Fragment Finder: a web-based software to identify similar three-dimensional structural motif.
Ontology highlight
ABSTRACT:
SUBMITTER: Ananthalakshmi P
PROVIDER: S-EPMC1160114 | biostudies-literature | 2005 Jul
REPOSITORIES: biostudies-literature
Publications
Fragment Finder: a web-based software to identify similar three-dimensional structural motif.
Ananthalakshmi P P Kumar Ch Kiran ChK Jeyasimhan M M Sumathi K K Sekar K K
Nucleic acids research 20050701 Web Server issue
FF (Fragment Finder) is a web-based interactive search engine developed to retrieve the user-desired similar 3D structural fragments from the selected subset of 25 or 90% non-homologous protein chains. The search is based on the comparison of the main chain backbone conformational angles (phi and ). Additionally, the queried motifs can be superimposed to find out how similar the structural fragments are, so that the information can be effectively used in molecular modeling. The engine has facili ...[more]
Similar Datasets
Project description:Photogrammetry-based three-dimensional reconstruction of objects is becoming increasingly appealing in research areas unrelated to computer vision. It has the potential to facilitate the assessment of forest inventory-related parameters by enabling or expediting resource measurements in the field. We hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment. The evaluation is based on (i) a virtual scene where the precise location and dimensionality of objects is known a priori and is thus conducive to a quantitative comparison and (ii) using series of in situ acquired photographs of vegetation with overlapping field of view where the photogrammetric outcomes are compared qualitatively. Performance is quantified by computing receiver operating characteristic curves that summarize the type-I and type-II errors between the reference and reconstructed tree models. Similar artefacts are observed in synthetic- and in situ-based reconstructions.
| S-EPMC6083669 | biostudies-literature
Project description:Carnocyclin A (CclA) is a potent antimicrobial peptide from Carnobacterium maltaromaticum UAL307 that displays a broad spectrum of activity against numerous Gram-positive organisms. An amide bond links the N and C termini of this bacteriocin, imparting stability and structural integrity to this 60-amino acid peptide. CclA interacts with lipid bilayers in a voltage-dependent manner and forms anion selective pores. Several other circular bacteriocins have been reported, yet only one (enterocin AS-48) has been structurally characterized. We have now determined the solution structure of CclA by NMR and further examined its anion binding and membrane channel properties. The results reveal that CclA preferentially binds halide anions and has a structure that is surprisingly similar to that of AS-48 despite low sequence identity, different oligomeric state, and disparate function. CclA folds into a compact globular bundle, comprised of four helices surrounding a hydrophobic core. NMR studies show two fluoride ion binding modes for CclA. Our findings suggest that although other circular bacteriocins are likely to have diverse mechanisms of action, many may have a common structural motif. This shared three-dimensional arrangement resembles the fold of mammalian saposins, peptides that either directly lyse membranes or serve as activators of lipid-degrading enzymes.
| S-EPMC2781411 | biostudies-literature
Project description:Protein phosphorylation, driven by specific recognition of substrates by kinases and phosphatases, plays central roles in a variety of important cellular processes such as signaling and enzyme activation. Mass spectrometry enables the determination of phosphorylated peptides (and thereby proteins) in scenarios ranging from targeted in vitro studies to in vivo cell lysates under particular conditions. The characterization of commonalities among identified phosphopeptides provides insights into the specificities of the kinases involved in a study. Several algorithms have been developed to uncover linear motifs representing position-specific amino acid patterns in sets of phosphopeptides. To more fully capture the available information, reduce sensitivity to both parameter choices and natural experimental variation, and develop more precise characterizations of kinase specificities, it is necessary to determine all statistically significant motifs represented in a dataset.We have developed MMFPh (Maximal Motif Finder for Phosphoproteomics datasets), which extends the approach of the popular phosphorylation motif software Motif-X (Schwartz and Gygi, 2005) to identify all statistically significant motifs and return the maximal ones (those not subsumed by motifs with more fixed amino acids). In tests with both synthetic and experimental data, we show that MMFPh finds important motifs missed by the greedy approach of Motif-X, while also finding more motifs that are more characteristic of the dataset relative to the background proteome. Thus MMFPh is in some sense both more sensitive and more specific in characterizing the involved kinases. We also show that MMFPh compares favorably to other recent methods for finding phosphorylation motifs. Furthermore, MMFPh is less dependent on parameter choices. We support this powerful new approach with a web interface so that it may become a useful tool for studies of kinase specificity and phosphorylation site prediction.A web server is at www.cs.dartmouth.edu/~cbk/.
