A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products
A Convolutional Neural Network-Based Approach for the Rapid Annotation of Molecularly Diverse Natural Products
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DOI：
10.1021/jacs.9b13786
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发表时间：
2020-03-04
影响因子：
15
通讯作者：
Gerwick, William H.
中科院分区：
文献类型：
--
作者：
Reher, Raphael;Kim, Hyun Woo;Gerwick, William H.
该报告描述了基于NMR的新型机器学习工具“小分子精确识别技术”（SMART 2.0）在混合物分析中的首次应用，以及随后加速发现和表征新的天然产物。这一概念被应用于一种丝状海洋蓝藻的提取物，这种蓝藻被认为是细胞毒性天然产物的多产者。这种环境的Symploca提取物被粗略地分级，然后通过癌细胞的细胞毒性、基于NMR的SMART 2.0和基于MS 2的分子网络进行优先级排序和指导。这导致了一种新的嵌合Swinholide样大环内酯（symplocolide A）的分离和快速鉴定，以及Swinholide A、samholides A-I和几种新衍生物的注释。通过1D/2D NMR和LC-MS 2分析，证实symplocolide A的平面结构是swinholide A和鲁米诺内酯B之间的结构杂合物。第二个例子适用于智能2.0的结构新颖的环肽的表征，并比较这种方法最近出现的“原子排序”的方法。这项研究证实了传统和深度学习辅助分析方法相结合的革命性潜力，以克服天然产物药物发现的长期挑战。
This report describes the first application of the novel NMR-based machine learning tool "Small Molecule Accurate Recognition Technology" (SMART 2.0) for mixture analysis and subsequent accelerated discovery and characterization of new natural products. The concept was applied to the extract of a filamentous marine cyanobacterium known to be a prolific producer of cytotoxic natural products. This environmental Symploca extract was roughly fractionated, and then prioritized and guided by cancer cell cytotoxicity, NMR-based SMART 2.0, and MS2-based molecular networking. This led to the isolation and rapid identification of a new chimeric swinholide-like macrolide, symplocolide A, as well as the annotation of swinholide A, samholides A-I, and several new derivatives. The planar structure of symplocolide A was confirmed to be a structural hybrid between swinholide A and luminaolide B by 1D/2D NMR and LC-MS2 analysis. A second example applies SMART 2.0 to the characterization of structurally novel cyclic peptides, and compares this approach to the recently appearing "atomic sort" method. This study exemplifies the revolutionary potential of combined traditional and deep learning-assisted analytical approaches to overcome longstanding challenges in natural products drug discovery.