Next generation free energy perturbation (FEP) calculations--enabled by a novel integration of quantum mechanics (QM) with molecular dynamics allowing a large QM region and no sampling compromises

下一代自由能微扰 (FEP) 计算——通过量子力学 (QM) 与分子动力学的新颖集成实现，允许较大的 QM 区域且不会影响采样

• 批准号: 10698836
• 负责人: David A Pearlman
• 金额: $ 14.89万
• 依托单位: QUANTUM SIMULATION TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10698836
• 关键词: Address; Benchmarking; Binding; Binding Sites; Biological Models; Cloud Computing; Complex; Decision Making; Docking; Evaluation; Failure; Free Energy; Grant; Lead; Length; Ligands; Mainstreaming; Methods; Modeling; Modernization; Molecular; Molecular Conformation; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Potential Energy; Process; Proteins; Protocols documentation; Publishing; Quantum Mechanics; Reliability of Results; Running; Sampling; Sampling Errors; Science; Series; Speed; System; Time; Triage; Trust; Update; Work; beta-Cyclodextrins; computational chemistry; cost; drug discovery; drug modification; expectation; improved; interest; mechanical energy; molecular dynamics; molecular mechanics; next generation; novel; novel strategies; programs; protein structure; protonation; receptor; simulation; usability; virtual screening; web platform

Project Summary The value of computational chemistry to commercial drug discovery is now well-established. Virtual screening (including molecular docking) now jumpstarts most discovery efforts. More recently, a combination of GPU and cloud based computing has vastly increased the realistic computational throughput available for drug discovery. In turn, this has ignited substantial interest in relative free energy calculations (e.g. Free Energy Perturbation, FEP) for drug lead enhancement. FEP has been applied at the fringes of drug discovery for decades, but massive parallelism in the more recent past has moved FEP to center stage, and FEP has helped shave months or years off discovery efforts where these calculations are reliable. The catch is that FEP calculations are not always reliable. While for some systems, the error in a FEP result is much less than one kcal/mol--and they have successfully steered slow/expensive bench efforts--there are other systems where the predictions are not very useful. Even where retrospective analysis is possible, it is often not very clear why FEP calculations are so good for some target systems, and so bad for others. Broadly, the limitations of FEP can be distilled down to three problems: A poor description of the energetics (force field); insufficient sampling; or a misunderstanding of the fundamental science (e.g., incorrect protein model, wrong binding site, wrong protonation state, etc.). It is generally believed that many issues arise from the first of these—and improving the evaluation of energetics using quantum mechanics (QM) will be the focus here. There is a huge interest in methods that can help obviate the existing problems with FEP. Herein, we propose a new platform for FEP, which incorporates a quantum mechanical description of the molecular interaction of central interest. The traditional force field used with FEP is a simplified analytic expression with fit coefficients termed Molecular Mechanics (MM). MM is a simple approximation of the true molecular interactions that can be described using quantum mechanics. But QM has been, until recently, far too expensive to use in the context of the molecular dynamics (MD) sampling required for FEP. At long last, we have determined how to integrate QM into the FEP paradigm, using a carefully programmed distributed processing platform that lends itself to use on commodity cloud computers, and by integrating a semiempirical QM implementation that provides predictions that are much better than those from MM, but at a cost far less than for a full DFT QM prediction. Our implementation allows FEP calculations with a realistic QM core region of hundreds of atoms to be carried out with the scale of sampling associated with accurate FEP calculations and with turnaround commensurate with modern drug discovery. Here, we propose to validate this platform against traditional MM-based FEP, to show it addresses many of the issues of that approach. We will also identify additional implementation ideas to further improve effective throughput and/or accuracy.

项目概要 计算化学对商业药物发现的价值现已得到证实。 （包括分子对接）现在启动了大多数发现工作，最近，GPU 和 GPU 的结合。 基于云的计算极大地提高了药物可用的实际计算吞吐量 反过来，这引发了人们对相对自由能计算（例如自由能）的浓厚兴趣。 用于药物先导增强的扰动（FEP）已应用于药物发现的边缘。 几十年来，但最近的大规模并行性已将 FEP 推到了中心舞台，并且 FEP 已经 在这些计算可靠的情况下，帮助减少了数月或数年的发现工作。 问题是 FEP 计算并不总是可靠，而对于某些系统，FEP 结果中存在错误。 远低于一千卡/摩尔——而且他们已经成功地控制了缓慢/昂贵的替补工作——有 即使可以进行回顾性分析，预测也不是很有用。 通常不太清楚为什么 FEP 计算对于某些目标系统如此好，而对于其他系统则如此糟糕。 FEP 的局限性可以归结为三个问题： 对能量学（力场）的描述不佳； 采样不足；或对基础科学的误解（例如，不正确的蛋白质模型、错误的 一般认为，许多问题都是由第一个引起的。 这些以及使用量子力学 (QM) 改进能量学评估将是这里的重点。 人们对有助于消除 FEP 现有问题的方法非常感兴趣，我们在此提出建议。 FEP 的新平台，结合了分子相互作用的量子力学描述 FEP 使用的传统力场是带有拟合系数的简化解析表达式。 分子力学 (MM) 是真实分子相互作用的简单近似。 但直到最近，量子力学在量子力学中的应用仍然过于昂贵。 FEP 所需的分子动力学 (MD) 采样背景。 最后，我们确定了如何使用精心编程的方法将 QM 集成到 FEP 范式中。 分布式处理平台，适合在商用云计算机上使用，并通过集成 半经验 QM 实现提供的预测比 MM 的预测要好得多，但速度较慢 我们的实现允许使用真实的 QM 进行 FEP 计算，其成本远低于完整的 DFT QM 预测。 数百个原子的核心区域将进行与精确 FEP 相关的采样规模 在这里，我们建议验证这一点。 与传统的基于 MM 的 FEP 相比，我们将展示它解决了该方法的许多问题。 还确定实施其他想法以进一步提高有效吞吐量和/或准确性。