Thirty-plus functional families from a single motif

Area of Science:

• Proteomics
• Bioinformatics
• Structural Biology

Background:

• WD-repeat proteins constitute a large protein family with diverse functions.
• Identifying functional roles and cellular pathways for these proteins is crucial for understanding biological processes.
• Previous methods for classifying WD-repeat proteins were limited.

Purpose of the Study:

• To develop and apply a novel computational approach for identifying functional subfamilies of WD-repeat-containing proteins.
• To assign known WD-repeat proteins to specific cellular pathways and processes.
• To assess the utility of this approach across different model organisms.

Main Methods:

• Combined traditional sequence similarity searches with a beta-propeller structural context model.
• Utilized Hidden Markov Models (HMMs) to represent the structural context.
• Developed an automated procedure for protein-protein surface similarity analysis.

Main Results:

• Identified over 30 distinct functional subfamilies of WD-repeat proteins.
• Successfully assigned a significant proportion of WD-repeat proteins from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, and Arabidopsis thaliana to these subfamilies.
• Demonstrated the applicability of the method to both completed and partial genomes.

Conclusions:

• The developed computational method effectively classifies WD-repeat proteins into functional subfamilies.
• This approach facilitates the assignment of proteins to cellular pathways, enhancing functional genomics studies.
• The automated procedure offers a scalable solution for analyzing large proteomes.