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07:49
Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
Published on: August 16, 2017
Aligning protein sequence and analysing substitution pattern using a class-specific matrix
Hai Song Xu1, Wen Ke Ren, Xiao Hui Liu
1College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
Journal of Biosciences
|August 7, 2010
Summary
Developing new protein sequence alignment score matrices tailored to specific structural classes significantly improves accuracy, especially in challenging
Area of Science:
- Biochemistry and Structural Biology
- Bioinformatics and Computational Biology
Background:
- Protein sequence alignment is crucial in biological research.
- Alignment in the 'twilight zone' (low sequence identity) remains a challenge.
- Advances in protein structure resolution necessitate updated alignment methods.
Purpose of the Study:
- To develop and evaluate novel, structure-based, class-specific amino acid score matrices for protein sequence alignment.
- To assess the performance of these matrices against existing generalized and structure-derived matrices.
- To investigate the utility of class-specific matrices in improving homology detection.
Main Methods:
- Construction of three structural class-specific score matrices (all-alpha, all-beta, alpha/beta) using structure alignment of low-identity proteins.
- Comparative alignment performance tests against generalized matrices (BLOSUM30, BLOSUM60, Gonnet250) and a structure-derived matrix (HSDM).
- Optimization of gap penalties to enhance alignment performance.
Main Results:
- Class-specific score matrices demonstrated significantly superior alignment performance compared to HSDM, BLOSUM30, BLOSUM60, and Gonnet250.
- Optimized gap penalties further improved alignment accuracy.
- Distinct amino acid substitution patterns were identified across different protein structural classes.
Conclusions:
- Protein structural class-specific amino acid score matrices are essential for accurate sequence alignment, particularly for low-identity proteins.
- These specialized matrices outperform generalized matrices and can enhance homology detection through profile construction.
- Future alignment strategies should incorporate structural class information for improved biological insights.
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