Arabidopsis proteins containing similarity to the universal stress protein domain of bacteria

We have collected a set of 44 Arabidopsis proteins with similarity to the USPA (universal stress protein A of Escherichia coli) domain of bacteria. The USPA domain is found either in small proteins, or it makes up the N-terminal portion of a larger protein, usually a protein kinase. Phylogenetic tree analysis based upon a multiple sequence alignment of the USPA domains shows that these domains of protein kinases 1.3.1 and 1.3.2 form distinct groups, as do the protein kinases 1.4.1. This indicates that their USPA domain structures have diverged appreciably and suggests that they may subserve distinct cellular functions. Two USPA fold classes have been proposed: one based on Methanococcus jannaschii MJ0577 (1MJH) that binds ATP, and the other based on the Haemophilus influenzae universal stress protein (1JMV), highly similar to E. coli UspA, which does not bind ATP. A set of common residues involved in ATP binding in 1MJH and conserved in similar bacterial sequences is also found in a distinct cluster of Arabidopsis sequences. Threading analysis, which examines aspects of secondary and tertiary structure, confirms this Arabidopsis sequence cluster as highly similar to 1MJH. This structural approach can distinguish between the characteristic fold differences of 1MJH-like and 1JMV-like bacterial proteins and was used to assign the complete set of candidate Arabidopsis proteins to one of these fold classes. It is clear that all the plant sequences have arisen from a 1MJH-like ancestor.     

Three-dimensional cluster analysis identifies interfaces and functional residue clusters in proteins

Three-dimensional cluster analysis offers a method for the prediction of functional residue clusters in proteins. This method requires a representative structure and a multiple sequence alignment as input data. Individual residues are represented in terms of regional alignments that reflect both their structural environment and their evolutionary variation, as defined by the alignment of homologous sequences. From the overall (global) and the residue-specific (regional) alignments, we calculate the global and regional similarity matrices, containing scores for all pairwise sequence comparisons in the respective alignments. Comparing the matrices yields two scores for each residue. The regional conservation score (C(R)(x)) defines the conservation of each residue x and its neighbors in 3D space relative to the protein as a whole. The similarity deviation score (S(x)) detects residue clusters with sequence similarities that deviate from the similarities suggested by the full-length sequences. We evaluated 3D cluster analysis on a set of 35 families of proteins with available cocrystal structures, showing small ligand interfaces, nucleic acid interfaces and two types of protein-protein interfaces (transient and stable). We present two examples in detail: fructose-1,6-bisphosphate aldolase and the mitogen-activated protein kinase ERK2. We found that the regional conservation score (C(R)(x)) identifies functional residue clusters better than a scoring scheme that does not take 3D information into account. C(R)(x) is particularly useful for the prediction of poorly conserved, transient protein-protein interfaces. Many of the proteins studied contained residue clusters with elevated similarity deviation scores. These residue clusters correlate with specificity-conferring regions: 3D cluster analysis therefore represents an easily applied method for the prediction of functionally relevant spatial clusters of residues in proteins.     

A structure and evolution-guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of proteins

MOTIVATION: Various multiple sequence alignment-based methods have been proposed to detect functional surfaces in proteins, such as active sites or protein interfaces. The effect that the choice of sequences has on the conclusions of such analysis has seldom been discussed. In particular, no method has been discussed in terms of its ability to optimize the sequence selection for the reliable detection of functional surfaces. RESULTS: Here we propose, for the case of proteins with known structure, a heuristic Metropolis Monte Carlo strategy to select sequences from a large set of homologues, in order to improve detection of functional surfaces. The quantity guiding the optimization is the clustering of residues which are under increased evolutionary pressure, according to the sample of sequences under consideration. We show that we can either improve the overlap of our prediction with known functional surfaces in comparison with the sequence similarity criteria of selection or match the quality of prediction obtained through more elaborate non-structure based-methods of sequence selection. For the purpose of demonstration we use a set of 50 homodimerizing enzymes which were co-crystallized with their substrates and cofactors.     

