@article {468, title = {PTMcode v2: a resource for functional associations of post-translational modifications within and between proteins.}, journal = {Nucleic Acids Res}, volume = {43}, year = {2015}, month = {2015 Jan}, pages = {D494-502}, abstract = {

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.

}, keywords = {Databases, Protein, Internet, Protein Interaction Mapping, Protein Processing, Post-Translational}, issn = {1362-4962}, doi = {10.1093/nar/gku1081}, author = {Minguez, Pablo and Letunic, Ivica and Parca, Luca and Garc{\'\i}a-Alonso, Luz and Dopazo, Joaquin and Huerta-Cepas, Jaime and Bork, Peer} } @article {519, title = {SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jan}, pages = {D935-9}, abstract = {

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.

}, keywords = {Databases, Protein, Humans, Internet, Meta-Analysis as Topic, Phenotype, Polymorphism, Single Nucleotide, Protein Conformation, Proteins}, issn = {1362-4962}, doi = {10.1093/nar/gkr996}, author = {De Baets, Greet and Van Durme, Joost and Reumers, Joke and Maurer-Stroh, Sebastian and Vanhee, Peter and Dopazo, Joaquin and Schymkowitz, Joost and Rousseau, Frederic} } @article {586, title = {SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks.}, journal = {Nucleic Acids Res}, volume = {37}, year = {2009}, month = {2009 Jul}, pages = {W109-14}, abstract = {

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{\textquoteright}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.

}, keywords = {Computer Graphics, Data Interpretation, Statistical, Databases, Protein, Humans, Internet, Protein Interaction Mapping, Software}, issn = {1362-4962}, doi = {10.1093/nar/gkp402}, author = {Minguez, Pablo and G{\"o}tz, Stefan and Montaner, David and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin} } @article {600, title = {Use of estimated evolutionary strength at the codon level improves the prediction of disease-related protein mutations in humans.}, journal = {Hum Mutat}, volume = {29}, year = {2008}, month = {2008 Jan}, pages = {198-204}, abstract = {

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.

}, keywords = {Algorithms, Codon, Computational Biology, Databases, Protein, DNA Mutational Analysis, Evolution, Molecular, Genetic Predisposition to Disease, Genetic Variation, Genome, Human, Humans, Iduronic Acid, Point Mutation, Polymorphism, Single Nucleotide, Proteins, Tumor Suppressor Protein p53}, issn = {1098-1004}, doi = {10.1002/humu.20628}, author = {Capriotti, Emidio and Arbiza, Leonardo and Casadio, Rita and Dopazo, Joaquin and Dopazo, Hern{\'a}n and Marti-Renom, Marc A} } @article {603, title = {DBAli tools: mining the protein structure space.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W393-7}, abstract = {

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.

}, keywords = {Algorithms, Amino Acid Sequence, Computational Biology, Data Interpretation, Statistical, Databases, Protein, Internet, Molecular Sequence Data, Protein Conformation, Proteins, Pseudomonas aeruginosa, Sequence Alignment, Sequence Analysis, Protein, Sequence Homology, Amino Acid, Software, Structure-Activity Relationship}, issn = {1362-4962}, doi = {10.1093/nar/gkm236}, author = {Marti-Renom, Marc A and Pieper, Ursula and Madhusudhan, M S and Rossi, Andrea and Eswar, Narayanan and Davis, Fred P and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin and Sali, Andrej} } @article {592, title = {Functional profiling of microarray experiments using text-mining derived bioentities.}, journal = {Bioinformatics}, volume = {23}, year = {2007}, month = {2007 Nov 15}, pages = {3098-9}, abstract = {

MOTIVATION: 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.

}, keywords = {Artificial Intelligence, Databases, Protein, Gene Expression Profiling, Information Storage and Retrieval, Natural Language Processing, Proteins, Research Design, Systems Integration}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btm445}, author = {Minguez, Pablo and Al-Shahrour, F{\'a}tima and Montaner, David and Dopazo, Joaquin} }