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Prediction of transmembrane segments in proteins utilising multiple sequence alignments.

A method for prediction of transmembrane segments from multiply aligned amino acid sequences is presented. For the calculations, two sets of propensity values were used: one for the middle, hydrophobic portion and one for the terminal regions of the transmembrane sequence spans. Average propensity values were calculated for each position along the alignment, with the contribution from each sequence weighted according to its dissimilarity relative to the other aligned sequences. Eight-residue segments were considered as potential cores of transmembrane segments and elongated if their middle propensity values were above a given threshold. End propensity values were also considered as stop signals. Only helices with length of 15 to 29 residues were allowed and corrections for strictly conserved charged residues were also made. The method is shown to be more successful than predictions based upon single sequences alone. In the test set of 28 families with 126 transmembrane segments, only five spans were not predicted or constituted false positives. The method is applied to sequence families for which data on transmembrane segments do not exist or are sparse or contradictory included voltage-gated potassium-channels, cytochrome c oxidases, NADH-ubiquinone oxidoreductase, beta-glucosides-specific phosphotransferase enzyme and major surface antigen of hepatitis B virus.

Algorithms↗

Improving sequence alignments for intrinsically disordered proteins.

Here we analyze sequence alignments for intrinsically disordered proteins. For 55 disordered protein families we measure the performance of different scoring matrices and propose one adjusted to disordered regions. An iterative algorithm of realigning sequences and recalculating matrices is designed and tested. For each matrix we also test a wide range of gap penalties. Results show an improvement in the ability to detect and discriminate related disordered proteins whose average sequence identity with the other family members is below 50%.

Amino Acid Sequence↗

Pattern-induced multi-sequence alignment (PIMA) algorithm employing secondary structure-dependent gap penalties for use in comparative protein modelling.

A multiple sequence alignment algorithm is described that uses a dynamic programming-based pattern construction method to align a set of homologous sequences based on their common pattern of conserved sequence elements. This pattern-induced multi-sequence alignment (PIMA) algorithm can employ secondary-structure dependent gap penalties for use in comparative modelling of new sequences when the three-dimensional structure of one or more members of the same family is known. We show that the use of secondary structure information can significantly improve the accuracy of aligning structure boundaries in a set of homologous sequences even when the structure of only one member of the family is known.

Algorithms↗

Multiple sequence alignment by parallel simulated annealing.

We have developed simulated annealing algorithms to solve the problem of multiple sequence alignment. The algorithm was shown to give the optimal solution as confirmed by the rigorous dynamic programming algorithm for three-sequence alignment. To overcome long execution times for simulated annealing, we utilized a parallel computer. A sequential algorithm, a simple parallel algorithm and the temperature parallel algorithm were tested on a problem. The results were compared with the result obtained by a conventional tree-based algorithm where alignments were merged by two-way dynamic programming. Every annealing algorithm produced a better energy value than the conventional algorithm. The best energy value, which probably represents the optimal solution, was reached within a reasonable time by both of the parallel annealing algorithms. We consider the temperature parallel algorithm of simulated annealing to be the most suitable for finding the optimal multiple sequence alignment because the algorithm does not require any scheduling for optimization. The algorithm is also useful for refining multiple alignments obtained by other heuristic methods.

Algorithms↗

Analysis and comparison of benchmarks for multiple sequence alignment.

The most popular way of comparing the performance of multiple sequence alignment programs is to use empirical testing on sets of test sequences. Several such test sets now exist, each with potential strengths and weaknesses. We apply several different alignment packages to 6 benchmark datasets, and compare their relative performances. HOMSTRAD, a collection of alignments of homologous proteins, is regularly used as a benchmark for sequence alignment though it is not designed as such, and lacks annotation of reliable regions within the alignment. We introduce this annotation into HOMSTRAD using protein structural superposition. Results on each database show that method performance is dependent on the input sequences. Alignment benchmarks are regularly used in combination to measure performance across a spectrum of alignment problems. Through combining benchmarks, it is possible to detect whether a program has been over-optimised for a single dataset, or alignment problem type.

Cluster Analysis↗

MUMMALS: multiple sequence alignment improved by using hidden Markov models with local structural information.

