A new algorithm for the alignment of multiple protein structures using Monte Carlo optimization

• Published in: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing p. 275
• Main Authors: Guda, C, Scheeff, E D, Bourne, P E, Shindyalov, I N
• Format: Journal Article
• Language: English
• Published: United States 2001
• Subjects: Algorithms; Amino Acid Sequence; Aspartic Acid Endopeptidases - chemistry; Aspartic Acid Endopeptidases - genetics; Databases, Factual; Molecular Sequence Data; Monte Carlo Method; Protein Kinases - chemistry; Protein Kinases - genetics; Proteins - chemistry; Proteins - genetics; Sequence Alignment - statistics & numerical data; Sequence Homology, Amino Acid
• ISSN: 2335-6936

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.