A strategy for the rapid multiple alignment of protein sequences. Confidence levels from tertiary structure comparisons

An algorithm is presented for the multiple alignment of protein sequences that is both accurate and rapid computationally. The approach is based on the conventional dynamic-programming method of pairwise alignment. Initially, two sequences are aligned, then the third sequence is aligned against the alignment of both sequences one and two. Similarly, the fourth sequence is aligned against one, two and three. This is repeated until all sequences have been aligned. Iteration is then performed to yield a final alignment. The accuracy of sequence alignment is evaluated from alignment of the secondary structures in a family of proteins. For the globins, the multiple alignment was on average 99% accurate compared to 90% for pairwise comparison of sequences. For the alignment of immunoglobulin constant and variable domains, the use of many sequences yielded an alignment of 63% average accuracy compared to 41% average for individual variable/constant alignments. The multiple alignment algorithm yields an assignment of disulphide connectivity in mammalian serotransferrin that is consistent with crystallographic data, whereas pairwise alignments give an alternative assignment.     

Improving accuracy of multiple sequence alignment algorithms based on alignment of neighboring residues

While most of the recent improvements in multiple sequence alignment accuracy are due to better use of vertical information, which include the incorporation of consistency-based pairwise alignments and the use of profile alignments, we observe that it is possible to further improve accuracy by taking into account alignment of neighboring residues when aligning two residues, thus making better use of horizontal information. By modifying existing multiple alignment algorithms to make use of horizontal information, we show that this strategy is able to consistently improve over existing algorithms on a few sets of benchmark alignments that are commonly used to measure alignment accuracy, and the average improvements in accuracy can be as much as 1–3% on protein sequence alignment and 5–10% on DNA/RNA sequence alignment. Unlike previous algorithms, consistent average improvements can be obtained across all identity levels.     

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 (>10000) 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.     

Analysis on sliding helices and strands in protein structural comparisons: a case study with protein kinases

Protein structural alignments are generally considered as 'golden standard' for the alignment at the level of amino acid residues. In this study we have compared the quality of pairwise and multiple structural alignments of about 5900 homologous proteins from 718 families of known 3-D structures. We observe shifts in the alignment of regular secondary structural elements (helices and strands) between pairwise and multiple structural alignments. The differences between pairwise and multiple structural alignments within helical and beta-strand regions often correspond to 4 and 2 residue positions respectively. Such shifts correspond approximately to "one turn" of these regular secondary structures. We have performed manual analysis explicitly on the family of protein kinases. We note shifts of one or two turns in helix-helix alignments obtained using pairwise and multiple structural alignments. Investigations on the quality of the equivalent helix-helix, strand-strand pairs in terms of their residue side-chain accessibilities have been made. Our results indicate that the quality of the pairwise alignments is comparable to that of the multiple structural alignments and, in fact, is often better. We propose that pairwise alignment of protein structures should also be used in formulation of methods for structure prediction and evolutionary analysis.     

Vorolign--fast structural alignment using Voronoi contacts

Vorolign, a fast and flexible structural alignment method for two or more protein structures is introduced. The method aligns protein structures using double dynamic programming and measures the similarity of two residues based on the evolutionary conservation of their corresponding Voronoi-contacts in the protein structure. This similarity function allows aligning protein structures even in cases where structural flexibilities exist. Multiple structural alignments are generated from a set of pairwise alignments using a consistency-based, progressive multiple alignment strategy. RESULTS: The performance of Vorolign is evaluated for different applications of protein structure comparison, including automatic family detection as well as pairwise and multiple structure alignment. Vorolign accurately detects the correct family, superfamily or fold of a protein with respect to the SCOP classification on a set of difficult target structures. A scan against a database of >4000 proteins takes on average 1 min per target. The performance of Vorolign in calculating pairwise and multiple alignments is found to be comparable with other pairwise and multiple protein structure alignment methods. AVAILABILITY: Vorolign is freely available for academic users as a web server at http://www.bio.ifi.lmu.de/Vorolign     

Analysis on sliding helices and strands in protein structural comparisons: A case study with protein kinases

