and download functionalities.
• The motif discovery tool now includes conservation and structural attributes to help identify biologically relevant motifs.

Conclusions:

• The updated ELM resource provides a more comprehensive and accessible platform for studying linear motifs.
• Enhanced discovery tools facilitate the identification of functional linear motifs, aiding research in cell signaling, protein turnover, and localization.
• The ELM resource continues to be a valuable community tool for advancing the understanding of protein regulatory elements.