Data-driven modeling of the vibrational spectroscopy of ion channels

离子通道振动光谱的数据驱动建模

• 批准号: 10715048
• 负责人: Alexei Kananenka
• 金额: $ 27.58万
• 依托单位: UNIVERSITY OF DELAWARE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10715048
• 关键词: Arrhythmia; Cardiovascular Diseases; Cell membrane; Computing Methodologies; Data; Dimensions; Disease; Environment; Epilepsy; Frequencies; Functional disorder; Future; Goals; Integral Membrane Protein; Ion Channel; Ion Transport; Ions; Light; Link; Maps; Measures; Methodology; Methods; Modeling; Molecular; Molecular Conformation; Myopathy; NMR Spectroscopy; Nuclear; Peptides; Physiologic pulse; Potassium Channel; Protein Dynamics; Proteins; Research; Solvents; Spectrum Analysis; Structural Models; Structure; System; Techniques; Vertebral column; X-Ray Crystallography; chemical bond; complex biological systems; data-driven model; design; electric field; experimental study; frontier; graph neural network; improved; machine learning framework; magnetic field; nervous system disorder; novel; programs; simulation; temporal measurement; therapeutic target; two-dimensional; vibration; voltage

Data-driven modeling of the vibrational spectroscopy of ion channels Abstract The long-term goals of this research program are (1) to develop new computational methods for accurate sim- ulations of linear and two-dimensional infrared (2D IR) spectra of proteins and use the developed methods to simulate recent and design new 2D IR experiments to (2) investigate the mechanisms of ion transport and molec- ular origins of selectivity in the KcsA ion channel and (3) elucidate the conformational and hydrational changes of the voltage-sensing domain of the KvAP channel during voltage activation. Despite decades of research, we still don’t have the direct information on ion channel dynamics and the effects of an applied voltage on ion channel structures. 2D IR spectroscopy is an emerging analytical technique that probes protein dynamics with chemi- cal bond-specific spatial and high temporal resolution. 2D IR spectroscopy is analogous to NMR spectroscopy, except that it uses pulses of infrared light to measure vibrations rather than pulsed magnetic fields for nuclear spins. New methodology improvements expand the frontiers of 2D IR spectroscopy, permitting the study of com- plex biological systems in their native environments. Particularly interesting are systems for which NMR and X-ray crystallography are difficult to apply, such as ion channels. Interpreting congested 2D IR spectra is difficult without simulations that can quantitatively connect spectral features to atomistic structural models. Currently, 2D IR spectra of proteins are modeled using model-driven, mostly empirical, spectroscopic maps that correlate solvent-induced electric field and backbone dihedral angles to vibrational frequencies and couplings. This ap- proach, however, lacks systematic improvability, has limited transferability, provides qualitative accuracy at best, and is inaccurate for peptides in heterogeneous environments. Shifting away from the model-driven paradigm, we will use ab initio-based data-driven approaches based on Graph Neural Networks to accurately model the vibrational spectra of proteins in realistic environments. The proposed methods will provide computational sup- port for the ongoing and future 2D IR experiments on ion channels. The results of the proposed studies will significantly enhance our understanding of the molecular-level mechanisms of function of ion channels. A large spectrum of neurological, cardiovascular, and muscle disorders result from defective ion channel functioning. A better understanding of the origins of these diseases will pave the way for improved therapeutics that target ion channels.

离子通道振动光谱的数据驱动建模 抽象的 该研究计划的长期目标是（1）开发新的计算方法来精确模拟 蛋白质的线性和二维红外（2D IR）光谱的计算，并使用开发的方法 模拟最近并设计新的 2D IR 实验，以 (2) 研究离子传输和分子的机制 KcsA 离子通道选择性的起源，以及 (3) 阐明 KcsA 离子通道的构象和水合变化 电压激活期间 KvAP 通道的电压感应域。尽管经过数十年的研究，我们仍然 没有有关离子通道动力学和施加电压对离子通道影响的直接信息 结构。 2D IR 光谱是一种新兴的分析技术，可通过化学方法探测蛋白质动力学。 校准键特定的空间和高时间分辨率。 2D IR 光谱类似于 NMR 光谱， 不同之处在于它使用红外光脉冲来测量振动而不是核脉冲磁场 旋转。新方法的改进扩展了二维红外光谱的前沿，允许研究复合材料 复杂的生物系统在其原生环境中。特别有趣的是 NMR 和 X射线晶体学很难应用，例如离子通道。解释拥挤的二维红外光谱很困难 没有可以定量地将光谱特征与原子结构模型联系起来的模拟。现在， 蛋白质的 2D 红外光谱是使用模型驱动的（主要是经验性的）光谱图进行建模，这些光谱图与 溶剂引起的电场和骨架二面角与振动频率和耦合。这个应用程序 然而，proach 缺乏系统的可改进性，可移植性有限，最多只能提供定性的准确性， 对于异质环境中的肽来说是不准确的。摆脱模型驱动的范式， 我们将使用基于图神经网络的从头开始的数据驱动方法来准确建模 现实环境中蛋白质的振动光谱。所提出的方法将提供计算支持 用于正在进行和未来的离子通道二维红外实验的端口。拟议研究的结果将 显着增强我们对离子通道功能的分子水平机制的理解。一个大 离子通道功能缺陷导致一系列神经、心血管和肌肉疾病。一个 更好地了解这些疾病的起源将为改进针对离子的治疗方法铺平道路 渠道。