02330nas a2200265   4500008004100000022001400041245012000055210006900175260001300244300001300257490000700270520140800277653002301685653001301708653003201721653004301753100001901796700001901815700001601834700002401850700002001874700002401894700001501918856013101933       2015                        eng d  a1362-496200aPTMcode v2: a resource for functional associations of post-translational modifications within and between proteins.0 aPTMcode v2 a resource for functional associations of posttransla  c2015 Jan  aD494-5020 v433 a<p>The post-translational regulation of proteins is mainly driven by two molecular events, their modification by several types of moieties and their interaction with other proteins. These two processes are interdependent and together are responsible for the function of the protein in a particular cell state. Several databases focus on the prediction and compilation of protein-protein interactions (PPIs) and no less on the collection and analysis of protein post-translational modifications (PTMs), however, there are no resources that concentrate on describing the regulatory role of PTMs in PPIs. We developed several methods based on residue co-evolution and proximity to predict the functional associations of pairs of PTMs that we apply to modifications in the same protein and between two interacting proteins. In order to make data available for understudied organisms, PTMcode v2 (http://ptmcode.embl.de) includes a new strategy to propagate PTMs from validated modified sites through orthologous proteins. The second release of PTMcode covers 19 eukaryotic species from which we collected more than 300,000 experimentally verified PTMs (>1,300,000 propagated) of 69 types extracting the post-translational regulation of >100,000 proteins and >100,000 interactions. In total, we report 8 million associations of PTMs regulating single proteins and over 9.4 million interplays tuning PPIs.</p>10aDatabases, Protein10aInternet10aProtein Interaction Mapping10aProtein Processing, Post-Translational1 aMinguez, Pablo1 aLetunic, Ivica1 aParca, Luca1 aGarcía-Alonso, Luz1 aDopazo, Joaquin1 aHuerta-Cepas, Jaime1 aBork, Peer  uhttps://www.clinbioinfosspa.es/content/ptmcode-v2-resource-functional-associations-post-translational-modifications-within-and02236nas a2200325   4500008004100000022001400041245010200055210006900157260001300226300001100239490000700250520119100257653002301448653001101471653001301482653002701495653001401522653003601536653002501572653001301597100002001610700002101630700001801651700002801669700001801697700002001715700002301735700002301758856012901781       2012                        eng d  a1362-496200aSNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants.0 aSNPeffect 40 online prediction of molecular and structural effec  c2012 Jan  aD935-90 v403 a<p>Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.</p>10aDatabases, Protein10aHumans10aInternet10aMeta-Analysis as Topic10aPhenotype10aPolymorphism, Single Nucleotide10aProtein Conformation10aProteins1 aDe Baets, Greet1 aVan Durme, Joost1 aReumers, Joke1 aMaurer-Stroh, Sebastian1 aVanhee, Peter1 aDopazo, Joaquin1 aSchymkowitz, Joost1 aRousseau, Frederic  uhttps://www.clinbioinfosspa.es/content/snpeffect-40-line-prediction-molecular-and-structural-effects-protein-coding-variants01737nas a2200277   4500008004100000022001400041245009700055210006900152260001300221300001200234490000700246520083000253653002201083653003701105653002301142653001101165653001301176653003201189653001301221100001901234700001801253700002001271700002501291700002001316856012301336       2009                        eng d  a1362-496200aSNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.0 aSNOW a webbased tool for the statistical analysis of proteinprot  c2009 Jul  aW109-140 v373 a<p>Understanding the structure and the dynamics of the complex intercellular network of interactions that contributes to the structure and function of a living cell is one of the main challenges of today's biology. SNOW inputs a collection of protein (or gene) identifiers and, by using the interactome as scaffold, draws the connections among them, calculates several relevant network parameters and, as a novelty among the rest of tools of its class, it estimates their statistical significance. The parameters calculated for each node are: connectivity, betweenness and clustering coefficient. It also calculates the number of components, number of bicomponents and articulation points. An interactive network viewer is also available to explore the resulting network. SNOW is available at http://snow.bioinfo.cipf.es.