High Accuracy Computational Methods for Biomolecular Nuclear Magnetic Resonance Spectroscopy

生物分子核磁共振波谱的高精度计算方法

• 批准号: 9219020
• 负责人: Martin Paul Head-Gordon
• 金额: $ 29.04万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9219020
• 关键词: Adopted; Biological; Biological Neural Networks; Biomolecular Nuclear Magnetic Resonance; Cell Nucleus; Characteristics; Chemicals; Collaborations; Complex; Computer Simulation; Computing Methodologies; Coupling; Data; Data Set; Databases; Development; Discrimination; Disease; Environment; Equation; Evaluation; Expert Systems; Geometry; Goals; Laboratories; Learning; Magnetism; Measurement; Measures; Mechanics; Methodology; Methods; Modeling; Molecular Conformation; NMR Spectroscopy; Nuclear; Nuclear Magnetic Resonance; Phase; Property; Proteins; Quantum Theory; Residual state; Resolution; Roentgen Rays; Sampling; Solvents; Spectrum Analysis; Statistical Models; Structure; Supervision; System; Testing; Training; Uncertainty; Work; X-Ray Crystallography; aqueous; base; cost; drug structure; experimental study; functional group; heuristics; improved; learning strategy; magnetic field; personalized approach; protein folding; protein function; protein structure; prototype; quantum; response; restraint; simulation; success; tool; vibration

High accuracy computational methods for biomolecular nuclear magnetic resonance spectroscopy Nuclear magnetic resonance (NMR) spectroscopy is one of the most important condensed phase probes of composition, structure and dynamics of biomolecules and bio-organic species. NMR observables such as chemical shifts and spin-spin splittings can be measured to very high accuracy, and are sensitive both to the functional groups that are present and to their detailed geometries and chemical environment. As such these NMR measurements could be used to develop protein structures with a quality equivalent to high resolution X-ray crystallography but in their native aqueous environments. The connection to structure, while true in principle, is nevertheless sometimes difficult to reveal in practice through direct assignment of the spectrum. Simulation methods that accurately predict spectral observables from structure are a key goal for spectral assignment. Such methods are even more crucial for the inverse problem of realizing high quality NMR structures of folded proteins from spectra, and as powerful restraints for determining the structural ensembles of intrinsically disordered proteins (IDPs). Existing approaches to this problem typically rely on semi-empirical heuristics, and while they have achieved considerable success, they also reveal limitations that significantly degrade the quality of structural prediction. In this proposal, we will develop a new, first principles quantum mechanical (QM) based approach to simulation of NMR spectral observables for condensed phase biomolecules and bio-organics. Rapid prototyping of new QM methods will be enabled by the development of a distinctive in-silico NMR laboratory that applies finite magnetic fields and nuclear spins. From this capability, new methods for chemical shifts and spin-spin splittings will emerge that offer improved accuracy versus cost tradeoffs, and will be employed to populate databases that reflect protein relevant and energetically accessible environments. With such data, both artificial neural networks and Bayesian supervised learning approaches will determine a quantitative relationship between structure and computed NMR observable, and the resulting eQMCalculator will be tested on the refinement of folded proteins and creation of structural ensembles for IDPs.

生物分子核磁共振的高精度计算方法 光谱学 核磁共振波谱是最重要的凝聚相之一 生物分子和生物有机物种的组成、结构和动力学的探讨。核磁共振 化学位移和自旋-自旋分裂等可观测性可以测量到很高 准确性，并且对存在的官能团和其详细结构都很敏感 几何和化学环境。因此，这些核磁共振测量可以用来 开发质量相当于高分辨率X射线结晶学的蛋白质结构，但 在它们的天然水环境中。与结构的联系，虽然原则上是正确的，但也是 然而，在实践中有时很难通过直接指配光谱来揭示。 从结构中准确预测光谱可观测值的模拟方法是 频谱分配。这样的方法对于实现的反问题更加关键 高质量的折叠蛋白质的核磁共振结构，并作为强有力的限制 确定固有无序蛋白质(IDPs)的结构集合。现有 解决这个问题的方法通常依赖于半经验启发式，尽管他们已经 虽然取得了相当大的成功，但它们也暴露了显著降低质量的局限性 关于结构预测的。在这个提议中，我们将发展一种新的，第一原理量子 基于力学(QM)的凝聚态核磁共振谱可观测值模拟 相态生物分子和生物有机物。新的QM方法的快速原型将通过 开发一种独特的In-Silico核磁共振实验室，它应用有限的磁场和 核子自转。通过这种能力，化学位移和自旋分裂的新方法将 Emerge提供更高的准确性与成本权衡，并将用于填充 反映蛋白质相关和能量可及环境的数据库。有了这些数据， 人工神经网络和贝叶斯监督学习方法都将确定 结构和计算的核磁共振可观测值之间的定量关系，以及结果 EQMCalculator将在精炼折叠蛋白质和创建结构方面进行测试 国内流离失所者合唱团。