Spiral Search: A Hydrophobic-Core Directed Local Search for Simplified PSP on 3D FCC Lattice
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Author(s)
Rashid, Mahmood A
Newton, MA Hakim
Hoque, Md Tamjidul
Shatabda, Swakkhar
Pham, Duc Nghia
Sattar, Abdul
Year published
2013
Metadata
Show full item recordAbstract
Background: Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic (FCC) lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (HC) is essential for the progress of the search. The hydrophobic-core (H-core) helps find a stable structure with the lowest possible free energy. Results: In order to build ...
View more >Background: Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic (FCC) lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (HC) is essential for the progress of the search. The hydrophobic-core (H-core) helps find a stable structure with the lowest possible free energy. Results: In order to build H-core, we present a new Spiral Search algorithm based on tabu-guided local search. Our algorithm uses a novel H-core directed guidance heuristic that squeezes the structure around a dynamic hydrophobic-core-centre (HCC). We applied random-walk for breaking premature H-cores to avoid early convergence. We used a novel relay-restart (RR) technique to handle stagnation. Conclusions: We tested our algorithms on a set of benchmark proteins. The experimental results show that our spiral search performs better in comparison to state-of-the art local search algorithms for simplified protein structure prediction. We also experimentally show the effectiveness of the relay-restart.
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View more >Background: Protein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic (FCC) lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (HC) is essential for the progress of the search. The hydrophobic-core (H-core) helps find a stable structure with the lowest possible free energy. Results: In order to build H-core, we present a new Spiral Search algorithm based on tabu-guided local search. Our algorithm uses a novel H-core directed guidance heuristic that squeezes the structure around a dynamic hydrophobic-core-centre (HCC). We applied random-walk for breaking premature H-cores to avoid early convergence. We used a novel relay-restart (RR) technique to handle stagnation. Conclusions: We tested our algorithms on a set of benchmark proteins. The experimental results show that our spiral search performs better in comparison to state-of-the art local search algorithms for simplified protein structure prediction. We also experimentally show the effectiveness of the relay-restart.
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Conference Title
BMC BIOINFORMATICS
Volume
14
Publisher URI
Http://www.apbc2013.org/
Copyright Statement
© 2013 Rashid et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Note
Page numbers are not for citation purposes. Instead, this article has the unique article number of S16.
Subject
Mathematical sciences
Biological sciences
Information and computing sciences
Artificial intelligence not elsewhere classified