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Updated: May 21, 2026
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Minimum message length inference of secondary structure from protein coordinate data.
Arun S Konagurthu1, Arthur M Lesk, Lloyd Allison
1Clayton School of Information Technology, Monash University, Clayton VIC 3800, Australia. arun.konagurthu@monash.edu
Bioinformatics (Oxford, England)
|June 13, 2012
Summary
A new Bayesian inference method provides reliable protein secondary structure assignments, even for low-resolution data. This approach offers a mathematically rigorous definition for secondary structure prediction.
Area of Science:
- Structural Biology
- Computational Biology
- Biophysics
Background:
- Protein secondary structure is crucial for folding, function, and architecture.
- Existing methods for secondary structure assignment lack mathematical rigor and consistency.
- Comparative studies reveal significant disagreements among current assignment algorithms.
Purpose of the Study:
- To develop a mathematically rigorous and consistent method for protein secondary structure assignment.
- To introduce a novel approach based on Bayesian inference for improved accuracy.
Main Methods:
- Utilized the Bayesian method of minimum message length (MML) inference.
- Treated secondary structure assignments as hypotheses to explain coordinate data.
- Developed a program, SST, implementing the MML approach.
Main Results:
- The SST program provides reliable secondary structure assignments.
- The method performs well even on low-resolution protein structures.
- Demonstrated superiority over established methods like DSSP and STRIDE in evaluations.
Conclusions:
- The Bayesian MML inference offers a robust framework for protein secondary structure assignment.
- SST provides a consistent and mathematically sound alternative for structural analysis.
- This method enhances the reliability of secondary structure prediction, particularly for challenging datasets.
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