演題概要

ポスター発表

第3日 6月19日(金)  P会場

MS-FINDER: ケムおよびバイオインフォマティクスリソースを統合することによる未知化合物の同定戦略

(1理研CSRS2UC Davis3ライフィクス4千葉大院5遺伝研)
o津川裕司1Kind, Tobias2中林亮1行平大地3斉藤和季1,4Fiehn, Oliver2有田正規1,5

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the preferred tool for untargeted metabolomics: detection, identification, and quantification of biomolecules. The main bottleneck in its discovery phase is the structure estimation of unknown metabolites. Since data-independent acquisition in combination with mass spectral deconvolution provides comprehensive MS/MS spectra for all precursor ions, we propose the following three-step approach. First, formula calculation predicts a molecular formula from both MS1 and MS/MS in combination with the seven golden rules and a database for neutral losses. Second, structure data that match with the formula are downloaded via the PubChem Rest Service, and are computationally fragmented with the Chemistry Development Kit to check consistency. Finally, the likelihood scores are calculated to rank candidate structures.