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PubMed2002

Aligning multiple protein sequences by parallel hybrid genetic algorithm.

This paper presents a parallel hybrid genetic algorithm (GA) for solving the sum-of-pairs multiple protein sequence alignment. A new chromosome representation and its corresponding genetic operators are proposed. A multi-population GENITOR-type GA is combined with local search heuristics. It is then extended to run in parallel on a multiprocessor system for speeding up. Experimental results of benchmarks from the BAliBASE show that the proposed method is superior to MSA, OMA, and SAGA methods with regard to quality of solution and running time. It can be used for finding multiple sequence alignment as well as testing cost functions.

Algorithms↗

PubMed1998

Dynamic sequence databank searching with templates and multiple alignment.

Sequence databank searches are often performed iteratively, taking the results of a search to form a probe (either a pattern or profile) for a subsequent scan of the databank. The advantage of this approach is that, as more sequences are drawn into the probe, it should, in principle be possible to detect increasingly distant members of the family. This approach works well when supervised by an "expert" who has a good "eye" for the quality of the sequence alignment and whether novel matches should be rejected or incorporated into the probe. However, all attempts to automate the process have proved difficult, as the process is inherently unstable. Errors in the alignment, or the misalignment of a non-family member, lead to a deterioration of the probe specificity, so allowing further incorrect sequences to be identified. Here, a combination of two methods is used to provide a check on such instability. A pattern matching (template) search method is used (with a BLAST-like pre-filter for speed) to return sequence segments for alignment in a standard multiple alignment program (MULTAL). Sequences are aligned only to a fixed limit of similarity and any sequences or sub-families that have not joined the original "seed" family are rejected. The remaining core family then provides the basis for a subsequent pattern derivation and databank search. The constant check by the multiple alignment phase allows the search phase to be pushed continually towards the boundary of similarity. This is maintained by lowering the cutoff on the scores of acceptable sequences each time the family remains the same over successive search cycles. The procedure was observed to be stable under misalignments and to have an ability to recognise distantly related family members across super-families that was comparable to Psi-BLAST. The method is applied to the analysis of the hormone-binding domains of the insulin and related growth-factor receptors.

Azurin↗

PubMed2006

EGenBio: a data management system for evolutionary genomics and biodiversity.

BACKGROUND: Evolutionary genomics requires management and filtering of large numbers of diverse genomic sequences for accurate analysis and inference on evolutionary processes of genomic and functional change. We developed Evolutionary Genomics and Biodiversity (EGenBio; http://egenbio.lsu.edu) to begin to address this. DESCRIPTION: EGenBio is a system for manipulation and filtering of large numbers of sequences, integrating curated sequence alignments and phylogenetic trees, managing evolutionary analyses, and visualizing their output. EGenBio is organized into three conceptual divisions, Evolution, Genomics, and Biodiversity. The Genomics division includes tools for selecting pre-aligned sequences from different genes and species, and for modifying and filtering these alignments for further analysis. Species searches are handled through queries that can be modified based on a tree-based navigation system and saved. The Biodiversity division contains tools for analyzing individual sequences or sequence alignments, whereas the Evolution division contains tools involving phylogenetic trees. Alignments are annotated with analytical results and modification history using our PRAED format. A miscellaneous Tools section and Help framework are also available. EGenBio was developed around our comparative genomic research and a prototype database of mtDNA genomes. It utilizes MySQL-relational databases and dynamic page generation, and calls numerous custom programs. CONCLUSION: EGenBio was designed to serve as a platform for tools and resources to ease combined analysis in evolution, genomics, and biodiversity.

Animals↗

PubMed1992

Evolutionary divergence plots of homologous proteins.

A simple and efficient method is described for analyzing quantitatively multiple protein sequence alignments and finding the most conserved blocks as well as the maxima of divergence within the set of aligned sequences. It consists of calculating the mean distance and the root-mean-square distance in each column of the multiple alignment, averaging the values in a window of defined length and plotting the results as a function of the position of the window. Due attention is paid to the presence of gaps in the columns. Several examples are provided, using the sequences of several cytochromes c, serine proteases, lysozymes and globins. Two distance matrices are compared, namely the matrix derived by Gribskov and Burgess from the Dayhoff matrix, and the Risler Structural Superposition Matrix. In each case, the divergence plots effectively point to the specific residues which are known to be essential for the catalytic activity of the proteins. In addition, the regions of maximum divergence are clearly delineated. Interestingly, they are generally observed in positions immediately flanking the most conserved blocks. The method should therefore be useful for delineating the peptide segments which will be good candidates for site-directed mutagenesis and for visualizing the evolutionary constraints along homologous polypeptide chains.

Amino Acid Sequence↗

PubMed2001

Omiga: a PC-based sequence analysis tool.

