SCPRED: A Method for Predicting Protein Structural Classes
Author Information
Author(s): Kurgan Lukasz, Cios Krzysztof, Chen Ke
Primary Institution: University of Alberta
Hypothesis
Can we improve the prediction accuracy of protein structural classes for sequences with low identity using a new method?
Conclusion
The SCPRED method can accurately predict structural classes for protein sequences that share low identity with known sequences.
Supporting Evidence
- SCPRED achieved 80.3% accuracy in predicting structural classes.
- The method was tested on datasets of 1673 protein chains.
- SCPRED outperformed over a dozen recent competing methods.
- Features used in SCPRED were based on secondary structure predictions.
- The method can be used as a post-processing filter to improve other classification methods.
Takeaway
This study created a new tool called SCPRED that helps scientists figure out the structure of proteins even when they don't look very similar to other proteins.
Methodology
The SCPRED method uses a support vector machine classifier with a custom-designed feature vector based on secondary structure predictions.
Limitations
The predictions for mixed structural classes (α+β and α/β) are of lower quality compared to all-α and all-β classes.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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