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PubMed1998

Fold assembly of small proteins using monte carlo simulations driven by restraints derived from multiple sequence alignments.

The feasibility of predicting the global fold of small proteins by incorporating predicted secondary and tertiary restraints into ab initio folding simulations has been demonstrated on a test set comprised of 20 non-homologous proteins, of which one was a blind prediction of target 42 in the recent CASP2 contest. These proteins contain from 37 to 100 residues and represent all secondary structural classes and a representative variety of global topologies. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Predicted tertiary restraints are derived from multiple sequence alignments via a two-step process. First, seed side-chain contacts are identified from correlated mutation analysis, and then a threading-based algorithm is used to expand the number of these seed contacts. A lattice-based reduced protein model and a folding algorithm designed to incorporate these predicted restraints is described. Depending upon fold complexity, it is possible to assemble native-like topologies whose coordinate root-mean-square deviation from native is between 3.0 A and 6.5 A. The requisite level of accuracy in side-chain contact map prediction can be roughly 25% on average, provided that about 60% of the contact predictions are correct within +/-1 residue and 95% of the predictions are correct within +/-4 residues. Precision in tertiary contact prediction is more critical than absolute accuracy. Furthermore, only a subset of the tertiary contacts, on the order of 25% of the total, is sufficient for successful topology assembly. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm holds considerable promise for the prediction of the global topology of small proteins.

Amino Acid Sequence↗

PubMed1997

Generating benchmarks for multiple sequence alignments and phylogenetic reconstructions.

We present a new probabilistic model of evolution of RNA-, DNA-, or protein-like sequences and a tool rose that implements this model. By insertion, deletion and substitution of characters, a family of sequences is created from a common ancestor. During this artificial evolutionary process, the "true" history is logged and the "correct" multiple sequence alignment is created simultaneously. We also allow for varying rates of mutation within the sequences making it possible to establish so-called sequence motifs. The results are suitable for the evaluation of methods in multiple sequence alignment computation and the prediction of phylogenetic relationships.

Amino Acid Sequence↗

PubMed1998

In unison: regularization of protein secondary structure predictions that makes use of multiple sequence alignments.

We present a method whose purpose is to post-process the fuzzy results of secondary structure prediction methods that use multiple sequence alignments, in order to obtain 'realistic' secondary structures, i.e., secondary structure elements whose length is greater than or equal to some predefined minimum length. This regularization helps with interpretation of the secondary structure prediction.

Algorithms↗

PubMed2000

Application of multiple sequence alignment profiles to improve protein secondary structure prediction.

The effect of training a neural network secondary structure prediction algorithm with different types of multiple sequence alignment profiles derived from the same sequences, is shown to provide a range of accuracy from 70.5% to 76.4%. The best accuracy of 76.4% (standard deviation 8.4%), is 3.1% (Q(3)) and 4.4% (SOV2) better than the PHD algorithm run on the same set of 406 sequence non-redundant proteins that were not used to train either method. Residues predicted by the new method with a confidence value of 5 or greater, have an average Q(3) accuracy of 84%, and cover 68% of the residues. Relative solvent accessibility based on a two state model, for 25, 5, and 0% accessibility are predicted at 76.2, 79.8, and 86. 6% accuracy respectively. The source of the improvements obtained from training with different representations of the same alignment data are described in detail. The new Jnet prediction method resulting from this study is available in the Jpred secondary structure prediction server, and as a stand-alone computer program from: http://barton.ebi.ac.uk/. Proteins 2000;40:502-511.

Algorithms↗

PubMed2000

Computational methods for protein secondary structure prediction using multiple sequence alignments.

Efforts to use computers in predicting the secondary structure of proteins based only on primary structure information started over a quarter century ago [1-3]. Although the results were encouraging initially, the accuracy of the pioneering methods generally did not attain the level required for using predictions of secondary structures reliably in modelling the three-dimensional topology of proteins. During the last decade, however, the introduction of new computational techniques as well as the use of multiple sequence information has lead to a dramatic increase in the success rate of prediction methods, such that successful 3D modelling based on predicted secondary structure has become feasible [e.g., Ref 4]. This review is aimed at presenting an overview of the scale of the secondary structure prediction problem and associated pitfalls, as well as the history of the development of computational prediction methods. As recent successful strategies for secondary structure prediction all rely on multiple sequence information, some methods for accurate protein multiple sequence alignments will also be described. While the main focus is on prediction methods for globular proteins, also the prediction of trans-membrane segments within membrane proteins will be briefly summarised. Finally, an integrated iterative approach tying secondary structure prediction and multiple alignment will be introduced [5].

Algorithms↗

PubMed2005

A tabu search algorithm for post-processing multiple sequence alignment.

