Evaluation of the QCxMS2 Method for the Calculation of Collision-Induced Dissociation Spectra via Automated Reaction Network Exploration

化学 碰撞诱导离解 离解(化学) 碰撞 谱线 分析化学(期刊) 质谱法 计算化学 物理化学 色谱法 串联质谱法 天文 计算机安全 计算机科学 物理
作者
Johannes Gorges,Marianne Engeser,Stefan Grimme
出处
期刊:Journal of the American Society for Mass Spectrometry [American Chemical Society]
卷期号:36 (10): 2276-2289
标识
DOI:10.1021/jasms.5c00234
摘要

Collision-induced dissociation mass spectrometry (CID-MS) is an important tool in analytical chemistry for the structural elucidation of unknown compounds. The theoretical prediction of the CID spectra plays a critical role in supporting and accelerating this process. To this end, we adapt the recently developed QCxMS2 program originally designed for the calculation of electron ionization (EI) spectra to enable the computation of CID-MS. To account for the fragmentation conditions characteristic of CID within the automated reaction network discovery approach of QCxMS2 we adapted the internal energy distribution to match the experimental conditions. This distribution can be adjusted via a single parameter to approximate various activation settings, thereby eliminating the need for explicit simulations of the collisional process. We evaluate our approach on a test set of 13 organic molecules with diverse functional groups, compiled specifically for this study. All reference spectra were recorded consistently under the same measurement conditions, including both CID and higher-energy collisional dissociation (HCD) modes. Overall, QCxMS2 achieves a good average entropy similarity score (ESS) of 0.687 for the HCD spectra and 0.773 for the CID spectra. The direct comparison to experimental data demonstrates that the QCxMS2 approach, even without explicit modeling of collisions, is generally capable of computing both CID and HCD spectra with reasonable accuracy and robustness. This highlights its potential as a valuable tool for integration into structure elucidation workflows in analytical mass spectrometry.
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
坚强冷荷完成签到,获得积分10
刚刚
呜呜呜完成签到,获得积分10
1秒前
1秒前
天天摸鱼完成签到,获得积分10
1秒前
1秒前
zz完成签到,获得积分10
1秒前
2秒前
揽月yue完成签到,获得积分10
2秒前
3秒前
ptyz霍建华发布了新的文献求助10
4秒前
ll发布了新的文献求助10
4秒前
4秒前
5秒前
scholar1234完成签到,获得积分10
5秒前
呜呜呜发布了新的文献求助10
6秒前
白白发布了新的文献求助10
6秒前
sunwx发布了新的文献求助10
6秒前
tttt发布了新的文献求助10
8秒前
8秒前
yimron发布了新的文献求助10
8秒前
9秒前
lhl完成签到,获得积分10
10秒前
完美世界应助江柚白采纳,获得10
10秒前
爆米花应助LYDZ2采纳,获得10
10秒前
Emper发布了新的文献求助10
12秒前
醉玉颓山完成签到,获得积分10
14秒前
15秒前
15秒前
16秒前
12完成签到,获得积分10
16秒前
小锦鲤关注了科研通微信公众号
16秒前
17秒前
大模型应助柔弱绝施采纳,获得10
18秒前
Lucas应助qingjiu采纳,获得10
18秒前
19秒前
乐观凝梦完成签到,获得积分10
20秒前
20秒前
jaci发布了新的文献求助10
20秒前
20秒前
汐月完成签到 ,获得积分10
21秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 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 7584040
求助须知:如何正确求助?哪些是违规求助? 9162784
关于积分的说明 19608034
捐赠科研通 7165941
什么是DOI,文献DOI怎么找? 3266349
关于科研通互助平台的介绍 2431328
邀请新用户注册赠送积分活动 2257917