AnnoSM: An Automated Annotation Tool for Determining the Substituent Modes on the Parent Skeleton Based on a Characteristic MS/MS Fragment Ion Library

化学 注释 黄酮类 取代基 片段(逻辑) 质谱法 软件 黄酮醇 触摸屏 立体化学 类黄酮 色谱法 人工智能 计算机科学 有机化学 程序设计语言 抗氧化剂 操作系统
作者
Xing Wang,An-Qi Guo,Rui Wang,Wen Gao,Hua Yang
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:96 (9): 3817-3828 被引量:5
标识
DOI:10.1021/acs.analchem.3c04946
摘要

Mass spectrometry (MS) is a powerful technology for the structural elucidation of known or unknown small molecules. However, the accuracy of MS-based structure annotation is still limited due to the presence of numerous isomers in complex matrices. There are still challenges in automatically interpreting the fine structure of molecules, such as the types and positions of substituents (substituent modes, SMs) in the structure. In this study, we employed flavones, flavonols, and isoflavones as examples to develop an automated annotation method for identifying the SMs on the parent molecular skeleton based on a characteristic MS/MS fragment ion library. Importantly, user-friendly software AnnoSM was built for the convenience of researchers with limited computational backgrounds. It achieved 76.87% top-1 accuracy on the 148 authentic standards. Among them, 22 sets of flavonoid isomers were successfully differentiated. Moreover, the developed method was successfully applied to complex matrices. One such example is the extract of Ginkgo biloba L. (EGB), in which 331 possible flavonoids with SM candidates were annotated. Among them, 23 flavonoids were verified by authentic standards. The correct SMs of 13 flavonoids were ranked first on the candidate list. In the future, this software can also be extrapolated to other classes of compounds.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
安详的从波完成签到,获得积分10
1秒前
4秒前
5秒前
6秒前
小蘑菇应助体贴鱼采纳,获得10
8秒前
9秒前
11秒前
搜集达人应助ccCherub采纳,获得10
11秒前
11秒前
ALBERT完成签到,获得积分10
13秒前
勤恳梦柏完成签到 ,获得积分10
14秒前
Orange应助小张兜里很有qian采纳,获得10
14秒前
14秒前
14秒前
wzy小号完成签到 ,获得积分10
17秒前
18秒前
zb发布了新的文献求助10
18秒前
jinze完成签到,获得积分10
18秒前
19秒前
Zheng发布了新的文献求助10
19秒前
19秒前
无心的谷槐完成签到,获得积分10
20秒前
科研通AI6.3应助LILI采纳,获得10
20秒前
24秒前
王广峰发布了新的文献求助10
24秒前
柔弱熊猫完成签到 ,获得积分10
24秒前
张欢馨应助site001采纳,获得10
24秒前
dagongren完成签到,获得积分0
26秒前
ccCherub完成签到,获得积分10
26秒前
欣慰松思完成签到,获得积分10
27秒前
29秒前
32秒前
33秒前
酷波er应助科研通管家采纳,获得10
35秒前
35秒前
35秒前
斯文败类应助科研通管家采纳,获得10
36秒前
lewellyn完成签到,获得积分10
36秒前
prigogin应助科研通管家采纳,获得10
36秒前
大个应助科研通管家采纳,获得10
36秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Health Psychology 800
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Electric machines: theory, operating applications, and controls 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
When Is Two-Stage Sample Robust Optimization Asymptotically Optimal? 500
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7593471
求助须知:如何正确求助?哪些是违规求助? 9170643
关于积分的说明 19629353
捐赠科研通 7171301
什么是DOI,文献DOI怎么找? 3267609
关于科研通互助平台的介绍 2432450
邀请新用户注册赠送积分活动 2260262