Secator: a program for inferring protein subfamilies from phylogenetic trees

Area of Science:

• Bioinformatics
• Computational Biology
• Structural Biology

Background:

• The exponential growth of protein sequence data necessitates efficient methods for subset identification.
• Analyzing large protein families requires robust tools for structural, functional, and evolutionary studies.

Purpose of the Study:

• To develop and validate a novel computational program, Secator, for automatically determining the optimal number of subsets within large protein families.
• To enhance the efficiency and accuracy of protein data analysis for downstream biological investigations.

Main Methods:

• Implementation of an ascending hierarchical clustering method based on a protein sequence multiple alignment distance matrix.
• Development of a novel stopping rule to automatically identify significant dissimilarity values within a phylogenetic tree.
• Validation of clustering quality using a Jackknife statistical study.

Main Results:

• Secator successfully demonstrated its utility across 24 diverse protein families with varying sequence and structural conservation.
• The program accurately identified meaningful subsets, as validated by Jackknife analysis.
• Case studies on Sm proteins and nuclear receptors highlighted the practical accuracy and usefulness of Secator.

Conclusions:

• Secator provides an automated and accurate solution for partitioning large protein families into biologically relevant subsets.
• The program facilitates in-depth structural, functional, and evolutionary analyses by improving data organization and reducing manual effort.