Simulating Collision-Induced Dissociation Tandem Mass Spectrometry (CID-MS/MS) for the Blood Exposome Database Using Quantum Chemistry Methods - A Pilot Study

A significant number of compounds in exposome databases and chemical inventories lack mass spectral data due to the nonavailability of reference standards. To address this limitation, computational chemistry methods can be utilized to extend mass spectral libraries for a set of chemicals. In this pilot study, we employed quantum-chemistry-based software QCxMS to generate collision-induced dissociation mass spectra for 121 compounds from the Blood Exposome Database. We developed a scalable computational framework that integrates QCxMS and additional tools, utilizing a grid-based parameter selection strategy and defined coverage criteria. Our approach systematically explored protomeric isomers and applied predefined parameter combination sets sequentially based on molecular structures. This workflow produced high-quality in silico spectra for 81 compounds that achieved entropy similarity scores ≥700 and at least two matching fragment ions against the NIST23 library, yielding 71% spectral coverage. These results highlight the importance of optimizing simulation parameters and accounting for protomeric diversity to enhance the spectral quality and computational efficiency. This workflow provides a practical strategy to add mass spectral data for most compounds in the Blood Exposome Database at reasonable computational cost, supporting the spectral library expansion for improved compound annotation in exposomics.

暴露组数据库（exposome databases）与化学物质名录中，大量化合物因缺乏参考标准品而未获取质谱数据。为解决这一局限，可借助计算化学方法拓展特定化学品的质谱库。本初步研究采用基于量子化学的QCxMS软件，为血液暴露组数据库（Blood Exposome Database）中的121种化合物生成碰撞诱导解离质谱。我们搭建了集成QCxMS与其他工具的可扩展计算框架，采用基于网格的参数选择策略与预设覆盖度准则；该框架基于分子结构，系统探索质子异构体（protomeric isomers）并依次应用预定义参数组合集。该工作流为81种化合物生成了高质量虚拟质谱（in silico spectra），这些谱图与NIST23质谱库（NIST23 library）比对时，熵相似性得分≥700且至少匹配2个碎片离子，最终谱图覆盖度达71%。上述结果凸显了优化模拟参数、考量质子异构多样性对提升谱图质量与计算效率的重要意义。本工作流可在合理计算成本下，为血液暴露组数据库中的多数化合物补充质谱数据，为拓展质谱库以提升暴露组学（exposomics）领域的化合物注释能力提供了切实可行的方案。