用于真实蛋白质结构预测的一种新的优化方法

用"相对熵"作为优化函数,提出了一个有效快速的折叠预测优化算法.使用了非格点模型,预测只关心蛋白质主链的走向.其中只用到了蛋白质主链上的两两连续的Cα原子间的距离信息以及20种氨基酸的接触势的一个扩展形式.对几个真实蛋白质做了算法测试,预测的初始结构都为比较大的去折叠态,预测构象相对于它们天然结构的均方根偏差(RMSD)为5～7 A.从原理上讲,该方法是对能量优化的改进.

A New Minimization Method for Real Protein Folding Prediction

A new effective and fast minimization approach is proposed for the prediction of protein folding, in which the“relative entropy”is used as minimization function. Unlike the energy minimization method, the essence of this approach is to search the conformation with high occupation probability, which corresponds to the state with low free energy instead of low energy. The off-lattice model is used, and the prediction just focuses on the frame of the main chain of protein. In this approach, only the distances between the consecutive Cα atoms along the peptide chain and a generalized form of the contact potential for 20 types of amino acids are used. Tests of the prediction algorithm are performed on real proteins with the initial structure fully denatured. The root mean square deviations (RMSD) of the structures of four folded target proteins versus the native structures are from 5 to 7. The advantage of this approach is its simple potential function and fast performance. Moreover, it can be considered as an improvement on the energy minimization method in principle.