Vol. 23 ECCB 2006, pages e205–e211 doi:10.1093/bioinformatics/btl294

*To whom correspondence should be addressed Summary: We introduce an algorithm that uses the information gained from simultaneous consideration of an entire group of related proteins to create multiple structure alignments. CBA (consistency-based alignment) first harnesses the information contained within regions that are consistently aligned among a set of pairwise superpositions in order to realign pairs of proteins through both global and local refinement methods. It then constructs a multiple alignment that is maximally consistent with the improved pairwise alignments. We validate CBA’s alignments by assessing their accuracy in regions where at least two of the aligned structures contain the same conserved sequence motif. Results: CBA correctly aligns well over 90 % of motif residues in superpositions of proteins belonging to the same family or superfamily, and outperforms a number of previously reported multiple structure alignment algorithms. Availability: CBA is available at

Root mean square deviation (RMSD) is often used to measure the difference between structures. We show mathematically that, for multiple structure alignment, the minimum RMSD (weighted at aligned positions or unweighted) for all pairs is the same as the RMSD to the average of the structures. Thus, using RMSD implies that the average is a consensus structure. We use this property to validate and improve algorithms for multiple structure alignment. In particular, we establish the properties of the average structure, and show that an iterative algorithm proposed by Sutcliffe and co-authors can find it efficiently – – each iteration takes linear time and the number of iterations is small. We explore the residuals after alignment and assign weights to positions to identify aligned cores of structures. Observing this property also calls into question whether global RMSD is the right way to compare multiple protein structures, and guides the search for more local techniques. 1.

Understanding how proteins fold is essential to our quest in discovering how life works at the molecular level. Current computation power enables researchers to produce a huge amount of folding simulation data. Hence there is a pressing need to be able to interpret and identify novel folding features from them. In this paper, we model each folding trajectory as a multi-dimensional curve. We then develop an effective multiple curve comparison (MCC) algorithm, called the enhanced partial order (EPO) algorithm, to extract features from a set of diverse folding trajectories, including both successful and unsuccessful simulation runs. The EPO algorithm addresses several new challenges presented by comparing high dimensional curves coming from folding trajectories. A detailed case study on miniprotein Trp-cage [1] demonstrates that our algorithm can detect similarities at rather low level, and extract biologically meaningful folding events. The EPO algorithm is general and applicable to a wide range of applications. We demonstrate its generality and effectiveness by applying it to aligning multiple protein structures with low similarities.

Extracting multiple structural alignments from pairwise alignments: a comparison of a rigorous and a heuristic approach

Root mean square deviation (RMSD) is often used to measure the difference between structures. We show mathematically that, for multiple structure alignment, the minimum RMSD (weighted at aligned positions or unweighted) for all pairs is the same as the RMSD to the average of the structures. Thus, using RMSD implies that the average is a consensus structure. We use this property to validate and improve algorithms for multiple structure alignment. In particular, we establish the properties of the average structure, and show that an iterative algorithm proposed by Sutcliffe and co-authors can find it efficiently – – each iteration takes linear time and the number of iterations is small. We explore the residuals after alignment and assign weights to positions to identify aligned cores of structures. Observing this property also calls into question whether global RMSD is the right way to compare multiple protein structures, and guides the search for more local techniques. 1.

Protein structures often show similarities to another which would not be seen at the sequence level. Given the coordinates of a protein chain, the SALAMI server at www.zbh.uni-hamburg.de/salami will search the protein data bank and return a set of similar structures without using sequence information. The results page lists the related proteins, details of the sequence and structure similarity and implied sequence alignments. Via a simple structure viewer, one can view superpositions of query and library structures and finally download superimposed coordinates. The alignment method is very tolerant of large gaps and insertions, and tends to produce slightly longer alignments than other similar programs.

The geometric configurations of atoms in protein structures can be viewed as approximate relations among them. Then, finding similar common substructures within a set of protein structures belongs to a new class of problems that generalizes that of finding repeated motifs. The novelty lies in the addition of constraints on the motifs in terms of relations that must hold between pairs of positions of the motifs. We will hence denote them as relational motifs. For this class of problems we present an algorithm that is a suitable extension of the KMR (Karp et al., 1972) paradigm and, in particular, of the KMRC (Soldano et al., 1995) as it uses a degenerate alphabet. Our algorithm contains several improvements with respect to (Soldano et al., 1995) that become especially useful when—as it is required for relational motifs—the inference is made by partially overlapping shorter motifs, rather than concatenating them like in (Karp et al., 1972). The efficiency, correctness and completeness of the algorithm is ensured by several non-trivial properties that are proven in this paper. The algorithm has been applied in the important field of protein common 3D substructure searching. The methods implemented have been tested on several examples of protein families such as serine proteases, globins and cytochromes P450 additionally. The detected motifs have been compared to those found by multiple structural alignments methods. 1 1

a web server for the identification, analysis