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

Paper ID Volume ID Publish Year Pages File Format Full-Text
14941 1362 2016 16 PDF Available
Title
Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction
Abstract

•Graded energy-model strategically mixes the 20 × 20 MJ potential matrix with 2 × 2 HP energy model.•HP guided macro-mutation operator within GA provides efficient sampling.•Proposed Algorithm outperformed other state-of-the-art approaches.•Splits the energy function related complexities into two less complex functions through macro-mutation operator.

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.

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Keywords
Ab initio protein structure prediction; Genetic algorithms; FCC lattice; Miyazawa–Jernigan model; Hydrophobic-polar model
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Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 61, April 2016, Pages 162–177
Authors
, , , , ,
Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us