CONRAD: a method for identification of variable and conserved regions within proteins by scale-space filtering

Herrmann, G.; Schön, Annemarie; Brack‐Werner, Ruth; Werner, Thomas

doi:10.1093/bioinformatics/12.3.197

Cited by 3 publications

(3 citation statements)

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“…In addition, a number of other methods are able to distinguish between conserved and variable regions of an alignment (Pesole et al 1992;Herrmann et al 1996; Thompson et al 1997), but they have not been specifically devised for efficiency in phylogenetic analysis and may not be able to distinguish important informative positions.…”

Section: Introductionmentioning

confidence: 99%

Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis

Castresana¹

2000

Molecular Biology and Evolution

9,069

6,134

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The use of some multiple-sequence alignments in phylogenetic analysis, particularly those that are not very well conserved, requires the elimination of poorly aligned positions and divergent regions, since they may not be homologous or may have been saturated by multiple substitutions. A computerized method that eliminates such positions and at the same time tries to minimize the loss of informative sites is presented here. The method is based on the selection of blocks of positions that fulfill a simple set of requirements with respect to the number of contiguous conserved positions, lack of gaps, and high conservation of flanking positions, making the final alignment more suitable for phylogenetic analysis. To illustrate the efficiency of this method, alignments of 10 mitochondrial proteins from several completely sequenced mitochondrial genomes belonging to diverse eukaryotes were used as examples. The percentages of removed positions were higher in the most divergent alignments. After removing divergent segments, the amino acid composition of the different sequences was more uniform, and pairwise distances became much smaller. Phylogenetic trees show that topologies can be different after removing conserved blocks, particularly when there are several poorly resolved nodes. Strong support was found for the grouping of animals and fungi but not for the position of more basal eukaryotes. The use of a computerized method such as the one presented here reduces to a certain extent the necessity of manually editing multiple alignments, makes the automation of phylogenetic analysis of large data sets feasible, and facilitates the reproduction of the final alignment by other researchers.

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Section: Introductionmentioning

confidence: 99%

Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis

Castresana¹

2000

Molecular Biology and Evolution

9,069

6,134

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“…Proteins have some regions that, due to their functional or structural importance, are very well conserved, whereas other regions evolve faster both in terms of nucleotide substitutions and insertions or deletions (Henikoff and Henikoff, 1994;Herrmann et al, 1996;Pesole et al, 1992). That is, evolutionary rate heterogeneity affects to whole regions in addition to single positions.…”

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confidence: 99%

Improvement of Phylogenies after Removing Divergent and Ambiguously Aligned Blocks from Protein Sequence Alignments

2007

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Alignment quality may have as much impact on phylogenetic reconstruction as the phylogenetic methods used. Not only the alignment algorithm, but also the method used to deal with the most problematic alignment regions, may have a critical effect on the final tree. Although some authors remove such problematic regions, either manually or using automatic methods, in order to improve phylogenetic performance, others prefer to keep such regions to avoid losing any information. Our aim in the present work was to examine whether phylogenetic reconstruction improves after alignment cleaning or not. Using simulated protein alignments with gaps, we tested the relative performance in diverse phylogenetic analyses of the whole alignments versus the alignments with problematic regions removed with our previously developed Gblocks program. We also tested the performance of more or less stringent conditions in the selection of blocks. Alignments constructed with different alignment methods (ClustalW, Mafft, and Probcons) were used to estimate phylogenetic trees by maximum likelihood, neighbor joining, and parsimony. We show that, in most alignment conditions, and for alignments that are not too short, removal of blocks leads to better trees. That is, despite losing some information, there is an increase in the actual phylogenetic signal. Overall, the best trees are obtained by maximum-likelihood reconstruction of alignments cleaned by Gblocks. In general, a relaxed selection of blocks is better for short alignment, whereas a stringent selection is more adequate for longer ones. Finally, we show that cleaned alignments produce better topologies although, paradoxically, with lower bootstrap. This indicates that divergent and problematic alignment regions may lead, when present, to apparently better supported although, in fact, more biased topologies.

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“…However, as far as I am aware, there are surprisingly few published methods for automatic identification of variable and conserved regions within a multiple sequence alignment. One of the first seems to be the approach of Herrmann et al (1996), using algorithmic scale‐space filtering. Stojanovic et al (1999) describe and compare five methods for the purpose; nevertheless, all five suffer from the disadvantage of requiring the user to specify arbitrary values for a series of parameters, e.g.…”

Section: Earlier Methods To Partition Sequence Alignmentsmentioning

confidence: 99%

Definition of the tempo of sequence diversity across an alignment and automatic identification of sequence motifs: Application to protein homologous families and superfamilies

May¹

2002

Protein Science

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It is often possible to identify sequence motifs that characterize a protein family in terms of its fold and/or function from aligned protein sequences. Such motifs can be used to search for new family members. Partitioning of sequence alignments into regions of similar amino acid variability is usually done by hand. Here, I present a completely automatic method for this purpose: one that is guaranteed to produce globally optimal solutions at all levels of partition granularity. The method is used to compare the tempo of sequence diversity across reliable three-dimensional (3D) structure-based alignments of 209 protein families (HOMSTRAD) and that for 69 superfamilies (CAMPASS). (The mean alignment length for HOMSTRAD and CAMPASS are very similar.) Surprisingly, the optimal segmentation distributions for the closely related proteins and distantly related ones are found to be very similar. Also, optimal segmentation identifies an unusual protein superfamily. Finally, protein 3D structure clues from the tempo of sequence diversity across alignments are examined. The method is general, and could be applied to any area of comparative biological sequence and 3D structure analysis where the constraint of the inherent linear organization of the data imposes an ordering on the set of objects to be clustered.Keywords: Sequence motif; multiple sequence alignment; optimal segmentation; protein homologous family; protein superfamily Importance of sequence alignmentMultiple alignment of molecular sequences/three-dimensional (3D) structures is a central method in molecular biology (for a recent review, see Smith 1999). By definition, sequence-invariant positions within a set of sequences can only be identified once the relationships within the set have been defined by multiple alignment. Sequence-invariant positions within meaningful multiple protein sequence alignments frequently correspond to residues conserved for function, for example, an enzyme active site or 3D structure, for example, disulphide bonds. (However, recent work demonstrates that "enzyme function places severe constraints on residue identities at positions showing evolutionary variability, and at exterior nonactive-site positions, in particular" [Axe 2000].) Furthermore, significant sequence identities between aligned proteins allow inference of homology, that is, a shared evolutionary origin. Despite divergence of amino acid sequence, homologous proteins share the same overall 3D structure (Chothia and Lesk 1986). This observation is the basis of comparative protein modeling (Blundell et al. 1987): it is possible to model usefully the 3D structure of a protein given significant sequence identity with protein(s) of known 3D structure. Usually, homologous proteins also share a common function. Again, this is helpful: the function of a protein encoded by a new seRequests for reprints to:

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CONRAD: a method for identification of variable and conserved regions within proteins by scale-space filtering

Cited by 3 publications

References 15 publications

Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis

Selection of Conserved Blocks from Multiple Alignments for Their Use in Phylogenetic Analysis

Improvement of Phylogenies after Removing Divergent and Ambiguously Aligned Blocks from Protein Sequence Alignments

Definition of the tempo of sequence diversity across an alignment and automatic identification of sequence motifs: Application to protein homologous families and superfamilies

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