Tabu search is a meta-heuristic approach that is proven to be useful in solving combinatorial optimization problems. We implement the adaptive memory features of tabu search to refine a multiple sequence alignment. Adaptive memory helps the search process to avoid local optima and explores the solution space economically and effectively without getting trapped into cycles. The algorithm is further enhanced by introducing extended tabu search features such as intensification and diversification. The neighborhoods of a solution are generated stochastically and a consistency-based objective function is employed to measure its quality. The algorithm is tested with the datasets from BAliBASE benchmarking database. We have observed through experiments that tabu search is able to improve the quality of multiple alignments generated by other software such as ClustalW and T-Coffee. The source code of our algorithm is available at http://www.bii.a-star.edu.sg/~tariq/tabu/.

Algorithms↗

PubMed1989

A fast and sensitive multiple sequence alignment algorithm.

A two-step multiple alignment strategy is presented that allows rapid alignment of a set of homologous sequences and comparison of pre-aligned groups of sequences. Examples are given demonstrating the improvement in the quality of alignments when comparing entire groups instead of single sequences. The modular design of computer programs based on this algorithm allows for storage of aligned sequences and successive alignment of any number of sequences.

Algorithms↗

PubMed2006

Optimal sum-of-pairs multiple sequence alignment using incremental Carrillo and Lipman bounds.

Alignment of sequences is an important routine in various areas of science, notably molecular biology. Multiple sequence alignment is a computationally hard optimization problem which involves the consideration of different possible alignments in order to find an optimal one, given a measure of goodness of alignments. Dynamic programming algorithms are generally well suited for the search of optimal alignments, but are constrained by unwieldy space requirements for large numbers of sequences. Carrillo and Lipman devised a method that helps to reduce the search space for an optimal alignment under a sum-of-pairs measure using bounds on the scores of its pairwise projections. In this paper, we generalize Carrillo and Lipman bounds and demonstrate a novel approach for finding optimal sum-of-pairs multiple alignments that allows incremental pruning of the optimal alignment search space. This approach can result in a drastic pruning of the final search space polytope (where we search for the optimal alignment) when compared to Carrillo and Lipman's approach and hence allows many runs that are not feasible with the original method.

Algorithms↗

PubMed2003

AltAVisT: comparing alternative multiple sequence alignments.

UNLABELLED: We introduce a WWW-based tool that is able to compare two alternative multiple alignments of a given sequence set. Regions where both alignments coincide are color-coded to visualize the local agreement between the two alignments and to identify those regions that can be considered to be reliably aligned. AVAILABILITY: http://bibiserv.techfak.uni-bielefeld.de/altavist/.

Color↗

PubMed2006

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↗

PubMed2006

Greene SCPrimer: a rapid comprehensive tool for designing degenerate primers from multiple sequence alignments.

Polymerase chain reaction (PCR) is widely applied in clinical and environmental microbiology. Primer design is key to the development of successful assays and is often performed manually by using multiple nucleic acid alignments. Few public software tools exist that allow comprehensive design of degenerate primers for large groups of related targets based on complex multiple sequence alignments. Here we present a method for designing such primers based on tree building followed by application of a set covering algorithm, and demonstrate its utility in compiling Multiplex PCR primer panels for detection and differentiation of viral pathogens.

Algorithms↗

PubMed1993

The limits of protein secondary structure prediction accuracy from multiple sequence alignment.

The expected best residue-by-residue accuracies for secondary structure prediction from multiple protein sequence alignment have been determined by an analysis of known protein structural families. The results show substantial variation is possible among homologous protein structures, and that 100% agreement is unlikely between a consensus prediction and one member of a protein structural family. The study provides the range of agreement to be expected between a perfect secondary structure prediction from a multiple alignment and each protein within the alignment. The results of this study overcome the difficulties inherent in the use of residue-by-residue accuracy for assessing the quality of consensus secondary structure predictions. The accuracies of recent consensus predictions for the annexins, SH2 domains and SH3 domains fall within the expected range for a perfect prediction.

Amino Acid Sequence↗

PubMed2000

The performance of several multiple-sequence alignment programs in relation to secondary-structure features for an rRNA sequence.

The performances of five global multiple-sequence alignment programs (CLUSTAL W, Divide and Conquer, Malign, PileUp, and TreeAlign) were evaluated using part of the animal mitochondrial small subunit (12S) rRNA molecule. Conserved sequence motifs derived from an alignment based on secondary structural information were used to score how well each program aligned a data set of five vertebrate and five invertebrate taxa over a range of parameter values. All of the programs could align the motifs with reasonable accuracy for at least one set of parameter conditions, although if the whole sequence was considered, similarity to the structural alignment was only 25%-34%. Use of small gap costs generally gave more accurate results, although Malign and TreeAlign generated longer alignments when gap costs were low. The programs differed in the consistency of the alignments when gap cost was varied; CLUSTAL W, Divide and Conquer, and TreeAlign were the most accurate and robust, while PileUp performed poorly as gap cost values increased, and the accuracy of Malign fluctuated. Default settings for the programs did not give the best results, and attempting to select similar parameter values in different programs did not always result in more similar alignments. Poor alignment of even well-conserved motifs can occur if these are near sites with insertions or deletions. Since there is no a priori way to determine gap costs and because such costs can vary over the gene, alignment of rRNA sequences, particularly the less well conserved regions, should be treated carefully and aided by secondary structure and conserved motifs. Some motifs are single bases and so are often invisible to alignment programs. Our tests involved the most conserved regions of the 12S rRNA gene, and alignment of less well conserved regions will be more problematical. None of the alignments we examined produced a fully resolved phylogeny for the data set, indicating that this portion of 12S rRNA is insufficient for resolution of distant evolutionary relationships.

