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PubMed2006

MODOMICS: a database of RNA modification pathways.

MODOMICS is the first comprehensive database resource for systems biology of RNA modification. It integrates information about the chemical structure of modified nucleosides, their localization in RNA sequences, pathways of their biosynthesis and enzymes that carry out the respective reactions. MODOMICS also provides literature information, and links to other databases, including the available protein sequence and structure data. The current list of modifications and pathways is comprehensive, while the dataset of enzymes is limited to Escherichia coli and Saccharomyces cerevisiae and sequence alignments are presented only for tRNAs from these organisms. RNAs and enzymes from other organisms will be included in the near future. MODOMICS can be queried by the type of nucleoside (e.g. A, G, C, U, I, m1A, nm5s2U, etc.), type of RNA, position of a particular nucleoside, type of reaction (e.g. methylation, thiolation, deamination, etc.) and name or sequence of an enzyme of interest. Options for data presentation include graphs of pathways involving the query nucleoside, multiple sequence alignments of RNA sequences and tabular forms with enzyme and literature data. The contents of MODOMICS can be accessed through the World Wide Web at http://genesilico.pl/modomics/.

Base Sequence↗

PubMed2000

The identification of conserved interactions within the SH3 domain by alignment of sequences and structures.

The SH3 domain, comprised of approximately 60 residues, is found within a wide variety of proteins, and is a mediator of protein-protein interactions. Due to the large number of SH3 domain sequences and structures in the databases, this domain provides one of the best available systems for the examination of sequence and structural conservation within a protein family. In this study, a large and diverse alignment of SH3 domain sequences was constructed, and the pattern of conservation within this alignment was compared to conserved structural features, as deduced from analysis of eighteen different SH3 domain structures. Seventeen SH3 domain structures solved in the presence of bound peptide were also examined to identify positions that are consistently most important in mediating the peptide-binding function of this domain. Although residues at the two most conserved positions in the alignment are directly involved in peptide binding, residues at most other conserved positions play structural roles, such as stabilizing turns or comprising the hydrophobic core. Surprisingly, several highly conserved side-chain to main-chain hydrogen bonds were observed in the functionally crucial RT-Src loop between residues with little direct involvement in peptide binding. These hydrogen bonds may be important for maintaining this region in the precise conformation necessary for specific peptide recognition. In addition, a previously unrecognized yet highly conserved beta-bulge was identified in the second beta-strand of the domain, which appears to provide a necessary kink in this strand, allowing it to hydrogen bond to both sheets comprising the fold.

Amino Acid Sequence↗

PubMed1995

Context-dependent optimal substitution matrices.

Substitution matrices are a key tool in important applications such as identifying sequence homologies, creating sequence alignments and more recently using evolutionary patterns for the prediction of protein structure. We have derived a novel approach to the derivation of these matrices that utilizes not only multiple sequence alignments, but also the associated evolutionary trees. The key to our method is the use of a Bayesian formalism to calculate the probability that a given substitution matrix fits the tree structures and multiple sequence alignment data. Using this procedure, we can determine optimal substitution matrices for various local environments, depending on parameters such as secondary structure and surface accessibility.

Evolution, Molecular↗

PubMed1994

Improving the sensitivity of the sequence profile method.

The sequence profile method (Gribskov M, McLachlan AD, Eisenberg D, 1987, Proc Natl Acad Sci USA 84:4355-4358) is a powerful tool to detect distant relationships between amino acid sequences. A profile is a table of position-specific scores and gap penalties, providing a generalized description of a protein motif, which can be used for sequence alignments and database searches instead of an individual sequence. A sequence profile is derived from a multiple sequence alignment. We have found 2 ways to improve the sensitivity of sequence profiles: (1) Sequence weights: Usage of individual weights for each sequence avoids bias toward closely related sequences. These weights are automatically assigned based on the distance of the sequences using a published procedure (Sibbald PR, Argos P, 1990, J Mol Biol 216:813-818). (2) Amino acid substitution table: In addition to the alignment, the construction of a profile also needs an amino acid substitution table. We have found that in some cases a new table, the BLOSUM45 table (Henikoff S, Henikoff JG, 1992, Proc Natl Acad Sci USA 89:10915-10919), is more sensitive than the original Dayhoff table or the modified Dayhoff table used in the current implementation. Profiles derived by the improved method are more sensitive and selective in a number of cases where previous methods have failed to completely separate true members from false positives.

