Next-generation integrated quantum force fields for biomedical applications

用于生物医学应用的下一代集成量子力场

• 批准号: 10202634
• 负责人: Darrin M York
• 金额: $ 32.25万
• 依托单位: RUTGERS, THE STATE UNIV OF N.J.
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10202634
• 关键词: Affinity; Attention; Binding; Binding Proteins; Books; Cations; Charge; Chemicals; Complex; Computer software; Coupled; Data; Data Set; Development; Drug Targeting; Electrostatics; Free Energy; Halogens; Inflammatory; Laboratories; Lead; Libraries; Ligand Binding; Ligands; Machine Learning; Mechanics; Metals; Methods; Modeling; Molecular; Molecular Conformation; Penetration; Periodicity; Pharmaceutical Preparations; Phase; Problem Solving; Proteins; Research; Sampling; Series; Structure; Surface; System; Technology; Testing; Thermodynamics; Universities; Variant; Work; base; density; design; drug discovery; flexibility; inhibitor/antagonist; innovation; insight; lead optimization; macrophage; mechanical force; nervous system disorder; next generation; novel; novel strategies; physical model; programs; quantum; response; simulation; tool; validation studies

Next-generation integrated quantum force fields for biomedical applications PI: Darrin M. York, Rutgers University, Piscataway, NJ 08854-8087 USA. We have recently developed novel framework for next-generation quantum mechanical force fields (QMFFs) designed to meet the challenges of biomolecular simulations and drug discovery applications. QMFFs have tremendous computational advantages relative to their fully QM counterparts, being inherently parallelizable and linearly scaling, offering tremendous computational speedup, and promising quantitative accuracy potentially superior to full QM methods. QMFFs accurately model multipolar electrostatics, charge penetration effects, and non-linear polarization response. QMFFs thus offer a transformative technology for drug discovery applications, in particular, for advancing the predictive capability of free energy simulations in lead refinement. These are critically important for the diverse chemical space of drug molecules, including halogen bonding, cation- and metal-ligand interactions. Further, QMFFs offer a mechanism for modeling covalent inhibitors. Specifically, we propose to: I. Develop new QMFFs for drug discovery. QMFFs will be developed based on both semiempirical and ab initio density-functional methods in the following stages: 1) determination of multipolar mapping parameters enhancing the DFTB electrostatic potential to reach greater accuracy, 2) augmentation of electronic response terms using chemical potential equalization (CPE) corrections using an orthogonal perturbation-response approach to solve the under-polarization problem of DFTB methods, 3) parameterization of non-electrostatic non-bonded interac- tion parameters using realistic potentials that capture many-body exchange and dispersion interactions, and 4) exploration of statistical potentials, using machine learning approaches applied to quantum data sets, to correct internal conformational energies and short-range interactions. II. Develop new free energy methods to enable protein-ligand binding predictions using QMFFs. We will develop a novel integrated free energy pipeline to pre- dict alchemical binding free energies for ligands and inhibitors. This will include new GPU-accelerated methods for -space self-adaptive mixture sampling ( -SAMS) and 2D-vFEP analysis, coupled with conformational space enhanced sampling methods for alchemical steps of the thermodynamic cycle, and advancements in free en- ergy “book-ending” methods (BBQm) to efficiently connect molecular mechanical force field and QMFF model representations. III. Test and validate QMFFs and free energy methods, and apply to MIF inhibitor binding. The methods will be broadly tested against established data sets for solvation free energies, and a drug discovery data set. More in-depth validation studies will be conducted by examining the relative binding free energies of inhibitors of the macrophage inhibitory factor (MIF). Finally, exploratory applications will examine mechanisms, characterize transition states and predict rates for covalent inhibition for a series of MIF inhibitors.

用于生物医学的下一代集成量子力fi场 派：达林·M·约克，罗格斯大学，美国新泽西州皮斯卡塔韦，邮编：08854-8087。 我们最近开发了下一代量子力fi场(Qmff)的新框架。 旨在应对生物分子模拟和药物发现应用的挑战。QMFF有 相对于完全的QM对应物，具有巨大的计算优势，本质上是可并行化的，并且 线性缩放，提供了极大的计算加速，并有可能保证定量精度 优于全质量管理方法。QMFR精确地模拟多极静电、电荷穿透效应和 非线性极化响应。因此，QMFC为药物发现应用提供了一种变革性的技术，在 特别是，用于提高铅元素中自由能模拟的预测能力。这些都是至关重要的 对药物分子的不同化学空间很重要，包括卤素键、阳离子和金属配体 互动。此外，QMFC提供了一种对共价抑制剂进行建模的机制。特别是fiCally，我们建议：I. 开发用于药物发现的新的QMFF。将在半经验和从头算的基础上开发QMFF 密度泛函方法在以下几个阶段的应用：1)多极映射参数增强的确定 DFTB静电势达到更高的精度，2)电子响应项的增强使用 用正交微扰-响应法求解化学势均衡(CPE)校正 DFTB方法的欠极化问题，3)非静电非键合作用的参数化。 使用真实势能捕捉多体交换和色散相互作用的运动参数，以及4) 使用应用于量子数据集的机器学习方法来探索统计势，以校正 内部构象能和短程相互作用。二、开发新的自由能方法，使 基于QMFFS的蛋白质-配体结合预测。我们将开发一种新型的集成自由能源管道，以预 配位体和抑制剂的化学结合自由能。这将包括新的GPU加速方法 FOR-空间自适应混合采样(-SAMS)和2D-vFEP分析，结合构象空间 改进的热力循环化学步骤的取样方法，以及在自由能量方面的进展。 fi分子机械力fi场与QMFF场模型的能量“收尾”方法 申述。测试和验证QMFF和自由能方法，并适用于MIF抑制剂结合。这个 这些方法将根据已建立的溶剂化自由能数据集和一项药物发现进行广泛测试 数据集。将通过检查相对结合自由能进行更深入的验证研究 巨噬细胞抑制因子(MIF)的抑制剂。最后，探索性应用程序将检查机制， 表征一系列MIF抑制剂的过渡态并预测共价抑制率。