Abstract
Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (Nos. 61672121, 61751203, 61772100, 61702070, 61572093), Program for Changjiang Scholars and Innovative Research Team in University (No. IRT_15R07), the Program for Liaoning Innovative Research Team in University (No. LT2015002), the Basic Research Program of the Key Lab in Liaoning Province Educational Department (No. LZ2015004).
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Zhou, C., Sun, C., Wang, B. et al. An improved stochastic fractal search algorithm for 3D protein structure prediction. J Mol Model 24, 125 (2018). https://doi.org/10.1007/s00894-018-3644-5
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DOI: https://doi.org/10.1007/s00894-018-3644-5