Amino Acid Sequence↗

PubMed1995

Confronting the problem of interconnected structural changes in the comparative modeling of proteins.

Comparative models of three proteins have been built using a variety of computational methods, heavily supplemented by visual inspection. We consider the accuracy obtained to be worse than expected. A careful analysis of the models shows that a major reason for the poor results is the interconnectedness of the structural differences between the target proteins and the template structures they were modeled from. Side chain conformations are often determined by details of the structure remote in the sequence, and can be influenced by relatively small main chain changes. Almost all of the regions of substantial main chain conformational change interact with at least one other such region, so that they often cannot be modeled independently. Visual inspection is sometimes effective in correcting errors in sequence alignment and in spotting when an alternative template structure is more appropriate. We expect some improvements in the near future through the development of structure-based sequence alignment tools, side chain interconnectedness rotamer choice algorithms, and a better understanding of the context sensitivity of conformational features.

Amino Acid Sequence↗

PubMed2004

COACH: profile-profile alignment of protein families using hidden Markov models.

MOTIVATION: Alignments of two multiple-sequence alignments, or statistical models of such alignments (profiles), have important applications in computational biology. The increased amount of information in a profile versus a single sequence can lead to more accurate alignments and more sensitive homolog detection in database searches. Several profile-profile alignment methods have been proposed and have been shown to improve sensitivity and alignment quality compared with sequence-sequence methods (such as BLAST) and profile-sequence methods (e.g. PSI-BLAST). Here we present a new approach to profile-profile alignment we call Comparison of Alignments by Constructing Hidden Markov Models (HMMs) (COACH). COACH aligns two multiple sequence alignments by constructing a profile HMM from one alignment and aligning the other to that HMM. RESULTS: We compare the alignment accuracy of COACH with two recently published methods: Yona and Levitt's prof_sim and Sadreyev and Grishin's COMPASS. On two sets of reference alignments selected from the FSSP database, we find that COACH is able, on average, to produce alignments giving the best coverage or the fewest errors, depending on the chosen parameter settings. AVAILABILITY: COACH is freely available from www.drive5.com/lobster

Algorithms↗

PubMed2001

Identification and molecular characterization of 18 paramyxoviruses isolated from snakes.

Viral agents from 18 different snake species (families Colubridae, Viperidae, and Crotalidae) showing respiratory symptoms and neuronal disease were identified as paramyxoviruses by typical cytopathogenic effect (CPE), electron microscopy, and hemagglutination inhibition. Detailed molecular characterization of the viruses was performed by partial L- and F-gene-specific reverse transcription polymerase chain reaction (RT-PCR) and sequencing, nucleotide and amino acid sequence alignment, and phylogenetic analysis (PHYLIP). RT-PCR of the partial L-gene (566 nt) was successful for all 18 viruses; amplicons of the partial F-gene (918 nt) could be obtained in 16 cases. F- and L-sequence alignment revealed similarities to Fer de Lance virus (FDLV) ranging from 79 to 88% on a nucleotide basis, and 94 to 99% on an amino acid basis. Phylogenetic analysis of the ophidian paramyxoviruses resulted in three clusters for the L-gene sequence and corresponding clusters for the F-gene sequence, indicating no species specificity. We analyzed the F-protein of the snake paramyxoviruses, which proved to have an identical conserved motif of heptad repeat A and predicted a furin cleavage site. This uniformity distinguishes the snake virus group from the other type species of the subfamily Paramyxovirinae. For further classification, we aligned the sequences of the ophidian paramyxoviruses and members of the Paramyxoviridae, such as Sendai virus (genus Respirovirus), mumps virus (genus Rubulavirus), measles virus (genus Morbillivirus), human respiratory syncytial virus (genus Pneumovirus) (Van Regenmortel and 10 co-authors, 2000) and Hendra virus, which have recently been suggested as type species of the genus Henipavirus (Wang et al., 2000). Maximum sequence similarity was found to the partial L-gene of Sendai virus, with 56% nucleotide and 61% amino acid identity. The FDLV and Sendai virus cluster in the phylogenetic analysis of L- and F-protein regarding the Paramyxovirus type species and Hendra virus and show the closest relationship. Regarding the biological properties, the antigenic distance, and particularly the low homology of available sequences, we propose a new genus for the reptilian paramyxoviruses within the Paramyxoviridae.

