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Protein structure prediction is considered as one of the most challenging andcomputationally intractable combinatorial problem. Thus, the efficient modelingof convoluted search space, the clever use of energy functions, and moreimportantly, the use of effective sampling algorithms become crucial to addressthis problem. For protein structure modeling, an off-lattice model provideslimited scopes to exercise and evaluate the algorithmic developments due to itsastronomically large set of data-points. In contrast, an on-lattice modelwidens the scopes and permits studying the relatively larger proteins becauseof its finite set of data-points. In this work, we took the full advantage ofan on-lattice model by using a face-centered-cube lattice that has the highestpacking density with the maximum degree of freedom. We proposed a gradedenergy-strategically mixes the Miyazawa-Jernigan (MJ) energy with thehydrophobic-polar (HP) energy-based genetic algorithm (GA) for conformationalsearch. In our application, we introduced a 2x2 HP energy guided macro-mutationoperator within the GA to explore the best possible local changes exhaustively.Conversely, the 20x20 MJ energy model-the ultimate objective function of our GAthat needs to be minimized-considers the impacts amongst the 20 different aminoacids and allow searching the globally acceptable conformations. On a set ofbenchmark proteins, our proposed approach outperformed state-of-the-artapproaches in terms of the free energy levels and the root-mean-squaredeviations.

Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction