CONFORMATIONAL ANALYSIS BY ENERGY EMBEDDING

用于构象计算的距离几何方法非常 成功地产生了满足严格几何条件的构象 约束，但一直没有办法对约束进行加权或 包括精力充沛的考虑。我正在开发一种延长距离的方法 不仅处理几何约束，而且产生低 能量构象。在众多这样的申请中， 技术，我对研究蛋白质折叠特别感兴趣，它 当然，这是分子生物学基本理解的核心。我的 初步结果表明，能量嵌入扩展到 距离几何处理几何约束的成功程度 总是，也会产生能量非常低的构象。作为算法 现在，计算是相当耗时的，给定的蛋白质 在给定的势函数下，总会有相同的结果 康格顿。部分过程可以通过更快的替换来加速 计算技术。意识到能量嵌入并不是 保证找到全局能量最小值，算法必须修改为 出于物理现实主义的考虑，产生了几个低能量结构 而不是只有一个。势函数的选择也必须 更彻底地检查了一下。由于最终的结构如此关键地依赖于 在所使用的潜力上，一些函数可能比其他函数在引导方面更好 最终得到令人满意的结果。用于预测蛋白质 三级构象，一个特别好的“潜力”或一组 约束可以从许多经验折叠规则中推导出来，这些规则是 现在已经知道了。

The distance geometry approach to conformational calculations has been very successful in producing conformations satisfying strictly geometric constraints, but there has been no way to weight the constraints or to include energetic considerations. I am developing an extension of distance geometry that not only handles geometric constraints, but also produces low energy conformations. Among the numerous applications for such a technique, I am particularly interested in studying protein folding, which is of course central to the basic understanding of molecular biology. My preliminary results indicate that the energy embedding extension to distance geometry deals with geometric constraints as successfully as always, and also produces conformers of very low energy. As the algorithm now stands, the calculations are quite time consuming, and a given protein under a given potential function will always come to the same final conformaton. Parts of the process can be speeded up by substituting faster computational techniques. Realizing that energy embedding is not guaranteed to find global energy minima, the algorithm must be modified for the sake of physical realism to produce several low-energy structures instead of just one. The choice of potential function must also be examined more thoroughly. Since the final structure depends so critically on the potential used, some functions may be better than others at guiding the molecule to a satisfactory final result. For predicting protein tertiary conformation, a particularly good "potential" or set of constraints can be derived from the many empirical folding rules that are now known.