Amino Acid Sequence↗

PubMed2006

Homology modeling of membrane proteins: a critical assessment.

Evaluation and validation of homology modeling protocols are indispensable for membrane proteins as experimental determination of their three-dimensional structure is an arduous task. The prediction ability of Modeller, MOE, InsightII-Homology and Swiss-PdbViewer (SPV) with different sequence alignments CLUSTALW, BLAST and 3D-JIGSAW have been assessed. The sequence identity of the target and template was chosen to be in the range of 25-35%. Validation protocols to assess the structure, fold and stereochemical quality, are employed by comparing with experimental structures. Two different ranking schemes are suggested to evaluate the performance of each methodology based on the validation scores. While unambiguous preference for any given procedure did not surface, statistically Modeller and the sequence alignment technique, 3D-JIGSAW, gave best results amongst the chosen protocols. The present study helps in selecting the right protocols when modeling membrane proteins, which form a major class of drug targets.

Computational Biology↗

PubMed2005

The limits of protein sequence comparison?

Modern sequence alignment algorithms are used routinely to identify homologous proteins, proteins that share a common ancestor. Homologous proteins always share similar structures and often have similar functions. Over the past 20 years, sequence comparison has become both more sensitive, largely because of profile-based methods, and more reliable, because of more accurate statistical estimates. As sequence and structure databases become larger, and comparison methods become more powerful, reliable statistical estimates will become even more important for distinguishing similarities that are due to homology from those that are due to analogy (convergence). The newest sequence alignment methods are more sensitive than older methods, but more accurate statistical estimates are needed for their full power to be realized.

Algorithms↗

PubMed2006

Exploring the relationship between sequence similarity and accurate phylogenetic trees.

We have characterized the relationship between accurate phylogenetic reconstruction and sequence similarity, testing whether high levels of sequence similarity can consistently produce accurate evolutionary trees. We generated protein families with known phylogenies using a modified version of the PAML/EVOLVER program that produces insertions and deletions as well as substitutions. Protein families were evolved over a range of 100-400 point accepted mutations; at these distances 63% of the families shared significant sequence similarity. Protein families were evolved using balanced and unbalanced trees, with ancient or recent radiations. In families sharing statistically significant similarity, about 60% of multiple sequence alignments were 95% identical to true alignments. To compare recovered topologies with true topologies, we used a score that reflects the fraction of clades that were correctly clustered. As expected, the accuracy of the phylogenies was greatest in the least divergent families. About 88% of phylogenies clustered over 80% of clades in families that shared significant sequence similarity, using Bayesian, parsimony, distance, and maximum likelihood methods. However, for protein families with short ancient branches (ancient radiation), only 30% of the most divergent (but statistically significant) families produced accurate phylogenies, and only about 70% of the second most highly conserved families, with median expectation values better than 10(-60), produced accurate trees. These values represent upper bounds on expected tree accuracy for sequences with a simple divergence history; proteins from 700 Giardia families, with a similar range of sequence similarities but considerably more gaps, produced much less accurate trees. For our simulated insertions and deletions, correct multiple sequence alignments did not perform much better than those produced by T-COFFEE, and including sequences with expressed sequence tag-like sequencing errors did not significantly decrease phylogenetic accuracy. In general, although less-divergent sequence families produce more accurate trees, the likelihood of estimating an accurate tree is most dependent on whether radiation in the family was ancient or recent. Accuracy can be improved by combining genes from the same organism when creating species trees or by selecting protein families with the best bootstrap values in comprehensive studies.

