拉氏图是一种经典的蛋白质结构验证工具，在蛋白质结构研究领域有广泛应用。然而，传统拉氏图定义的合理区域范围太广，容错率高，包含了一些不准确的结构。针对这一问题，本文提出一种基于支持向量机和贝叶斯优化的方法SVM-Rama，对传统拉氏图的合理区域定义进行优化和细分，使细分后的合理区域的范围精确到具体的二级结构种类，SVM-Rama可以提高蛋白质结构验证准确率，并简便精确地标注二级结构。结果表明，本方法在二级结构标记中，准确率接近传统方法取得的最好结果，但训练和计算成本远小于传统方法。

The Ramachandran plot is among the most central concept for validating the conformation of protein structures, which plays an important role in structural biology. However, the favored regions defined by the traditional Ramachandran plot are too wide and contain inaccurate structures. For this lack, a method based on Support Vector Machine and Bayesian Optimization, SVM-Rama, is proposed to optimize and subdivide the definition of favored regions for the Ramachandran plot. The present study aims to improve the accuracy of the favored regions to specific secondary structure species of proteins and then to validate and annotate protein secondary structures simply and accurately. The results show that it has a high accuracy close to the best performance of traditional methods in secondary structure annotation but at lower training and computational costs than traditional methods do.