Mixed Heuristic Local Search for Protein Structure Prediction

Protein structure prediction is an unsolved problem in computational biology. One great difficulty is due to the unknown factors in the actual energy function. Moreover, the energy models available are often not very informative particularly when spatially similar structures are compared during search. We introduce several novel heuristics to augment the energy model and present a new local search algorithm that exploits these heuristics in a mixed fashion. Although the heuristics individually are weaker in performance than the energy function, their combination interestingly produces stronger results. For standard benchmark proteins on the face centered cubic lattice and a realistic 20 × 20 energy model, we obtain structures with significantly lower energy than those obtained by the state-of-the-art algorithms. We also report results for these proteins using the same energy model on the cubic lattice.

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

[2]  Alessandro Dal Palù,et al.  Heuristics, optimizations, and parallelism for protein structure prediction in CLP(FD) , 2005, PPDP '05.

[3]  Kathleen Steinhöfel,et al.  Population-based local search for protein folding simulation in the MJ energy model and cubic lattices , 2009, Comput. Biol. Chem..

[4]  Sue Whitesides,et al.  A complete and effective move set for simplified protein folding , 2003, RECOMB '03.

[5]  Alessandro Dal Palù,et al.  A constraint solver for discrete lattices, its parallelization, and application to protein structure prediction , 2007, Softw. Pract. Exp..

[6]  Michael J. Maher,et al.  Kangaroo: An Efficient Constraint-Based Local Search System Using Lazy Propagation , 2011, CP.

[7]  Kathleen Steinhöfel,et al.  Stochastic Protein Folding Simulation in the d-Dimensional HP-Model , 2007, BIRD.

[8]  Barry Cipra,et al.  Packing Challenge Mastered At Last , 1998, Science.

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

[10]  Abdul Sattar,et al.  The road not taken: retreat and diverge in local search for simplified protein structure prediction , 2013, BMC Bioinformatics.

[11]  Alessandro Dal Palù,et al.  Constraint Based Protein Structure Prediction Exploiting Secondary Structure Information , 2004 .

[12]  Jeffrey Skolnick,et al.  Fast procedure for reconstruction of full‐atom protein models from reduced representations , 2008, J. Comput. Chem..

[13]  K. Binder,et al.  A Guide to Monte Carlo Simulations in Statistical Physics: Preface , 2005 .

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

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

[16]  R. Jernigan,et al.  Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation , 1985 .

[17]  Fernando Niño,et al.  A novel ab-initio genetic-based approach for protein folding prediction , 2007, GECCO '07.

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

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

[20]  Alessandro Dal Palù,et al.  Exploring Protein Fragment Assembly Using CLP , 2011, IJCAI.