Statistical mechanics with quantum potentials: Application to protein-ligand binding affinities

量子势统计力学：在蛋白质-配体结合亲和力中的应用

• 批准号: 9795701
• 负责人: Simon Webb
• 金额: $ 71.52万
• 依托单位: VERACHEM, LLC
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9795701
• 关键词: Achievement; Active Sites; Address; Affinity; Binding; Binding Proteins; Binding Sites; Cations; Charge; Chemicals; Computer Assisted; Computer software; Consumption; Dependence; Disease; Docking; Drug Design; Drug Industry; Drug Kinetics; Environment; Free Energy; Goals; Industry; Laboratories; Ligand Binding; Ligands; Measures; Mechanics; Metals; Methodology; Methods; Mining; Modeling; Molecular Conformation; Nature; Pharmaceutical Preparations; Phase; Physics; Potential Energy; Process; Property; Proteins; Quantum Mechanics; Resources; Scheme; Scientist; Scoring Method; Series; Small Business Innovation Research Grant; Solubility; Speed; Statistical Mechanics; System; Testing; Time; base; chemical synthesis; computational chemistry; computer cluster; design; drug candidate; drug development; drug discovery; experimental study; improved; inhibitor/antagonist; lead optimization; molecular mechanics; novel therapeutics; parallelization; programs; quantum; research and development; small molecule libraries

During the drug development process, lead optimization requires intensive chemical synthesis and testing efforts. The process can be highly iterative in nature with multiple rounds of synthesis required, because changes made to improve, for example, pharmacokinetic factors such as solubility can also decrease potency, requiring further changes to recover potency, and so on. Consistently accurate computational predictions of protein-ligand binding affinities would significantly reduce this expensive and time consuming burden, by providing medicinal chemists the ability to more aggressively prioritize ligands for synthesis and testing based on computational results. However, currently, achievement of consistent accuracy in protein-ligand binding affinity prediction is an unmet goal in the field of computational chemistry. Conventional docking and scoring methods have been shown to provide enrichment of active vs. inactive ligands in chemical libraries, but still are very limited in their ability to rank candidate ligands by their binding affinities. Even advances like free energy perturbation (FEP) and VeraChem's own mining minima free energy method VM2, remain limited in their ability to consistently provide the accuracy levels needed. Importantly, all of these methods have in common a dependency on classical molecular mechanics (MM) force fields, and even the best force fields for proteins and drug-like molecules are not guaranteed to have optimal parameters nor to provide adequate descriptions of chemical interactions involving, for example, π-stacking, polarization, charge transfer, or metal cations. In fact, the approximations inherent in typical force fields are thought to be a key factor limiting accuracy. In this fast- track SBIR proposal, we aim to address this key limitation by integrating VeraChem's free energy method VM2 with quantum mechanical (QM) potentials, producing a new software package for QM based protein-ligand free energy calculations called PLQM-VM2. This package will be distinct from other free energy methods, such as FEP, which is not readily implemented with QM potentials. Similarly, although QM has been applied to protein-ligand systems, existing methods are limited to focusing on a single conformation, whereas PLQM- VM2 will integrate existing force field-based conformational searching with QM energy and free energy refinement. Phase I will provide a first level of QM protein-ligand free energy capability, integrating VM2 with a fast semi-empirical QM treatment of the ligand and protein active site. In Phase II, a capability to allow fast and accurate inclusion of protein atoms beyond the active site will be added through a SEQM/polarizable force field method, and a very efficient QM fragmentation scheme will enable energy corrections at higher-level QM. Parallelization on CPUs and GPUs will provide fast enough turnaround to support industry R&D, and submission of calculations to both local computer clusters and cloud resources will be supported. The package will be tested and best practices defined through application to multiple protein targets each with high quality measured affinities for a large series of non-covalent inhibitors.

在药物开发过程中，先导化合物优化需要大量的化学合成和测试 该过程本质上可能是高度迭代的，需要多轮综合，因为 例如，为改善药代动力学因素（例如溶解度）而进行的改变也会降低效力， 需要进一步的改变来恢复效力，等等。 蛋白质-配体结合亲和力将显着减少这种昂贵且耗时的负担，通过 为药物化学家提供更积极地优先考虑配体的能力，以进行基于合成和测试的配体 然而，目前，蛋白质-配体结合的准确性已达到一致。 传统的对接和评分领域中，亲和力预测是一个未实现的目标。 方法已被证明可以在化学库中富集活性配体与非活性配体，但仍然存在 他们通过结合亲和力对候选配体进行排序的能力非常有限，甚至像自由能这样的进步也是如此。 扰动 (FEP) 和 VeraChem 自己的采矿最小自由能方法 VM2，在其方面仍然有限 能够始终如一地提供所需的准确度水平 重要的是，所有这些方法都有一个共同点。 依赖于经典分子力学 (MM) 力场，甚至是蛋白质和蛋白质的最佳力场 类药物分子不能保证具有最佳参数，也不能提供充分的描述 化学相互作用涉及例如 π 堆积、极化、电荷转移或金属阳离子。 典型力场固有的近似值被认为是限制这种快速精度的关键因素。 跟踪 SBIR 提案，我们的目标是通过集成 VeraChem 的自由能方法 VM2 来解决这一关键限制 具有量子力学 (QM) 潜力，为基于 QM 的蛋白质配体生成新的软件包 称为 PLQM-VM2 的自由能计算该软件包将不同于其他自由能方法，例如 作为 FEP，它不容易通过 QM 潜力实现。尽管 QM 已类似地应用于 对于蛋白质-配体系统，现有方法仅限于关注单一构象，而 PLQM- VM2将现有的基于力场的构象搜索与QM能量和自由能量相结合 第一阶段将提供第一级 QM 蛋白质-配体自由能能力，将 VM2 与 对配体和蛋白质活性位点进行快速半经验 QM 处理，能够快速进行。 并通过 SEQM/极化力精确包含活性位点之外的蛋白质原子 场方法和非常有效的 QM 碎片方案将能够在更高级别的 QM 上进行能量校正。 CPU 和 GPU 上的并行化将提供足够快的周转速度来支持行业研发，并且 将支持向本地计算机集群和云资源提交计算。 将通过应用于多个蛋白质目标（每个目标均具有高质量）来进行测试并定义最佳实践 测量了一系列非共价抑制剂的亲和力。