Estimation of amino acid residue substitution rates at local spatial regions and application in protein function inference: a Bayesian Monte Carlo approach

The amino acid sequences of proteins provide rich information for inferring distant phylogenetic relationships and for predicting protein functions. Estimating the rate matrix of residue substitutions from amino acid sequences is also important because the rate matrix can be used to develop scoring matrices for sequence alignment. Here we use a continuous time Markov process to model the substitution rates of residues and develop a Bayesian Markov chain Monte Carlo method for rate estimation. We validate our method using simulated artificial protein sequences. Because different local regions such as binding surfaces and the protein interior core experience different selection pressures due to functional or stability constraints, we use our method to estimate the substitution rates of local regions. Our results show that the substitution rates are very different for residues in the buried core and residues on the solvent-exposed surfaces. In addition, the rest of the proteins on the binding surfaces also have very different substitution rates from residues. Based on these findings, we further develop a method for protein function prediction by surface matching using scoring matrices derived from estimated substitution rates for residues located on the binding surfaces. We show with examples that our method is effective in identifying functionally related proteins that have overall low sequence identity, a task known to be very challenging.     

Accurate detection of very sparse sequence motifs.

Protein sequence alignments are more reliable the shorter the evolutionary distance. Here, we align distantly related proteins using many closely spaced intermediate sequences as stepping stones. Such transitive alignments can be generated between any two proteins in a connected set, whether they are direct or indirect sequence neighbors in the underlying library of pairwise alignments. We have implemented a greedy algorithm, MaxFlow, using a novel consistency score to estimate the relative likelihood of alternative paths of transitive alignment. In contrast to traditional profile models of amino acid preferences, MaxFlow models the probability that two positions are structurally equivalent and retains high information content across large distances in sequence space. Thus, MaxFlow is able to identify sparse and narrow active-site sequence signatures which are embedded in high-entropy sequence segments in the structure based multiple alignment of large diverse enzyme superfamilies. In a challenging benchmark based on the urease superfamily, MaxFlow yields better reliability and double coverage compared to available sequence alignment software. This promises to increase information returns from functional and structural genomics, where reliable sequence alignment is a bottleneck to transferring the functional or structural characterization of model proteins to entire protein superfamilies.     

Joint Evolutionary Trees: A Large-Scale Method To Predict Protein Interfaces Based on Sequence Sampling

The Joint Evolutionary Trees (JET) method detects protein interfaces, the core residues involved in the folding process, and residues susceptible to site-directed mutagenesis and relevant to molecular recognition. The approach, based on the Evolutionary Trace (ET) method, introduces a novel way to treat evolutionary information. Families of homologous sequences are analyzed through a Gibbs-like sampling of distance trees to reduce effects of erroneous multiple alignment and impacts of weakly homologous sequences on distance tree construction. The sampling method makes sequence analysis more sensitive to functional and structural importance of individual residues by avoiding effects of the overrepresentation of highly homologous sequences and improves computational efficiency. A carefully designed clustering method is parametrized on the target structure to detect and extend patches on protein surfaces into predicted interaction sites. Clustering takes into account residues' physical-chemical properties as well as conservation. Large-scale application of JET requires the system to be adjustable for different datasets and to guarantee predictions even if the signal is low. Flexibility was achieved by a careful treatment of the number of retrieved sequences, the amino acid distance between sequences, and the selective thresholds for cluster identification. An iterative version of JET (iJET) that guarantees finding the most likely interface residues is proposed as the appropriate tool for large-scale predictions. Tests are carried out on the Huang database of 62 heterodimer, homodimer, and transient complexes and on 265 interfaces belonging to signal transduction proteins, enzymes, inhibitors, antibodies, antigens, and others. A specific set of proteins chosen for their special functional and structural properties illustrate JET behavior on a large variety of interactions covering proteins, ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf, Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significant improvement in performance and computational efficiency is shown.     

Structural clusters of evolutionary trace residues are statistically significant and common in proteins

