Wang Y, Zhang H, Scott R A
Department of Molecular Biology, Jilin University, Changchun, People's Republic of China.
Protein Sci. 1995 Jul;4(7):1402-11. doi: 10.1002/pro.5560040714.
A new model for calculating the solvation energy of proteins is developed and tested for its ability to identify the native conformation as the global energy minimum among a group of thousands of computationally generated compact non-native conformations for a series of globular proteins. In the model (called the WZS model), solvation preferences for a set of 17 chemically derived molecular fragments of the 20 amino acids are learned by a training algorithm based on maximizing the solvation energy difference between native and non-native conformations for a training set of proteins. The performance of the WZS model confirms the success of this learning approach; the WZS model misrecognizes (as more stable than native) only 7 of 8,200 non-native structures. Possible applications of this model to the prediction of protein structure from sequence are discussed.
开发了一种用于计算蛋白质溶剂化能的新模型，并对其进行了测试，以检验该模型能否在一系列球状蛋白质的数千个通过计算生成的紧密非天然构象中，将天然构象识别为全局能量最低构象。在该模型（称为WZS模型）中，基于使一组训练蛋白质的天然构象和非天然构象之间的溶剂化能差异最大化的训练算法，学习了20种氨基酸的一组17个化学衍生分子片段的溶剂化偏好。WZS模型的性能证实了这种学习方法的成功；在8200个非天然结构中，WZS模型仅误识别出7个（将其识别为比天然结构更稳定）。讨论了该模型在从序列预测蛋白质结构方面的可能应用。