Animals↗

PubMed2006

Prediction of protein subcellular localization.

Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.

Algorithms↗

PubMed1994

Conserved patterns in the Cu,Zn superoxide dismutase family.

Conserved structural and functional features of Cu,Zn superoxide dismutase enzymes have been studied by comparison of known three-dimensional structures and analysis of the currently available amino acid sequences. For this purpose, the three-dimensional structures of the bovine, spinach and yeast enzymes have been superimposed and the structure-based sequence alignment of 38 different superoxide dismutases has been produced. The evolutionary tree obtained from the alignment indicates that cytosolic and extracellular enzymes followed independent evolutionary paths, and that horizontal gene transfer, if any, occurred at an early stage in eukaryota evolution. Based on the sequence alignment and on the analysis of clusters of spatially neighboring residues, the conservation/variation of functionally relevant intramolecular interactions has been investigated. Seven alternative residue arrangements have been identified in the upper rim of the active site, which form an important determinant of the electrostatic field at the catalytic center. The total nominal charge of this region is constantly -1 through the phyla. The seven residues which coordinate the two metal ions at the active site are conserved, with only one known exception. Among the residues involved in maintenance of the active site structure, Gly59, Gly80, Gly136 and Gly139 are fully conserved; mutations of Gly42 and Pro64 have been observed, concerted with replacements in their structural surroundings. Coordinated mutations affecting residue pairs which maintain the packing geometry of the Greek-key beta-barrel have been identified. Furthermore, the unique disulfide bridge involving Cys55-Cys144 in eukaryota, shows the alternative Cys50A-Cys144 arrangement in prokaryotic enzymes.

Amino Acid Sequence↗

PubMed2006

Defining the fold space of membrane proteins: the CAMPS database.

Recent progress in structure determination techniques has led to a significant growth in the number of known membrane protein structures, and the first structural genomics projects focusing on membrane proteins have been initiated, warranting an investigation of appropriate bioinformatics strategies for optimal structural target selection for these molecules. What determines a membrane protein fold? How many membrane structures need to be solved to provide sufficient structural coverage of the membrane protein sequence space? We present the CAMPS database (Computational Analysis of the Membrane Protein Space) containing almost 45,000 proteins with three or more predicted transmembrane helices (TMH) from 120 bacterial species. This large set of membrane proteins was subjected to single-linkage clustering using only sequence alignments covering at least 40% of the TMH present in a given family. This process yielded 266 sequence clusters with at least 15 members, roughly corresponding to membrane structural folds, sufficiently structurally homogeneous in terms of the variation of TMH number between individual sequences. These clusters were further subdivided into functionally homogeneous subclusters according to the COG (Clusters of Orthologous Groups) system as well as more stringently defined families sharing at least 30% identity. The CAMPS sequence clusters are thus designed to reflect three main levels of interest for structural genomics: fold, function, and modeling distance. We present a library of Hidden Markov Models (HMM) derived from sequence alignments of TMH at these three levels of sequence similarity. Given that 24 out of 266 clusters corresponding to membrane folds already have associated known structures, we estimate that 242 additional new structures, one for each remaining cluster, would provide structural coverage at the fold level of roughly 70% of prokaryotic membrane proteins belonging to the currently most populated families.

Bacterial Proteins↗

PubMed2005

MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes.

BACKGROUND: Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity. RESULTS: We describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method. CONCLUSION: The search engine, available at http://mapper.chip.org, allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation.

Algorithms↗

PubMed2005

Scoring docking models with evolutionary information.

We have developed methods for the extraction of evolutionary information from multiple sequence alignments for use in the study of the evolution of protein interaction networks and in the prediction of protein interaction. For Rounds 3, 4, and 5 of the CAPRI experiment, we used scores derived from the analysis of multiple sequence alignments to submit predictions for 7 of the 12 targets. Our docking models were generated with Hex and GRAMM, but all our predictions were selected using methods based on multiple sequence alignments and on the available experimental evidence. With this approach, we were able to predict acceptable level models for 4 of the targets, and for a fifth target, we located the residues involved in the binding surface. Here we detail our successes and highlight several of the limitations and problems that we faced while dealing with particular docking cases.