Given the massive increase in the number of new sequences and structures, a critical problem is how to integrate these raw data into meaningful biological information. One approach, the Evolutionary Trace, or ET, uses phylogenetic information to rank the residues in a protein sequence by evolutionary importance and then maps those ranked at the top onto a representative structure. If these residues form structural clusters, they can identify functional surfaces such as those involved in molecular recognition. Now that a number of examples have shown that ET can identify binding sites and focus mutational studies on their relevant functional determinants, we ask whether the method can be improved so as to be applicable on a large scale. To address this question, we introduce a new treatment of gaps resulting from insertions and deletions, which streamlines the selection of sequences used as input. We also introduce objective statistics to assess the significance of the total number of clusters and of the size of the largest one. As a result of the novel treatment of gaps, ET performance improves measurably. We find evolutionarily privileged clusters that are significant at the 5% level in 45 out of 46 (98%) proteins drawn from a variety of structural classes and biological functions. In 37 of the 38 proteins for which a protein-ligand complex is available, the dominant cluster contacts the ligand. We conclude that spatial clustering of evolutionarily important residues is a general phenomenon, consistent with the cooperative nature of residues that determine structure and function. In practice, these results suggest that ET can be applied on a large scale to identify functional sites in a significant fraction of the structures in the protein databank (PDB). This approach to combining raw sequences and structure to obtain detailed insights into the molecular basis of function should prove valuable in the context of the Structural Genomics Initiative.     

CLEMAPS: multiple alignment of protein structures based on conformational letters

CLEMAPS is a tool for multiple alignment of protein structures. It distinguishes itself from other existing algorithms for multiple structure alignment by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of three angles formed by C(alpha) pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. The input 3D structures are first converted to sequences of conformational letters. Each string of a fixed length is then taken as the center seed to search other sequences for neighbors of the seed, which are strings similar to the seed. A seed and its neighbors form a center-star, which corresponds to a fragment set of local structural similarity shared by many proteins. The detection of center-stars using CLESUM is extremely efficient. Local similarity is a necessary, but insufficient, condition for structural alignment. Once center-stars are found, the spatial consistency between any two stars are examined to find consistent star duads using atomic coordinates. Consistent duads are later joined to create a core for multiple alignment, which is further polished to produce the final alignment. The utility of CLEMAPS is tested on various protein structure ensembles.     

Mapping the hyaluronan-binding site on the link module from human tumor necrosis factor-stimulated gene-6 by site-directed mutagenesis

Link modules are hyaluronan-binding domains found in extracellular proteins involved in matrix assembly, development, and immune cell migration. Previously we have expressed the Link module from the inflammation-associated protein tumor necrosis factor-stimulated gene-6 (TSG-6) and determined its tertiary structure in solution. Here we generated 21 Link module mutants, and these were analyzed by nuclear magnetic resonance spectroscopy and a hyaluronan-binding assay. The individual mutation of five amino acids, which form a cluster on one face of the Link module, caused large reductions in functional activity but did not affect the Link module fold. This ligand-binding site in TSG-6 is similar to that determined previously for the hyaluronan receptor, CD44, suggesting that the location of the interaction surfaces may also be conserved in other Link module-containing proteins. Analysis of the sequences of TSG-6 and CD44 indicates that the molecular details of their association with hyaluronan are likely to be significantly different. This comparison identifies key sequence positions that may be important in mediating hyaluronan binding, across the Link module superfamily. The use of multiple sequence alignment and molecular modeling allowed the prediction of functional residues in link protein, and this approach can be extended to all members of the superfamily.     

SnapDRAGON: a method to delineate protein structural domains from sequence data

We describe a method to identify protein domain boundaries from sequence information alone based on the assumption that hydrophobic residues cluster together in space. SnapDRAGON is a suite of programs developed to predict domain boundaries based on the consistency observed in a set of alternative ab initio three-dimensional (3D) models generated for a given protein multiple sequence alignment. This is achieved by running a distance geometry-based folding technique in conjunction with a 3D-domain assignment algorithm. The overall accuracy of our method in predicting the number of domains for a non-redundant data set of 414 multiple alignments, representing 185 single and 231 multiple-domain proteins, is 72.4 %. Using domain linker regions observed in the tertiary structures associated with each query alignment as the standard of truth, inter-domain boundary positions are delineated with an accuracy of 63.9 % for proteins comprising continuous domains only, and 35.4 % for proteins with discontinuous domains. Overall, domain boundaries are delineated with an accuracy of 51.8 %. The prediction accuracy values are independent of the pair-wise sequence similarities within each of the alignments. These results demonstrate the capability of our method to delineate domains in protein sequences associated with a wide variety of structural domain organisation.     

