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Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction

Rashid, Mahmood and Iqbal, Sumaiya and Khatib, Firas and Hoque, Md Tamjidul and Sattar, Abdul (2016) Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction. Computational Biology and Chemistry , 61 . 162 - 177. ISSN 1476-9271

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Protein structure prediction is considered as one of the most challenging and computationally intractable combinatorial problem. Thus, the efficient modeling of convoluted search space, the clever use of energy functions, and more importantly, the use of effective sampling algorithms become crucial to address this problem. For protein structure modeling, an off-lattice model provides limited scopes to exercise and evaluate the algorithmic developments due to its astronomically large set of data-points. In contrast, an on-lattice model widens the scopes and permits studying the relatively larger proteins because of its finite set of data-points. In this work, we took the full advantage of an on-lattice model by using a face-centered-cube lattice that has the highest packing density with the maximum degree of freedom. We proposed a graded energy—strategically mixes the Miyazawa–Jernigan (MJ) energy with the hydrophobic-polar (HP) energy—based genetic algorithm (GA) for conformational search. In our application, we introduced a 2 × 2 HP energy guided macro-mutation operator within the GA to explore the best possible local changes exhaustively. Conversely, the 20 × 20 MJ energy model—the ultimate objective function of our GA that needs to be minimized—considers the impacts amongst the 20 different amino acids and allow searching the globally acceptable conformations. On a set of benchmark proteins, our proposed approach outperformed state-of-the-art approaches in terms of the free energy levels and the root-mean-square deviations.

Item Type: Journal Article
Additional Information: Subject Areas- Bioinformatics, Computational Biology, Genetic Algorithms, Artificial Intelligence
Uncontrolled Keywords: Hydrophobic-polar model
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Q Science > QD Chemistry
Q Science > QH Natural history > QH301 Biology
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: Mahmood Rashid
Date Deposited: 07 Apr 2016 03:36
Last Modified: 20 Mar 2017 23:23

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