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Evaluating eukaryotic secreted protein prediction.

ABSTRACT:

Background

Improvements in protein sequence annotation and an increase in the number of annotated protein databases has fueled development of an increasing number of software tools to predict secreted proteins. Six software programs capable of high throughput and employing a wide range of prediction methods, SignalP 3.0, SignalP 2.0, TargetP 1.01, PrediSi, Phobius, and ProtComp 6.0, are evaluated.

Results

Prediction accuracies were evaluated using 372 unbiased, eukaryotic, SwissProt protein sequences. TargetP, SignalP 3.0 maximum S-score and SignalP 3.0 D-score were the most accurate single scores (90-91% accurate). The combination of a positive TargetP prediction, SignalP 2.0 maximum Y-score, and SignalP 3.0 maximum S-score increased accuracy by six percent.

Conclusion

Single predictive scores could be highly accurate, but almost all accuracies were slightly less than those reported by program authors. Predictive accuracy could be substantially improved by combining scores from multiple methods into a single composite prediction.

SUBMITTER: Klee EW

PROVIDER: S-EPMC1276785 | biostudies-literature | 2005 Oct

REPOSITORIES: biostudies-literature

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Evaluating eukaryotic secreted protein prediction.

Klee Eric W EW Ellis Lynda B M LB

BMC bioinformatics 20051014

<h4>Background</h4>Improvements in protein sequence annotation and an increase in the number of annotated protein databases has fueled development of an increasing number of software tools to predict secreted proteins. Six software programs capable of high throughput and employing a wide range of prediction methods, SignalP 3.0, SignalP 2.0, TargetP 1.01, PrediSi, Phobius, and ProtComp 6.0, are evaluated.<h4>Results</h4>Prediction accuracies were evaluated using 372 unbiased, eukaryotic, SwissPr ...[more]

PMID: 16225690

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