Heat Shock Protein 70 Family: Multiple Sequence Comparisons, Function, and Evolution

The heat shock protein 70 kDa sequences (HSP70) are of great importance as molecular chaperones in protein folding and transport. They are abundant under conditions of cellular stress. They are highly conserved in all domains of life: Archaea, eubacteria, eukaryotes, and organelles (mitochondria, chloroplasts). A multiple alignment of a large collection of these sequences was obtained employing our symmetric-iterative ITERALIGN program (Brocchieri and Karlin 1998). Assessments of conservation are interpreted in evolutionary terms and with respect to functional implications. Many archaeal sequences (methanogens and halophiles) tend to align best with the Gram-positive sequences. These two groups also miss a signature segment [about 25 amino acids (aa) long] present in all other HSP70 species (Gupta and Golding 1993). We observed a second signature sequence of about 4 aa absent from all eukaryotic homologues, significantly aligned in all prokaryotic sequences. Consensus sequences were developed for eight groups [Archaea, Gram-positive, proteobacterial Gram-negative, singular bacteria, mitochondria, plastids, eukaryotic endoplasmic reticulum (ER) isoforms, eukaryotic cytoplasmic isoforms]. All group consensus comparisons tend to summarize better the alignments than do the individual sequence comparisons. The global individual consensus ``matches' 87% with the consensus of consensuses sequence. A functional analysis of the global consensus identifies a (new) highly significant mixed charge cluster proximal to the carboxyl terminus of the sequence highlighting the hypercharge run EEDKKRRER (one-letter aa code used). The individual Archaea and Gram-positive sequences contain a corresponding significant mixed charge cluster in the location of the charge cluster of the consensus sequence. In contrast, the four Gram-negative proteobacterial sequences of the alignment do not have a charge cluster (even at the 5% significance level). All eukaryotic HSP70 sequences have the analogous charge cluster. Strikingly, several of the eukaryotic isoforms show multiple mixed charged clusters. These clusters were interpreted with supporting data related to HSP70 activity in facilitating chaperone, transport, and secretion function. We observed that the consensus contains only a single tryptophan residue and a single conserved cysteine. This is interpreted with respect to the target rule for disaggregating misfolded proteins. The mitochondrial HSP70 connections to bacterial HSP70 are analyzed, suggesting a polyphyletic split of Trypanosoma and Leishmania protist mitochondrial (Mt) homologues separated from Mt-animal/fungal/plant homologues. Moreover, the HSP70 sequences from the amitochondrial Entamoeba histolytica and Trichomonas vaginalis species were analyzed. The E. histolytica HSP70 is most similar to the higher eukaryotic cytoplasmic sequences, with significantly weaker alignments to ER sequences and much diminished matching to all eubacterial, mitochondrial, and chloroplast sequences. This appears to be at variance with the hypothesis that E. histolytica rather recently lost its mitochondrial organelle. T. vaginalis contains two HSP70 sequences, one Mt-like and the second similar to eukaryotic cytoplasmic sequences suggesting two diverse origins. Received: 29 January 1998 / Accepted: 14 May 1998     

Lipid-binding surfaces of membrane proteins: evidence from evolutionary and structural analysis

Membrane proteins function in the diverse environment of the lipid bilayer. Experimental evidence suggests that some lipid molecules bind tightly to specific sites on the membrane protein surface. These lipid molecules often act as co-factors and play important functional roles. In this study, we have assessed the evolutionary selection pressure experienced at lipid-binding sites in a set of α-helical and β-barrel membrane proteins using posterior probability analysis of the ratio of synonymous vs. nonsynonymous substitutions (ω-ratio). We have also carried out a geometric analysis of the membrane protein structures to identify residues in close contact with co-crystallized lipids. We found that residues forming cholesterol-binding sites in both β(2)-adrenergic receptor and Na(+)-K(+)-ATPase exhibit strong conservation, which can be characterized by an expanded cholesterol consensus motif for GPCRs. Our results suggest the functional importance of aromatic stacking interactions and interhelical hydrogen bonds in facilitating protein-cholesterol interactions, which is now reflected in the expanded motif. We also find that residues forming the cardiolipin-binding site in formate dehydrogenase-N γ-subunit and the phosphatidylglycerol binding site in KcsA are under strong purifying selection pressure. Although the lipopolysaccharide (LPS)-binding site in ferric hydroxamate uptake receptor (FhuA) is only weakly conserved, we show using a statistical mechanical model that LPS binds to the least stable FhuA β-strand and protects it from the bulk lipid. Our results suggest that specific lipid binding may be a general mechanism employed by β-barrel membrane proteins to stabilize weakly stable regions. Overall, we find that the residues forming specific lipid binding sites on the surfaces of membrane proteins often experience strong purifying selection pressure.     

