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XFINGER: a tool for searching and visualising protein fingerprints and patterns
1Department of Biochemistry, University of Leeds, UK.
Computer Applications in the Biosciences : CABIOS
|April 1, 1996
Summary
A new tool integrates pattern and fingerprint database searching for biological sequences. This system enhances motif discovery by allowing simultaneous scans against PROSITE and PRINTS databases, improving sequence analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Structural Biology
Background:
- Traditional sequence analysis tools often use either regular-expression patterns or fingerprint databases separately.
- Existing databases like PROSITE (patterns) and PRINTS (fingerprints) employ different data formats, limiting unified searching capabilities.
- A need exists for a flexible tool capable of searching diverse biological sequence databases efficiently.
Purpose of the Study:
- To develop a versatile tool for searching both pattern and fingerprint databases.
- To enable iterative scanning of sequence alignments using motifs derived from conserved regions.
- To provide enhanced visualization of search results, including 3D structural context.
Main Methods:
- Developed a unified search system for PROSITE patterns and PRINTS fingerprints.
- Implemented iterative database scanning using excised motifs from sequence alignments.
- Integrated text and graphical displays for fingerprint search results.
- Enabled visualization of identified motifs within 3D structural coordinates.
Main Results:
- The system successfully allows query sequences to be scanned against PROSITE, the full PRINTS database, or individual fingerprints.
- Search results are presented simultaneously in text and graphical windows for improved user comprehension.
- Demonstrated the tool's utility by locating low-density lipoprotein-binding (LDL) motifs and leucine-rich repeats in a G-protein-coupled receptor (GPCR).
Conclusions:
- The developed tool offers a flexible and integrated approach to searching pattern and fingerprint databases.
- Enhanced visualization features improve the interpretability of motif identification in biological sequences.
- This system advances sequence analysis by bridging the gap between different database types and incorporating structural information.
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