A wealth of protein and DNA sequence data is being generated by genome projects and other sequencing efforts. A crucial barrier to deciphering these sequences and understanding the relations among them is the difficulty of detecting subtle local residue patterns common to multiple sequences. Such patterns frequently reflect similar molecular structures and biological properties. A mathematical definition of this "local multiple alignment" problem suitable for full computer automation has been used to develop a new and sensitive algorithm, based on the statistical method of iterative sampling. This algorithm finds an optimized local alignment model for N sequences in N-linear time, requiring only seconds on current workstations, and allows the simultaneous detection and optimization of multiple patterns and pattern repeats. The method is illustrated as applied to helix-turn-helix proteins, lipocalins, and prenyltransferases.

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Detecting Subtle Sequence Signals: A Gibbs Sampling Strategy for Multiple Alignment

The reconstruction of phylogenetic history is predicated on being able to accurately establish hypotheses of character homology, which involves sequence alignment for studies based on molecular sequence data. In an empirical study investigating nucleotide sequence alignment, we inferred phylogenetic trees for 43 species of the Apicomplexa and 3 of Dinozoa based on complete small-subunit rDNA sequences, using six different multiple-alignment procedures: manual alignment based on the secondary structure of the 18S rRNA molecule, and automated similarity-based alignment algorithms using the PileUp, ClustalW, TreeAlign, MALIGN, and SAM computer programs. Trees were constructed using neighboring-joining, weighted-parsimony, and maximum-likelihood methods. All of the multiple sequence alignment procedures yielded the same basic structure for the estimate of the phylogenetic relationship among the taxa, which presumably represents the underlying phylogenetic signal. However, the placement of many of the taxa was sensitive to the alignment procedure used; and the different alignments produced trees that were on average more dissimilar from each other than did the different tree-building methods used. The multiple alignments from the different procedures varied greatly in length, but aligned sequence length was not a good predictor of the similarity of the resulting phylogenetic trees. We also systematically varied the gap weights (the relative cost of inserting a new gap into a sequence or extending an already-existing gap) for the ClustalW program, and this produced alignments that were at least as different from each other as those produced by the different alignment algorithms. Furthermore, there was no combination of gap weights that produced the same tree as that from the structure alignment, in spite of the fact that many of the alignments were similar in length to the structure alignment. We also investigated the phylogenetic information content of the helical and nonhelical regions of the rDNA, and conclude that the helical regions are the most informative. We therefore conclude that many of the literature disagreements concerning the phylogeny of the Apicomplexa are probably based on differences in sequence alignment strategies rather than differences in data or tree-building methods.

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Effects of nucleotide sequence alignment on phylogeny estimation: a case study of 18S rDNAs of apicomplexa.

I have addressed the biological rather than bioinformatics aspects of molecular sequence alignment by covering a series of topics that have been under-valued, particularly within the context of phylogenetic analysis. First, phylogenetic analysis is only one of the many objectives of sequence alignment, and the most appropriate multiple alignment may not be the same for all of these purposes. Phylogenetic alignment thus occupies a specific place within a broader context. Second, homology assessment plays an intricate role in phylogenetic analysis, with sequence alignment consisting of primary homology assessment and tree building being secondary homology assessment. The objective of phylogenetic alignment thus distinguishes it from other sorts of alignment. Third, I summarise what is known about the serious limitations of using phenetic similarity as a criterion for automated multiple alignment, and provide an overview of what is currently being done to improve these computerised procedures. This synthesises information that is apparently not widely known among phylogeneticists. Fourth, I then consider the recent development of automated procedures for combining alignment and tree building, thus integrating primary and secondary homology assessment. Finally, I outline various strategies for increasing the biological content of sequence alignment procedures, which consists of taking into account known evolutionary processes when making alignment decisions. These procedures can be objective and repeatable, and can involve computerised algorithms to automate much of the work. Perhaps the most important suggestion is that alignment should be seen as a process where new sequences are added to a pre-existing alignment that has been manually curated by the biologist.

