Metalloenzyme binding affinity prediction with VM2

使用 VM2 预测金属酶结合亲和力

• 批准号: 10697593
• 负责人: Simon Webb
• 金额: $ 31.15万
• 依托单位: VERACHEM, LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10697593
• 关键词: Acceleration; Active Sites; Affinity; Algorithms; Anti-Inflammatory Agents; Antibiotics; Antineoplastic Agents; Antiviral Agents; Area; Binding; Charge; Complex; Computer software; Consumption; Crystallography; Docking; Drug Design; Drug Industry; Drug Kinetics; Drug Targeting; Environment; Enzymes; Exhibits; FDA approved; Face; Free Energy; Generations; Geometry; Ions; Ligand Binding; Ligands; Metalloproteins; Metals; Methodology; Methods; Mining; Modeling; Molecular Conformation; Nature; Output; Pharmaceutical Preparations; Phase; Potential Energy; Preclinical Testing; Preparation; Process; Program Development; Property; Proteins; Quantum Mechanics; Reproduction; Research; Resources; Scientist; Scoring Method; Series; Small Business Innovation Research Grant; Software Tools; Speed; Statistical Mechanics; Structure; System; Therapeutic; Time; United States; Viral Cancer; anti-cancer; computer cluster; density; design; drug candidate; drug development; drug-like compound; electronic structure; experimental study; human disease; improved; inhibitor; metalloenzyme; molecular modeling; neglect; novel; oxidation; parallelization; perturbation theory; predictive modeling; quantum chemistry; research and development; small molecule libraries; theories; therapeutic target; therapy development; virtual

Project summary: It is estimated that 40 to 50% of known enzymes can be characterized as metalloenzymes, while currently only 7% of FDA-approved drugs in the United States target this class of protein. This is despite the fact that there are many dozens of already identified metalloenzyme targets involved in virtually every therapeutic area, including anti-inflammatory, antibiotics, antivirals, anticancer drugs, and more. This is in large part because the already very difficult drug design requirement to maintain/increase the potency of an initial ligand (drug-like molecule) while improving/maintaining its target selectivity and pharmacokinetic properties, is made even harder by the complicated and often non-intuitive nature of metal-ligand and metal-protein interactions. Accurate molecular modeling predictions of metalloenzyme-ligand binding affinities, then, would be highly impactful in pharmaceutical industry drug research and development programs, because they would allow R&D scientists to carry out computational experiments drastically reducing the number of expensive and time-consuming bench experiments required to overcome the difficult metalloenzyme inhibitor design challenges they face. However, currently available molecular modeling approaches are unable to make predictions reliable enough to do this. Docking and scoring methods are able to determine, in many cases, the pose of inhibitors in metalloenzyme active sites, but they cannot correctly rank candidate inhibitors in order of binding affinity as they lack the required detail in their energy models. Recently, free energy-based methods have advanced to the point of providing reliable binding affinity predictions for many non-metal protein-ligand series and can, therefore, help speed ligand discovery efforts for these systems. They cannot provide good binding affinities for metalloenzyme-ligand systems though, because to-date they are all entirely based on classical forcefields, which fundamentally limits the accuracy of their descriptions of metal-ligand and metal-protein interactions. This is due, in part, to lack of inclusion of important polarization and charge transfer effects, but it is also because the complex electronic structure, which metals often exhibit, is intrinsically quantum mechanical. This fast-track SBIR proposal will address this by developing a new and unique molecular modeling software tool called Mzyme-QM-VM2, which will provide reliably accurate binding free energies for metalloenzyme- inhibitor complexes by a novel combination of statistical mechanics and highly scalable quantum chemistry methods. This software will be based on mining minima free energy calculation methodology and will be developed as an extension of VeraChem's VM2 free energy software platform.

项目摘要：据估计，已知酶的40％至50％可以被描述为金属酶，即 尽管目前，美国只有7％的FDA批准药物以这种蛋白质为目标。尽管如此 几乎每个都涉及的金属酶靶标已经有很多已经确定的金属酶靶标这个事实 治疗区域，包括抗炎，抗生素，抗病毒药，抗癌药等。这很大 部分是因为已经非常困难的药物设计要求保持/增加初始效力 配体（类似药物的分子），同时改善/维持其靶标的选择性和药代动力学特性， 由于金属配体和金属蛋白质的复杂且通常是非直觉的性质，使得更加困难 互动。因此 在制药行业的药物研发计划中具有很高的影响，因为它们会 允许研发科学家进行计算实验，以大大减少昂贵的数量和 克服困难的金属酶抑制剂设计所需的耗时台式实验 他们面临的挑战。但是，目前可用的分子建模方法无法做出 预测足以做到这一点。对接和评分方法在许多情况下能够确定 金属酶活性位点的抑制剂姿势，但不能按顺序正确对候选抑制剂进行排名 具有绑定亲和力，因为它们缺乏能量模型中所需的细节。最近，基于自由能的方法具有 提前到为许多非金属蛋白质序列提供可靠的结合亲和力预测 因此，可以帮助加快这些系统的配体发现工作。他们不能提供良好的约束力 但是，金属酶 - 配体系统的亲和力，因为迄今 力场，从根本上限制了其对金属配体和金属蛋白的描述的准确性 互动。这部分原因是缺乏重要的极化和电荷传递效应，但 这也是因为金属经常表现出的复杂电子结构本质上是量子机械的。 这个快速轨道SBIR提案将通过开发新的独特分子建模软件来解决这一问题 称为Mzyme-QM-VM2的工具，它将为金属酶提供可靠的准确结合自由能 抑制剂复合物通过统计力学和高度可扩展的量子化学的新型组合 方法。该软件将基于采矿最小自由能计算方法，将是 开发为Verachem的VM2自由能量软件平台的扩展。