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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
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A novel approach for predicting disordered regions in a protein sequence.
Meijing Li1, Seong Beom Cho2, Keun Ho Ryu1
1Database/Bioinformatics Laboratory, Chungbuk National University, Cheongju, Korea.
Osong Public Health and Research Perspectives
|November 8, 2014
Summary
A novel emerging subsequence (ES) mining approach improves protein disordered region prediction accuracy. This method outperforms existing predictors by identifying more varying disordered regions for enhanced protein structure analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Structural Biology
Background:
- Protein disorder prediction is crucial for understanding protein function and structure.
- Existing prediction methods vary in accuracy and the disordered regions they identify.
- Ongoing research aims to improve the efficiency and precision of disordered region prediction.
Purpose of the Study:
- To develop a novel approach for more accurate prediction of disordered protein regions.
- To introduce "emerging subsequence (ES) mining" as a new method for protein sequence analysis.
- To enhance the prediction of protein structures by identifying varying disordered regions.
Main Methods:
- Adapted emerging subsequence (ES) mining to generate subsequences from public protein data.
- Utilized a sliding window approach to search for generated subsequences within protein sequences.
- Calculated scores for overlapping regions based on support and growth rate, comparing target and source classes.
Main Results:
- Disordered and ordered sequence data were sourced from DisProt 6.02 and PDB for training.
- Test data from CASP 9 and CASP 10 were used to evaluate prediction accuracy.
- The proposed method demonstrated higher accuracy rates compared to existing predictors.
Conclusions:
- The emerging subsequence (ES) mining approach offers improved accuracy in predicting disordered protein regions.
- This novel method provides a more efficient and precise tool for structural biology research.
- The findings suggest a significant advancement in the field of protein disorder prediction.
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