Protein structural alignments are generally considered as ‘golden standard’ for the alignment at the level of amino acid residues. In this study we have compared the quality of pairwise and multiple structural alignments of about 5900 homologous proteins from 718 families of known 3-D structures. We observe shifts in the alignment of regular secondary structural elements (helices and strands) between pairwise and multiple structural alignments. The differences between pairwise and multiple structural alignments within helical and β-strand regions often correspond to 4 and 2 residue positions respectively. Such shifts correspond approximately to “one turn” of these regular secondary structures. We have performed manual analysis explicitly on the family of protein kinases. We note shifts of one or two turns in helix-helix alignments obtained using pairwise and multiple structural alignments. Investigations on the quality of the equivalent helix-helix, strand-strand pairs in terms of their residue side-chain accessibilities have been made. Our results indicate that the quality of the pairwise alignments is comparable to that of the multiple structural alignments and, in fact, is often better. We propose that pairwise alignment of protein structures should also be used in formulation of methods for structure prediction and evolutionary analysis.     

Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments

Background  

While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.     

Multiple sequence alignment by conformational space annealing

We present a new method for multiple sequence alignment (MSA), which we call MSACSA. The method is based on the direct application of a global optimization method called the conformational space annealing (CSA) to a consistency-based score function constructed from pairwise sequence alignments between constituting sequences. We applied MSACSA to two MSA databases, the 82 families from the BAliBASE reference set 1 and the 366 families from the HOMSTRAD set. In all 450 cases, we obtained well optimized alignments satisfying more pairwise constraints producing, in consequence, more accurate alignments on average compared with a recent alignment method SPEM. One of the advantages of MSACSA is that it provides not just the global minimum alignment but also many distinct low-lying suboptimal alignments for a given objective function. This is due to the fact that conformational space annealing can maintain conformational diversity while searching for the conformations with low energies. This characteristics can help us to alleviate the problem arising from using an inaccurate score function. The method was the key factor for our success in the recent blind protein structure prediction experiment.     

A polynomial time solvable formulation of multiple sequence alignment.

Since traditional multiple alignment formulations are NP-hard, heuristics are commonly employed to find acceptable alignments with no guaranteed performance bound. This causes a substantial difficulty in understanding what the resulting alignment means and in assessing the quality of these alignments. We propose an alternative formulation of multiple alignment based on the idea of finding a multiple alignment of k sequences which preserves k - 1 pairwise alignments as specified by edges of a given tree. Although it is well known that such a preserving alignment always exists, it did not become a mainstream method for multiple alignment since it seems that a lot of information is lost from ignoring pairwise similarities outside the tree. In contrast, by using pairwise alignments that incorporate consistency information from other sequences, we show that it is possible to obtain very good accuracy with the preserving alignment formulation. We show that a reasonable objective function to use is to find the shortest preserving alignment, and, by a reduction to a graph-theoretic problem, that the problem of finding the shortest preserving multiple alignment can be solved in polynomial time. We demonstrate the success of this approach on three sets of benchmark multiple alignments by using consistency-based pairwise alignments from the first stage of two of the best performing progressive alignment algorithms TCoffee and ProbCons and replace the second heuristic progressive step of these algorithms by the exact preserving alignment step. We apply this strategy to TCoffee and show that our approach outperforms TCoffee on two of the three test sets. We apply the strategy to a variant of ProbCons with no iterative refinements and show that our approach achieves similar or better accuracy except on one test set. We also compare our performance to ProbCons with iterative refinements and show that our approach achieves similar or better accuracy on many subcategories even without further refinements. The most important advantage of the preserving alignment formulation is that we are certain that we can solve the problem in polynomial time without using a heuristic. A software program implementing this approach (PSAlign) is available at http://faculty.cs.tamu.edu/shsze/psalign.     

