McAllister Scott R, Floudas Christodoulos A
Department of Chemical Engineering, Princeton University, Princeton, NJ 08544-5263, USA.
Comput Optim Appl. 2010 Mar 1;45(2):377-413. doi: 10.1007/s10589-009-9277-y.
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.
用于蛋白质结构预测问题的第一性原理方法必须在巨大的构象空间中进行搜索，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表述为一个非线性约束最小化问题，其目标是在扭转角和原子间距离的约束下，使蛋白质构象的能量最小化。所提出算法的核心是一种混合全局优化方法，该方法结合了αBB确定性全局优化方法和构象空间退火的优点。这些全局优化技术采用一种局部最小化策略，该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，然后应用序列二次规划方法在约束条件下进一步最小化蛋白质构象的能量。所提出的算法展示了识别更低能量蛋白质结构以及更大的低能量构象集合的能力。
