STRUCT DEPENDENT PKA SHIFTS IN POLYPENTAPEPTIDE OF POLY[FV(IPGVG), FE(IPGEG)]

基于结构的蛋白质抑制剂设计需要有能力 预测构象和自由能的变化 蛋白质-配体结合。原则上，这可以通过找到 最低能量构象(全局最小)和自由能构象 关联的宏状态。但在实践中，数字的复杂性 的计算能力超出了能力许多数量级。 我们正在开发一种新的算法，它可以发现和利用 大分子势能的内在层次结构 能源景观，从而极大地提高了这一 进程。该方法是从重整化群方法发展而来的 它们在统计物理学中非常成功，取代了 传统的固定比例最小化和抽样 以一系列规模递减的规模运营。在过去 去年，我们发展了许多必要的数学形式主义和 已经对包括小肽在内的模型系统进行了测试。我们 目前正在将我们的测试扩展到更大的多肽并开发 应用于较大蛋白质所需的工具。

Structure-based design of protein inhibitors requires the ability to predict conformational and free-energy changes occurring during protein-ligand binding. In principle this can be done by finding the conformation of lowest energy (global minimum) and the free-energy of the associated macrostate. But, in practice, the numerical complexity of the computation exceeds capabilities by many orders-of-magnitude. We are developing a new class of algorithms that uncovers and exploits the intrinsic hierarchical structure of macromolecular potential energy landscapes to thereby greatly increase the efficiency of this process. The approach, adapted from renormalization group methods which have been very successful in statistical physics, replaces conventional fixed-scale minimization and sampling with a sequence of operations at a series of decreasing size scales. During the past year we have developed much of the needed mathematical formalism and have performed tests on model systems including small peptides. We are currently extending our tests to larger peptides and developing the tools needed for application to larger proteins.