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Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm

Islam, Md. L. and Shatabda, Swakkhar and Rashid, Mahmood A. and Khan, Mohammad G.M. and Rahman, M.S. (2019) Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm. Computational Biology and Chemistry, 79 . pp. 6-15. ISSN 1476-9271

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    Nuclear Magnetic Resonance Spectroscopy (most commonly known as NMR Spectroscopy) is used to generate approximate and partial distances between pairs of atoms of the native structure of a protein. To predict protein structure from these partial distances by solving the Euclidean distance geometry problem from the partial distances obtained from NMR Spectroscopy, we can predict three-dimensional (3D) structure of a protein. In this paper, a new genetic algorithm is proposed to efficiently address the Euclidean distance geometry problem towards building 3D structure of a given protein applying NMR's sparse data. Our genetic algorithm uses (i) a greedy mutation and crossover operator to intensify the search; (ii) a twin removal technique for diversification in the population; (iii) a random restart method to recover from stagnation; and (iv) a compaction factor to reduce the search space. Reducing the search space drastically, our approach improves the quality of the search. We tested our algorithms on a set of standard benchmarks. Experimentally, we show that our enhanced genetic algorithms significantly outperforms the traditional genetic algorithms and a previously proposed state-of-theart method. Our method is capable of producing structures that are very close to the native structures and hence, the experimental biologists could adopt it to determine more accurate protein structures from NMR data.

    Item Type: Journal Article
    Subjects: Q Science > QA Mathematics
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Depositing User: Komal Devi
    Date Deposited: 25 Feb 2019 11:26
    Last Modified: 25 Feb 2019 11:26

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