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DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines.

ABSTRACT: The proper prediction of the location of disulfide bridges is efficient in helping to solve the protein folding problem. Most of the previous works on the prediction of disulfide connectivity pattern use the prior knowledge of the bonding state of cysteines. The DBCP web server provides prediction of disulfide bonding connectivity pattern without the prior knowledge of the bonding state of cysteines. The method used in this server improves the accuracy of disulfide connectivity pattern prediction (Q(p)) over the previous studies reported in the literature. This DBCP server can be accessed at http://120.107.8.16/dbcp or http://140.120.14.136/dbcp.

SUBMITTER: Lin HH

PROVIDER: S-EPMC2896133 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines.

Lin Hsuan-Hung HH Tseng Lin-Yu LY

Nucleic acids research 20100608 Web Server issue

The proper prediction of the location of disulfide bridges is efficient in helping to solve the protein folding problem. Most of the previous works on the prediction of disulfide connectivity pattern use the prior knowledge of the bonding state of cysteines. The DBCP web server provides prediction of disulfide bonding connectivity pattern without the prior knowledge of the bonding state of cysteines. The method used in this server improves the accuracy of disulfide connectivity pattern predictio ...[more]

PMID: 20530534

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Project description:BACKGROUND:Dysregulated gene expression patterns have been reported in several mental disorders. Limited by the difficulty of obtaining samples, psychiatric molecular mechanism research still relies heavily on clues from genetics studies. By using reference data from brain expression studies, multiple types of comprehensive gene expression pattern analysis have been performed on psychiatric genetic results. These systems-level spatial-temporal expression pattern analyses provided evidence on specific brain regions, developmental stages and molecular pathways that are possibly involved in psychiatric pathophysiology. At present, there is no online tool for such systematic analysis, which hinders the applications of analysis by non-informatics researchers such as experimental biologists and clinical molecular biologists. RESULTS:We developed the BEST web server to support Brain Expression Spatio-Temporal pattern analysis. There are three highlighted features of BEST: 1) visualization: it generates user-friendly visual results that are easy to interpret, including heatmaps, Venn diagrams, gene co-expression networks and cluster-based Manhattan gene plots; these results illustrate the complex spatio-temporal expression patterns, including expression quantification and correlation between genes; 2) integration: it provides comprehensive human brain spatio-temporal expression patterns by integrating data from currently available databases; 3) multi-dimensionality: it analyses input genes as both a whole set and several subsets (clusters) which are enriched according to co-expression patterns, and it also presents the correlation between genetic and expression data. CONCLUSIONS:To the best of our knowledge, BEST is the first data tool to support comprehensive human brain spatial-temporal expression pattern analysis. It helps to bridge disease-related genetic studies and mechanism studies, provides clues for key gene and molecular system identification, and supports the analysis of disease sensitive brain region and age stages. BEST is freely available at http://best.psych.ac.cn.

Dataset Information

DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines.

Publications

DBCP: a web server for disulfide bonding connectivity pattern prediction without the prior knowledge of the bonding state of cysteines.

Similar Datasets

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