A flexible multiple sequence alignment program

The 'regions' method for multisequence alignment used in the previously reported program MALIGN has been generalized to include recursive refinement so that unaligned portions between two regions at the current level of resolution can be handled with increased resolution. Additionally, there is incorporated a limiting of the number of regions to be used at any level of resolution from which to abstract an alignment. This provides a significant increase in speed over the unlimited version. The program GENALIGN uses this improved regions method to execute fast pairwise alignments in the framework of Taylor's multisequence alignment procedure using clustered pairwise alignments. Pairwise alignments by dynamic programming are also provided in the program.     

CLUSTAL: a package for performing multiple sequence alignment on a microcomputer

An approach for performing multiple alignments of large numbers of amino acid or nucleotide sequences is described. The method is based on first deriving a phylogenetic tree from a matrix of all pairwise sequence similarity scores, obtained using a fast pairwise alignment algorithm. Then the multiple alignment is achieved from a series of pairwise alignments of clusters of sequences, following the order of branching in the tree. The method is sufficiently fast and economical with memory to be easily implemented on a microcomputer, and yet the results obtained are comparable to those from packages requiring mainframe computer facilities.     

The deterministic effects of alignment bias in phylogenetic inference

Alignment of nucleotide and/or amino acid sequences is a fundamental component of sequence‐based molecular phylogenetic studies. Here we examined how different alignment methods affect the phylogenetic trees that are inferred from the alignments. We used simulations to determine how alignment errors can lead to systematic biases that affect phylogenetic inference from those sequences. We compared four approaches to sequence alignment: progressive pairwise alignment, simultaneous multiple alignment of sequence fragments, local pairwise alignment and direct optimization. When taking into account branch support, implied alignments produced by direct optimization were found to show the most extreme behaviour (based on the alignment programs for which nearly equivalent alignment parameters could be set) in that they provided the strongest support for the correct tree in the simulations in which it was easy to resolve the correct tree and the strongest support for the incorrect tree in our long‐branch‐attraction simulations. When applied to alignment‐sensitive process partitions with different histories, direct optimization showed the strongest mutual influence between the process partitions when they were aligned and phylogenetically analysed together, which makes detecting recombination more difficult. Simultaneous alignment performed well relative to direct optimization and progressive pairwise alignment across all simulations. Rather than relying upon methods that integrate alignment and tree search into a single step without accounting for alignment uncertainty, as with implied alignments, we suggest that simultaneous alignment using the similarity criterion, within the context of information available on biological processes and function, be applied whenever possible for sequence‐based phylogenetic analyses.     

Accuracy of structure-based sequence alignment of automatic methods

Background  

Accurate sequence alignments are essential for homology searches and for building three-dimensional structural models of proteins. Since structure is better conserved than sequence, structure alignments have been used to guide sequence alignments and are commonly used as the gold standard for sequence alignment evaluation. Nonetheless, as far as we know, there is no report of a systematic evaluation of pairwise structure alignment programs in terms of the sequence alignment accuracy.     

MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons

MOTIVATION: Due to the importance of considering secondary structures in aligning functional RNAs, several pairwise sequence-structure alignment methods have been developed. They use extended alignment scores that evaluate secondary structure information in addition to sequence information. However, two problems for the multiple alignment step remain. First, how to combine pairwise sequence-structure alignments into a multiple alignment and second, how to generate secondary structure information for sequences whose explicit structural information is missing. RESULTS: We describe a novel approach for multiple alignment of RNAs (MARNA) taking into consideration both the primary and the secondary structures. It is based on pairwise sequence-structure comparisons of RNAs. From these sequence-structure alignments, libraries of weighted alignment edges are generated. The weights reflect the sequential and structural conservation. For sequences whose secondary structures are missing, the libraries are generated by sampling low energy conformations. The libraries are then processed by the T-Coffee system, which is a consistency based multiple alignment method. Furthermore, we are able to extract a consensus-sequence and -structure from a multiple alignment. We have successfully tested MARNA on several datasets taken from the Rfam database.     

