A Firefly-inspired method for protein structure prediction in lattice models

Maher, Brian, Albrecht, Andreas A., Loomes, Martin J., Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556 and Steinhofel, Kathleen (2014) A Firefly-inspired method for protein structure prediction in lattice models. Biomolecules, 4 (1). pp. 56-75. ISSN 2218-273X (doi:https://doi.org/10.3390/biom4010056)

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Abstract

We introduce a Firefly-inspired algorithmic approach for
protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function valuations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > SensoLab group
A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 13044
Notes on copyright: Creative Commons Attribution Licence. Authors are encouraged to submit their published articles to institutional repositories.
Useful Links:
Depositing User: Andreas Albrecht
Date Deposited: 19 Feb 2014 11:43
Last Modified: 14 Jun 2019 05:33
URI: https://eprints.mdx.ac.uk/id/eprint/13044

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