1D-O1-1605 PDF
タンデム質量分析とケモ・ネットワークインフォマティクスの連携：低分子化合物構造推定への展開
High-throughput and highly reliable compound annotation from complex MS/MS dataset is still a challenging task. We are developing a MS/MS data analysis framework employing MS/MS spectral network concept together with chemo-, network informatics for visualization, classification and compound annotation from MS/MS spectral data. In MS/MS spectral network approach, similarities among MS/MS spectra are systematically assessed to create networks displaying the relation of spectra derived from structurally related compounds. Further we apply network informatics such as multilayer network structure with MassBank “reference network”, and “MS/MS community detection”to improve visualization and classification for complex MS/MS spectral datasets. Compound annotation is systematically performed by combining MS/MS spectral matching and chemoinformatics-based structure matching by taking advantage of the network structure. This method is versatile and can be applied to a wide range of classes of small compounds, such as metabolites and endogenous peptides in complex matrices. We will discuss the advantages of the proposed method, as well as practical applications.