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Probabilistic description of protein alignments for sequences and structures.
Ryotaro Koike1, Kengo Kinoshita, Akinori Kidera
1Department of Chemistry, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan.
Proteins
|May 27, 2004
Summary
This study introduces a probabilistic alignment method for protein sequences and structures. The method accurately identifies circular permutations by systematically representing sub-optimal alignments.
Area of Science:
- Bioinformatics
- Computational Biology
- Structural Biology
Background:
- Multiple optimal alignments exist for protein sequence and structure comparisons.
- Sub-optimal alignments are challenging to represent systematically.
Purpose of the Study:
- To develop a method for generating probabilistic alignments of protein sequences and structures.
- To systematically represent and analyze sub-optimal alignments.
Main Methods:
- Probabilistic alignment generation for sequences and structures.
- Incorporation of periodic boundary conditions to mitigate entropy artifacts.
- Application of mean-field approximation for environmental effects in structure comparison.
Main Results:
- Developed a consistent probabilistic framework for sequence and structure alignments.
- Demonstrated the method's effectiveness on proteins with internal symmetry (TIM-barrel and beta-trefoil folds).
- Showcased that highest probability alignments correspond to circular permutations.
Conclusions:
- The probabilistic alignment method provides a robust way to handle sub-optimal alignments.
- The findings highlight the utility of probabilistic alignments in identifying complex protein relationships like circular permutation.