Computer-based sequence analysis, notation, and manipulation are a necessity for all molecular biologists working with any but the most simple DNA sequences. As sequence data become increasingly available, tools that can be used to manipulate and annotate individual sequences and sequence elements will become an even more vital implement in the molecular biologist's arsenal. The Omiga DNA and Protein Sequence Analysis Software tool, version 2.0 provides an effective and comprehensive tool for the analysis of both nucleic acid and protein sequences that runs on a standard PC available in every molecular biology laboratory. Omiga allows the import of sequences in several common formats. Upon importing sequences and assigning them to various projects, Omiga allows the user to produce, analyze, and edit sequence alignments. Sequences may also be queried for the presence of restriction sites, sequence motifs, and other sequence features, all of which can be added into the notations accompanying each sequence. This newest version of Omiga also allows for sequencing and polymerase chain reaction (PCR) primer prediction, a functionality missing in earlier versions. Finally, Omiga allows rapid searches for putative coding regions, and Basic Local Alignment Search Tool (BLAST) queries against public databases at the National Center for Biotechnology Information (NCBI).

Humans↗

PubMed1994

Toward the unification of sequence and structural data for identification of structural and functional constraints.

The identification and characterization of local residue patterns or conserved segments shared by a set of biopolymers has provided a number of insights in molecular biology. Biopolymer sequences are observations from macro molecules that share common structural or function features. The approach taken here rests on the notion that information may be most efficiently extracted from these observations through the use of a model that faithfully represents macro-molecular characteristics. Accordingly, our efforts are focused on statistical models which attempt to capture central features of protein structure, function, and change. Here the assumptions that underlie two new methods for the analysis of protein sequence data are explicitly delineated. (1) Threading of a sequence through structural motifs seeks to determine if a protein sequence fits a known protein structure. The assumptions delineated here also generally apply to other contact based threading methods that have been recently described. (2) Multiple sequence alignment via the Gibbs sampling algorithm seeks to identify position specific empirical free energy models for residue sites in common motifs and simultaneously the align sequence observations form these motifs.

Algorithms↗

PubMed2004

SAD--a normalized structural alignment database: improving sequence-structure alignments.

MOTIVATION: We present a structural alignment database that is specifically targeted for use in derivation and optimization of sequence-structure alignment algorithms for homology modeling. We have paid attention to ensure that fold-space is properly sampled, that the structures involved in alignments are of significant resolution (better than 2.5 A) and the alignments are accurate and reliable. RESULTS: Alignments have been taken from the HOMSTRAD, BAliBASE and SCOP-based Gerstein databases along with alignments generated by a global structural alignment method described here. In order to discriminate between equivalent alignments from these different sources, we have developed a novel scoring function, Contact Alignment Quality score, which evaluates trial alignments by their statistical significance combined with their ability to reproduce conserved three-dimensional residue contacts. The resulting non-redundant, unbiased database contains 1927 alignments from across fold-space with high-resolution structures and a wide range of sequence identities. AVAILABILITY: The database can be interactively queried either over the web at http://abagyan.scripps.edu/lab/web/sad/show.cgi or by using MySQL, and is also available to download over the web.

Algorithms↗

PubMed2005

[Alignment-free biomolecular sequence comparison method].

Biosequence analysis is the primary research field of bioinformatics. In this field, useful information can be extracted by comparison analysis methods. Among them, sequence alignment is the most common comparison method. However the sequence comparison by alignment, which assumes conservation of contiguity between homologous segments, is at odds with genetic recombination. Especially for the multisequence alignment, there exists the difficulty in the complexity of calculation. Therefore, alignment-free sequence comparison methods are required. In this paper, two main categories of alignment-free sequence comparison methods are reviewed. The first one is based on the word (oligomer) frequency and its distribution. The sequences are compared using the distances defined in a Cartesian space by the frequency vectors. In the second category, sequences are compared using Kolmogorov complexity and chaos theory.

Algorithms↗

PubMed1999

Effective use of sequence correlation and conservation in fold recognition.

Protein families are a rich source of information; sequence conservation and sequence correlation are two of the main properties that can be derived from the analysis of multiple sequence alignments. Sequence conservation is related to the direct evolutionary pressure to retain the chemical characteristics of some positions in order to maintain a given function. Sequence correlation is attributed to the small sequence adjustments needed to maintain protein stability against constant mutational drift. Here, we showed that sequence conservation and correlation were each frequently informative enough to detect incorrectly folded proteins. Furthermore, combining conservation, correlation, and polarity, we achieved an almost perfect discrimination between native and incorrectly folded proteins. Thus, we made use of this information for threading by evaluating the models suggested by a threading method according to the degree of proximity of the corresponding correlated, conserved, and apolar residues. The results showed that the fold recognition capacity of a given threading approach could be improved almost fourfold by selecting the alignments that score best under the three different sequence-based approaches.

