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Structural annotation of small molecules in tandem mass spectrometry has always been a central challenge in mass spectrometry analysis, especially using a miniaturized mass spectrometer for on-site testing. Here, we propose the Transformer enabled Fragment Tree (TeFT) method, which combines various types of fragmentation tree models and a deep learning Transformer module. It is aimed to generate the specific structure of molecules de novo solely from mass spectrometry spectra. The evaluation results on different open-source databases indicated that the proposed model achieved remarkable results in that the majority of molecular structures of compounds in the test can be successfully recognized. Also, the TeFT has been validated on a miniaturized mass spectrometer with low-resolution spectra for 16 flavonoid alcohols, achieving complete structure prediction for 8 substances. Finally, TeFT confirmed the structure of the compound contained in a Chinese medicine substance called the Anweiyang capsule. These results indicate that the TeFT method is suitable for annotating fragmentation peaks with clear fragmentation rules, particularly when applied to on-site mass spectrometry with lower mass resolution. Tandem mass spectrometry spectra contain structural information of analyzed small molecules, however structural annotation and prediction from MS spectra remain challenging. Here, the authors combine the fragmentation tree models with a deep-learning transformer module, to annotate the fragmentation peaks and generate de novo molecular structures from a low-resolution mass spectrometer. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • An optimized T-trap with uniform electric field was obtained based on electric field analysis and SIMION simulation. • A high correlation was found between the in-trap electric field gradient distribution and the ion emission characteristics. • Directional ion excitation ability can be achieved with appropriate structure modification. • The finding can be used to implement structure optimization of asymmetric ion trap analyzers. Abstract The toroidal-cylindrical ion trap (TCIT) is a small multi-function tandem ion trap analyzer with a simple structure and a compact design. The asymmetric structure of the out toroidal ion trap (T-trap) confers the in-trap region with a non-uniform electric field distribution, particularly when a plate ring electrode is used. To optimize the electric field distribution in the trap, the shapes of the toroidal electrodes were modified to compensate for the electric field. For this purpose, we developed a simple simulation-based method for evaluating the optimized asymmetric structure of the ion trap electrode. Specifically, static electric field distribution in the trap was acquired using COMSOL software and then simply analyzed, while the SIMION simulation platform was adopted to characterize both the ion motion and the performance of the T-trap with an modified structure. The results revealed that electrode optimization can not only improve the non-uniformity of the in-trap electric field distribution but also implement unidirectional emission of the ions upon resonance excitation, thereby significantly increasing the utilization efficiency of the ions in TCIT. In addition, a high correlation was found between the electric field gradient distribution in the trap and the ion emission characteristics, which can provide an simple but useful guiding significance for the performance and structural optimization of T-trap. [ABSTRACT FROM AUTHOR]