化合物结构鉴定新技术 大环类
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化合物结构鉴定新技术大环类英文回答:
Advances in Compound Structure Identification Techniques for Macrocycles.
Macrocycles, cyclic compounds with large ring sizes, have garnered significant attention in drug discovery and development due to their unique structural and pharmacological properties. Traditional methods for identifying the structures of macrocycles, such as nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography, can be time-consuming and require large sample amounts. To address these challenges, novel techniques have emerged that offer improved efficiency, accuracy, and sensitivity for macrocycle structure identification.
Mass Spectrometry (MS)-Based Techniques:
Electrospray ionization mass spectrometry (ESI-MS) is a soft ionization technique that preserves non-covalent interactions within macrocycles, providing insights into their stoichiometry and connectivity.
Tandem mass spectrometry (MS/MS) involves fragmenting parent ions to analyze their structural components. When coupled with advanced algorithms, MS/MS can deduce the amino acid sequence of macrocyclic peptides and identify post-translational modifications.
Ion Mobility Spectrometry (IMS)-Based Techniques:
IMS separates ions based on their size, shape, and charge. Coupling IMS with MS (IMS-MS) provides additional structural information by resolving macrocyclic conformers and distinguishing between isomers.
Traveling-wave ion mobility spectrometry (TWIMS) offers high resolving power and can differentiate between macrocycle enantiomers.
Optical Spectroscopy Techniques:
Circular dichroism (CD) spectroscopy measures the differential absorption of leftand right-circularly polarized light. It can provide information about the secondary structure and conformational changes of macrocycles.
UV-visible spectroscopy can determine the electronic structure and chromophores present in macrocycles, aiding
in their identification.
Computational Techniques:
Molecular modeling can predict the three-dimensional structure of macrocycles based on their chemical composition. It can complement experimental data and guide the identification of unknown structures.
Machine learning algorithms can analyze large datasets of known macrocycle structures to identify patterns and develop models for predicting the structural properties of
novel macrocycles.
中文回答:
大环类化合物结构鉴定新技术。
大环类化合物是一种具有大环尺寸的环状化合物,由于其独特
的结构和药理特性,在大药物发现和开发中备受关注。
传统的用于
鉴定大环类化合物结构的方法,如核磁共振 (NMR) 光谱和 X 射线
晶体学,可能是耗时的,并且需要大量的样品。
为了解决这些难题,出现了新的技术,这些技术为大环类化合物结构鉴定提供了改进的
效果、准确度和灵敏度。
基于质谱 (MS) 的技术:
电喷雾电离质谱 (ESI-MS) 是一种软电离技术,可以保留大环
类化合物中的非共价相互作用,从而深入了解它们的空间化学计量
和连通性。
串联质谱 (MS/MS) 涉及使母离子碎片化以分析它们的结构成分。
当使用先进算法时,MS/MS 可以推断出大环肽类化合物的氨基
酸序列,并鉴定翻译后修饰。
基于离子淌度谱 (IMS) 的技术:
IMS 根据离子的尺寸、形状和电荷对离子进行分离。
将 IMS 与 MS 结合使用 (IMS-MS) 可以通过解析大环类化合物的构象异构体和区分异构体,提供一些额外的结构信息。
行波离子淌度谱 (TWIMS) 提供了很高的分辨能力,并且可以区分大环类化合物对映异构体。
光谱技术:
圆二色性 (CD) 光谱法测量左、右旋圆偏振光的差吸收。
它可以提供大环类化合物二级结构和构象变化的信息。
紫外-可见光谱法可以确定大环类化合物中存在的电子结构和生色团,从而帮助识别它们。
计算技术:
分子建模可以根据大环类化合物的化学组成预测它们的立体结构。
它可以补充实验数据,并指导对未知结构的鉴定。
机器学习算法可以分析已知大环类化合物结构的大型数据集,以识别模式并建立预测新型大环类化合物结构特性的模型。