Entropy for End-Point and FFT-Based Binding Free Energy Calculations

项目摘要 计算机经常被用来预测两个分子结合的紧密程度，即它们的结合自由能。这些 预测有助于设计药物，预测遗传变异的后果， 分子如何相互作用维持生命不幸的是，目前可用的方法要么快速要么准确， 但不是两者都有。一般来说，快速方法在熵方面做得很差，熵是计算的重要部分。 自由能源该项目的主要目标是开发更好的方法来解释两种流行的熵 研究分子相互作用的技术：结合复合物的“终点”模拟及其 未结合的对应物;和基于快速傅立叶变换的分子对接。特别是，新的方法， 分析计算结果将被导出、实施、评估和优化。此外，方法 将与增强的采样技术相结合。 我们的新终点和基于FFT的方法将通过其再现基准结果的能力进行评估 从更慢但更准确的计算方法，以及实验结果。基准数据集 将包括具有已知结合亲和力的蛋白质-配体复合物和蛋白质-蛋白质复合物， 结构，以及蛋白质-蛋白质复合物，其中错义突变对结合的影响 测量了我们还将对肿瘤抑制基因p53的突变体进行基准计算， 激活新蛋白质和促进肿瘤生长的能力。除了作为基准， 计算可以提供关于蛋白质如何结合各种配体以及p53突变体如何获得 新的合作伙伴。 我们的新方法也将在反复出现的社区挑战中进行测试：“药物设计数据资源” (D3R)预测蛋白质-配体复合物结构和亲和力的巨大挑战以及“对蛋白质-配体复合物结构和亲和力的关键评估”。 蛋白质-蛋白质结构预测的“相互作用预测”（卡普里）挑战。这些盲目的挑战 将允许我们的方法与其他研究小组的方法进行公正的比较。 最后，我们将在一个药物发现项目中评估我们的方法。我们将使用现有的方法和新的 针对一对结构相似的细菌代谢酶虚拟筛选化学文库的方法。 一种酶与活跃的细菌有关，另一种与休眠的细菌有关。预测选择性结合的化合物 细菌（相对于人类）酶将在生化分析中进行实验测试。我们预计 我们改进后的方法将比现有方法更准确， 从相互作用预测到药物发现。

Project Summary Computers are often used to predict how tightly two molecules associate, their binding free energy. These predictions are helpful for designing drugs, predicting the consequences of genetic variation, and understanding how molecules interact to sustain life. Unfortunately, currently available methods are either fast or accurate, but not both. In general, fast methods do a poor job accounting for entropy, which is an important part of the free energy. The main objective of this project is to develop better ways to account for entropy in two popular techniques for studying molecular interactions: “end-point” simulations of the bound complexes and their unbound counterparts; and molecular docking based on the Fast Fourier Transform. Specifically, new ways to analyze calculation results will be derived, implemented, assessed, and optimized. Additionally, the methods will be combined with enhanced sampling techniques. Our new end-point and FFT-based methods will be assessed by their ability to reproduce benchmark results from slower but more accurate computational methods, as well as experimental results. The benchmark dataset will include protein-ligand complexes and protein-protein complexes with known binding affinities and crystal structures, as well as protein-protein complexes for which the effect of missense mutations on binding have been measured. We will also perform benchmark calculations on mutants of the tumor suppressor p53 that gain the ability to activate new proteins and promote tumor growth. In addition to serving as benchmarks, these calculations may provide mechanistic insight into how proteins bind various ligands and how p53 mutants gain new binding partners. Our new methods will also be tested in recurring community challenges: the “Drug Design Data Resource” (D3R) grand challenge to predict protein-ligand complex structures and affinities and the “Critical Assessment of PRediction of Interactions” (CAPRI) challenge for protein-protein structure prediction. These blinded challenges will allow for an unbiased comparison of our methods to those from other research groups. Finally, we will assess our methods in a drug discovery project. We will use established methods and our new methods to virtually screen a chemical library against a pair of structurally similar bacterial metabolic enzymes. One enzyme is relevant to active and the other to dormant bacteria. Compounds predicted to selectively bind the bacterial (opposed to human) enzymes will be experimentally tested in biochemical assays. We anticipate that our improved methods will be significantly more accurate than established approaches, advancing research ranging from interactome prediction to drug discovery.