SKPDB: A structural database of shikimate pathway enzymes

Background: The functional and structural characterisation of enzymes that belong to microbial metabolic pathways is very important for structure-based drug design. The main interest in studying shikimate pathway enzymes involves the fact that they are essential for bacteria but do not occur in humans, making them selective targets for design of drugs that do not directly impact humans.Description: The ShiKimate Pathway DataBase (SKPDB) is a relational database applied to the study of shikimate pathway enzymes in microorganisms and plants. The current database is updated regularly with the addition of new data; there are currently 8902 enzymes of the shikimate pathway from different sources. The database contains extensive information on each enzyme, including detailed descriptions about sequence, references, and structural and functional studies. All files (primary sequence, atomic coordinates and quality scores) are available for downloading. The modeled structures can be viewed using the Jmol program.Conclusions: The SKPDB provides a large number of structural models to be used in docking simulations, virtual screening initiatives and drug design. It is freely accessible at http://lsbzix.rc.unesp.br/skpdb/. © 2010 Arcuri et al; licensee BioMed Central Ltd.

BtoxDB: a comprehensive database of protein structural data on toxin-antitoxin systems

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Molecular models of protein targets from Mycobacterium tuberculosis

Structural characterization of enzymes that belong to microbial metabolic pathways is very important for structure-based drug design since some of these proteins may be present in the bacterial genome, but absent in humans. Thus, metabolic pathways became potential targets for drug design. The motivation of this work is the fact that Mycobacterium tuberculosis is the cause of the deaths of millions of people in the world, so that the structural characterization of protein targets to propose new drugs has become essential. DBMODELING is a relational database, created to highlight the importance of methods of molecular modeling applied to the Mycobacterium tuberculosis genome with the aim of proposing protein-ligand docking analysis. There are currently more than 300 models for proteins from Mycobacterium tuberculosis genome in the database. The database contains a detailed description of the reaction catalyzed by each enzyme and their atomic coordinates. Information about structures, a tool for animated gif image, a table with a specification of the metabolic pathway, modeled protein, inputs used in modeling, and analysis methods used in this project are available in the database for download. The search tool can be used for reseachers to find specific pathways or enzymes.

Docking e análise do modo de ligação de três moléculas pequenas, um benzimidazol e dois compostos de crômio, nos sulcos do DNA 5'-CGCGAATTCGCG-3

Pós-graduação em Ciência e Tecnologia de Materiais - FC

DBMODELING - A database applied to the study of protein targets from genome projects

Genome sequencing efforts are providing us with complete genetic blueprints for hundreds of organisms. We are now faced with assigning, understanding, and modifying the functions of proteins encoded by these genomes. DBMODELING is a relational database of annotated comparative protein structure models and their metabolic pathway characterization, when identified. This procedure was applied to complete genomes such as Mycobacteritum tuberculosis and Xylella fastidiosa. The main interest in the study of metabolic pathways is that some of these pathways are not present in humans, which makes them selective targets for drug design, decreasing the impact of drugs in humans. In the database, there are currently 1116 proteins from two genomes. It can be accessed by any researcher at http://www.biocristalografia.df.ibilce.unesp.br/tools/. This project confirms that homology modeling is a useful tool in structural bioinformatics and that it can be very valuable in annotating genome sequence information, contributing to structural and functional genomics, and analyzing protein-ligand docking.

Application of ontologies in peer-to-peer database with routing queries based on ant colony algorithm

A significant set of information stored in different databases around the world, can be shared through peer-topeer databases. With that, is obtained a large base of knowledge, without the need for large investments because they are used existing databases, as well as the infrastructure in place. However, the structural characteristics of peer-topeer, makes complex the process of finding such information. On the other side, these databases are often heterogeneous in their schemas, but semantically similar in their content. A good peer-to-peer databases systems should allow the user access information from databases scattered across the network and receive only the information really relate to your topic of interest. This paper proposes to use ontologies in peer-to-peer database queries to represent the semantics inherent to the data. The main contribution of this work is enable integration between heterogeneous databases, improve the performance of such queries and use the algorithm of optimization Ant Colony to solve the problem of locating information on peer-to-peer networks, which presents an improve of 18% in results. © 2011 IEEE.

Optimization of query results in peer-to-peer database supported by semantics

The development of new technologies that use peer-to-peer networks grows every day, with the object to supply the need of sharing information, resources and services of databases around the world. Among them are the peer-to-peer databases that take advantage of peer-to-peer networks to manage distributed knowledge bases, allowing the sharing of information semantically related but syntactically heterogeneous. However, it is a challenge to ensure the efficient search for information without compromising the autonomy of each node and network flexibility, given the structural characteristics of these networks. On the other hand, some studies propose the use of ontology semantics by assigning standardized categorization of information. The main original contribution of this work is the approach of this problem with a proposal for optimization of queries supported by the Ant Colony algorithm and classification though ontologies. The results show that this strategy enables the semantic support to the searches in peer-to-peer databases, aiming to expand the results without compromising network performance. © 2011 IEEE.

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.

Ditregra - An auxiliary program for structural determination of diterpenes

This work describes the creation of heuristics rules based on 13C-NMR spectroscopy that characterize several skeletal types of diterpenes. Using a collection of 2745 spectra we built a database linked to the expert system SISTEMAT. Several programs were applied to the database in order to discover characteristic signals that identify with a good performance, a large diversity of skeletal types. The heuristic approach used was able to differentiate groups of skeletons based firstly on the number of primary, secondary, tertiary and quaternary carbons, and secondly the program searches, for each group, if there are ranges of chemical shifts that identifies specific skeletal type. The program was checked with 100 new structures recently published and was able to identify the correct skeleton in 65 of the studied cases. When the skeleton has several hundreds of compounds, for example, the labdanes, the program employs the concept of subskeletal, and does not classify in the same group labdanes with double bounds at different positions. The chemical shift ranges for each subskeletal types and the structures of all skeletal types are given. The consultation program can be obtained from the authors. © 1997 - IOS Press. All rights reserved.