/HjDE-3D

A GPU-Based Hybrid jDE Algorithm Applied to the 3D-AB Protein Structure Prediction

Primary LanguageCudaMIT LicenseMIT

HjDE-3D: GPU-based Hybrid jDE to the 3D-AB PSP

If you use my approach or any adaptation of it, please refer to the following papers:

Boiani M., Parpinelli R. S. A GPU-based hybrid jDE algorithm applied to the 3D-AB protein structure prediction. Swarm and Evolutionary Computation (2020): 100711. doi: 10.1016/j.swevo.2020.100711, URL

Boiani M., Dominico G., Parpinelli R. S. (2019) A GPU-Based Hybrid jDE Algorithm Applied to the 2D-AB Protein Structure Prediction Problem. 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador, Brazil, 2019, pp. 317-322, doi: 10.1109/BRACIS.2019.00063, URL

Boiani M., Dominico G., Parpinelli R. S. (2020) A GPU-Based jDE Algorithm Applied to Continuous Unconstrained Optimization. In: Abraham A., Cherukuri A., Melin P., Gandhi N. (eds) Intelligent Systems Design and Applications. ISDA 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. doi: 10.1007/978-3-030-16657-1_85, URL

Description: Protein Structure Prediction (PSP) problem is an open problem in bioinformatics and, as the problem scales, complexity and processing time increases. In this way, robust methods and massively parallel architectures are required. This repository provide a GPU-based hybrid algorithm, named cuHjDE, to handle the 3D-AB off-lattice PSP problem. The cuHjDE is composed of the jDE algorithm and the Hooke-Jeeves local search algorithm. An important feature present in the proposed method is the use of a crowding mechanism to avoid premature convergence promoting diversification in the search space.
Requirements
Architecture

The Figure below depicts a flow chart of the GPU-based implementation. In kernels’ boxes, the labels S1, S2, and S3 represent the kernel structure and letters R,SM, and C denote the use of random number generator, shared memory and constant memory, respectively

Compile
$ cd repo
$ make
Parameters Setting
$ "runs, r"      - Number of Executions
$ "pop_size, p"  - Population Size
$ "dim, d"       - Protein Length {13, 21, 34, 38, 55, 64, 98, 120}
$ "max_eval, e"  - Number of Function Evaluations
$ "help, h"      - Show this help
Proteins Added
PDB ID PSL D AB Sequence
1BXP 13 21 ABBBBBBABBBAB
1CB3 13 21 BABBBAABBAAAB
1BXL 16 27 ABAABBAAAAABBABB
1EDP 17 29 ABABBAABBBAABBABA
2ZNF 18 31 ABABBAABBABAABBABA
1EDN 21 37 ABABBAABBBAABBABABAAB
2H3S 25 45 AABBAABBBBBABBBABAABBBBBB
1ARE 29 53 BBBAABAABBABABBBAABBBBBBBBBBB
2KGU 34 63 ABAABBAABABBABAABAABABABABABAAABBB
1TZ4 37 69 BABBABBAABBAAABBAABBAABABBBABAABBBBBB
1TZ5 37 69 AAABAABAABBABABBAABBBBAABBBABAABBABBB
1AGT 38 71 AAAABABABABABAABAABBAAABBABAABBBABABAB
1CRN 46 87 BBAAABAAABBBBBAABAAABABAAAABBBAAAAAAAABAAABBAB
2KAP 60 115 BBAABBABABABABBABABBBBABAABABAABBBBBBABBBAABAAABBABBABBAAAAB
1HVV 75 145 BAABBABBBBBBAABABBBABBABBABABAAAAABBBABAABBABBBABBAABBABBAABBBBBAABBBBBABBB
1GK4 84 163 ABABAABABBBBABBBABBABBBBAABAABBBBBAABABBBABBABBBAABBABBBBBAABABAAABABAABBBBAABABBBBA
1PCH 88 171 ABBBAAABBBAAABABAABAAABBABBBBBBABAAABBBBABABBAABAAAAAABBABBABABABABBABBAABAABBBAABBAAABA
2EWH 98 191 AABABAAAAAAABBBAAAAAABAABAABBAABABAAABBBAAAABABAAABABBAAABAAABAAABAABBAABAAAAABAAABABBBABBAAABAABA
Execute
$ cd repo
$ ./demo <parameter setting> or make run (with default parameters)
Clean up
$ make clean
TODO
  • Empty list for a while

[1] J. Brest, V. Zumer and M. S. Maucec, "Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization," 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, 2006, pp. 215-222. doi: 10.1109/CEC.2006.1688311, URL

[2] CUDA is a parallel computing platform and programming model developed by NVIDIA for GPGPU