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Multiple sequence alignment for phylogenetic purposes

Original algorithms for simultaneous alignment of protein sequences are presented, including sequence clustering and within- or between-groups multiple alignment. The way of matching similar regions is fundamentally new. Complete matches are formed by segments more similar than expected by random, according to a given probability limit. Any classic or user-defined score matrix can be used to express the similarity between the residues. The algorithm seeks for complete matches common to all the sequences without performing pairwise alignment and regardless of gap weighting. An automatic screening delineates all the similar regions (boxes) that may be defined for a given maximal shift between the sequences. The shift can be large enough to allow the matching of any region of a sequence with any region of another one. It can also be short and used to refine the alignment around anchor points. The algorithm provides the most likely optimal alignment and a comprehensive list of the alignment dilemma. Duality between automatism and interactivity is provided. Depending on the problem complexity, a final alignment is obtained fully automatically or requires some interactive handling to discriminate alternative pathways.

MATCH-BOX: a fundamentally new algorithm for the simultaneous alignment of several protein sequences.

The rigorous alignment of multiple protein sequences becomes impractical even with a modest number of sequences, since computer memory and time requirements increase as the product of the lengths of the sequences. We have devised a strategy to approach such an optimal alignment, which modifies the intensive computer storage and time requirements of dynamic programming. Our algorithm randomly divides a group of unaligned sequences into two subgroups, between which an optimal alignment is then obtained by a Needleman-Wunsch style of algorithm. Our algorithm uses a matrix with dimensions corresponding to the lengths of the two aligned sequence subgroups. The pairwise alignment process is repeated using different random divisions of the whole group into two subgroups. Compared with the rigorous approach of solving the n-dimensional lattice by dynamic programming, our iterative algorithm results in alignments that match or are close to the optimal solution, on a limited set of test problems. We have implemented this algorithm in a computer program that runs on the IBM PC class of machines, together with a user-friendly environment for interactively selecting sequences or groups of sequences to be aligned either simultaneously or progressively.

A novel randomized iterative strategy for aligning multiple protein sequences.

The programs described herein function as part of a suite of programs designed for pairwise alignment, multiple alignment, generation of randomized sequences, production of alignment scores and a sorting routine for analysis of the alignments produced. The sequence alignment programs penalize gaps (absences of residues) within regions of protein secondary structure and have the added option of 'fingerprinting' structurally or functionally important protein-residues. The multiple alignment program is based upon the sequence alignment method of Needleman and Wunsch and the multiple alignment extension of Barton and Sternberg. Our application includes the feature of optionally weighting active site, monomer--monomer, ligand contact or other important template residues to bias the alignment toward matching these residues. A sum-score for the alignments is introduced, which is independent of gap penalties. This score more adequately reflects the character of the alignments for a given scoring matrix than the gap-penalty-dependent total score described previously in the literature. In addition, individual amino acid similarity scores at each residue position in the alignments are printed with the alignment output to enable immediate quantitative assessment of homology at key sections of the aligned chains.

A multiple sequence alignment algorithm for homologous proteins using secondary structure information and optionally keying alignments to functionally important sites

A program that generates amino acid sequences that are compatible with the Greek key protein motif is presented. Using statistical data derived from the structures of molecules from the protein databank, the novel algorithm generates amino acid sequences compatible with an 8-stranded perfect Greek key jellyroll motif. In this motif, all hydrogen bonds present in the theoretical originating beta-hairpin stay in register as the whole 8-stranded domain folds at once in an anticlockwise swirl. Eight residues are generated per strand and 32 residues per sheet making 64 residues in the antiparallel beta-barrel. The seven loops between beta-strands contain an additional 27 residues. All recognized features of beta-sheets and beta-strands, such as alternating hydrophobic, hydrophilic residues with hydrophobics on the narrow-hydrogen-bond-pair, concave side of the theoretical originating beta-ribbon; sheet twist, strand twist, side chain rotation about the strands; the theories of side chain packing between the sheets; an average 30 degrees rotation between beta-sheets; the theoretical anticomplementary patch residues of each sheet; and the anticomplementary, isotropically stressed hyperbolic parabloid shape of each sheet, are taken into account in the program. The sequences of the loops between strands are designed by turn type and strand twist is considered in the design of the motif's single beta-hairpin turn. Secondary structure parameters and between-strand amino acid pair correlations also figure importantly in the novel algorithm.

Christina M. Henneke

Papers

A multiple sequence alignment algorithm for homologous proteins using secondary structure information and optionally keying alignments to functionally important sites

Protein motif by computer: the perfect Greek key jellyroll designer.