A Systematic Mapping Study on Applications for Multi-core and Many-core Architectures for Protein Structure Prediction
Resumo
Proteins are the most abundant organic compounds of living matter and perform essential functions to the life's process. Given a proteins amino acid sequence, the Protein Structure Prediction (PSP) problem is to find a three-dimensional structure that has the native energy level. It can help in the design of new drugs and medicine. However, despite advances made in recent years, the development of methodologies capable of achieving a high degree of predictability and accuracy remains a major challenge. This systematic mapping aims to find related studies and research opportunities of how multi-core and many-core architectures have been used to solve the PSP problem. We have defined a systematic mapping process and applied it to complete a systematic mapping study. Thirty-two primary studies were selected for discussions on advances and opportunities for further investigations. The results show that there is an increasing interest to apply solutions based on multi-core and many-core architectures for this computing hard problem.
Referências
Baker, D. (2000). A surprising simplicity to protein folding. Nature, 405(6782):39.
Brasil, C. R. S., Delbem, A. C. B., and da Silva, F. L. B. (2013). Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction. Journal of computational chemistry, 34(20):1719–1734.
Dieste, O. and Padua, A. G. (2007). Developing search strategies for detecting relevant experiments for systematic reviews. In First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), pages 215–224.
Dorn, M., e Silva, M. B., Buriol, L. S., and Lamb, L. C. (2014). Three-dimensional protein structure prediction: Methods and computational strategies. Comp. biology and chemistry, 53:251–276.
Gang, W., Xiaoguang, L., and Jing, L. (2006). Parallel algorithm for protein folds prediction. In 2006 International Conference on Computational Intelligence and Security, volume 1, pages 470–473.
Levinthal, Cyrus (1968). Are there pathways for protein folding? Journal de chimie physique, 65:44–45.
Llanes, A., Mu˜noz, A., Bueno-Crespo, A., Garc´ýa-Valverde, T., S´anchez, A., Arcas-T´unez, F., P´erez-S´anchez, H., and M Cecilia, J. (2016). Soft computing techniques for the protein folding problem on high performance computing architectures. Current drug targets, 17(14):1626–1648.
Lopes, G. R., de Souza, P. S. L., and Delbem, A. C. B. (2019). A systematic mapping on high-performance computing for protein structure prediction. In Senger, H., Marques,
O., Garcia, R., Pinheiro de Brito, T., Iope, R., Stanzani, S., and Gil-Costa, V., editors, High Performance Computing for Computational Science – VECPAR 2018, pages 77– 91, Cham. Springer International Publishing.
Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., and Tramontano, A. (2014). Critical assessment of methods of protein structure prediction (casp) - round x. Proteins: Structure, Function, and Bioinformatics, 82:1–6.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE’08, pages 68–77, Swindon, UK. BCS Learning & Development Ltd.
Sar, E. and Acharyya, S. (2014). Genetic algorithm variants in predicting protein structure. In 2014 Int. Conf. on Comm. and Signal Proc., pages 321–325.
Verli, H. (2014). Bioinform´atica: da biologia `a flexibilidade molecular. Sociedade Brasileira de Bioqu´ýmica e Biologia Molecular, S˜ao Paulo, SP, BR, 1a edition.
Webster, D. M. (2000). Protein structure prediction: methods and protocols, volume 143. Springer Science & Business Media.