Sat, 05 Jul 2008 23:05:52 BST CiteULike: neils's algorithms http://www.citeulike.org/user/neils/tag/algorithms CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/neils/article/2903932 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:Non-homology based methods such as phylogenetic profiles are effective for predicting functional relationships between proteins with no considerable sequence or structure similarity. Those methods rely heavily on traditional similarity metrics defined on pairs of phylogenetic patterns. Proteins do not exclusively interact in pairs as the final biological function of a protein in the cellular context is often hold by a group of proteins. In order to accurately infer modules of functionally interacting proteins, the consideration of not only direct but also indirect relationships is required. In this paper, we used the Bond Energy Algorithm (BEA) to predict functionally related groups of proteins. BEA creates clusters of phylogenetic profiles based on the associations of the surrounding elements of the analyzed data using a metric that considers linked relationships among elements in the data set.RESULTS:Using phylogenetic profiles obtained from the Cluster of Orthologous Groups of Proteins (COG) database, we conducted a series of clustering experiments using BEA to predict (upper level) relationships between profiles. We validated the results of the proposed method using COG's functional categories. In addition, we tested our results by comparing them with the experimentally determined functional relationships between proteins provided by the DIP and ECOCYC databases. Our results demonstrate that BEA is capable of predicting meaningful modules of functionally related proteins. BEA outperforms traditionally used clustering methods, such as k-means and hierarchical clustering by predicting functional relationships between proteins with higher accuracy.CONCLUSIONS:This study shows that the linked relationships of phylogenetic profiles obtained by BEA is useful for detecting functional associations between profiles and extending functional modules not detected by traditional methods. BEA is capable of detecting relationship among phylogenetic patterns by linking them through a common element shared in a group. Additionally, we discuss how the proposed method may become more powerful if other criteria to classify different levels of protein functional interactions, as gene neighborhood or protein fusion information, is provided. Inferring modules of functionally interacting proteins using the Bond Energy Algorithm. Ryosuke Watanabe Enrique Morett Edgar Vallejo doi:10.1186/1471-2105-9-285 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-06-17T23:56:28-00:00 2008 BMC Bioinformatics 9 1 algorithms energy interaction prediction protein-protein http://www.citeulike.org/user/neils/article/2402373 <i>Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3635-3641.</i><br /><br />Scansite identifies short protein sequence motifs that are recognized by modular signaling domains, phosphorylated by protein Ser/Thr- or Tyr-kinases or mediate specific interactions with protein or phospholipid ligands. Each sequence motif is represented as a position-specific scoring matrix (PSSM) based on results from oriented peptide library and phage display experiments. Predicted domain-motif interactions from Scansite can be sequentially combined, allowing segments of biological pathways to be constructed in silico. The current release of Scansite, version 2.0, includes 62 motifs characterizing the binding and/or substrate specificities of many families of Ser/Thr- or Tyr-kinases, SH2, SH3, PDZ, 14-3-3 and PTB domains, together with signature motifs for PtdIns(3,4,5)P(3)-specific PH domains. Scansite 2.0 contains significant improvements to its original interface, including a number of new generalized user features and significantly enhanced performance. Searches of all SWISS-PROT, TrEMBL, Genpept and Ensembl protein database entries are now possible with run times reduced by approximately 60% when compared with Scansite version 1.0. Scansite 2.0 allows restricted searching of species-specific proteins, as well as isoelectric point and molecular weight sorting to facilitate comparison of predictions with results from two-dimensional gel electrophoresis experiments. Support for user-defined motifs has been increased, allowing easier input of user-defined matrices and permitting user-defined motifs to be combined with pre-compiled Scansite motifs for dual motif searching. In addition, a new series of Sequence Match programs for non-quantitative user-defined motifs has been implemented. Scansite is available via the World Wide Web at http://scansite.mit.edu. Scansite 2.0: Proteome-wide prediction of cell signaling interactions using short sequence motifs. JC Obenauer LC Cantley MB Yaffe doi:10.1093/nar/gkg584 Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3635-3641. 