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This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2005

Open Access Research article

High performance workflow implementation for protein surface characterization using grid technology

Ivan Merelli1, Giulia Morra1, Daniele D'Agostino2, Andrea Clematis2 and Luciano Milanesi1*

Author Affiliations

1 Istituto di Tecnologie Biomediche (ITB – CNR), via F.lli Cervi, Segrate (Milano), Italy

2 Istituto di Matematica Applicata e Tecnologie Informatiche (IMATI – CNR), via de Marini, Genova, Italy

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BMC Bioinformatics 2005, 6(Suppl 4):S19  doi:10.1186/1471-2105-6-S4-S19

Published: 1 December 2005



This study concerns the development of a high performance workflow that, using grid technology, correlates different kinds of Bioinformatics data, starting from the base pairs of the nucleotide sequence to the exposed residues of the protein surface. The implementation of this workflow is based on the Italian project infrastructure, that is a network of several computational resources and storage facilities distributed at different grid sites.


Workflows are very common in Bioinformatics because they allow to process large quantities of data by delegating the management of resources to the information streaming. Grid technology optimizes the computational load during the different workflow steps, dividing the more expensive tasks into a set of small jobs.


Grid technology allows efficient database management, a crucial problem for obtaining good results in Bioinformatics applications. The proposed workflow is implemented to integrate huge amounts of data and the results themselves must be stored into a relational database, which results as the added value to the global knowledge.


A web interface has been developed to make this technology accessible to grid users. Once the workflow has started, by means of the simplified interface, it is possible to follow all the different steps throughout the data processing. Eventually, when the workflow has been terminated, the different features of the protein, like the amino acids exposed on the protein surface, can be compared with the data present in the output database.