Advances in Bioinformatics and Computational Biology: Third by Roberto T. Alves, Myriam R. Delgado, Alex A. Freitas

By Roberto T. Alves, Myriam R. Delgado, Alex A. Freitas (auth.), Ana L. C. Bazzan, Mark Craven, Natália F. Martins (eds.)

This ebook constitutes the refereed complaints of the 3rd Brazilian Symposium on Bioinformatics, BSB 2008, held in Sao Paulo, Brazil, in August 2008 - co-located with IWGD 2008, the overseas Workshop on Genomic Databases.

The 14 revised complete papers and five prolonged abstracts have been conscientiously reviewed and chosen from forty-one submissions. The papers deal with a huge variety of present themes in computational biology and bioinformatics that includes unique learn in computing device technology, arithmetic and information in addition to in molecular biology, biochemistry, genetics, drugs, microbiology and different existence sciences.

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Extra info for Advances in Bioinformatics and Computational Biology: Third Brazilian Symposium on Bioinformatics, BSB 2008, Santo André, Brazil, August 28-30, 2008. Proceedings

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Table 1. Discrete State models used in the work of Park and Levitt [5]. The ∗ signals the models used in this work. [6] (-87,-47),(77,22),(107,-174) An Evaluation of the Impact of Side Chain Positioning on the Accuracy Non GLY/PRO GLY 27 PRO Fig. 2. Ramachandran plots for the proline (right), glycine (center) and other types of amino acids (left). com/bio/). If we analyze the angle sets in terms of physical correction, we can notice that only the best model, C, is near the probable zones of the Ramachandran plot [16].

In essence, the protein signatures used in the datasets have the following characteristics. Prosite signatures are regular expressions or patterns describing short fragments of protein sequences that can be used to identify protein domains, families and functional sites. Currently, the Prosite database stores patterns and profiles specific for more than a thousand protein families or domains. Each of these signatures comes with documentation providing background information on the structure and function of these proteins [15].

However, for larger proteins, especially for proteins with beta sheets, the accuracy is significantly lower than the one obtained for the backbone only tests. However, it is possible to obtain structures with root mean square error close or lower than 4 angstrom, which is still a very good, low detail, representation for the protein and a good starting point for the refinement algorithms. Moreover, for many proteins the results are near the 2 angstrom RMSD value and have, therefore, an accuracy equivalent to the backbone discrete models.

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