基于二次优化的蛋白质构象预测非线性控制
Quadratic Optimization-Based Nonlinear Control for Protein Conformation Prediction
摘要：
预测蛋白质分子的最终折叠结构并模拟其折叠途径对于设计病毒药物和在分子水平上研究阿尔茨海默氏症等疾病至关重要。为此，本文研究了在避免折叠过程中的高熵损失路径的约束下的蛋白质构象预测问题。利用蛋白质分子基于动态顺应性（KCM）的非线性动力学模型，将蛋白质构象预测问题转化为一个二次规划（QP）的逐点最优控制综合问题.结果表明，在蛋白质折叠文献中的KCM扭矩可以用来定义一个参考向量场的基于QP的控制生成问题。所得到的动静态控制扭矩输入将接近基于KCM的参考矢量场，并保证受到预定界限的约束;因此，在折叠期间避免了高熵损失路线，同时分子的能量降低。
英文摘要：
Predicting the final folded structure of protein molecules and simulating their folding pathways is of crucial importance for designing viral drugs and studying diseases such as Alzheimer's at the molecular level. To this end, this paper investigates the problem of protein conformation prediction under the constraint of avoiding high-entropy-loss routes during folding. Using the well-established kinetostatic compliance (KCM)-based nonlinear dynamics of a protein molecule, this paper formulates the protein conformation prediction as a pointwise optimal control synthesis problem cast as a quadratic program (QP). It is shown that the KCM torques in the protein folding literature can be utilized for defining a reference vector field for the QP-based control generation problem. The resulting kinetostatic control torque inputs will be close to the KCM-based reference vector field and guaranteed to be constrained by a predetermined bound; hence, high-entropy-loss routes during folding are avoided while the energy of the molecule is decreased.
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