Theory and Modeling of Noncovalent Binding

项目摘要 识别与目标蛋白紧密结合的小分子是一个耗时且昂贵的步骤 在许多药物发现项目中。显式溶剂自由能方法可以用来预测 小分子-蛋白质结合亲和力，从而有助于这一步骤。然而，他们并没有 提供始终如一的准确预测，他们使用的力场的限制是 被牵连为错误的关键来源。因此，我们的主要目标是帮助产生更多 值得信赖的力场。特别是，我们的目标是证明使用实验的原则 主-客系统的绑定数据，以及传统使用的液体属性，以进行提炼 力场参数。我们的目的还在于证明自由能方法可以帮助预测配体。 结合的姿势，并根据目标蛋白对化合物文库进行排序。 首先，我们将通过开发简便的方法来扩大主客体系统的化学多样性 衍生环糊精主体分子，并使用这些方法创造新的，水- 可溶性环糊精衍生物。我们将用以下方法测量它们的结合自由能和结合热 不同的客体分子，并将使用这些新数据来测试和改进力场。 我们还旨在证明使用敏感性分析来提炼Lennard-Jones(LJ)的原理 现有原子型力场中的参数，基于主-客体结合数据和液体 特性数据。除了调整现有的原子类型参数外，我们还将开发一个 分子中原子将分子的量子计算映射到LJ的方法 该分子的参数。通过减少参数的数量，相对于原子类型 方法时，此方法应支持全局参数优化，而不仅仅是优化 现有参数的。 最后，我们将自动化和优化我们实验室的附加-拉-释放(APR)计算方法 结合自由能，以使其可用于对结合位中配体的候选姿势进行排序； 然后，最稳定的几个姿势将被用于完全束缚自由能计算。在以下方面取得成功 这一努力将使自由能方法能够用于虚拟化合物筛选。在……里面 此外，我们将使用APR方法来测试上面在 蛋白质-配体结合的背景。

Project Summary Identifying a small molecule that tightly binds a targeted protein is a time-consuming, costly step in many drug discovery projects. Explicit solvent free energy methods can be used to predict small molecule-protein binding affinities and thus assist with this step. However, they do not provide consistently accurate predictions, and limitations in the force fields they use are implicated as a key source of error. Our main goal, therefore, is to help generate more trustworthy force fields. In particular, we aim to prove principle for the use of experimental binding data for host-guest systems, along with traditionally used liquid properties, to refine force field parameters. We also aim show that free energy methods can help predict ligand binding poses and rank compound libraries against targeted proteins. First, we will expand the chemical diversity of host-guest systems, by developing facile methods of derivatizing cyclodextrin host molecules, and using these methods to create new, water- soluble cyclodextrin derivatives. We will measure their binding free energies and enthalpies with varied guest molecules, and will use these new data to test and refine force fields. We also aim to prove principle for the use of sensitivity analysis to refine Lennard-Jones (LJ) parameters in existing atom-typed force fields, based on host-guest binding data and liquid property data. In addition to adjusting existing atom-typed parameters, we will develop an atoms-in-molecules approach to mapping a quantum calculation for a molecule to LJ parameters for that molecule. By reducing the number of parameters, relative to atom-typed methods, this approach should enable global parameter optimization, rather than just refinement of existing parameters. Finally, we will automate and optimize our lab’s attach-pull-release (APR) method of computing binding free energies so that it can be used to rank candidate poses of a ligand in a binding site; the most stable few poses will then be used for full binding free energy calculations. Success in this effort will enable free energy methods to be used in virtual compound screening. In addition, we will use the APR method to test the new parameters generated above in the context of protein-ligand binding.