2008-02-20T10:21:00-00:00 2003 Nucleic Acids Res 1362-4962 31 13 3635 3641 algorithms amino analysis article-nar article-predikin binding databases internet motifs phosphorylation protein proteins proteome sequence signal sites software structure transduction http://www.citeulike.org/user/neils/article/2568624 <i>Brief Bioinform (20 March 2008), bbn007.</i><br /><br />Mass spectrometry (MS) is a technique that is used for biological studies. It consists in associating a spectrum to a biological sample. A spectrum consists of couples of values (intensity, m/z), where intensity measures the abundance of biomolecules (as proteins) with a mass-to-charge ratio (m/z) present in the originating sample. In proteomics experiments, MS spectra are used to identify pattern expressions in clinical samples that may be responsible of diseases. Recently, to improve the identification of peptides/proteins related to patterns, MS/MS process is used, consisting in performing cascade of mass spectrometric analysis on selected peaks. Latter technique has been demonstrated to improve the identification and quantification of proteins/peptide in samples. Nevertheless, MS analysis deals with a huge amount of data, often affected by noises, thus requiring automatic data management systems. Tools have been developed and most of the time furnished with the instruments allowing: (i) spectra analysis and visualization, (ii) pattern recognition, (iii) protein databases querying, (iv) peptides/proteins quantification and identification. Currently most of the tools supporting such phases need to be optimized to improve the protein (and their functionalities) identification processes. In this article we survey on applications supporting spectrometrists and biologists in obtaining information from biological samples, analyzing available software for different phases. We consider different mass spectrometry techniques, and thus different requirements. We focus on tools for (i) data preprocessing, allowing to prepare results obtained from spectrometers to be analyzed; (ii) spectra analysis, representation and mining, aimed to identify common and/or hidden patterns in spectra sets or in classifying data; (iii) databases querying to identify peptides; and (iv) improving and boosting the identification and quantification of selected peaks. We trace some open problems and report on requirements that represent new challenges for bioinformatics. 10.1093/bib/bbn007 Algorithms and tools for analysis and management of mass spectrometry data Pierangelo Veltri doi:10.1093/bib/bbn007 Brief Bioinform (20 March 2008), bbn007. 2008-03-21T04:15:23-00:00 2008 Brief Bioinform bbn007 algorithms bioinformatics data mass proteomics spectrometry statistics http://www.citeulike.org/user/neils/article/2054458 <i>Genome Res, Vol. 12, No. 10. (Oct 2002), pp. 1611-1618.</i><br /><br />The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort. The Bioperl toolkit: Perl modules for the life sciences. Jason Stajich David Block Kris Boulez Steven Brenner Stephen Chervitz Chris Dagdigian Georg Fuellen James Gilbert Ian Korf Hilmar Lapp Heikki Lehväslaiho Chad Matsalla Chris Mungall Brian Osborne Matthew Pocock Peter Schattner Martin Senger Lincoln Stein Elia Stupka Mark Wilkinson Ewan Birney Genome Res, Vol. 12, No. 10. (Oct 2002), pp. 1611-1618. 2007-12-04T03:22:10-00:00 2002 Genome Res 12 10 1611 1618 algorithms animals article-nar article-predikin biological biology computational computer database databases design genetic graphics humans integration internet management online perl sciences software systems http://www.citeulike.org/user/neils/article/2054436 <i>Bioinformatics, Vol. 20, No. 17. (Nov 2004), pp. 3179-3184.