Holm L, Sander C
European Molecular Biology Laboratory, Heidelberg, Federal Republic of Germany.
Proteins. 1992 Oct;14(2):213-23. doi: 10.1002/prot.340140208.
An unknown protein structure can be predicted with fair accuracy once an evolutionary connection at the sequence level has been made to a protein of known 3-D structure. In model building by homology, one typically starts with a backbone framework, rebuilds new loop regions, and replaces nonconserved side chains. Here, we use an extremely efficient Monte Carlo algorithm in rotamer space with simulated annealing and simple potential energy functions to optimize the packing of side chains on given backbone models. Optimized models are generated within minutes on a workstation, with reasonable accuracy (average of 81% side chain chi 1 dihedral angles correct in the cores of proteins determined at better than 2.5 A resolution). As expected, the quality of the models decreases with decreasing accuracy of backbone coordinates. If the back-bone was taken from a homologous rather than the same protein, about 70% side chain chi 1 angles were modeled correctly in the core in a case of strong homology and about 60% in a case of medium homology. The algorithm can be used in automated, fast, and reproducible model building by homology.
一旦在序列水平上与已知三维结构的蛋白质建立了进化联系，就可以相当准确地预测未知蛋白质结构。在同源性建模中，通常从一个主链框架开始，重建新的环区域，并替换非保守的侧链。在这里，我们在旋转异构体空间中使用一种极其高效的蒙特卡罗算法，结合模拟退火和简单的势能函数，来优化给定主链模型上侧链的堆积。在工作站上几分钟内就能生成优化后的模型，且具有合理的准确性（在分辨率优于2.5埃的蛋白质核心区域，平均81%的侧链χ1二面角正确）。正如预期的那样，模型的质量会随着主链坐标准确性的降低而下降。如果主链取自同源而非相同的蛋白质，在强同源性的情况下，核心区域约70%的侧链χ1角建模正确，在中等同源性的情况下约为60%。该算法可用于同源性自动、快速且可重复的模型构建。
