A local search embedded genetic algorithm for simplified protein structure prediction

No single algorithm suits the best for the protein structure prediction problem. Therefore, researchers have tried hybrid techniques to mix the power of different strategies to gain improvements. In this paper, we present a hybrid search framework that embeds a tabu-based local search within a population based genetic algorithm. We applied our hybrid algorithm on simplified protein structure prediction problem. We use a low-resolution ab initio search method with the hydrophobic-polar energy model and face-centred-cubic lattice. Within the genetic algorithm, we apply local search in two different situations: i) only once at the beginning and ii) every time at search stagnation. At the beginning, we apply local search to improve the randomly generated individuals and use them as an initial population for the genetic algorithm. Later, we apply local search after applying a random-walk at situations where the genetic algorithm gets stuck. In both cases, the use of local search is to improve the randomised solutions quickly. We experimentally show that our hybrid approach outperforms the state-of-the-art approaches.

[1]  David Baker,et al.  Protein Structure Prediction Using Rosetta , 2004, Numerical Computer Methods, Part D.

[2]  C. Levinthal.  Are there pathways for protein folding , 1968 .

[3]  Abdul Sattar,et al.  Spiral search: a hydrophobic-core directed local search for simplified PSP on 3D FCC lattice , 2013, BMC Bioinformatics.

[4]  Abdul Sattar,et al.  Protein folding prediction in 3D FCC HP lattice model using genetic algorithm , 2007, 2007 IEEE Congress on Evolutionary Computation.

[5]  Kathleen Steinhöfel,et al.  Protein Folding Simulation by Two-Stage Optimization , 2009 .

[6]  K. Dill,et al.  A lattice statistical mechanics model of the conformational and sequence spaces of proteins , 1989 .

[7]  Ron Unger,et al.  On the applicability of genetic algorithms to protein folding , 1993, [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences.

[8]  Pascal Van Hentenryck,et al.  On Lattice Protein Structure Prediction Revisited , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[9]  So Much More to Know … , 2005, Science.

[10]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[11]  El-Ghazali Talbi,et al.  A grid-based genetic algorithm combined with an adaptive simulated annealing for protein structure prediction , 2008, Soft Comput..

[12]  Pascal Van Hentenryck,et al.  Protein Structure Prediction on the Face Centered Cubic Lattice by Local Search , 2008, AAAI.

[13]  Hans-Joachim Böckenhauer,et al.  A Local Move Set for Protein Folding in Triangular Lattice Models , 2008, WABI.

[14]  Federico Fogolari,et al.  Amino acid empirical contact energy definitions for fold recognition in the space of contact maps , 2003, BMC Bioinformatics.

[15]  T. Hales.  The Kepler conjecture , 1998, math/9811078.

[16]  M. Stefani,et al.  Protein Folding and Misfolding on Surfaces , 2008, International journal of molecular sciences.

[17]  Ron Unger,et al.  Genetic Algorithm for 3D Protein Folding Simulations , 1993, ICGA.

[18]  Vincenzo Cutello,et al.  An Immune Algorithm for Protein Structure Prediction on Lattice Models , 2007, IEEE Transactions on Evolutionary Computation.

[19]  Kathleen Steinhöfel,et al.  A hybrid approach to protein folding problem integrating constraint programming with local search , 2010, BMC Bioinformatics.

[20]  Andrew Lewis,et al.  Twin Removal in Genetic Algorithms for Protein Structure Prediction Using Low-Resolution Model , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[21]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .

[22]  Abdul Sattar,et al.  A New Genetic Algorithm for Simplified Protein Structure Prediction , 2012, Australasian Conference on Artificial Intelligence.

[23]  Sitao Wu,et al.  Ab Initio Protein Structure Prediction , 2009 .

[24]  Joe Marks,et al.  Human-guided tabu search , 2002, AAAI/IAAI.

[25]  Songde Ma,et al.  Protein folding simulations of the hydrophobic–hydrophilic model by combining tabu search with genetic algorithms , 2003 .

[26]  Yue,et al.  Sequence-structure relationships in proteins and copolymers. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[27]  Erik D. Goodman,et al.  A Standard GA Approach to Native Protein Conformation Prediction , 1995 .

[28]  Adam Smith.  Protein misfolding , 2003, Nature.

[29]  Frank Thomson Leighton,et al.  Protein folding in the hydrophobic-hydrophilic (HP) is NP-complete , 1998, RECOMB '98.

[30]  Abdul Sattar,et al.  Random-walk: a stagnation recovery technique for simplified protein structure prediction , 2012, BCB '12.

[31]  Andrew Lewis,et al.  DFS-generated pathways in GA crossover for protein structure prediction , 2010, Neurocomputing.

[32]  Abdul Sattar,et al.  Memory-based local search for simplified protein structure prediction , 2012, BCB.

[33]  C. Anfinsen.  Principles that govern the folding of protein chains. , 1973, Science.

[34]  Holger H. Hoos,et al.  A replica exchange Monte Carlo algorithm for protein folding in the HP model , 2007, BMC Bioinformatics.

[35]  Charles Seife,et al.  What Is the Universe Made Of? , 2005, Science.

[36]  Richard Bonneau,et al.  Ab initio protein structure prediction: progress and prospects. , 2001, Annual review of biophysics and biomolecular structure.

[37]  R Samudrala,et al.  Ab initio construction of protein tertiary structures using a hierarchical approach. , 2000, Journal of molecular biology.

[38]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..