</i><br /><br />MOTIVATION: Phosphorylation is involved in diverse signal transduction pathways. By predicting phosphorylation sites and their kinases from primary protein sequences, we can obtain much valuable information that can form the basis for further research. Using support vector machines, we attempted to predict phosphorylation sites and the type of kinase that acts at each site. RESULTS: Our prediction system was limited to phosphorylation sites catalyzed by four protein kinase families and four protein kinase groups. The accuracy of the predictions ranged from 83 to 95\% at the kinase family level, and 76-91\% at the kinase group level. The prediction system used-PredPhospho-can be applied to the functional study of proteins, and can help predict the changes in phosphorylation sites caused by amino acid variations at intra- and interspecies levels. Prediction of phosphorylation sites using SVMs. Jong Kim Juyoung Lee Bermseok Oh Kuchan Kimm Insong Koh Bioinformatics, Vol. 20, No. 17. (Nov 2004), pp. 3179-3184. 2007-12-04T03:22:10-00:00 2004 Bioinformatics 20 17 3179 3184 algorithms alignment analysis article-predikin artificial binding chemical computer intelligence models molecular phosphorylation phosphotransferases protein proteins relationship sequence simulation sites structure-activity http://www.citeulike.org/user/neils/article/2054428 <i>Proc Natl Acad Sci U S A, Vol. 89, No. 22. (Nov 1992), pp. 10915-10919.</i><br /><br />Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups. Amino acid substitution matrices from protein blocks. S Henikoff JG Henikoff Proc Natl Acad Sci U S A, Vol. 89, No. 22. (Nov 1992), pp. 10915-10919. 2007-12-04T03:22:10-00:00 1992 Proc Natl Acad Sci U S A 89 22 10915 10919 acid algorithms amino animals article-predikin caenorhabditis data drosophila elegans homology lod mathematics molecular probability proteins score sequence software http://www.citeulike.org/user/neils/article/2054413 <i>Bioinformatics, Vol. 22, No. 18. (Sep 2006), pp. 2192-2195.</i><br /><br />MOTIVATION: Epithelial Na(+) channels (ENaC) mediate the transport of sodium (Na) across epithelia in the kidney, gut and lungs and are required for blood pressure regulation. They are inhibited by ubiquitin protein ligases, such as Nedd4-2. These ligases bind to proline-rich motifs (PY motifs) present in the C-termini of ENaC subunits. Loss of this inhibition leads to hypertension. We have previously reported that ENaC channels are maintained in the active state by the G protein coupled receptor kinase, GRK2. The enzyme has been implicated in the development of essential hypertension [R. D. Feldman (2002) Mol. Pharmacol., 61, 707-709]. Additional findings in our lab pointed towards a possible role for GRK2 in the phosphorylation and inactivation of Nedd4-2. RESULTS: We have predicted GRK2 phosphorylation sites on Nedd4-2 by combining sequence analysis, homology modeling and surface accessibility calculations. A total of 24 potential phosphorylation sites were predicted by sequence analysis. Of these, 16 could be modeled using homology modeling and 6 of these were found to have sufficient surface exposure to be accessible to the GRK2 enzyme responsible for the phosphorylation of Nedd4-2. The method provides an ordered list of the most probable GRK2 phosphorylation sites on Nedd4-2 providing invaluable guidance to future experimental studies aimed at mutating certain Nedd4-2 residues in order to prevent phosphorylation by GRK2. The method developed could be applied in a wide variety of biological applications involving the binding of one molecule to a protein. The relative effectiveness of the technique is determined mainly by the quality of the homology model built for the protein of interest. Contact: jarthur@med.usyd.edu.au Scoring of predicted GRK2 phosphorylation sites in Nedd4-2. Jonathan Arthur Angeles Perez David Cook Bioinformatics, Vol. 22, No. 18. (Sep 2006), pp. 2192-2195. 2007-12-04T03:22:09-00:00 2006 Bioinformatics 22 18 2192 2195 acid algorithms alignment amino analysis article-predikin artificial beta-adrenergic binding chemical computer data g-protein-coupled homology intelligence interaction kinase kinases ligases mapping models molecular phosphorylation protein receptor sequence simulation sites ubiquitin-protein