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Defining and Computing Optimum RMSD for Gapped and Weighted Multiple-Structure Alignment
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Source IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) archive
Volume 5 ,  Issue 4  (October 2008) table of contents
Pages 525-533  
Year of Publication: 2008
ISSN:1545-5963
Authors
Xueyi Wang  Northwest Nazarene University, Nampa
Jack Snoeyink  UNC Chapel Hill, Chapel Hill
Publisher
IEEE Computer Society Press  Los Alamitos, CA, USA
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DOI Bookmark: 10.1109/TCBB.2008.92

ABSTRACT

Pairwise structure alignment commonly uses root mean square deviation (RMSD) to measure the structural similarity, and methods for optimizing RMSD are well established. We extend RMSD to weighted RMSD for multiple structures. By using multiplicative weights, we show that weighted RMSD for all pairs is the same as weighted RMSD to an average of the structures. Thus, using RMSD or weighted RMSD implies that the average is a consensus structure. Although we show that in general, the two tasks of finding the optimal translations and rotations for minimizing weighted RMSD cannot be separated for multiple structures like they can for pairs, an inherent difficulty and a fact ignored by previous work, we develop a near-linear iterative algorithm to converge weighted RMSD to a local minimum. 10,000 experiments of gapped alignment done on each of 23 protein families from HOMSTRAD (where each structure starts with a random translation and rotation) converge rapidly to the same minimum. Finally we propose a heuristic method to iteratively remove the effect of outliers and find well-aligned positions that determine the structural conserved region by modeling B-factors and deviations from the average positions as weights and iteratively assigning higher weights to better aligned atoms.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Xueyi Wang: colleagues
Jack Snoeyink: colleagues