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A framework for protein structure prediction on the grid

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Abstract

The large number of protein sequences, provided by genomic projects at an increasing pace, constitutes a challenge for large scale computational studies of protein structure and thermodynamics. Grid technology is very suitable to face this challenge, since it provides a way to access the resources needed in compute and data intensive applications. In this paper, we show the procedure to adapt to the Grid an algorithm for the prediction of protein thermodynamics, using the GridWay tool. GridWay allows the resolution of large computational experiments by reacting to events dynamically generated by both the Grid and the application.

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Authors and Affiliations

  1. Centro de Astrobiología (CSIC-INTA), 28850, Torrejón de Ardoz, Spain

    Eduardo Huedo, Ugo Bastolla & Ignacio M. Llorente

  2. Dpto. de Arquitectura de Computadores y Automática, Universidad Complutense, 28040, Madrid, Spain

    Rubén S. Montero & Ignacio M. Llorente

Authors
  1. Eduardo Huedo
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  2. Ugo Bastolla
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  3. Rubén S. Montero
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  4. Ignacio M. Llorente
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Corresponding author

Correspondence to Eduardo Huedo.

Additional information

Eduardo Huedo, Ph.D.: He is a Computer Engineer (1999) and Ph.D. in Computer Architecture (2004) by the Universidad Complutense de Madrid (UCM). He is Scientist in the Advanced Computing Laboratory at Centro de Astrobiología (CSIC-INTA), associated to NASA Astrobiology Institute. He had one appointment in 2000 as a Summer Student in High Performance Computing and Applied Mathematics at ICASE (NASA Langley Research Center). His research areas are Performance Management and Tuning, High Performance Computing and Grid Technology.

Ugo Bastolla, Ph.D.: He received his degree and Ph.D. in Physics in Rome University, with L. Peliti and G. Parisi respectively. He was interested from the beginning in biologically motivated problems, therefore, studied models of Population Genetics, Boolean Networks, Neural Networks, Statistical Mechanics of Polymers, Ecological and Biodiversity. His main research interest is constituted by studies of protein folding thermodynamics and evolution. Thereby, he set up an effective energy function allowing prediction of protein folding thermodynamics, and applied it to protein structure prediction, to simulate protein evolution and to analyze protein sequences from a thermodynamical point of view. He is currently in the Bioinformatic Unit of the Centro de Astrobiología of Madrid.

Rubén S. Montero, Ph.D.: He received his B.S. in Physics (1996), M.S in Computer Science (1998) and Ph.D. in Computer Architecture (2002) from the Universidad Complutense de Madrid (UCM). He is Assistant Professor of Computer Architecture and Technology at UCM since 1999. He has held several research appointments at ICASE (NASA Langley Research Center), where he worked on computational fluid dynamics, parallel multigrid algorithms and Cluster computing. Nowadays, his research interests lie mainly in Grid Technology, in particular in adaptive scheduling, adaptive execution and distributed algorithms.

Ignacio M. Llorente, Ph.D.: He received his B.S. in Physics (1990), M.S in Computer Science (1992) and Ph.D. in Computer Architecture (1995) from the Universidad Complutense de Madrid (UCM). He is Executive M.B.A. by Instituto de Empresa since 2003. He is Associate Professor of Computer Architecture and Technology in the Department of Computer Architecture and System Engineering at UCM and Senior Scientist at Centro de Astrobiología (CSIC-INTA), associated to NASA Astrobiology Institute. He has held several appointments since 1997 as a Consultant in High Performance Computing and Applied Mathematics at ICASE (NASA Langley Research Center). His research areas are Information Security, High Performance Computing and Grid Technology.

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Huedo, E., Bastolla, U., Montero, R.S. et al. A framework for protein structure prediction on the grid. New Gener Comput 23, 277–290 (2005). https://doi.org/10.1007/BF03037634

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  • DOI: https://doi.org/10.1007/BF03037634

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