We have developed MUMMALS, a program to construct multiple protein sequence alignment using probabilistic consistency. MUMMALS improves alignment quality by using pairwise alignment hidden Markov models (HMMs) with multiple match states that describe local structural information without exploiting explicit structure predictions. Parameters for such models have been estimated from a large library of structure-based alignments. We show that (i) on remote homologs, MUMMALS achieves statistically best accuracy among several leading aligners, such as ProbCons, MAFFT and MUSCLE, albeit the average improvement is small, in the order of several percent; (ii) a large collection (>10 000) of automatically computed pairwise structure alignments of divergent protein domains is superior to smaller but carefully curated datasets for estimation of alignment parameters and performance tests; (iii) reference-independent evaluation of alignment quality using sequence alignment-dependent structure superpositions correlates well with reference-dependent evaluation that compares sequence-based alignments to structure-based reference alignments.

Markov Chains↗

SEAVIEW and PHYLO_WIN: two graphic tools for sequence alignment and molecular phylogeny.

SEAVIEW and PHYLO_WIN are two graphic tools for X Windows-Unix computers dedicated to sequence alignment and molecular phylogenetics. SEAVIEW is a sequence alignment editor allowing manual or automatic alignment through an interface with CLUSTALW program. Alignment of large sequences with extensive length differences is made easier by a dot-plot-based routine. The PHYLO_WIN program allows phylogenetic tree building according to most usual methods (neighbor joining with numerous distance estimates, maximum parsimony, maximum likelihood), and a bootstrap analysis with any of them. Reconstructed trees can be drawn, edited, printed, stored, evaluated according to numerous criteria. Taxonomic species groups and sets of conserved regions can be defined by mouse and stored into sequence files, thus avoiding multiple data files. Both tools are entirely mouse driven. On-line help makes them easy to use. They are freely available by anonymous ftp at biom3.univ-lyon1.fr/pub/ mol_phylogeny or http:@acnuc.univ-lyon1.fr/, or by e-mail to galtier@biomserv.univ-lyon1.fr.

Animals↗

SABmark--a benchmark for sequence alignment that covers the entire known fold space.

The Sequence Alignment Benchmark (SABmark) provides sets of multiple alignment problems derived from the SCOP classification. These sets, Twilight Zone and Superfamilies, both cover the entire known fold space using sequences with very low to low, and low to intermediate similarity, respectively. In addition, each set has an alternate version in which unalignable but apparently similar sequences are added to each problem.

Algorithms↗

Grouping of amino acid types and extraction of amino acid properties from multiple sequence alignments using variance maximization.

Understanding of amino acid type co-occurrence in trusted multiple sequence alignments is a prerequisite for improved sequence alignment and remote homology detection algorithms. Two objective approaches were used to investigate co-occurrence, both based on variance maximization of the weighted residue frequencies in columns taken from a large alignment database. The first approach discretely grouped amino acid types, and the second approach extracted orthogonal properties of amino acids using principal components analysis. The grouping results corresponded to amino acid physical properties such as side chain hydrophobicity, size, or backbone flexibility, and an optimal arrangement of approximately eight groups was observed. However, interpretation of the orthogonal properties was more complex. Although the principal components accounting for the largest variances exhibited modest correlations with hydrophobicity and conservation of glycine, in general principal components did not correspond to physical properties of amino acids. Although not intuitive, these amino acid mathematical properties were demonstrated to be robust and to improve local pairwise alignment accuracy, relative to 20 amino acid frequencies alone, for a simple test case.

Amino Acids↗

Homology assessment and molecular sequence alignment.

Hypotheses of homology are the basis of phylogenetic analysis. All character data are considered to be equivalent regardless of the source of those characters. Putative homology statements are designated based on observations of similarity. Pairwise sequence alignment using the Needleman-Wunsch algorithm is the basis for similarity maximization between molecular sequences. Multiple sequence alignment uses this algorithm in a topologically hierarchical framework. The resulting hypotheses of homology are tested in conjunction with character congruence through parsimony. This review introduces some underlying principles of phylogenetic analysis as they pertain homology testing and DNA sequence alignment.

Algorithms↗

S4: structure-based sequence alignments of SCOP superfamilies.

S4 is an automatically generated database of multiple structure-based sequence alignments of protein superfamilies in the SCOP database. All structural domains that do not share more than 40% sequence identity as defined by the ASTRAL compendium of protein structures are included. The alignments are constructed using pairwise structural alignments to generate residue equivalences that are then integrated into multiple alignments using sequence alignment tools. We describe the database and give examples showing how the automatically generated S4 alignments compare favourably to hand-crafted alignments. Available at: http://compbio.mds.qmw.ac.uk/S4.html.

Algorithms↗

Bellerophon: a program to detect chimeric sequences in multiple sequence alignments.