| S-EPMC3371830 | biostudies-literature
Project description:Motivation:RNA structural motif finding is a relevant problem that becomes computationally hard when working on high-throughput data (e.g. eCLIP, PAR-CLIP), often represented by thousands of RNA molecules. Currently, the BEAM server is the only web tool capable to handle tens of thousands of RNA in input with a motif discovery procedure that is only limited by the current secondary structure prediction accuracies. Results:The recently developed method BEAM (BEAr Motifs finder) can analyze tens of thousands of RNA molecules and identify RNA secondary structure motifs associated to a measure of their statistical significance. BEAM is extremely fast thanks to the BEAR encoding that transforms each RNA secondary structure in a string of characters. BEAM also exploits the evolutionary knowledge contained in a substitution matrix of secondary structure elements, extracted from the RFAM database of families of homologous RNAs. The BEAM web server has been designed to streamline data pre-processing by automatically handling folding and encoding of RNA sequences, giving users a choice for the preferred folding program. The server provides an intuitive and informative results page with the list of secondary structure motifs identified, the logo of each motif, its significance, graphic representation and information about its position in the RNA molecules sharing it. Availability and implementation:The web server is freely available at http://beam.uniroma2.it/ and it is implemented in NodeJS and Python with all major browsers supported. Contact:marco.pietrosanto@uniroma2.it. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5860439 | biostudies-literature
Project description:BACKGROUND:With the advent of high throughput sequencing techniques, large amounts of sequencing data are readily available for analysis. Natural biological signals are intrinsically highly variable making their complete identification a computationally challenging problem. Many attempts in using statistical or combinatorial approaches have been made with great success in the past. However, identifying highly degenerate and long (>20 nucleotides) motifs still remains an unmet challenge as high degeneracy will diminish statistical significance of biological signals and increasing motif size will cause combinatorial explosion. In this report, we present a novel rule-based method that is focused on finding degenerate and long motifs. Our proposed method, named iTriplet, avoids costly enumeration present in existing combinatorial methods and is amenable to parallel processing. RESULTS:We have conducted a comprehensive assessment on the performance and sensitivity-specificity of iTriplet in analyzing artificial and real biological sequences in various genomic regions. The results show that iTriplet is able to solve challenging cases. Furthermore we have confirmed the utility of iTriplet by showing it accurately predicts polyA-site-related motifs using a dual Luciferase reporter assay. CONCLUSION:iTriplet is a novel rule-based combinatorial or enumerative motif finding method that is able to process highly degenerate and long motifs that have resisted analysis by other methods. In addition, iTriplet is distinguished from other methods of the same family by its parallelizability, which allows it to leverage the power of today's readily available high-performance computing systems.
| S-EPMC2784457 | biostudies-literature
Project description:A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship between the species into account in order to distinguish conservation that is due to the occurrence of functional sites from spurious conservation that is due to evolutionary proximity. Second, one has to deal with the complexities of multiple alignments of orthologous intergenic regions, and one has to consider the possibility that functional sites may occur outside of conserved segments. Here we present a new motif sampling algorithm, PhyloGibbs, that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and "background" intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account. The algorithm uses simulated annealing and Monte Carlo Markov-chain sampling to rigorously assign posterior probabilities to all the binding sites that it reports. In tests on synthetic data and real data from five Saccharomyces species our algorithm performs significantly better than four other motif-finding algorithms, including algorithms that also take phylogeny into account. Our results also show that, in contrast to the other algorithms, PhyloGibbs can make realistic estimates of the reliability of its predictions. Our tests suggest that, running on the five-species multiple alignment of a single gene's upstream region, PhyloGibbs on average recovers over 50% of all binding sites in S. cerevisiae at a specificity of about 50%, and 33% of all binding sites at a specificity of about 85%. We also tested PhyloGibbs on collections of multiple alignments of intergenic regions that were recently annotated, based on ChIP-on-chip data, to contain binding sites for the same TF. We compared PhyloGibbs's results with the previous analysis of these data using six other motif-finding algorithms. For 16 of 21 TFs for which all other motif-finding methods failed to find a significant motif, PhyloGibbs did recover a motif that matches the literature consensus. In 11 cases where there was disagreement in the results we compiled lists of known target genes from the literature, and found that running PhyloGibbs on their regulatory regions yielded a binding motif matching the literature consensus in all but one of the cases. Interestingly, these literature gene lists had little overlap with the targets annotated based on the ChIP-on-chip data. The PhyloGibbs code can be downloaded from http://www.biozentrum.unibas.ch/~nimwegen/cgi-bin/phylogibbs.cgi or http://www.imsc.res.in/~rsidd/phylogibbs. The full set of predicted sites from our tests on yeast are available at http://www.swissregulon.unibas.ch.
