Abstract
The three-dimensional structure of a protein molecule provides significant insight into its biological function. Structural alignment of proteins is an important and widely performed task in the analysis of protein structures, whereby functionally and evolutionarily important segments are identified. However, structural alignment is a computationally difficult problem and a large number of heuristics introduced to solve it do not agree on their results. Consequently, there is no widely accepted solution to the structure alignment problem. In this study, we present a meta-analysis approach to generate a re- optimized, best-of-all result using the alignments generated from several popular methods. Evaluations of the methods on a large set of benchmark pairwise alignments indicate that TMalign (Template Modeling Alignment) provides superior alignments (except for RMSD, root mean square deviation), compared to other methods we have surveyed. Smolign (Spatial Motifs Based Multiple Protein Structure Alignment) provides smaller cores than other methods with best RMSD values. The re-optimization of the alignments using TM-align’s optimization method does not alter the relative performance of the methods. Additionally, visualization approaches to delineate the relationships of the alignment methods have been performed and their results provided
Highlights
Fold comparison software is important by itself for a number of reasons, not the least of which is the determination of function
The results substantiate the suggestion that the newer structural alignment programs are better than the older ones
If not for the extremely low RMSD that Smolign achieves, the fact that it by far possesses the lowest N-align ratio is a negative facet that does not act in its favor
Summary
Fold comparison software is important by itself for a number of reasons, not the least of which is the determination of function. If a protein’s folds have already been determined and its function is known, a new protein with similar folds should have similar function. Making new protein families may be possible with fold comparison software. Given a particular set of proteins and their structures, one can cluster them in families based on their structural similarities. Such a classification may take some time to determine accurately for all potential families, but is possible in theory, by defining a consensus structure for each family, solving an MSTA (multiple structural alignment) problem
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