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Protein Sequence Comparison Based on Physicochemical Properties and the Position-Feature Energy Matrix
Lulu Yu1, Yusen Zhang2, Ivan Gutman3
1School of Mathematics and Statistics, Shandong University at Weihai, Weihai, 264209, China.
Scientific Reports
|April 11, 2017
Summary
A new position-feature model analyzes protein sequences using amino acid properties and graph energy. This method effectively captures sequence order and dynamics, outperforming existing tools like Clustal W.
Area of Science:
- Biochemistry
- Computational Biology
- Bioinformatics
Background:
- Protein sequence analysis is crucial for understanding biological function.
- Existing methods may not fully capture sequence order and local dynamics.
- Developing novel computational models is essential for advancing protein sequence analysis.
Purpose of the Study:
- To introduce a novel position-feature-based model for protein sequence analysis.
- To incorporate physicochemical properties of amino acids and graph energy into the model.
- To evaluate the model's performance against established methods.
Main Methods:
- Developed a position-feature model utilizing physicochemical properties of 20 amino acids.
- Employed graph energy measure to capture sequence order and local dynamic distributions.
- Generated characteristic B-vectors representing sequence features.
- Applied relative entropy to B-vectors for similarity/dissimilarity measurement.
Main Results:
- The proposed model yields meaningful results in protein sequence analysis.
- Demonstrated effective capture of sequence order and local dynamic distributions.
- Numerical results show competitive or superior performance compared to Clustal W.
Conclusions:
- The novel position-feature model offers a robust approach to protein sequence analysis.
- Physicochemical properties and graph energy are valuable features for sequence representation.
- The method provides a promising alternative for sequence similarity assessment.