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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
A novel randomized iterative strategy for aligning multiple protein sequences
1Analytical Biostatistics Section, National Institutes of Health, Bethesda, MD 20892.
Computer Applications in the Biosciences : CABIOS
|October 1, 1991
Summary
This study introduces an iterative algorithm for multiple protein sequence alignment, reducing computational demands. The method achieves optimal or near-optimal alignments efficiently, making complex analyses feasible.
Area of Science:
- Bioinformatics
- Computational Biology
- Molecular Biology
Background:
- Multiple sequence alignment is computationally intensive, with requirements growing exponentially with sequence number and length.
- Traditional dynamic programming methods face limitations in memory and processing time for large datasets.
Purpose of the Study:
- To develop a more efficient algorithm for multiple protein sequence alignment.
- To reduce the computational complexity associated with optimal alignment of numerous sequences.
Main Methods:
- An iterative algorithm that randomly divides sequence groups into two subgroups.
- Utilizes a Needleman-Wunsch style algorithm for pairwise alignment within subgroups.
- Repeats the pairwise alignment process with different random divisions.
Main Results:
- The iterative algorithm significantly reduces memory and time requirements compared to rigorous dynamic programming.
- Achieves alignments that match or closely approximate optimal solutions on test datasets.
- Implemented in a user-friendly program for interactive sequence alignment on PC-class machines.
Conclusions:
- The proposed iterative strategy offers a practical approach to multiple protein sequence alignment.
- This method overcomes the computational bottlenecks of traditional algorithms.
- Enables efficient and interactive alignment of large sequence datasets.
