A hybrid approach to protein folding problem integrating constraint programming with local search

<p>Abstract</p> <p>Background</p> <p>The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.</p> <p>Results</p> <p>Using the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach.</p> <p>Conclusion</p> <p>Constraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.</p

Uphill unfolding of native protein conformations in cubic lattices

We present results from simulations of unfolding in cubic lattices with two types of simplified energy functions, namely the Miyazawa–Jernigan (MJ) energy function and the hydrophobic-polar (HP) model. The simulations are executed on six benchmark problems for the MJ model proposed by Faisca and Plaxco [7] and ten well-known benchmark problems for the HP model devised by Beutler and Dill [2]. The unfolding procedure utilizes the pull-move set as a neighbourhood relation and a new population-based search method. For all sixteen benchmark problems we establish the existence of short pathways with monotonically increasing energy functions from ground states to contact-free unfolded states, which includes the three sequences with a high contact order number studied in the MJ model. The number of pull-move transitions (length of unfolding pathways) differs only slightly for the sixteen benchmark problems and ranges from 27 to 31 for both types of benchmarks. The computational effort of finding unfolding paths and subsequent refolding is discussed in the context of one-way functions

Population-based local search for protein folding simulation in the MJ energy model and cubic lattices

We present experimental results on benchmark problems in 3D cubic lattice structures with the Miyazawa–Jernigan energy function for two local search procedures that utilise the pull-move set: (i) population-based local search (PLS) that traverses the energy landscape with greedy steps towards (potential) local minima followed by upward steps up to a certain level of the objective function; (ii) simulated annealing with a logarithmic cooling schedule (LSA). The parameter settings for PLS are derived from short LSA-runs executed in pre-processing and the procedure utilises tabu lists generated for each member of the population. In terms of the total number of energy function evaluations both methods perform equally well, however, PLS has the potential of being parallelised with an expected speed-up in the region of the population size. Furthermore, both methods require a significant smaller number of function evaluations when compared to Monte Carlo simulations with kink-jump moves