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Updated: Jul 17, 2026
08:57
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
Using phylogeny to improve genome-wide distant homology recognition
Sanne Abeln1, Carlo Teubner, Charlotte M Deane
1Department of Statistics, University of Oxford, Oxford, United Kingdom.
Plos Computational Biology
|January 24, 2007
Summary
We developed a method to detect false positives in genome-wide protein structure assignments. Isolated occurrences in protein phylogenies indicate unreliable assignments, improving structural prediction accuracy.
Area of Science:
- Bioinformatics
- Structural Biology
- Genomics
Background:
- The number of protein sequences vastly exceeds known structures, creating a need for automated structural assignment.
- Genome-wide fold-recognition methods aim to assign structures to all genes in completed genomes.
- Identifying false positives in these assignments is crucial for data accuracy.
Purpose of the Study:
- To develop a method for detecting false positives in genome-wide structural assignments.
- To identify patterns indicative of unreliable protein structure predictions.
- To enhance the quality of automated structural assignment methods.
Main Methods:
- Developed a method to identify "isolated occurrences" within protein phylogenies.
- Applied the method to analyze assignments from SUPERFAMILY and PSI-BLAST on 150 genomes.
- Utilized a parsimony algorithm to detect gains at leaf level in the phylogeny.
Main Results:
- Isolated occurrences correlate with high e-values, particularly above 10^-8 (SUPERFAMILY) and 10^-4 (PSI-BLAST).
- The method successfully predicted false positives, confirmed by independent tests.
- Fold-recognition accuracy may depend on protein secondary structure content and sequence length.
Conclusions:
- Structural occurrence patterns in phylogenies can identify false positives from fold-recognition methods.
- Isolated occurrences within a genome phylogeny suggest less reliable structural assignments.
- This method offers an independent approach to assess and improve genome-wide fold assignment quality.
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