</p>10aComputer Graphics10aData Interpretation, Statistical10aDatabases, Protein10aHumans10aInternet10aProtein Interaction Mapping10aSoftware1 aMinguez, Pablo1 aGötz, Stefan1 aMontaner, David1 aAl-Shahrour, Fátima1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/snow-web-based-tool-statistical-analysis-protein-protein-interaction-networks-003094nas a2200385   4500008004100000022001400041245013400055210006900189260001300258300001200271490000700283520182800290653001502118653001002133653002602143653002302169653002802192653002502220653003802245653002202283653001802305653001102323653001802334653001902352653003602371653001302407653003302420100002202453700002102475700001802496700002002514700002002534700002502554856012902579       2008                        eng d  a1098-100400aUse of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.0 aUse of estimated evolutionary strength at the codon level improv  c2008 Jan  a198-2040 v293 a<p>Predicting the functional impact of protein variation is one of the most challenging problems in bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data, allowing the application of rigorous statistical approaches for predicting whether a given single point mutation has an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likelihood that a given protein variant is associated with human disease or not. Our method relies on a support vector machine (SVM) classifier trained using three sources of information: protein sequence, multiple protein sequence alignments, and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59, indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at the codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT (available for download at http://sgu.bioinfo.cipf.es/services/Omidios/; last accessed: 24 August 2007), could be used to support clinical studies.</p>10aAlgorithms10aCodon10aComputational Biology10aDatabases, Protein10aDNA Mutational Analysis10aEvolution, Molecular10aGenetic Predisposition to Disease10aGenetic Variation10aGenome, Human10aHumans10aIduronic Acid10aPoint Mutation10aPolymorphism, Single Nucleotide10aProteins10aTumor Suppressor Protein p531 aCapriotti, Emidio1 aArbiza, Leonardo1 aCasadio, Rita1 aDopazo, Joaquin1 aDopazo, Hernán1 aMarti-Renom, Marc, A  uhttps://www.clinbioinfosspa.es/content/use-estimated-evolutionary-strength-codon-level-improves-prediction-disease-related-002301nas a2200421   4500008004100000022001400041245005300055210005100108260001300159300001100172490000700183520104700190653001501237653002401252653002601276653003701302653002301339653001301362653002801375653002501403653001301428653002701441653002301468653003101491653003401522653001301556653003601569100002501605700001901630700002201649700001801671700002101689700001901710700002501729700002001754700001701774856008801791       2007                        eng d  a1362-496200aDBAli tools: mining the protein structure space.0 aDBAli tools mining the protein structure space  c2007 Jul  aW393-70 v353 a<p>The DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.</p>10aAlgorithms10aAmino Acid Sequence10aComputational Biology10aData Interpretation, Statistical10aDatabases, Protein10aInternet10aMolecular Sequence Data10aProtein Conformation10aProteins10aPseudomonas aeruginosa10aSequence Alignment10aSequence Analysis, Protein10aSequence Homology, Amino Acid10aSoftware10aStructure-Activity Relationship1 aMarti-Renom, Marc, A1 aPieper, Ursula1 aMadhusudhan, M, S1 aRossi, Andrea1 aEswar, Narayanan1 aDavis, Fred, P1 aAl-Shahrour, Fátima1 aDopazo, Joaquin1 aSali, Andrej  uhttps://www.clinbioinfosspa.es/content/dbali-tools-mining-protein-structure-space-001723nas a2200277   4500008004100000022001400041245009000055210006900145260001600214300001100230490000700241520077800248653002801026653002301054653003001077653003801107653003201145653001301177653002001190653002401210100001901234700002501253700002001278700002001298856012701318       2007                        eng d  a1367-481100aFunctional profiling of microarray experiments using text-mining derived bioentities.0 aFunctional profiling of microarray experiments using textmining   c2007 Nov 15  a3098-90 v233 a<p><b>MOTIVATION: </b>The increasing use of microarray technologies brought about a parallel demand in methods for the functional interpretation of the results. Beyond the conventional functional annotations for genes, such as gene ontology, pathways, etc. other sources of information are still to be exploited. Text-mining methods allow extracting informative terms (bioentities) with different functional, chemical, clinical, etc. meanings, that can be associated to genes. We show how to use these associations within an appropriate statistical framework and how to apply them through easy-to-use, web-based environments to the functional interpretation of microarray experiments. Functional enrichment and gene set enrichment tests using bioentities are presented.</p>10aArtificial Intelligence10aDatabases, Protein10aGene Expression Profiling10aInformation Storage and Retrieval10aNatural Language Processing10aProteins10aResearch Design10aSystems Integration1 aMinguez, Pablo1 aAl-Shahrour, Fátima1 aMontaner, David1 aDopazo, Joaquin  uhttps://www.clinbioinfosspa.es/content/functional-profiling-microarray-experiments-using-text-mining-derived-bioentities-0