Protein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm |
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| Affiliation: | 1. Department of Computer Science, Indiana University, Bloomington, USA;2. Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh;3. Institute for Integrated & Intelligent Systems, Griffith University, Brisbane, Australia;4. School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Suva, Fiji;5. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh;1. University of Banjaluka, Faculty of Natural Sciences and Mathematics, Mladena Stojanovića 2, 78000 Banjaluka, Bosnia and Herzegovina;2. University of Belgrade, Faculty of Mathematics, Studentski trg 16/IV 11 000, Belgrade, Serbia;1. Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China;2. Department of Physics and Electronic information engineering, Wenzhou University, Wenzhou, 325000, Zhejiang, China;3. College of Information engineering, Wenzhou Vocational & Technology College, Wenzhou, 325000, Zhejiang, China;4. Department of Computer and Information Science, Fordham University, New York, NY, 10023, USA;1. Centre for Advanced Studies in Plant Biotechnology and Genetic Engineering, Department of Biosciences, Saurashtra University, Rajkot 360005, Gujarat, India;2. Agricultural Research Station, Ummedganj, Kota 324001. Rajasthan, India;3. DST-Centre for Policy Research, Entrepreneurship Development Institute of India, P.O. Bhat 382428. Gandhinagar, Gujarat, India;4. College of Agriculture, Sri Karan Narendra Agriculture University, Jobner, Jaipur, Rajasthan, India |
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| Abstract: | 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-the-art 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. |
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| Keywords: | Protein structure prediction Sparse data Molecular distance geometry Nuclear magnetic resonance spectroscopy Genetic algorithms |
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