High Accuracy Computational Methods for Biomolecular Nuclear Magnetic Resonance Spectroscopy

生物分子核磁共振的高精度计算方法 光谱 核磁共振（NMR）光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、结构和动力学的探针。NMR 可以测量到非常高的可观测值，如化学位移和自旋-自旋分裂。 准确性，并且对存在的官能团及其详细的 几何形状和化学环境。因此，这些NMR测量可以用于 开发出质量相当于高分辨率X射线晶体学的蛋白质结构， 在它们的自然水环境中。与结构的联系，虽然原则上是正确的， 然而，在实践中有时难以通过直接分配频谱来揭示。 从结构中准确预测光谱可观测量的模拟方法是 光谱分配这样的方法对于实现的逆问题更是至关重要 高质量的NMR结构的折叠蛋白质的光谱，并作为强大的限制， 确定内在无序蛋白质（IDP）的结构集合。现有 解决这个问题的方法通常依赖于半经验主义，虽然他们有 取得了相当大的成功，他们也揭示了限制，显着降低质量 结构预测。在这个建议中，我们将开发一个新的，第一原理量子 基于量子力学（QM）的方法来模拟凝聚态的NMR光谱观测值 相生物分子和生物有机物。新质量管理方法的快速原型将通过以下方式实现： 一个独特的硅核磁共振实验室的发展，应用有限的磁场， 核自旋从这种能力，化学位移和自旋-自旋分裂的新方法将 出现，提供改进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访问环境的数据库。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一个 结构和计算的NMR观测之间的定量关系，以及由此产生的 eQMCalculator将在折叠蛋白质的优化和结构的创建方面进行测试。 为国内流离失所者举办的合唱团。

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.