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Updated: Jul 16, 2026
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
AutoSCOP: automated prediction of SCOP classifications using unique pattern-class mappings
Jan E Gewehr1, Volker Hintermair, Ralf Zimmer
1Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University Munich, Amalienstr. 17, D-80333 Munich, Germany. jan.gewehr@ifi.lmu.de
Bioinformatics (Oxford, England)
|March 24, 2007
Summary
AutoSCOP leverages sequence patterns for accurate protein domain classification, achieving high sensitivity and specificity. This method aids in predicting SCOP classifications and serves as a valuable first step in prediction pipelines.
Area of Science:
- Computational Biology
- Bioinformatics
- Structural Biology
Background:
- Protein sequence annotation relies on motif and hidden Markov model (HMM) databases.
- Predicting protein structure and fold recognition benefits from analyzing sequence patterns.
- The ASTRAL compendium provides a protein domain hierarchy for classification.
Purpose of the Study:
- To develop a method for predicting SCOP classifications using sequence patterns.
- To achieve high-confidence predictions for protein domains, abstaining from uncertain cases.
- To create a robust first-step tool for protein domain classification pipelines.
Main Methods:
- Computed mappings from pattern databases to the ASTRAL protein domain hierarchy.
- Applied AutoSCOP approach for large-scale SCOP classification predictions.
- Integrated AutoSCOP with profile-profile alignments and structure alignment methods.
Main Results:
- AutoSCOP achieves high sensitivity (>93%) and specificity (>98%) in SCOP classification.
- The method demonstrates high accuracy (99%) when combined with structure alignment.
- AutoSCOP correctly abstains from predicting classifications for over 70% of new fold/superfamily domains.
Conclusions:
- AutoSCOP is a valuable tool for protein domain classification, offering high accuracy and reliability.
- The method effectively identifies unique sequence patterns for SCOP classifications.
- AutoSCOP enhances existing prediction pipelines by providing confident initial classifications.
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