SUMMARY: Bellerophon is a program for detecting chimeric sequences in multiple sequence datasets by an adaption of partial treeing analysis. Bellerophon was specifically developed to detect 16S rRNA gene chimeras in PCR-clone libraries of environmental samples but can be applied to other nucleotide sequence alignments. AVAILABILITY: Bellerophon is available as an interactive web server at http://foo.maths.uq.edu.au/~huber/bellerophon.pl

Algorithms↗

Sequence alignment by cross-correlation.

Many recent advances in biology and medicine have resulted from DNA sequence alignment algorithms and technology. Traditional approaches for the matching of DNA sequences are based either on global alignment schemes or heuristic schemes that seek to approximate global alignment algorithms while providing higher computational efficiency. This report describes an approach using the mathematical operation of cross-correlation to compare sequences. It can be implemented using the fast fourier transform for computational efficiency. The algorithm is summarized and sample applications are given. These include gene sequence alignment in long stretches of genomic DNA, finding sequence similarity in distantly related organisms, demonstrating sequence similarity in the presence of massive (approximately 90%) random point mutations, comparing sequences related by internal rearrangements (tandem repeats) within a gene, and investigating fusion proteins. Application to RNA and protein sequence alignment is also discussed. The method is efficient, sensitive, and robust, being able to find sequence similarities where other alignment algorithms may perform poorly.

Algorithms↗

Database of protein sequence alignments: PIR-ALN.

The Protein Information Resource (PIR) has been maintaining a database of curated protein sequence alignments since 1991. The collection includes superfamily, family and homology domain alignments. CLUSTAL V/W is used to generate multiple sequence alignments and ALNED, an interactive alignment editor, is used to check and correct them. The database has helped in classifying sequences, in defining new homology domains, and in spreading and standardizing protein names, features and keywords among members of a family or superfamily. The ATLAS information retrieval system can be used to browse and query the PIR-ALN alignments. The quarterly and weekly updates can be accessed via the WWW at http://www-nbrf. georgetown.edu/pir/

Databases, Factual↗

A new method for building protein conformations from sequence alignments with homologues of known structure.

We describe a largely automatic procedure for building protein structures from sequence alignments with homologues of known structure. This procedure uses simple rules by which multiple sequence alignments can be translated into distance and chirality constraints, which are then used as input for distance geometry calculations. By this means one obtains an ensemble of conformations for the unknown structure that are compatible with the rules employed, and the differences among these conformations provide an indication of the reliability of the structure prediction. The overall approach is demonstrated here by applying it to several Kazal-type trypsin inhibitors, for which experimentally determined structures are available. On the basis of our experience with these test problems, we have further predicted the conformation of the human pancreatic secretory trypsin inhibitor, for which no experimentally determined structure is presently available.

Algorithms↗

SAGA: sequence alignment by genetic algorithm.

We describe a new approach to multiple sequence alignment using genetic algorithms and an associated software package called SAGA. The method involves evolving a population of alignments in a quasi evolutionary manner and gradually improving the fitness of the population as measured by an objective function which measures multiple alignment quality. SAGA uses an automatic scheduling scheme to control the usage of 22 different operators for combining alignments or mutating them between generations. When used to optimise the well known sums of pairs objective function, SAGA performs better than some of the widely used alternative packages. This is seen with respect to the ability to achieve an optimal solution and with regard to the accuracy of alignment by comparison with reference alignments based on sequences of known tertiary structure. The general attraction of the approach is the ability to optimise any objective function that one can invent.

Algorithms↗

COMBOSA3D: combining sequence alignments with three-dimensional structures.

UNLABELLED: COMBOSA3D is a program that allows sequence conservation to be viewed in its proper three-dimensional environment. It superimposes sequence alignment information onto a protein structure using a customizable color scheme, which is also applied to a textual sequence alignment for reference. AVAILABILITY: The program can be tested at http://www.bioinformatics.org/combosa3d/, and the source code is freely available.

Animals↗

Multiple sequence alignment of protein families showing low sequence homology: a methodological approach using database pattern-matching discriminators for G-protein-linked receptors.

A multiple alignment has been constructed, containing 37 sequences from related families of membrane-bound receptors believed to share the same structural framework as rhodopsin. Sequence homology within families was high (occasionally greater than 90%), but homology between them was generally low (20% or less). Database pattern-scanning methods were therefore used to construct a set of discriminators to aid both the task of alignment and the identification of distantly related sequences showing similar rhodopsin-like transmembrane helices. The results indicate that these discriminators are uniquely able to identify each of the transmembrane helices without major cross-reaction with similar regions in unrelated integral membrane proteins. This ability engenders more accurate alignments of the sequences and facilitates structural analysis and model building of the receptors.

Amino Acid Sequence↗