55 research outputs found

    The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored

    Get PDF
    An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein–protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org

    Literature mining and network analysis in Biology

    Get PDF
    Η παρούσα διπλωματική παρουσιάζει το OnTheFly2.0, ένα διαδικτυακό εργαλείο που επικεντρώνεται στην εξαγωγή και επακόλουθη ανάλυση βιοϊατρικών όρων από μεμονωμένα αρχεία. Συγκεκριμένα, το OnTheFly2.0 υποστηρίζει πολλούς διαφορετικούς επιτρέποντας τον παράλληλο χειρισμό τους. Μέσω της ενσωμάτωσης της υπηρεσίας EXTRACT υλοποιείται η Αναγνώριση Ονοματικών Οντοτήτων (Named Entity Recognition) για γονίδια/πρωτεΐνες, χημικές ουσίες, οργανισμούς, ιστούς, περιβάλλοντα, ασθένειες, φαινοτύπους και όρους οντολογίας γονιδίων (Gene Ontology terms), καθώς και η δημιουργία αναδυόμενων παραθύρων που παρέχουν πληροφορίες για τον αναγνωρισμένο όρο, συνοδευόμενες από σύνδεσμο για διάφορες βάσεις δεδομένων. Οι αναγνωρισμένες πρωτεΐνες, τα γονίδια και οι χημικές ουσίες μπορούν να επεξεργαστούν περαιτέρω μέσω αναλύσεων εμπλουτισμού για τη λειτουργικότητα και τη βιβλιογραφία ή να συσχετιστούν με ασθένειες και πρωτεϊνικές δομές. Τέλος, είναι δυνατή η απεικόνιση αλληλεπιδράσεων μεταξύ πρωτεϊνών ή μεταξύ πρωτεϊνών και χημικών ουσιών μέσω της δημιουργίας διαδραστικών δικτύων από τις βάσεις STRING και STITCH αντίστοιχα. Το OnTheFly2.0 υποστηρίζει 197 διαφορετικά είδη οργανισμών και είναι διαθέσιμο στον παρακάτω σύνδεσμο: http://onthefly.pavlopouloslab.info.The particular thesis presents OnTheFly2.0, a web-based, versatile tool dedicated to the extraction and subsequent analysis of biomedical terms from individual files. More specifically, OnTheFly2.0 supports different file formats, enabling simultaneous file handling. The integration of the EXTRACT tagging service allows the implementation of Named Entity Recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and Gene Ontology terms, as well as the generation of popup windows which provide concise, context related information about the identified term, accompanied by links to various databases. Once named entities, such as proteins, genes and chemicals are identified, they can be further explored via functional and publication enrichment analysis or be associated with diseases and protein domains reporting from protein family databases. Finally, visualization of protein-protein and protein-chemical associations is possible through the generation of interactive networks from the STRING and STITCH services, respectively. OnTheFly2.0 currently supports 197 species and is available at http://onthefly.pavlopouloslab.info

    System biology modeling : the insights for computational drug discovery

    Get PDF
    Indiana University-Purdue University Indianapolis (IUPUI)Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features

    Systems biology approaches to a rational drug discovery paradigm

    Full text link
    The published manuscript is available at EurekaSelect via http://www.eurekaselect.com/openurl/content.php?genre=article&doi=10.2174/1568026615666150826114524.Prathipati P., Mizuguchi K.. Systems biology approaches to a rational drug discovery paradigm. Current Topics in Medicinal Chemistry, 16, 9, 1009. https://doi.org/10.2174/1568026615666150826114524

    Network-driven strategies to integrate and exploit biomedical data

    Get PDF
    [eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina

    Computational Approaches for Predicting Drug Targets

    Get PDF
    This thesis reports the development of several computational approaches to predict human disease proteins and to assess their value as drug targets, using in-house domain functional families (CATH FunFams). CATH-FunFams comprise evolutionary related protein domains with high structural and functional similarity. External resources were used to identify proteins associated with disease and their genetic variations. These were then mapped to the CATH-FunFams together with information on drugs bound to any relatives within the FunFam. A number of novel approaches were then used to predict the proteins likely to be driving disease and to assess whether drugs could be repurposed within the FunFams for targeting these putative driver proteins. The first work chapter of this thesis reports the mapping of drugs to CATHFunFams to identify druggable FunFams based on statistical overrepresentation of drug targets within the FunFam. 81 druggable CATH-FunFams were identified and the dispersion of their relatives on a human protein interaction network was analysed to assess their propensity to be associated with side effects. In the second work chapter, putative drug targets for bladder cancer were identified using a novel computational protocol that expands a set of known bladder cancer genes with genes highly expressed in bladder cancer and highly associated with known bladder cancer genes in a human protein interaction network. 35 new bladder cancer targets were identified in druggable FunFams, for some of which FDA approved drugs could be repurposed from other protein domains in the FunFam. In the final work chapter, protein kinases and kinase inhibitors were analysed. These are an important class of human drug targets. A novel classification protocol was applied to give a comprehensive classification of the kinases which was benchmarked and compared with other widely used kinase classifications. Druginformation from ChEMBL was mapped to the Kinase-FunFams and analyses of protein network characteristics of the kinase relatives in each FunFam used to identify those families likely to be associated with side effects
    corecore