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Statistical significance of threading scores
Afshin Fayyaz Movaghar1, Guillaume Launay, Sophie Schbath
1Mathématique, Informatique, et Génome, INRA, Jouy-en-Josas, France.
Summary
We developed a method to assess protein threading score significance. By comparing scores to a Weibull distribution of random sequences, small p-values indicate significant results.
Area of Science:
- Computational Biology
- Bioinformatics
- Structural Biology
Background:
- Assessing the statistical significance of protein threading scores is crucial for accurately predicting protein structures.
- Current methods may lack a robust statistical foundation for evaluating sequence-structure alignment significance.
Purpose of the Study:
- To introduce a general and statistically rigorous method for assessing protein threading score significance.
- To establish a theoretical framework for the distribution of threading scores from random sequences.
Main Methods:
- Developed a method comparing protein sequence threading scores against a reference distribution.
- Modeled the reference distribution using a Weibull extreme value distribution based on protein contact map properties.
- Estimated distribution parameters offline using simulated sequences and interpolated for query-specific lengths.
Main Results:
- Demonstrated that threading scores of random sequences follow a Weibull extreme value distribution.
- Showcased that distribution parameters are dependent on threading method, structure, and sequence length.
- Enabled rapid computation of p-values for assessing score significance.
Conclusions:
- The proposed method provides a statistically sound approach for evaluating protein threading significance.
- The Weibull distribution offers a reliable reference for random sequence alignments.
- This facilitates more accurate protein structure prediction and analysis.
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