Detection of Signature Sequences in Overlapping Genes and Prediction of a Novel Overlapping Gene in Hepatitis G Virus

In viruses an increased coding ability is provided by overlapping genes, in which two alternative open reading frames (ORFs) may be translated to yield two distinct proteins. The identification of signature sequences in overlapping genes is a topic of particular interest, since additional out-of-frame coding regions can be nested within known genes. In this work, a novel feature peculiar to overlapping coding regions is presented. It was detected by analysis of a sample set of 21 virus genomic sequences and consisted in the repeated occurrence of a cluster of basic amino acid residues, encoded by a frame, combined to a stretch of acidic residues, encoded by the corresponding overlapping frame. A computer scan of an additional set of virus sequences demonstrated that this feature is common to several other known overlapping ORFs and led to prediction of a novel overlapping gene in hepatitis G virus (HGV). The occurrence of a bifunctional coding region in HGV was also supported by its extremely lower rate of synonymous nucleotide substitutions compared to that observed in the other gene regions of the HGV genome. Analysis of the amino acid sequence that was deduced from the putative overlapping gene revealed a high content of basic residues and the presence of a nuclear targeting signal; these characteristics suggest that a core-like protein may be expressed by this novel ORF. Received: 21 July 1999 / Accepted: 26 October 1999     

Revealing the set of mutually correlated positions for the protein families of immunoglobulin fold

In this study, I explain the observation that a rather limited number of residues (about 10) establishes the immunoglobulin fold for the sequences of about 100 residues. Immunoglobulin fold proteins (IgF) comprise SCOP protein superfamilies with rather different functions and with less than 10% sequence identity; their alignment can be accomplished only taking into account the 3D structure. Therefore, I believe that discovering the additional common features of the sequences is necessary to explain the existence of a common fold for these SCOP superfamilies. We propose a method for analysis of pair-wise interconnections between residues of the multiple sequence alignment which helps us to reveal the set of mutually correlated positions, inherent to almost every superfamily of this protein fold. Hence, the set of constant positions (comprising the hydrophobic common core) and the set of variable but mutually correlated ones can serve as a basis of having the common 3D structure for rather distinct protein sequences.     

TRIAL: a tool for finding distant structural similarities

Finding structural similarities in distantly related proteins can reveal functional relationships that can not be identified using sequence comparison. Given two proteins A and B and threshold ε ?, we develop an algorithm, TRiplet-based Iterative ALignment (TRIAL) for computing the transformation of B that maximizes the number of aligned residues such that the root mean square deviation (RMSD) of the alignment is at most ε ?. Our algorithm is designed with the specific goal of effectively handling proteins with low similarity in primary structure, where existing algorithms perform particularly poorly. Experiments show that our method outperforms existing methods. TRIAL alignment brings the secondary structures of distantly related proteins to similar orientations. It also finds larger number of secondary structure matches at lower RMSD values and increased overall alignment lengths. Its classification accuracy is up to 63 percent better than other methods, including CE and DALI. TRIAL successfully aligns 83 percent of the residues from the smaller protein in reasonable time while other methods align only 29 to 65 percent of the residues for the same set of proteins.     

Identifying sequence-structure pairs undetected by sequence alignments

We examine how effectively simple potential functions previously developed can identify compatibilities between sequences and structures of proteins for database searches. The potential function consists of pairwise contact energies, repulsive packing potentials of residues for overly dense arrangement and short-range potentials for secondary structures, all of which were estimated from statistical preferences observed in known protein structures. Each potential energy term was modified to represent compatibilities between sequences and structures for globular proteins. Pairwise contact interactions in a sequence-structure alignment are evaluated in a mean field approximation on the basis of probabilities of site pairs to be aligned. Gap penalties are assumed to be proportional to the number of contacts at each residue position, and as a result gaps will be more frequently placed on protein surfaces than in cores. In addition to minimum energy alignments, we use probability alignments made by successively aligning site pairs in order by pairwise alignment probabilities. The results show that the present energy function and alignment method can detect well both folds compatible with a given sequence and, inversely, sequences compatible with a given fold, and yield mostly similar alignments for these two types of sequence and structure pairs. Probability alignments consisting of most reliable site pairs only can yield extremely small root mean square deviations, and including less reliable pairs increases the deviations. Also, it is observed that secondary structure potentials are usefully complementary to yield improved alignments with this method. Remarkably, by this method some individual sequence-structure pairs are detected having only 5-20% sequence identity.     