Amino Acid Sequence↗

PubMed2002

Super pairwise alignment (SPA): an efficient approach to global alignment for homologous sequences.

Sequence analysis is the basis of bioinformatics, while sequence alignment is a fundamental task for sequence analysis. The widely used alignment algorithm, Dynamic Programming, though generating optimal alignment, takes too much time due to its high computation complexity O(N(2)). In order to reduce computation complexity without sacrificing too much accuracy, we have developed a new approach to align two homologous sequences. The new approach presented here, adopting our novel algorithm which combines the methods of probabilistic and combinatorial analysis, reduces the computation complexity to as low as O(N). The computation speed by our program is at least 15 times faster than traditional pairwise alignment algorithms without a loss of much accuracy. We hence named the algorithm Super Pairwise Alignment (SPA). The pairwise alignment execution program based on SPA and the detailed results of the aligned sequences discussed in this article are available upon request.

Algorithms↗

PubMed2005

QUASAR--scoring and ranking of sequence-structure alignments.

SUMMARY: Sequence-structure alignments are a common means for protein structure prediction in the fields of fold recognition and homology modeling, and there is a broad variety of programs that provide such alignments based on sequence similarity, secondary structure or contact potentials. Nevertheless, finding the best sequence-structure alignment in a pool of alignments remains a difficult problem. QUASAR (quality of sequence-structure alignments ranking) provides a unifying framework for scoring sequence-structure alignments that aids finding well-performing combinations of well-known and custom-made scoring schemes. Those scoring functions can be benchmarked against widely accepted quality scores like MaxSub, TMScore, Touch and APDB, thus enabling users to test their own alignment scores against 'standard-of-truth' structure-based scores. Furthermore, individual score combinations can be optimized with respect to benchmark sets based on known structural relationships using QUASAR's in-built optimization routines.

Computational Biology↗

PubMed2004

WebLogo: a sequence logo generator.

WebLogo generates sequence logos, graphical representations of the patterns within a multiple sequence alignment. Sequence logos provide a richer and more precise description of sequence similarity than consensus sequences and can rapidly reveal significant features of the alignment otherwise difficult to perceive. Each logo consists of stacks of letters, one stack for each position in the sequence. The overall height of each stack indicates the sequence conservation at that position (measured in bits), whereas the height of symbols within the stack reflects the relative frequency of the corresponding amino or nucleic acid at that position. WebLogo has been enhanced recently with additional features and options, to provide a convenient and highly configurable sequence logo generator. A command line interface and the complete, open WebLogo source code are available for local installation and customization.

Amino Acid Sequence↗

PubMed2005

Scoredist: a simple and robust protein sequence distance estimator.

BACKGROUND: Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used. RESULTS: We propose a correction-based protein sequence estimator called Scoredist. It uses a logarithmic correction of observed divergence based on the alignment score according to the BLOSUM62 score matrix. We evaluated Scoredist and a number of optimal matrix methods using three evolutionary models for both training and testing Dayhoff, Jones-Taylor-Thornton, and Muller-Vingron, as well as Whelan and Goldman solely for testing. Test alignments with known distances between 0.01 and 2 substitutions per position (1-200 PAM) were simulated using ROSE. Scoredist proved as accurate as the optimal matrix methods, yet substantially more robust. When trained on one model but tested on another one, Scoredist was nearly always more accurate. The Jukes-Cantor and Kimura correction methods were also tested, but were substantially less accurate. CONCLUSION: The Scoredist distance estimator is fast to implement and run, and combines robustness with accuracy. Scoredist has been incorporated into the Belvu alignment viewer, which is available at ftp://ftp.cgb.ki.se/pub/prog/belvu/.

Algorithms↗

PubMed2003

The PSSH database of alignments between protein sequences and tertiary structures.

We introduce the PSSH ('Protein Sequence-to-Structure Homologies') database derived from HSSP2, an improved version of the HSSP ('Homology-derived Secondary Structure of Proteins') database [Dodge et al. (1998) Nucleic Acids Res., 26, 313-315]. Whereas each HSSP entry lists all protein sequences related to a given 3D structure, PSSH is the 'inverse', with each entry listing all structures related to a given sequence. In addition, we introduce two other derived databases: HSSPchain, in which each entry lists all sequences related to a given PDB chain, and HSSPalign, in which each entry gives details of one sequence aligned onto one PDB chain. This re-organization makes it easier to navigate from sequence to structure, and to map sequence features onto 3D structures. Currently (September 2002), PSSH provides structural information for over 400 000 protein sequences, covering 48% of SWALL and 61% of SWISS-PROT sequences; HSSPchain provides sequence information for over 25 000 PDB chains, and HSSPalign gives over 14 million sequence-to-structure alignments. The databases can be accessed via SRS 3D, an extension to the SRS system, at http://srs3d.ebi.ac.uk/.

