Absolute binding free energies for virtual screening: A novel implementation of quantum mechanics/molecular mechanics (QM/MM) for FEP that allows substantial sampling and a significant quantum region

用于虚拟筛选的绝对结合自由能：用于 FEP 的量子力学/分子力学 (QM/MM) 的新颖实现，允许大量采样和重要的量子区域

• 批准号: 10759829
• 负责人: David A Pearlman
• 金额: $ 27.34万
• 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: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10759829
• 关键词: Address; Adoption; Binding; CDK2 gene; Computers; Computing Methodologies; Data; Databases; Docking; Drug Targeting; Evaluation; Explosion; Free Energy; Goals; Head; Lead; Ligand Binding; Ligands; Mechanics; Methods; Modification; Molecular; Output; Process; Proteins; Protocols documentation; Publications; Quantum Mechanics; Reliability of Results; Resources; Sampling; Savings; Speed; System; Tail; Testing; Thrombin; Time; Triage; Virtual Tool; Work; beta-site APP cleaving enzyme 1; computational chemistry; cost; data modeling; drug candidate; drug discovery; falls; improved; interest; lead candidate; lead optimization; mechanical force; molecular mechanics; novel; novel strategies; programs; quantum; receptor; restraint; screening; simulation; timeline; virtual; virtual screening

Project Summary Computational chemistry has revolutionized drug discovery, reducing by months or even years the amount of time it takes to discover and refine a lead candidate. Nowhere has the contribution of computational chemistry been greater than in the realm of virtual screening (VS) to identify an initial hit to a drug target receptor. It is now routine to screen 106-108 virtual compounds via molecular docking to identify potential binders. A small number of these will be purchased and screened, which is a slower and more expensive process. While docking is demonstrably useful for brute force triage, it is also generally unreliable for rank-ordering the compounds that survive the triage. There is a substantial and unmet need for computational methods that are better at rank ordering that can further reduce the number of compounds that survive to purchase/screening. Interest is growing in an approach termed ABFE, in which the Absolute Binding Free Energies of diverse ligands can be evaluated for a common protein receptor target. This approach is a natural outgrowth of relative binding free energy (RBFE) methods, the most well-known of which is Free Energy Perturbation (FEP). In recent years, the application of FEP for hit-to-lead optimization has exploded, thanks to increasing computer resources and automized workflows. With a reliable ABFE approach, further enrichment of the potential binders that come from dock-based screening can be obtained, improving the cost/hit ratio for the expensive experimental tail of the screening campaign. Based on FEP, the computational formalism that would make ABFE calculations possible within the screening paradigm has been described, and a few publications have demonstrated that it is, indeed, capable of further enriching the compounds that survive the molecular docking screen. These calculations have still been limited by two issues: 1) Computational throughput; 2) Limitations of the Molecular Mechanics (MM) force field that has been exclusively used in these ABFE/FEP simulations. The limitations of computational throughput are increasingly addressed by expansion in the availability of cloud resources, so the limitations of the MM force field are the primary issue. We propose an ABFE/FEP approach that replaces the limited MM representation with one based on a combined quantum mechanics (QM) +MM approach: QM/MM—where the region of ligand binding is treated using QM. In contrast to MM, QM describes molecular energetics much more exactly, and is broadly appliable to all classes of molecular ligands, unlike MM, which has a large number of known limitations/deficiencies. We will apply ABFE/FEP calculations to a variety of systems to validate the approach within the context of virtual screening, and to demonstrate the improvements that QM/MM allows versus traditional MM approaches.

项目摘要 计算化学已经彻底改变了药物发现，减少了数月甚至数年的数量。 发现和完善一个主要候选人所需的时间。计算化学的贡献 比在虚拟筛选（VS）领域更大，以确定对药物靶受体的初始命中。是 现在常规地通过分子对接筛选106-108个虚拟化合物以鉴定潜在的结合剂。一个小 其中一些将被购买和筛选，这是一个缓慢和昂贵的过程。而 对接对于蛮力分类是非常有用的，但是对于排序来说，它通常也是不可靠的。 在检伤分类中幸存下来的化合物存在对计算方法的大量且未满足的需求， 更好地进行排序，这可以进一步减少存活到购买/筛选的化合物的数量。 人们对一种称为ABFE的方法越来越感兴趣，在这种方法中，不同分子的绝对结合自由能 可以针对共同的蛋白质受体靶来评估配体。这种方法是相对的自然产物 结合自由能（RBFE）方法，其中最著名的是自由能微扰（FEP）。在 近年来，由于计算机的不断增加，FEP在命中率优化中的应用呈爆炸式增长 资源和自动化工作流程。 通过可靠的ABFE方法，进一步丰富了来自码头的潜在粘合剂， 可以获得筛选，提高成本/命中率为昂贵的实验尾部的筛选 运动中 基于FEP，计算形式主义，使ABFE计算在筛选范围内成为可能 已经描述了一个范例，一些出版物已经证明，它确实能够进一步 富集在分子对接筛选中存活的化合物。这些计算仍然有限 两个问题：1）计算吞吐量; 2）分子力学（MM）力场的限制， 专门用于ABFE/FEP模拟。计算吞吐量的限制是 越来越多地通过云资源可用性的扩展来解决，因此MM部队的局限性 领域是首要问题。 我们提出了一种ABFE/FEP方法，该方法将有限的MM表示替换为基于 结合量子力学（QM）+MM方法：QM/MM-其中处理配体结合区域 使用QM。与MM相比，QM对分子能量学的描述更为精确，具有广泛的适用性 与MM不同，MM具有大量已知的限制/缺陷。 我们将ABFE/FEP计算应用于各种系统，以验证该方法的背景下， 虚拟筛选，并证明改进QM/MM允许与传统的MM方法。