Can functional regions of proteins be predicted from their coding sequences? The case study of G-protein coupled receptors

A filter based on a set of unsupervised neural networks trained with a winner-take-all strategy discloses signals along the coding sequences of G-protein coupled receptors. By comparing with the existing experimental data it appears that these signals correlate with putative functional domains of the proteins. After protein alignment within subfamilies, signals cluster in protein regions which, according to the presently available experimental results, are described as possible functional domains of the folded proteins. The mapping procedure reveals characteristic regions in the coding sequences common and/or characteristic of the receptor subtype. This is particularly noticeable for the third cytoplasmic loop, which is likely to be involved in the molecular coupling of all the subfamilies with G-proteins. The results indicate that our mapping can highlight intrinsic representative features of the coding sequences which, in the case of G-protein coupled receptors, are characteristic of protein functional regions and suggest a possible application of the filter for predicting functional determinants in proteins starting from the coding sequence.     

Sequence motifs specific for cytosine methyltransferases

Using a new alignment method, the sequences of 13 m5C methyltransferases (MTases) have been examined. Five extremely well-conserved blocks of sequence have been detected and have been used as fixed points for the alignment of the 13 sequences. Following this initial alignment, five further blocks of similarity have been identified to give a total of ten recognizable blocks of sequence homology that are all arranged in a common order. The structures of these MTases consist of a variable-length N-terminal arm followed by eight well-conserved blocks each separated by small variable-length regions. A large variable-length segment of 90 to 270 amino acids (aa) then follows. After this are two blocks, and a variable-length C-terminal segment completes the sequence. Within the final alignment, 20 aa in the protein sequences, and 86 nucleotides in the nucleotide sequences are invariant. The strongest conservation is found in proximity to a suspected functional site that contains the dipeptide proline-cysteine. Consensus patterns can be defined for the five best conserved blocks and, when used as search motifs, are able to clearly distinguish between the m5C MTases and all other identified proteins in the PIR database. This suggests they may be of use in identifying putative MTases among protein sequences of unknown function.     

Identification of novel families of membrane proteins from the model plant Arabidopsis thaliana

MOTIVATION: The completion of the Arabidopsis genome offers the first opportunity to analyze all of the membrane protein sequences of a plant. The majority of integral membrane proteins including transporters, channels, and pumps contain hydrophobic alpha-helices and can be selected based on TransMembrane Spanning (TMS) domain prediction. By clustering the predicted membrane proteins based on sequence, it is possible to sort the membrane proteins into families of known function, based on experimental evidence or homology, or unknown function. This provides a way to identify target sequences for future functional analysis. RESULTS: An automated approach was used to select potential membrane protein sequences from the set of all predicted proteins and cluster the sequences into related families. The recently completed sequence of Arabidopsis thaliana, a model plant, was analyzed. Of the 25,470 predicted protein sequences 4589 (18%) were identified as containing two or more membrane spanning domains. The membrane protein sequences clustered into 628 distinct families containing 3208 sequences. Of these, 211 families (1764 sequences) either contained proteins of known function or showed homology to proteins of known function in other species. However, 417 families (1444 sequences) contained only sequences with no known function and no homology to proteins of known function. In addition, 1381 sequences did not cluster with any family and no function could be assigned to 1337 of these.     

MuSiC: a tool for multiple sequence alignment with constraints

SUMMARY: MuSiC is a web server to perform the constrained alignment of a set of sequences, such that the user-specified residues/nucleotides are aligned with each other. The input of the MuSiC system consists of a set of protein/DNA/RNA sequences and a set of user-specified constraints, each with a fragment of residue/nucleotide that (approximately) appears in all input sequences. The output of MuSiC is a constrained multiple sequence alignment in which the fragments of the input sequences whose residues/nucleotides exhibit a given degree of similarity to a constraint are aligned together. The current MuSiC system is implemented in Java language and can be accessed via a simple web interface. AVAILABILITY: http://genome.life.nctu.edu.tw/MUSIC