社论:结构和立体化学分析的见解:2022

J. Batista, Q. Cass
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引用次数: 0

摘要

包含四篇文章,包括一篇观点,两篇评论和一篇原创研究文章,研究主题洞察结构和立体化学分析:2022涵盖了在分析天然产物(NP)分子的结构和3D排列方面进行的最先进的研究。在Lopes和da Silva的展望文章中,质谱法和一些先进的里程碑在复杂分子结构的化学表征方面的主要挑战和影响得到了强调。在这方面,数据分析的复杂性被讨论为时间限制任务的主要限制之一。此外,在质谱数据库中观测到的扩展不足以进行必要的数据解释。正如作者所强调的那样,ESI-MS/ MS在公共领域的光谱估计数量约为60,000个分子,远远低于化学空间,并且“传统上,大多数光谱技术依赖于先前表征信号的数据库匹配”。同时,最近创建的大型公共数据库的重要性被认为是表征复杂样本(如NP提取)中获得的大型数据集的一种手段。还研究了基于机器学习(ML)的方法对光谱库中表现不佳的分子类别的结构预测的贡献。利用MALDI-MS对小分子进行成像,可以对原位NPs进行空间测绘,从而更好地了解它们的功能和分布。随着技术在设备方面的进步,可以同时生成图像和MS/MS数据,从而可以直接注释分子。此外,通过离子迁移率引入的第三维度揭示了通过空间分辨率进行单细胞分析的可能性。Lopes和da Silva的这篇观点文章阐明了这些重要问题以及NP研究中我们面临的所有机会。Queiroz等人的这篇研究主题的原始文章是关于Alzatea verticillata Ruiz & Pav叶和茎的有机极性和极性提取物的化学特征。(Alzateaceae)。半制备液相色谱法分离出12个毫克级化合物,其中3个为未知化合物。通过核磁共振和质谱分析对分离得到的化合物进行了结构鉴定。先前描述的化合物包括二聚体内酯,二甲基山葵素和两种不寻常的二聚体二苯基环丁烷二羧酸(β-truxinic衍生物)。新二聚内酯的x射线晶体学显示其为(S,S), (R,R)外消旋体。本文提供了一个与所得外消旋体一致的生物遗传学建议。此外,非目标LC-HRMS数据OPEN ACCESS
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Editorial: Insights in structural and stereochemical analysis: 2022
With four articles, including one perspective, two reviews, and one original research article, the Research Topic Insights in Structural and Stereochemical Analysis: 2022 covers the state-of-the-art research being conducted in the analysis of the structure and 3D arrangements of natural product (NP) molecules. In the perspective article by Lopes and da Silva, mass spectrometry and some advanced landmarks are highlighted in respect to the main challenges and impacts of the chemical characterization of complex molecular structures. In this respect, the complexity of data analysis is discussed as one of the main limitations for time-limited tasks. Moreover, the observed expansion in the mass spectral database is not sufficient for the necessary data interpretation. As emphasized by the authors, the estimated number of spectra for ESI-MS/ MS in the public domain is in the order of 60,000 molecules, which is far below the chemical space, and “traditionally, most spectrometry techniques rely on database matching of previously characterized signals.” Meanwhile, the importance of the recently created large public databases is acknowledged as a means of characterizing large datasets acquired in complex samples such as NP extracts. Machine learning (ML)-based methods are also examined regarding their contribution to the structural prediction of classes of molecules that are poorly represented in spectral libraries. The imaging of small molecules by MALDI-MS grants spatial mapping of in situ NPs, allowing a better understanding of their function and distribution. As technological advances in terms of equipment, images and MS/MS data can be simultaneously generated, allowing the direct annotation of molecules. Additionally, the introduction of a third dimension by ionic mobility reveals the possibility of single-cell analysis by spatial resolution. This perspective article by Lopes and da Silva shines light on these significant issues and all the opportunities ahead of us in NP research. The original article of this Research Topic by Queiroz et al. is about the chemical profile of organic apolar and polar extracts from leaves and stems of Alzatea verticillata Ruiz & Pav. (Alzateaceae). By semipreparative LC, twelve compounds were isolated in a milligram scale, of which three were unknown. The structure elucidation of the isolated compounds was carried out based on NMR and HRMS analyses. The previously undescribed compounds included a dimeric lactone, dimethyl anemonin, and two unusual dimeric diphenyl cyclobutane dicarboxylic acids (β-truxinic derivatives). The X-ray crystallography of the new dimeric lactone revealed it as an (S,S), (R,R) racemate. This article provides a biogenetic proposal that is in line with the obtained racemate. Additionally, non-target LC-HRMS data OPEN ACCESS
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