Subtractive genomics approach for in silico identification and characterization of novel drug targets in Neisseria meningitides serogroup B

Meningococcal disease is a life-threatening illness with annual incidence rates varying from 1 to 1000 per 100 000 persons in different parts of the world. Effective polysaccharide and polysaccharide-protein conjugate vaccines that offer protection against infection with meningococcal serogroups A, C, Y and W-135 have been licensed and are available worldwide. Serogroup B remains the most prevalent cause of meningococcal disease responsible for 32% of all meningococcal disease in the United States, 45 to 80% of the cases in Europe, and for the majority of cases in the rest of the world. The development of a vaccine against serogroup B poses the biggest problem due to the similarity between the B capsular polysaccharide structure and a polysialic acid containing glycopeptides that are a part of human brain tissue. Prevention of meningococcal disease will require the development of an effective vaccine to combat serogroup B, which is the cause of most meningococcal cases in developed countries. The availability of the complete sequence information of Neisseria meningitides serogroup B proteome has made it possible to carry out the in silico analysis of its genome for identification of potential vaccine and drug targets. Our study revealed 1413 proteins which are non-homologous to human genome. Screening these proteins using the Database of Essential Genes (DEG) resulted in the identification of 362 proteins as essential proteins of the bacterium. Analysis of the identified essential proteins, using the KEGG Automated Annotation Server (KAAS) housed at Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways database, revealed 35 enzymes of N. Meningitides that may be used as potential drug targets, as they belongs to pathways present only in the bacterium and not present in humans. Subcelluler localization prediction of these essential proteins revealed that 9 proteins lie on the outer membrane of the pathogen which could be potential vaccine targets. Screening of the functional inhibitors against these novel targets may result in discovery of novel therapeutic compounds that can be effective against Neisseria meningitides Serogroup B

PA-GOSUB: a searchable database of model organism protein sequences with their predicted Gene Ontology molecular function and subcellular localization

PA-GOSUB (Proteome Analyst: Gene Ontology Molecular Function and Subcellular Localization) is a publicly available, web-based, searchable and downloadable database that contains the sequences, predicted GO molecular functions and predicted subcellular localizations of more than 107 000 proteins from 10 model organisms (and growing), covering the major kingdoms and phyla for which annotated proteomes exist (http://www.cs.ualberta.ca/~bioinfo/PA/GOSUB). The PA-GOSUB database effectively expands the coverage of subcellular localization and GO function annotations by a significant factor (already over five for subcellular localization, compared with Swiss-Prot v42.7), and more model organisms are being added to PA-GOSUB as their sequenced proteomes become available. PA-GOSUB can be used in three main ways. First, a researcher can browse the pre-computed PA-GOSUB annotations on a per-organism and per-protein basis using annotation-based and text-based filters. Second, a user can perform BLAST searches against the PA-GOSUB database and use the annotations from the homologs as simple predictors for the new sequences. Third, the whole of PA-GOSUB can be downloaded in either FASTA or comma-separated values (CSV) formats

Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machine

Predicting the destination of a protein in a cell is important for annotating the function of the protein. Recent advances have allowed us to develop more accurate methods for predicting the subcellular localization of proteins. One of the most important factors for improving the accuracy of these methods is related to the introduction of new useful features for protein sequences. In this paper we present a new method for extracting appropriate features from the sequence data by computing pairwise sequence alignment scores. As a classifier, support vector machine (SVM) is used. The overall prediction accuracy evaluated by the jackknife validation technique reached 94.70% for the eukaryotic non-plant data set and 92.10% for the eukaryotic plant data set, which is the highest prediction accuracy among the methods reported so far with such data sets. Our experimental results confirm that our feature extraction method based on pairwise sequence alignment is useful for this classification problem

LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST

Subcellular location of a protein is one of the key functional characters as proteins must be localized correctly at the subcellular level to have normal biological function. In this paper, a novel method named LOCSVMPSI has been introduced, which is based on the support vector machine (SVM) and the position-specific scoring matrix generated from profiles of PSI-BLAST. With a jackknife test on the RH2427 data set, LOCSVMPSI achieved a high overall prediction accuracy of 90.2%, which is higher than the prediction results by SubLoc and ESLpred on this data set. In addition, prediction performance of LOCSVMPSI was evaluated with 5-fold cross validation test on the PK7579 data set and the prediction results were consistently better than the previous method based on several SVMs using composition of both amino acids and amino acid pairs. Further test on the SWISSPROT new-unique data set showed that LOCSVMPSI also performed better than some widely used prediction methods, such as PSORTII, TargetP and LOCnet. All these results indicate that LOCSVMPSI is a powerful tool for the prediction of eukaryotic protein subcellular localization. An online web server (current version is 1.3) based on this method has been developed and is freely available to both academic and commercial users, which can be accessed by at

PSORTdb: a protein subcellular localization database for bacteria

Information about bacterial subcellular localization (SCL) is important for protein function prediction and identification of suitable drug/vaccine/diagnostic targets. PSORTdb (http://db.psort.org/) is a web-accessible database of SCL for bacteria that contains both information determined through laboratory experimentation and computational predictions. The dataset of experimentally verified information (∼2000 proteins) was manually curated by us and represents the largest dataset of its kind. Earlier versions have been used for training SCL predictors, and its incorporation now into this new PSORTdb resource, with its associated additional annotation information and dataset version control, should aid researchers in future development of improved SCL predictors. The second component of this database contains computational analyses of proteins deduced from the most recent NCBI dataset of completely sequenced genomes. Analyses are currently calculated using PSORTb, the most precise automated SCL predictor for bacterial proteins. Both datasets can be accessed through the web using a very flexible text search engine, a data browser, or using BLAST, and the entire database or search results may be downloaded in various formats. Features such as GO ontologies and multiple accession numbers are incorporated to facilitate integration with other bioinformatics resources. PSORTdb is freely available under GNU General Public License

Agregación de métricas de minería de datos usando funciones de lógica continua

En la línea de investigación aquí presentada, nos ocupamos de la propuesta y aplicación de una Lógica Continua [Duj08] para la evaluación y comparación de técnicas de Minería de Datos.Eje: Ingeniería de softwareRed de Universidades con Carreras en Informática (RedUNCI

Agregación de métricas de minería de datos usando funciones de lógica continua

En la línea de investigación aquí presentada, nos ocupamos de la propuesta y aplicación de una Lógica Continua [Duj08] para la evaluación y comparación de técnicas de Minería de Datos.Eje: Ingeniería de softwareRed de Universidades con Carreras en Informática (RedUNCI

Agregación de métricas de minería de datos usando funciones de lógica continua

En la línea de investigación aquí presentada, nos ocupamos de la propuesta y aplicación de una Lógica Continua [Duj08] para la evaluación y comparación de técnicas de Minería de Datos.Eje: Ingeniería de softwareRed de Universidades con Carreras en Informática (RedUNCI

Desarrollo de modelos de evaluación usando operadores de una lógica continua

En la línea de investigación aquí presentada, nos ocupamos del desarrollo de modelos para la evaluación de sistemas usando el método LSP (Logic Score of Preference) [Duj08]. Actualmente, nos encontramos avocados al desarrollo de modelos para la evaluación de bienes inmobiliarios, particularmente –aunque no exclusivamente– modelos que puedan ser aplicados al cálculo del avalúo fiscal, aunque también pueda ser aplicado a otras áreas. El empleo del método LSP permite expresar aspectos en la evaluación que otras técnicas meramente aditivas no permiten, ofreciéndonos la posibilidad de construir modelos que se ajusten con una mayor precisión a las necesidades del usuario, sea este un ente recaudador de impuestos fiscales, un agente inmobiliario o cualquier otro interesado en obtener una tasación de un bien inmueble.Eje: Ingeniería de SoftwareRed de Universidades con Carreras en Informática (RedUNCI