Algorithms↗

PubMed2004

Local homology recognition and distance measures in linear time using compressed amino acid alphabets.

Methods for discovery of local similarities and estimation of evolutionary distance by identifying k-mers (contiguous subsequences of length k) common to two sequences are described. Given unaligned sequences of length L, these methods have O(L) time complexity. The ability of compressed amino acid alphabets to extend these techniques to distantly related proteins was investigated. The performance of these algorithms was evaluated for different alphabets and choices of k using a test set of 1848 pairs of structurally alignable sequences selected from the FSSP database. Distance measures derived from k-mer counting were found to correlate well with percentage identity derived from sequence alignments. Compressed alphabets were seen to improve performance in local similarity discovery, but no evidence was found of improvements when applied to distance estimates. The performance of our local similarity discovery method was compared with the fast Fourier transform (FFT) used in MAFFT, which has O(L log L) time complexity. The method for achieving comparable coverage to FFT is revealed here, and is more than an order of magnitude faster. We suggest using k-mer distance for fast, approximate phylogenetic tree construction, and show that a speed improvement of more than three orders of magnitude can be achieved relative to standard distance methods, which require alignments.

Algorithms↗

PubMed1994

The sequence of a subtilisin-type protease (aerolysin) from the hyperthermophilic archaeum Pyrobaculum aerophilum reveals sites important to thermostability.

The hyperthermophilic archaeum Pyrobaculum aerophilum grows optimally at 100 degrees C and pH 7.0. Cell homogenates exhibit strong proteolytic activity within a temperature range of 80-130 degrees C. During an analysis of cDNA and genomic sequence tags, a genomic clone was recovered showing strong sequence homology to alkaline subtilisins of Bacillus sp. The total DNA sequence of the gene encoding the protease (named "aerolysin") was determined. Multiple sequence alignment with 15 different serine-type proteases showed greatest homology with subtilisins from gram-positive bacteria rather than archaeal or eukaryal serine proteases. Models of secondary and tertiary structure based on sequence alignments and the tertiary structures of subtilisin Carlsberg, BPN', thermitase, and protease K were generated for P. aerophilum subtilisin. This allowed identification of sites potentially contributing to the thermostability of the protein. One common transition put alanines at the beginning and end of surface alpha-helices. Aspartic acids were found at the N-terminus of several surface helices, possibly increasing stability by interacting with the helix dipole. Several of the substitutions in regions expected to form surface loops were adjacent to each other in the tertiary structure model.

Amino Acid Sequence↗

PubMed1997

cDNAs derived from primary and small cytoplasmic Alu (scAlu) transcripts.

We have isolated and sequenced twenty-six cDNAs derived from primary Alu transcripts. Most cDNAs (22/26) sequenced end in multiple T residues, known to be at the termination for RNA polymerase III-directed transcripts. We conclude that these cDNAs were derived from authentic, RNA polymerase III-directed primary Alu transcripts. Sequence alignment of the cDNAs with Alu consensus sequences show that the cDNAs belong to different, previously described Alu subfamilies. The sequence variation observed in the 3' non-Alu regions of each of the cDNAs led us to conclude that they were derived from different genomic loci, thus demonstrating that multiple Alu loci are transcriptionally active. The subfamily distribution of the cDNAs suggests that transcriptional activity is biased towards evolutionarily younger Alu subfamilies, with a strong selection for the consensus sequence in the first 42 bases and the promoter B box. Sequence data from seven cDNAs derived from small cytoplasmic Alu (scAlu) transcripts, a processed form of Alu transcripts, also have a similar bias towards younger Alu subfamilies. About half of these cDNAs are due to processing or degradation, but the other half appear to be due to the formation of a cryptic RNA polymerase III termination signal in multiple loci. Using our sequence data, we have isolated a transcriptionally active genomic Alu element belonging to the Ya5 subfamily. In vitro transcription studies of this element suggest that its flanking sequences contribute to its transcriptional activity. The role of flanking sequences and other factors involved in transcriptional activity of Alu elements are discussed.

Base Sequence↗