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A new algorithm for the alignment of multiple protein structures using Monte Carlo optimization
C Guda1, E D Scheeff, P E Bourne
1San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0537, USA.
Abstract:
We have developed a new algorithm for the alignment of multiple protein structures based on a Monte Carlo optimization technique. The algorithm uses pair-wise structural alignments as a starting point. Four different types of moves were designed to generate random changes in the alignment. A distance-based score is calculated for each trial move and moves are accepted or rejected based on the improvement in the alignment score until the alignment is converged. Initial tests on 66 protein structural families show promising results, the score increases by 69% on average. The increase in score is accompanied by an increase (12%) in the number of residue positions incorporated into the alignment. Two specific families, protein kinases and aspartic proteinases were tested and compared against curated alignments from HOMSTRAD and manual alignments. This algorithm has improved the overall number of aligned residues while preserving key catalytic residues. Further refinement of the method and its application to generate multiple alignments for all protein families in the PDB, is currently in progress.
Insights
A new Monte Carlo algorithm enhances multiple protein structure alignment by improving alignment scores by 69% and incorporating more residue positions. This method preserves crucial catalytic residues in protein families.
Area of Science:
- Computational Biology
- Structural Bioinformatics
- Biochemistry
Background:
- Multiple protein structure alignment is crucial for understanding protein function and evolution.
- Existing methods may not fully capture complex structural relationships.
- Accurate alignment is essential for identifying conserved regions and functional sites.
Purpose of the Study:
- To develop and evaluate a novel algorithm for multiple protein structure alignment.
- To improve the accuracy and completeness of protein structural alignments.
- To provide a robust tool for analyzing protein structural families.
Main Methods:
- Development of a Monte Carlo optimization technique for structural alignment.
- Utilizing pair-wise structural alignments as initial input.
- Implementing four distinct move types for random alignment modifications.
- Employing a distance-based scoring system for move acceptance/rejection.
- Convergence criteria based on alignment score improvement.
Main Results:
- Demonstrated significant improvement in alignment scores (69% average increase) across 66 protein structural families.
- Increased the number of aligned residue positions by 12%.
- Successfully aligned protein kinases and aspartic proteinases, outperforming existing curated and manual alignments.
- Preserved key catalytic residues in tested protein families.
Conclusions:
- The developed Monte Carlo algorithm offers a significant advancement in multiple protein structure alignment.
- The method enhances alignment quality by increasing aligned residues and improving scores while maintaining functional site integrity.
- Future work includes refining the algorithm for broader application across the Protein Data Bank (PDB).
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