Animals↗

PubMed1997

Correlating patterns in alignments of polymorphic sequences with experimental assays.

A general algorithm is presented for identifying sets of positions in multiple sequence alignments that best characterize an a priori partitioning such as those determined by inhibition studies or other experimental techniques. The algorithm explores combinations of polymorphic columns in the alignment and evaluates how well these sites reflect the original input partition. Partitions across the polymorphic columns are derived using a tree building procedure with conventional amino acid substitution matrices. Elucidation of those amino acids which govern the biochemical behaviour of a protein with a given substrate or inhibitor can provide insights towards an understanding of the tertiary conformation of the protein. Since it is likely that such positions will be spatially clustered in the protein fold, these positions may give rise to useful distance constraints for substantiating model protein structures. The method is exemplified using data for a set of human mu class glutathione S-transferases. A novel aspect for predicting the behaviour of new polymorphic sequences is also discussed.

Algorithms↗

PubMed2003

Increased detection of structural templates using alignments of designed sequences.

Protein structure prediction by comparative modeling benefits greatly from the use of multiple sequence alignment information to improve the accuracy of structural template identification and the alignment of target sequences to structural templates. Unfortunately, this benefit is limited to those protein sequences for which at least several natural sequence homologues exist. We show here that the use of large diverse alignments of computationally designed protein sequences confers many of the same benefits as natural sequences in identifying structural templates for comparative modeling targets. A large-scale massively parallelized application of an all-atom protein design algorithm, including a simple model of peptide backbone flexibility, has allowed us to generate 500 diverse, non-native, high-quality sequences for each of 264 protein structures in our test set. PSI-BLAST searches using the sequence profiles generated from the designed sequences ("reverse" BLAST searches) give near-perfect accuracy in identifying true structural homologues of the parent structure, with 54% coverage. In 41 of 49 genomes scanned using reverse BLAST searches, at least one novel structural template (not found by the standard method of PSI-BLAST against PDB) is identified. Further improvements in coverage, through optimizing the scoring function used to design sequences and continued application to new protein structures beyond the test set, will allow this method to mature into a useful strategy for identifying distantly related structural templates.

Algorithms↗

PubMedDate not supplied

Genomic divergence between human and chimpanzee estimated from large-scale alignments of genomic sequences.

To study the genomic divergence between human and chimpanzee, large-scale genomic sequence alignments were performed. The genomic sequences of human and chimpanzee were first masked with the RepeatMasker and the repeats were excluded before alignments. The repeats were then reinserted into the alignments of nonrepetitive segments and entire sequences were aligned again. A total of 2.3 million base pairs (Mb) of genomic sequences, including repeats, were aligned and the average nucleotide divergence was estimated to be 1.22%. The Jukes-Cantor (JC) distances (nucleotide divergences) in nonrepetitive (1.44 Mb) and repetitive sequences (0.86 Mb) are 1.14% and 1.34%, respectively, suggesting a slightly higher average rate in repetitive sequences. Annotated coding and noncoding regions of homologous chimpanzee genes were also retrieved from GenBank and compared. The average synonymous and nonsynonymous divergences in 88 coding genes are 1.48% and 0.55%, respectively. The JC distances in intron, 5' flanking, 3' flanking, promoter, and pseudogene regions are 1.47%, 1.41%, 1.68%, 0.75%, and 1.39%, respectively. It is not clear why the genetic distances in most of these regions are somewhat higher than those in genomic sequences. One possible explanation is that some of the genes may be located in regions with higher mutation rates.

Animals↗

PubMed2005

An Eulerian path approach to local multiple alignment for DNA sequences.

Expensive computation in handling a large number of sequences limits the application of local multiple sequence alignment. We present an Eulerian path approach to local multiple alignment for DNA sequences. The computational time and memory usage of this approach is approximately linear to the total size of sequences analyzed; hence, it can handle thousands of sequences or millions of letters simultaneously. By constructing a De Bruijn graph, most of the conserved segments are amplified as heavy Eulerian paths in the graph, and the original patterns distributed in sequences are recovered even if they do not exist in any single sequence. This approach can accurately detect unknown conserved regions, for both short and long, conserved and degenerate patterns. We further present a Poisson heuristic to estimate the significance of a local multiple alignment. The performance of our method is demonstrated by finding Alu repeats in the human genome. We compare the results with Alus marked by repeatmasker, where the two programs are in good agreement. Our method is robust under various conditions and superior to other methods in terms of efficiency and accuracy.

Base Sequence↗