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，铅的优化需要密集的化学合成和测试 努力。这个过程本质上可能是高度迭代的，需要多轮合成，因为 例如，为改善药物动力学因素(如溶解度)而进行的改变也会降低效力， 需要进一步改变以恢复效力，以此类推。始终如一准确的计算预测 蛋白质-配体结合的亲和力将显著减少这一昂贵和耗时的负担，通过 为药物化学家提供了更积极地优先选择用于合成和测试的配体的能力 对计算结果的影响。然而，目前，在蛋白质-配体结合方面实现了一致的准确性 亲和力预测是计算化学领域尚未达到的目标。常规对接和得分 已经证明了在化学文库中提供活性配体和非活性配体的富集化的方法，但仍然是 根据它们的结合亲和力对候选配体进行排名的能力非常有限。即使是像自由能这样的进步 摄动(FEP)和VeraChem自己的挖掘最小自由能方法VM2，在它们的应用中仍然受到限制 能够始终如一地提供所需的精度水平。重要的是，所有这些方法都有一个共同点 依赖于经典的分子力学(MM)力场，甚至蛋白质和 类药物分子不能保证有最佳的参数，也不能提供充分的描述 例如，涉及π堆积、极化、电荷转移或金属阳离子的化学相互作用。事实上, 典型力场固有的近似被认为是限制精度的关键因素。在这么快的速度下- 跟踪SBIR建议，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 利用量子力学(QM)势，生成基于量子力学(QM)的蛋白质配体的新软件包 自由能计算称为PLQM-VM2。这套方案将有别于其他自由能方法，如 作为FEP，它不容易用QM势来实现。同样，尽管质量管理已被应用于 蛋白质-配基系统，现有的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能量和自由能相结合 精致。第一阶段将提供第一级QM蛋白质-配体自由能能力，将VM2与 一种快速的半经验QM处理配体和蛋白质的活性部位。在第二阶段，能够实现FAST 并通过SEQM/可极化力将蛋白质原子精确地包含在活性中心之外 场方法，一个非常有效的QM碎片方案将在更高级别的QM实现能量校正。 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，以及 将支持向本地计算机集群和云资源提交计算。套餐 将通过应用于多个高质量的蛋白质靶标来测试和定义最佳实践 测量了一大系列非共价抑制剂的亲和力。