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At least 19 recordsLinked to original sources
PubMed1989

A tool for multiple sequence alignment.

Multiple sequence alignment can be a useful technique for studying molecular evolution and analyzing sequence-structure relationships. Until recently, it has been impractical to apply dynamic programming, the most widely accepted method for producing pairwise alignments, to comparisons of more than three sequences. We describe the design and application of a tool for multiple alignment of amino acid sequences that implements a new algorithm that greatly reduces the computational demands of dynamic programming. This tool is able to align in reasonable time as many as eight sequences the length of an average protein.

Algorithms↗
PubMed2026

A reinforcement learning-enhanced fuzzy multi-objective equilibrium optimization framework for multiple sequence alignment.

Multiple sequence alignment (MSA) is a fundamental task in bioinformatics, underpinning comparative genomics, structural analysis, and evolutionary inference. However, MSA remains a challenging multi-objective optimization problem due to the need to simultaneously maximize alignment accuracy, preserve conserved regions, and control gap proliferation, particularly in large and heterogeneous sequence collections. In this work, we propose MOFSACEO-MSA, a novel hybrid optimization framework for multiple sequence alignment that integrates a fuzzy multi-objective evaluation scheme with the Equilibrium Optimizer (EO) and a Soft Actor-Critic (SAC)-based adaptive control mechanism. The proposed framework formulates MSA as a dynamic multi-objective optimization problem, in which alignment quality is assessed using complementary residue-level and column-level criteria, including Sum-of-Pairs score, column conservation, entropy, and gap statistics. Fuzzy membership functions are employed to harmonize competing objectives into a unified optimization landscape, while EO provides robust global exploration. To further enhance adaptability, SAC dynamically regulates key EO parameters during the search process, enabling an effective balance between exploration and exploitation across datasets of varying size and heterogeneity. Extensive experiments werew conducted on diverse biological sequence datasets, with a primary focus on RNA benchmarks, including structured families from Rfam, large-scale repositories from RNAcentral and GenBank, and organism-specific tRNA datasets from GtRNAdb. Comparative evaluations against classical alignment tools (ClustalW, MAFFT, MUSCLE, PRANK, KAlign, and T-Coffee), metaheuristic methods (SAGA, Sequoya and EAFSA), and a reinforcement learning-based approach (RLALIGN) demonstrate that MOFSACEO-MSA consistently achieves competitive or superior Sum-of-Pairs scores while significantly reducing gap proportions and maintaining compact alignment lengths. Notably, the proposed framework exhibits improved robustness on large and highly heterogeneous datasets, where existing methods often suffer from excessive gap insertion or unstable convergence. Overall, MOFSACEO-MSA provides a flexible and extensible optimization paradigm that effectively bridges evolutionary search and reinforcement learning for high-quality multiple sequence alignment, with demonstrated effectiveness on challenging RNA alignment tasks.

Sequence Alignment↗
PubMed1992

MALIGNED: a multiple sequence alignment editor.

A multiple sequence alignment editor is described which runs on a VAX/VMS system and can exchange data with a number of other programs, including those of the Genetics Computer Group (GCG). Up to 199 sequences can be aligned. The quality of the alignment can be easily judged during its development because the display attributes to each character are determined by the way it matches the other sequences. Four methods are available for calculating the highlighting to emphasize different aspects of the relationships of the sequences and up to four styles of highlighting can be used at the same time. Laser printer output is suitable for publication without modification.

Molecular Sequence Data↗
PubMed1989

Sequence alignment of citrate synthase proteins using a multiple sequence alignment algorithm and multiple scoring matrices.

The alignment of Escherichia coli citrate synthase to pig heart citrate synthase and the multiple alignment of the known sequences of the citrate synthase family of enzymes have been performed using six different amino acid similarity scoring matrices and a large range of gap penalty ratios for insertions and deletions of amino acids. The alignment studies have been performed as the first step in a project aimed at homology modelling E. coli citrate synthase (a hexamer) from pig heart citrate synthase (a dimer) in a molecular modelling approach to the study of multi-subunit enzymes. The effects of several important variables in producing realistic alignments have been investigated. The difference between multiple alignment of the family of enzymes versus simple pairwise alignment of the pig heart and E. coli proteins was explored. The effects of initial separate multiple alignments of the most highly related or most homologous species of the family of enzymes upon a subsequent pairwise alignment between species was evaluated. The value of 'fingerprinting' certain residues to bias the alignment in favour of matching those residues, as well as the worth of the computerized approach compared to an intuitive alignment technique, were assessed.

Algorithms↗
PubMed1987

Multiple sequence alignment by a pairwise algorithm.

