Method development for simultaneous automatic assignment and structure determination in protein NMR

蛋白质 NMR 中同时自动分配和结构测定的方法开发

• 批准号: 10373305
• 负责人: Rongjie Lai
• 金额: $ 22.34万
• 依托单位: RENSSELAER POLYTECHNIC INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-12 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10373305
• 关键词: 3-Dimensional; Algorithms; Amino Acid Sequence; Benchmarking; Biochemistry; Biomedical Research; Chemicals; Collection; Communities; Computer software; Consumption; Crystallization; Data; Data Analyses; Data Set; Enzymatic Biochemistry; Euclidean Space; Geometry; Goals; Homology Modeling; Literature; Manuals; Mathematics; Methods; Modeling; NOESY; Proteins; Protons; Resolution; Science; Shapes; Structure; TOCSY; Testing; Time; Ubiquitin; Validation; base; cost; cyclophilin D; deep learning; design; drug discovery; experimental study; mathematical methods; method development; novel; pointed protein; programs; protein structure; reconstruction; sound; theories; three dimensional structure; tool

Protein NMR is a powerful tool for studying protein structure, dynamics and interactions at atomic resolution. However, currently it is time-consuming and laborious to obtain NMR resonance assignment and the NMR structure of a protein. Here we will leverage recent advances in Euclidean distance geometry to develop a method for simultaneous determination of resonance assignment and protein structure using only the NOESY spectra. We have two aims: 1. Method development and validation using synthetic data based on known protein structure; 2 Method development and validation using experimental NMR data.

蛋白质NMR是研究蛋白质结构、动力学和相互作用的有力工具， 原子分辨率然而，目前获得NMR是费时费力的 共振分配和蛋白质的NMR结构。在这里，我们将利用最近的 欧几里德距离几何的进展，以开发一种方法，同时 仅使用NOESY光谱确定共振归属和蛋白质结构。 我们有两个目标：1。使用基于以下的合成数据进行方法开发和验证： 已知蛋白质结构; 2使用实验NMR的方法开发和验证 数据