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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Bastolla. U., “Sequence-Structure Alignments with the Protfinder Algorithm,” inAbstracts of the 5th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction, Dec. 2002, Available at http://predictioncenter.llnl.gov/casp5/doc/Abstr.doc.
Bastolla, U., Farwer, J., Knapp, E. W. and Vendruscolo, M., “How to Guarantee Optimal Stability for Most Protein Native Structures in the Protein Data Bank,”Proteins: Structure, Function, and Genetics, 44, pp. 79–96, 2001.
Bastolla, U., Moya, A., Viguera, E. and Ham, R. van, “Genomic Determinants of Protein Folding Thermodynamics in Prokanotic Organisms,”Journal of Molecular Biology, 343, 5, pp. 1451–1466, 2004.
Bastolla, U., Roman, H. E. and Vendruscolo, M., “Neutral Evolution of Model Proteins: Diffusion in Sequence Space and Overdispersion,”Journal of Theoretical Biology, 200, pp. 49–64, 1999.
Bastolla, U., Vendruscolo, M. and Knapp, E. W., “A Statistical Mechanical Method to Optimize Energy Parameters for Protein Folding,” inProc. of the National Academy of Sciences (PNAS) of USA, 97, pp. 3977–3981, 2000.
Berman, F., Wolski, R., Casanova, H., Cirne, W., Dail, H., Faerman, M., Figueira, S., Hayes, J., Obertelli, G., Schopf, J., Shao, G., Smallen, S., Spring, N., Su, A. and Zagorodnov, D., “Adaptive Computing on the Grid Using AppLeS,”IEEE Transactions on Parallel and Distributed Systems, 14, 4, pp. 369–382, 2003.
Buyya, R., Abramson, D. and Giddy, J., “Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computation Grid,” inProc. of the 4th IEEE International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia), IEEE Computer Society Press, pp. 283–289, 2000.
Casanova, H., Legrand, A., Zagorodnov, D. and Berman, F., “Heuristics for Scheduling Parameter Sweep Applications in Grid Environments,” inProc. of the 9th Heterogeneous Computing Workshop, IEEE Computer Society Press, pp. 349–363, 2000.
El-Ghazawi, T., Gaj, K., Alexandinis, N. and Schott, B., “Conceptual Comparative Study of Job Management Systems,” Technical report, George Mason University, Feb. 2001, Available at http://ece.gmu.edu/lucite/reports/
Epema, D. H. J., Livny, M., Dantzig, R van., Evers, X. and Pruyne, J., “A Worldwide Flock of Condors: Load Sharing among Workstation Clusters,”Future Generation Computer Systems, 12, 1, pp. 53–65, 1996.
Foster, I. and Kesselman, C., “Globus: A Metacomputing Infrastructure Toolkit,”International Journal of Supercomputer Applications, 11, 2, pp. 115–128, 1997.
Gutin, A. M., Abkevich, V. I. and Shakhnovich, E. I., “Evolution-Like Selection of Fast-Folding Model Proteins,” inProc. of National Academy of Sciences (PNAS) of USA, 92, pp. 1282–1286, 1995.
Ham, R. van, Kamerbeek, J., Palacios, C., Rausell, C., Abascal, F., Bastolla, U., Fernandez, J. M., Jimenez, L., Postigo, M., Silva, F. J., Tamames, J., Viguera, E., Latorre, A., Valencia, A., Morán, F. and Moya, A., “Reductive Genome Evolution in Buchnera Aphidicola,” inProc. of National Academy of Sciences (PNAS) of USA, 100, pp. 581–586, 2003.
Huedo, E., Montero, R. S. and Llorente, I. M., “A Framework for Adaptive Execution on Grids,”Journal of Software — Practice and Experience, 34, 7, pp. 631–651, 2004.
Huedo, E., Montero, R. S. and Llorente, I. M., “Experiences on Grid Resource Selection Considering Resource Proximity,” inProc. of the 1st European Across Grids Conference, LNCS, 2970, Springer-Verlag, pp. 1–8, Feb. 2003.
Lanfermann, G., Allen, G., Radke, T. and Seidel, E., “Nomadic Migration: A New Tool for Dynamic Grid Computing,” inProc. of the 10th IEEE International Symposium on High Performance Distributed Computing (HPDC’01), IEEE Computer Society Press, pp. 429–430, 2001.
Montero, R. S., Huedo, E. and Llorente, I. M., “Grid Resource Selection for Opportunistic Job Migration,” inProc. of the 9th International Conference on Parallel and Distributed Computing (Euro-Par 2003), LNCS, 2790, Springer-Verlag, pp. 366–373, Aug. 2003.
Nagano, N., Orengo, C. A. and Thornton, J. M., “One Fold with Many Functions: The Evolutionary Relationships Between TIM Barrel Families Based on their Sequences, Structures and Functions,”Journal of Molecular Biology, 321, pp. 741–765, 2002.
Schopf, J. M., “Ten Actions when Superscheduling,”Scheduling Working Group — The Global Grid Forum, GFD-1.4, 2001.
Sun Microsystems, “How Sun Grid Engine, Enterprise Edition 5.3 Works,”Technical report, 2002. Available at http://www.sun.com/software/gridware/sgeee53/wp-sgeee
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