This paper presents a two-phase protein folding optimization on a three-dimensional AB off-lattice model. The first phase is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second phase, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two phases until the stopping condition is satisfied. An auxiliary fitness function was designed for the first phase, while the original fitness function is used in the second phase. The auxiliary fitness function includes an expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of two-phase optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-phase optimization improves the efficiency of our algorithm significantly and that the proposed algorithm is superior to other state-of-the-art algorithms.

本文提出了在三维AB离格模型上的两阶段蛋白质折叠优化。第一阶段负责形成具有良好疏水核或一组紧密的疏水氨基酸位置的构象。这些构象被转发到第二阶段，在该阶段进行精确搜索，目的是找到具有最佳能量值的构象。优化过程在这两个阶段之间切换，直到满足停止条件为止。为第一阶段设计了辅助适应功能，而在第二阶段中使用了原始适应功能。辅助适应度函数包括关于疏水核的质量的表达。这种表达对于将搜索过程引向具有良好疏水性核心的有前途的解决方案至关重要，因此，提高了整个优化过程的效率。我们的差分进化算法用于证明两阶段优化的效率。用文献中经常使用的众所周知的氨基酸序列进行了分析。获得的实验结果表明，采用的两阶段优化大大提高了我们算法的效率，并且该算法优于其他最新算法。