Abstract
Searching similarities among 3D protein structures deposited in macromolecular data repositories, like Protein Data Bank, is one of the time-consuming processes performed in structural bioinformatics. When performed in one-to-many or many-to-many model, the process requires increased computational resources. Moreover, exponential growth of protein structures in the Protein Data Bank causes the necessity to prepare computer systems to be able to deal with such huge volumes of data. Cloud computing provides both, theoretically infinite computational resources and a great possibility of scaling systems out and up. In this paper, we show how 3D protein structure similarity searching can be scaled out on Microsoft Azure cloud and performed by a loosely coupled, many-task computing system with local replicas of macromolecular data.
This project was supported by Microsoft Research in USA within Microsoft Azure for Research Award granted for the Cloud4Psi project.
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Acknowledgements
We would like to thank Microsoft Research for providing us with free access to the computational resources of the Microsoft Azure cloud under the Microsoft Azure for Research Award program. Further development of the system will be carried out by the Cloud4Proteins non-profit, scientific group (http://www.zti.aei.polsl.pl/w3/dmrozek/science/cloud4proteins.htm).
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Mrozek, D., Kutyła, T., Małysiak-Mrozek, B. (2016). Accelerating 3D Protein Structure Similarity Searching on Microsoft Azure Cloud with Local Replicas of Macromolecular Data. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. Lecture Notes in Computer Science(), vol 9574. Springer, Cham. https://doi.org/10.1007/978-3-319-32152-3_24
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