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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
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DASP3: identification of protein sequences belonging to functionally relevant groups
Janelle B Leuthaeuser1,2, John H Morris3, Angela F Harper4
1Molecular Genetics and Genomics Program, Wake Forest University, Winston-Salem, NC, 27106, USA. jleuthae@gmail.edu.
BMC Bioinformatics
|November 13, 2016
Summary
Automated protein clustering is essential. Enhancements to the Deacon Active Site Profiler (DASP) tool, resulting in DASP3, significantly improve the efficiency and accuracy of grouping proteins by function.
Area of Science:
- Bioinformatics
- Computational Biology
- Structural Biology
Background:
- Automated protein clustering is crucial for functional annotation as sequence databases grow.
- Experimental methods for protein function determination cannot keep pace with sequence identification.
- The Deacon Active Site Profiler (DASP) was developed to identify proteins with similar active sites.
Purpose of the Study:
- To improve the automatable clustering of proteins into functionally relevant groups.
- To address algorithmic limitations identified in the original DASP tool.
Main Methods:
- Implemented six algorithmic enhancements in two stages, creating DASP2 and DASP3.
- Validated DASP3's performance against previous versions and manual curation for protein clustering.
Main Results:
- DASP3 demonstrated significantly improved accuracy and efficiency in clustering protein sequences.
- DASP3 achieved greater score separation between true and false positives compared to earlier versions.
- DASP3 showed comparable performance to previous versions in clustering protein structures but superior efficiency for sequence clustering.
Conclusions:
- Algorithmic enhancements to DASP led to improved efficiency and accuracy in identifying proteins with similar active site features.
- DASP3 offers incremental improvements for structure and initial sequence database searches.
- DASP3 shows significant advantages for iterative sequence searches, making it suitable for automated protein functional clustering.
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