An algorithm is described that processes the results of a conventional pairwise sequence alignment program to automatically produce an unambiguous multiple alignment of many sequences. Unlike other, more complex, multiple alignment programs, the method described here is fast enough to be used on almost any multiple sequence alignment problem.

Algorithms↗
PubMed1989

A method for multiple sequence alignment with gaps.

A method that performs multiple sequence alignment by cyclical use of the standard pairwise Needleman-Wunsch algorithm is presented. The required central processor unit time is of the same order of magnitude as the standard Needleman-Wunsch pairwise implementation. Comparison with the one known case where the optimal multiple sequence alignment has been rigorously determined shows that in practice the proposed method finds the mathematically optimal solution. The more interesting question of the biological usefulness of such multiple sequence alignment over pairwise approaches is assessed using protein families whose X-ray structures are known. The two such cases studied, the subdomains of the ricin B-chain and the S-domains of virus coat proteins, have low pairwise similarity and thus fail to align correctly under standard pairwise sequence comparison. In both cases the multiple sequence alignment produced by the proposed technique, apart from minor deviations at loop regions, correctly predicts the true structural alignment. Thus, given many sequences of low pairwise similarity, the proposed multiple sequence method, can extract any familial similarity and so produce a sequence alignment consistent with the underlying structural homology.

Algorithms↗
PubMed2026

Fast, accurate construction of multiple sequence alignments from protein language embeddings.

Multiple sequence alignment (MSA) is a foundational task in computational biology, underpinning protein structure prediction, evolutionary analysis, and domain annotation. Traditional MSA algorithms rely on pairwise amino acid substitution matrices derived from conserved protein families. While effective for aligning closely related sequences, these scoring schemes struggle in the low-identity "twilight zone." Here, we present a new approach for constructing MSAs leveraging amino acid embeddings generated by protein language models (PLMs), which capture rich evolutionary and contextual information from massive and diverse sequence datasets. We introduce a windowed reciprocal-weighted embedding similarity metric that is surprisingly effective in identifying corresponding amino acids across sequences. Building on this metric, we develop ARIES (Alignment via RecIprocal Embedding Similarity), an algorithm that constructs a PLM-generated template embedding and aligns each sequence to this template via dynamic time warping in order to build a global MSA. Across diverse benchmark datasets, ARIES achieves higher accuracies than existing state-of-the-art approaches, especially in low-identity regimes where traditional methods degrade, while scaling almost linearly with the number of sequences to be aligned. Together, these results provide the first large-scale demonstration of the power of PLMs for accurate and scalable MSA construction across protein families of varying sizes and levels of similarity, highlighting the potential of PLMs to transform comparative sequence analysis.

Deep Learning↗
PubMed1992

CLUSTAL V: improved software for multiple sequence alignment.

The CLUSTAL package of multiple sequence alignment programs has been completely rewritten and many new features added. The new software is a single program called CLUSTAL V, which is written in C and can be used on any machine with a standard C compiler. The main new features are the ability to store and reuse old alignments and the ability to calculate phylogenetic trees after alignment. The program is simple to use, completely menu driven and on-line help is provided.

Phylogeny↗
PubMed1988

Multiple sequence alignment with hierarchical clustering.

An algorithm is presented for the multiple alignment of sequences, either proteins or nucleic acids, that is both accurate and easy to use on microcomputers. The approach is based on the conventional dynamic-programming method of pairwise alignment. Initially, a hierarchical clustering of the sequences is performed using the matrix of the pairwise alignment scores. The closest sequences are aligned creating groups of aligned sequences. Then close groups are aligned until all sequences are aligned in one group. The pairwise alignments included in the multiple alignment form a new matrix that is used to produce a hierarchical clustering. If it is different from the first one, iteration of the process can be performed. The method is illustrated by an example: a global alignment of 39 sequences of cytochrome c.

Algorithms↗
PubMed1986

Multiple sequence alignment by consensus.

An algorithm for multiple sequence alignment is given that matches words of length and degree of mismatch chosen by the user. The alignment maximizes an alignment scoring function. The method is based on a novel extension of our consensus sequence methods. The algorithm works for both DNA and protein sequences, and from earlier work on consensus sequences, it is possible to estimate statistical significance.

Algorithms↗
PubMed1988

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.

Algorithms↗
PubMed1989

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

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.

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↗
PubMed1991

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↗
PubMed2025

CREMSA: compressed indexing of (ultra) large multiple sequence alignments.

