SKPDB: a structural database of shikimate pathway enzymes

<p>Abstract</p> <p>Background</p> <p>The functional and <b>s</b>tructural 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.</p> <p>Description</p> <p>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.</p> <p>Conclusions</p> <p>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 <url>http://lsbzix.rc.unesp.br/skpdb/</url>.</p

Improvements in the score matrix calculation method using parallel score estimating algorithm

The increasing amount of sequences stored in genomic databases has become unfeasible to the sequential analysis. Then, the parallel computing brought its power to the Bioinformatics through parallel algorithms to align and analyze the sequences, providing improvements mainly in the running time of these algorithms. In many situations, the parallel strategy contributes to reducing the computational complexity of the big problems. This work shows some results obtained by an implementation of a parallel score estimating technique for the score matrix calculation stage, which is the first stage of a progressive multiple sequence alignment. The performance and quality of the parallel score estimating are compared with the results of a dynamic programming approach also implemented in parallel. This comparison shows a significant reduction of running time. Moreover, the quality of the final alignment, using the new strategy, is analyzed and compared with the quality of the approach with dynamic programming

Using Threads to Overcome Synchronization Delays in Parallel Multiple Progressive Alignment Algorithms

Problem statement: The parallelization of multiple progressive alignment algorithms is a difficult task. All known methods have strong bottlenecks resulting from synchronization delays. This is even more constraining in distributed memory systems, where message passing also delays the interprocess communication. Despite these drawbacks, parallel computing is becoming increasingly necessary to perform multiple sequence alignment. Approach: In this study, it is introduced a solution for parallelizing multiple progressive alignments in distributed memory systems that overcomes such delays. Results: The proposed approach uses threads to separate actual alignment from synchronization and communication. It also uses a different approach to schedule independent tasks. Conclusion/Recommendations: The approach was intensively tested, producing a performance remarkably better than a largely used algorithm. It is suggested that it can be applied to improve the performance of some multiple alignment tools, as CLUSTALW and MUSCLE.Sao Paulo State Research Foundation-FAPES

An Efficient Parallel Algorithm for Multiple Sequence Similarities Calculation Using a Low Complexity Method

With the advance of genomic researches, the number of sequences involved in comparative methods has grown immensely. Among them, there are methods for similarities calculation, which are used by many bioinformatics applications. Due the huge amount of data, the union of low complexity methods with the use of parallel computing is becoming desirable. The k-mers counting is a very efficient method with good biological results. In this work, the development of a parallel algorithm for multiple sequence similarities calculation using the k-mers counting method is proposed. Tests show that the algorithm presents a very good scalability and a nearly linear speedup. For 14 nodes was obtained 12x speedup. This algorithm can be used in the parallelization of some multiple sequence alignment tools, such as MAFFT and MUSCLE.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Expression of 19 microRNAs in glioblastoma and comparison with other brain neoplasia of grades I-III

Several biomarkers have been proposed as useful parameters to better specify the prognosis or to delineate new target therapy strategies for glioblastoma patients. MicroRNAs could represent putative target molecules, considering their role in tumorigenesis, cancer progression and their specific tissue expression. Although several studies have tried to identify microRNA signature for glioblastoma, a microRNA profile is still far from being well-defined.In this work the expression of 19 microRNAs (miR-7, miR-9, miR-9*, miR-10a, miR-10b, miR-17, miR-20a, miR-21, miR-26a, miR-27a, miR-31, miR-34a, miR-101, miR-137, miR-182, miR-221, miR-222, miR-330, miR-519d) was evaluated in sixty formalin-fixed and paraffin-embedded glioblastoma samples using a locked nucleic acid real-time PCR. Moreover, a comparison of miRNA expressions was performed between primary brain neoplasias of different grades (grades IV-I).The analysis of 14 validated miRNA expression in the 60 glioblastomas, using three different non-neoplastic references as controls, revealed a putative miRNA signature: mir-10b and miR-21 were up-regulated, while miR-7, miR-31, miR-101, miR-137, miR-222 and miR-330 were down-regulated in glioblastomas. Comparing miRNA expression between glioblastoma group and gliomas of grades I-III, 3 miRNAs (miR-10b, mir-34a and miR-101) showed different regulation statuses between high-grade and low-grade tumors. miR-10b was up-regulated in high grade and significantly down-regulated in low-grade gliomas, suggesting that could be a candidate for a GBM target therapy.This study provides further data for the identification of a miRNA profile for glioblastoma and suggests that different-grade neoplasia could be characterized by different expression of specific miRNAs