Algorithms↗

PubMed1994

CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

Algorithms↗

PubMed2005

Using reconfigurable hardware to accelerate multiple sequence alignment with ClustalW.

Aligning hundreds of sequences using progressive alignment tools such as ClustalW requires several hours on state-of-the-art workstations. We present a new approach to compute multiple sequence alignments in far shorter time using reconfigurable hardware. This results in an implementation of ClustalW with significant runtime savings on a standard off-the-shelf FPGA.

Computers↗

PubMed2005

Evolutionary profiles from the QR factorization of multiple sequence alignments.

We present an algorithm to generate complete evolutionary profiles that represent the topology of the molecular phylogenetic tree of the homologous group. The method, based on the multidimensional QR factorization of numerically encoded multiple sequence alignments, removes redundancy from the alignments and orders the protein sequences by increasing linear dependence, resulting in the identification of a minimal basis set of sequences that spans the evolutionary space of the homologous group of proteins. We observe a general trend that these smaller, more evolutionarily balanced profiles have comparable and, in many cases, better performance in database searches than conventional profiles containing hundreds of sequences, constructed in an iterative and computationally intensive procedure. For more diverse families or superfamilies, with sequence identity <30%, structural alignments, based purely on the geometry of the protein structures, provide better alignments than pure sequence-based methods. Merging the structure and sequence information allows the construction of accurate profiles for distantly related groups. These structure-based profiles outperformed other sequence-based methods for finding distant homologs and were used to identify a putative class II cysteinyl-tRNA synthetase (CysRS) in several archaea that eluded previous annotation studies. Phylogenetic analysis showed the putative class II CysRSs to be a monophyletic group and homology modeling revealed a constellation of active site residues similar to that in the known class I CysRS.

Algorithms↗

PubMed1998

Tertiary structure prediction of the KIX domain of CBP using Monte Carlo simulations driven by restraints derived from multiple sequence alignments.

Using a recently developed protein folding algorithm, a prediction of the tertiary structure of the KIX domain of the CREB binding protein is described. The method incorporates predicted secondary and tertiary restraints derived from multiple sequence alignments in a reduced protein model whose conformational space is explored by Monte Carlo dynamics. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that was modified for the presence of predicted U-turns, i.e., regions where the chain reverses global direction. Tertiary restraints are obtained via a two-step process: First, seed side-chain contacts are identified from a correlated mutation analysis, and then, a threading-based algorithm expands the number of these seed contacts. Blind predictions indicate that the KIX domain is a putative three-helix bundle, although the chirality of the bundle could not be uniquely determined. The expected root-mean-square deviation for the correct chirality of the KIX domain is between 5.0 and 6.2 A. This is to be compared with the estimate of 12.9 A that would be expected by a random prediction, using the model of F. Cohen and M. Sternberg (J. Mol. Biol. 138:321-333, 1980).

Algorithms↗

PubMed2005

Relationship between multiple sequence alignments and quality of protein comparative models.

Comparative modeling is the method of choice, whenever applicable, for protein structure prediction, not only because of its higher accuracy compared to alternative methods, but also because it is possible to estimate a priori the quality of the models that it can produce, thereby allowing the usefulness of a model for a given application to be assessed beforehand. By and large, the quality of a comparative model depends on two factors: the extent of structural divergence between the target and the template and the quality of the sequence alignment between the two protein sequences. The latter is usually derived from a multiple sequence alignment (MSA) of as many proteins of the family as possible, and its accuracy depends on the number and similarity distribution of the sequences of the protein family. Here we describe a method to evaluate the expected difficulty, and by extension accuracy, of a comparative model on the basis of the MSA used to build it. The parameter that we derive is used to compare the results obtained in the last two editions of the Critical Assessment of Methods for Structure Prediction (CASP) experiment as a function of the difficulty of the modeling exercise. Our analysis demonstrates that the improvement in the scope and quality of comparative models between the two experiments is largely due to the increased number of available protein sequences and to the consequent increased chance that a large and appropriately spaced set of protein sequences homologous to the proteins of interest is available.

Amino Acid Sequence↗