A new progressive-iterative algorithm for multiple structure alignment

Lupyan, Dmitry; Leo‐Macias, Alejandra; Ortíz, Ángel R.

doi:10.1093/bioinformatics/bti527

Cited by 131 publications

(142 citation statements)

References 45 publications

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“…The performance of our approach (mulPBA) was extensively assessed against the alignments in HOMSTRAD [50], the reference dataset used to assess the performance of different alignment methods [18,31]. The results of this comparison clearly show that the alignments are improved significantly with mulPBA approach.…”

Section: Resultsmentioning

confidence: 96%

“…The servers used for comparison involve both rigid-body alignment tools and flexible MSTAs. As highlighted in Table 1, for 8 out of the 12 families mulPBA gives the best quality alignments when compared to the rigid-body alignment methods SALIGN [57], MAMMOTH [31] and MASS [10]. Both SALIGN and MASS had the top scores for the alignment of two families.…”

Section: Resultsmentioning

confidence: 97%

“…STACCATO [59] was used to generate sequence alignments corresponding to the structure superposition obtained with MULTIPROT. STACCATO considers amino acid substitution weights while generating sequence alignments based on superposed structures and it is often used to obtain the equivalences from MULTIPROT alignments [31]. With respect to MULTIPROT, the average gain based on the different scores was about 86.6% (N rms : 82.4, N dist : 87.3, N 3.5 : 90.1) ( Figure 2B).…”

Section: Resultsmentioning

confidence: 99%

“…This measure is termed as N dist . A fixed distance cut-off of 4Å was used to assess MAMMOTH-MULT [31].…”

Section: Multiple Alignment Scoresmentioning

confidence: 99%

“…A guide tree generated based on a relative similarity measure determines the order in which structures are added to the alignment. To reduce the bias dependant on this order of fit, iterative refinements are carried out [14,16,17,31]. Consistency of residue equivalences among the pairwise alignments is also learned to refine the multiple alignment [12,32].…”

Section: Introductionmentioning

confidence: 99%

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Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

2012

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Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string.A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition.Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.3

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

“…This measure is termed as N dist . A fixed distance cut-off of 4Å was used to assess MAMMOTH-MULT [31].…”

Section: Multiple Alignment Scoresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

2012

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Comparative Protein Structure Modeling Using MODELLER

Webb

Šali

2014

CP in Bioinformatics

903

474

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Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

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Structure Comparison and Alignment

Bourne

Shindyalov

2003

Structural Bioinformatics

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Since the 1970s, after the seminal work of Rossmann and Argos (1978) comparing binding sites of known enzyme structures, the comparison and alignment of protein structures has come to be a fundamental and widely used task in computational structure biology. Three main steps are needed for comparing two protein structures: first, the detection of their common similarities; second, the alignment of the structures based on such similarities; and third, a statistical measure of the similarity. Considering the first two steps, structure comparison refers to the analysis of the similarities and differences between two or more structures, and structure alignment refers to establishing which amino acid residues are equivalent between them. The majority of commonly used methods do a reasonably good job in recognizing obvious similarities between protein structures. However, the alignment of two or more structures is a more difficult task, and its accuracy may depend on the method or program used as well as what the user is trying to accomplish, which will be discussed subsequently. All programs that are briefly described in this chapter perform both steps and are commonly known as protein structure alignment methods.It is also important to immediately clear up any confusion between structure alignment and structure superposition since such terms are often interchanged in the literature. As mentioned above, structure alignment tries to identify the equivalences between pairs of amino acid residues from the structures to superpose, while structure superposition requires the previous knowledge of such equivalences. Thus, structure superposition tries to solve the simpler geometrical task of minimizing the distance between already known equivalent residues of the superimposed structures by finding a transformation that produces either the lowest root-mean-square deviation (RMSD) or the maximal equivalences within an

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A new progressive-iterative algorithm for multiple structure alignment

Abstract: http://ub.cbm.uam.es/mammoth/mult.

Cited by 131 publications

References 45 publications

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Comparative Protein Structure Modeling Using MODELLER

Structure Comparison and Alignment

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