First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the αBB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations.

中文翻译：

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一种改进的蛋白质三级结构预测混合全局优化方法。

蛋白质结构预测问题的第一性原理方法必须搜索巨大的构象空间，以识别低能量、接近天然的结构。在本文中，我们将三级结构预测问题的公式描述为非线性约束最小化问题，其目标是在受扭转角和原子间距离约束的情况下最小化蛋白质构象的能量。所提出算法的核心是一种混合全局优化方法，它结合了α具有构象空间退火的 BB 确定性全局优化方法。这些全局优化技术采用局部最小化策略，结合扭转角动力学和旋转异构体优化来识别和改进初始构象的选择，然后应用顺序二次规划方法进一步最小化受约束的蛋白质构象的能量。所提出的算法证明了识别低能量蛋白质结构以及更大的低能量构象集合的能力。