Bioinformatics Advance Access published online on March 22, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm089
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AutoSCOP: Automated Prediction of SCOP Classifications using Unique Pattern-Class Mappings
Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University Munich, Amalienstr. 17, D-80333 Munich, Germany
*To whom correspondence should be addressed. Jan E. Gewehr, E-mail: jan.gewehr{at}ifi.lmu.de
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Abstract |
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Motivation: The sequence patterns contained in the available motif and HMM databases are a valuable source of information for protein sequence annotation. For structure prediction and fold recognition purposes, we computed mappings from such pattern databases to the protein domain hierarchy given by the ASTRAL compendium and applied them to the prediction of SCOP classifications. Our aim is to make highly confident predictions also for non-trivial cases if possible and abstain from a prediction otherwise, and thus to provide a method that can be used as a first step in a pipeline of prediction methods. We describe two successful examples for such pipelines. With the AutoSCOP approach it is possible to make predictions in a large-scale manner for many domains of the available sequences in the well-known protein sequence databases.
Results: AutoSCOP computes unique sequence patterns and pattern combinations for SCOP classifications. For instance, we assign a SCOP superfamily to a pattern found in its members whenever the pattern does not occur in any other SCOP superfamily. Especially on the fold and superfamily level, our method achieves both high sensitivity (above 93%) and high specificity (above 98%) on the difference set between two ASTRAL versions, due to being able to abstain from unreliable predictions. Further, on a harder test set filtered at low sequence identity, the combination with profile-profile alignments improves accuracy and performs comparably even to structure alignment methods. Integrating our method with structure alignment, we are able to achieve an accuracy of 99% on SCOP fold classifications on this set. In an analysis of false assignments of domains from new folds/superfamilies/families to existing SCOP classifications, Auto- SCOP correctly abstains for more than 70% of the domains belonging to new folds and superfamilies, and more than 80% of the domains belonging to new families. These findings show that our approach is a useful additional filter for SCOP classification prediction of protein domains in combination with well-known methods such as profile-profile alignment.
Availability: A web server where users can input their domain sequences is available at http://www.bio.ifi.lmu.de/autoscop.
Associate Editor: Prof. Anna Tramontano
Received on November 6, 2006; revised on February 28, 2007; accepted on March 5, 2007
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  F. Birzele, J. E. Gewehr, and R. Zimmer AutoPSI: a database for automatic structural classification of protein sequences and structures Nucleic Acids Res., January 11, 2008; 36(suppl_1): D398 - D401. [Abstract] [Full Text] [PDF]
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