Consistency of optimal sequence alignments

Pairwise optimal alignments between three or more sequences are not necessarily consistent as a whole, but consistent and inconsistent residues are usually distributed in clusters. An efficient method has been developed for locating consistent regions when each pairwise alignment is given in the form of a “skeletal representation” (Bull. math. Biol. 52, 359–373). This method is further extended so that the combination of pairwise alignments that gives the greatest consistency is found when possibly many alignments are equally optimal for each pairwise comparison. A method for acceleration of simultaneous multiple sequence alignment is proposed in which consistent regions serve as “anchor points” limiting application of direct multi-way alignment to the rest of “inconsistent” regions. Dedicated to Prof. Akiyoshi Wada on the occasion of his 60^th birthday.     

Development and validation of a consistency based multiple structure alignment algorithm

SUMMARY: We introduce an algorithm that uses the information gained from simultaneous consideration of an entire group of related proteins to create multiple structure alignments (MSTAs). Consistency-based alignment (CBA) first harnesses the information contained within regions that are consistently aligned among a set of pairwise superpositions in order to realign pairs of proteins through both global and local refinement methods. It then constructs a multiple alignment that is maximally consistent with the improved pairwise alignments. We validate CBA's alignments by assessing their accuracy in regions where at least two of the aligned structures contain the same conserved sequence motif. RESULTS: CBA correctly aligns well over 90% of motif residues in superpositions of proteins belonging to the same family or superfamily, and it outperforms a number of previously reported MSTA algorithms.     

SFESA: a web server for pairwise alignment refinement by secondary structure shifts

Background

Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate.

Results

We developed the SFESA web server to refine pairwise protein sequence alignments. Compared to the previous version of SFESA, which required a set of 3D coordinates for a protein, the new server will search a sequence database for the closest homolog with an available 3D structure to be used as a template. For each alignment block defined by secondary structure elements in the template, SFESA evaluates alignment variants generated by local shifts and selects the best-scoring alignment variant. A scoring function that combines the sequence score of profile-profile comparison and the structure score of template-derived contact energy is used for evaluation of alignments. PROMALS pairwise alignments refined by SFESA are more accurate than those produced by current advanced alignment methods such as HHpred and CNFpred. In addition, SFESA also improves alignments generated by other software.

Conclusions

SFESA is a web-based tool for alignment refinement, designed for researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. To our knowledge, the SFESA web server is the only tool that refines alignments by evaluating local shifts of secondary structure elements. The SFESA web server is available at http://prodata.swmed.edu/sfesa.     

Visualization of near-optimal sequence alignments

MOTIVATION: Mathematically optimal alignments do not always properly align active site residues or well-recognized structural elements. Most near-optimal sequence alignment algorithms display alternative alignment paths, rather than the conventional residue-by-residue pairwise alignment. Typically, these methods do not provide mechanisms for finding effectively the most biologically meaningful alignment in the potentially large set of options. RESULTS: We have developed Web-based software that displays near optimal or alternative alignments of two protein or DNA sequences as a continuous moving picture. A WWW interface to a C++ program generates near optimal alignments, which are sent to a Java Applet, which displays them in a series of alignment frames. The Applet aligns residues so that consistently aligned regions remain at a fixed position on the display, while variable regions move. The display can be stopped to examine alignment details.     

PSAR: measuring multiple sequence alignment reliability by probabilistic sampling

Multiple sequence alignment, which is of fundamental importance for comparative genomics, is a difficult problem and error-prone. Therefore, it is essential to measure the reliability of the alignments and incorporate it into downstream analyses. We propose a new probabilistic sampling-based alignment reliability (PSAR) score. Instead of relying on heuristic assumptions, such as the correlation between alignment quality and guide tree uncertainty in progressive alignment methods, we directly generate suboptimal alignments from an input multiple sequence alignment by a probabilistic sampling method, and compute the agreement of the input alignment with the suboptimal alignments as the alignment reliability score. We construct the suboptimal alignments by an approximate method that is based on pairwise comparisons between each single sequence and the sub-alignment of the input alignment where the chosen sequence is left out. By using simulation-based benchmarks, we find that our approach is superior to existing ones, supporting that the suboptimal alignments are highly informative source for assessing alignment reliability. We apply the PSAR method to the alignments in the UCSC Genome Browser to measure the reliability of alignments in different types of regions, such as coding exons and conserved non-coding regions, and use it to guide cross-species conservation study.