| S-EPMC1309704 | biostudies-literature
Project description:Exon skipping using antisense oligonucleotides (ASOs) has recently proven to be a powerful tool for mRNA splicing modulation. Several exon-skipping ASOs have been approved to treat genetic diseases worldwide. However, a significant challenge is the difficulty in selecting an optimal sequence for exon skipping. The efficacy of ASOs is often unpredictable, because of the numerous factors involved in exon skipping. To address this gap, we have developed a computational method using machine-learning algorithms that factors in many parameters as well as experimental data to design highly effective ASOs for exon skipping. eSkip-Finder (https://eskip-finder.org) is the first web-based resource for helping researchers identify effective exon skipping ASOs. eSkip-Finder features two sections: (i) a predictor of the exon skipping efficacy of novel ASOs and (ii) a database of exon skipping ASOs. The predictor facilitates rapid analysis of a given set of exon/intron sequences and ASO lengths to identify effective ASOs for exon skipping based on a machine learning model trained by experimental data. We confirmed that predictions correlated well with in vitro skipping efficacy of sequences that were not included in the training data. The database enables users to search for ASOs using queries such as gene name, species, and exon number.
| S-EPMC8265194 | biostudies-literature
Project description:BACKGROUND: Microbial communities in their natural environments exhibit phenotypes that can directly cause particular diseases, convert biomass or wastewater to energy, or degrade various environmental contaminants. Understanding how these communities realize specific phenotypic traits (e.g., carbon fixation, hydrogen production) is critical for addressing health, bioremediation, or bioenergy problems. RESULTS: In this paper, we describe a graph-theoretical method for in silico prediction of the cellular subsystems that are related to the expression of a target phenotype. The proposed (?, ?)-motif finder approach allows for identification of these phenotype-related subsystems that, in addition to metabolic subsystems, could include their regulators, sensors, transporters, and even uncharacterized proteins. By comparing dozens of genome-scale networks of functionally associated proteins, our method efficiently identifies those statistically significant functional modules that are in at least ? networks of phenotype-expressing organisms but appear in no more than ? networks of organisms that do not exhibit the target phenotype. It has been shown via various experiments that the enumerated modules are indeed related to phenotype-expression when tested with different target phenotypes like hydrogen production, motility, aerobic respiration, and acid-tolerance. CONCLUSION: Thus, we have proposed a methodology that can identify potential statistically significant phenotype-related functional modules. The functional module is modeled as an (?, ?)-clique, where ? and ? are two criteria introduced in this work. We also propose a novel network model, called the two-typed, divided network. The new network model and the criteria make the problem tractable even while very large networks are being compared. The code can be downloaded from http://www.freescience.org/cs/ABClique/
| S-EPMC3287386 | biostudies-literature
Project description:BackgroundAlthough a large number of allergens have been characterised, the structural, functional, and biochemical features that these molecules have in common, and that could explain their ability to elicit powerful IgE antibody responses, are still uncertain. Recently, there has been considerable interest in the role of the cysteine protease activity of the house dust mite allergen Der p 1 in biasing the immune response in favour of IgE production.AimsTo search for remote homologues of Der p 1 with sequences similar to the 30 conserved amino acids surrounding the catalytic cysteine residue (Cys34).MethodsPotential homologues were analysed by examining their three dimensional structures and multiple sequence alignments using the programs PROPSEARCH, ClustalW, GeneDoc, and Swiss Pdb Viewer.ResultsDiverse allergens (for example, the plant cysteine protease papain, the transport protein lipocalin Mus m 1, and the ragweed allergen Amb a 5) have a similar structural motif; namely, a groove resembling the substrate binding groove of Der p 1. The groove is located inside an alpha-beta motif, between an alpha helix on one side and an antiparallel beta sheet on the other side. A similar common motif (a cysteine stabilised alpha-beta fold) can also be found in some toxins and defensins.ConclusionAllergens of diverse sources have a common structural motif, namely a groove located inside an alpha-beta motif, which could potentially serve as a ligand binding site.
| S-EPMC1187054 | biostudies-literature
Project description:A major problem in structural biology is the recognition of errors in experimental and theoretical models of protein structures. The ProSA program (Protein Structure Analysis) is an established tool which has a large user base and is frequently employed in the refinement and validation of experimental protein structures and in structure prediction and modeling. The analysis of protein structures is generally a difficult and cumbersome exercise. The new service presented here is a straightforward and easy to use extension of the classic ProSA program which exploits the advantages of interactive web-based applications for the display of scores and energy plots that highlight potential problems spotted in protein structures. In particular, the quality scores of a protein are displayed in the context of all known protein structures and problematic parts of a structure are shown and highlighted in a 3D molecule viewer. The service specifically addresses the needs encountered in the validation of protein structures obtained from X-ray analysis, NMR spectroscopy and theoretical calculations. ProSA-web is accessible at https://prosa.services.came.sbg.ac.at.
| S-EPMC1933241 | biostudies-literature