MOTIVATION: Recent viral outbreaks motivate the systematic collection of pathogenic genomes in order to accelerate their study and monitor the apparition/spread of variants. Due to their limited length and temporal proximity of their sequencing, viral genomes are usually organized, and analyzed as oversized Multiple Sequence Alignments (MSAs). Such MSAs are largely ungapped, and mostly homogeneous on a column-wise level but not at a sequential level due to local variations, hindering the performances of sequential compression algorithms. RESULTS: In order to enable an efficient handling of MSAs, including subsequent statistical analyses, we introduce CREMSA (Column-wise Run-length Encoding for MSAs), a new index that builds on sparse bitvector representations to compress an existing or streamed MSA, all the while allowing for an expressive set of accelerated requests to query the alignment without prior decompression. Using CREMSA, a 65 GB MSA consisting of 1.9M SARS-CoV 2 genomes could be compressed into 22 MB using less than half a gigabyte of main memory, while executing access requests in the order of 100 ns. Such a speed up enables a comprehensive analysis of covariation over this very large MSA. We further assess the impact of the sequence ordering on the compressibility of MSAs and propose a resorting strategy that, despite the proven NP-hardness of an optimal sort, induces greatly increased compression ratios at a marginal computational cost. AVAILABILITY AND IMPLEMENTATION: CREMSA is freely accessible at https://gitlab.univ-lille.fr/cremsa/cremsa. The Snakemake workflow for the benchmarks is available at: https://gitlab.univ-lille.fr/cremsa/bench. The data used in the paper is on Zenodo at https://zenodo.org/records/14698859 and https://zenodo.org/records/15100011.

SARS-CoV-2↗
PubMed1989

Gap costs for multiple sequence alignment.

Standard methods for aligning pairs of biological sequences charge for the most common mutations, which are substitutions, deletions and insertions. Because a single mutation may insert or delete several nucleotides, gap costs that are not directly proportional to gap length are usually the most effective. How to extend such gap costs to alignments of three or more sequences is not immediately obvious, and a variety of approaches have been taken. This paper argues that, since gap and substitution costs together specify optimal alignments, they should be defined using a common rationale. Specifically, a new definition of gap costs for multiple alignments is proposed and compared with previous ones. Since the new definition links a multiple alignment's cost to that of its pairwise projections, it allows knowledge gained about two-sequence alignments to bear on the multiple alignment problem. Also, such linkage is a key element of recent algorithms that have rendered practical the simultaneous alignment of as many as six sequences.

Animals↗
PubMed1992

Mast cell tryptases: examination of unusual characteristics by multiple sequence alignment and molecular modeling.

Tryptases are trypsin-like serine proteinases found in the granules of mast cells. Although they show 40% sequence identity with trypsin and contain only 20 or 21 additional residues, tryptases display several unusual features. Unlike trypsin, the tryptases only make limited cleavages in a few proteins and are not inhibited by natural trypsin inhibitors, they form tetramers, bind heparin, and their activity on synthetic substrates is progressively inhibited as the concentration of salt increases above 0.2 M. Unique sequence features of seven tryptases were identified by comparison to other serine proteinases. The three-dimensional structures of the tryptases were then predicted by molecular modeling based on the crystal structure of bovine trypsin. The models show two large insertions to lie on either side of the active-site cleft, suggesting an explanation for the limited activity of tryptases on protein substrates and the lack of inhibition by natural inhibitors. A group of conserved Trp residues and a unique proline-rich region make two surface hydrophobic patches that may account for the formation of tetramers and/or inhibition with increasing salt. Although they contain no consensus heparin-binding sequence, the tryptases have 10-13 more His residues than trypsin, and these are positioned on the surface of the model. In addition, clustering of Arg and Lys residues may also contribute to heparin binding. Putative Asn-linked glycosylation sites are found on the opposite side of the model from the active site. The model provides structural explanations for some to the unusual characteristics of the tryptases and a rational basis for future experiments, such as site-directed mutagenesis.

Amino Acid Sequence↗
PubMed1986

Multiple sequence alignment.

A method has been developed for aligning segments of several sequences at once. The number of search steps depends only polynomially on the number of sequences, instead of exponentially, because most alignments are rejected without being evaluated explicitly. A data structure herein called the "heap" facilitates this process. For a set of n sequence segments, the overall similarity is taken to be the sum of all the constituent segment pair similarities, which are in turn sums of corresponding residue similarity scores from a Table. The statistical models that test alignments for significance make it possible to group sequences objectively, even when most or all of the interrelationships are weak. These tests are very sensitive, while remaining quite conservative, and discourage the addition of "misfit" sequences to an existing set. The new techniques are applied to a set of five DNA-binding proteins, to a group of three enzymes that employ the coenzyme FAD, and to a control set. The alignment previously proposed for the DNA-binding proteins on the basis of structural comparisons and inspection of sequences is supported quite dramatically, and a highly significant alignment is found for the FAD-binding proteins.

4-Hydroxybenzoate-3-Monooxygenase↗