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Tandem mass spectrometry is an essential technology for characterizing chemical compounds at high sensitivity and throughput, and is commonly adopted in many fields. However, computational methods for automated compound identification from their MS/MS spectra are still limited, especially for novel compounds that have not been previously characterized. In recent years, in silico methods were proposed to predict the MS/MS spectra of compounds, which can then be used to expand the reference spectral libraries for compound identification. However, these methods did not consider the compounds’ 3D conformations, and thus neglected critical structural information.

We present the 3D Molecular Network for Mass Spectra Prediction (3DMolMS), a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. We evaluated the model on the experimental spectra collected in several spectral libraries. The results showed that 3DMolMS predicted the spectra with the average cosine similarity of 0.691 and 0.478 with the experimental MS/MS spectra acquired in positive and negative ion modes, respectively. Furthermore, 3DMolMS model can be generalized to the prediction of MS/MS spectra acquired by different labs on different instruments through minor fine-tuning on a small set of spectra. Finally, we demonstrate that the molecular representation learned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time in the liquid chromatography and the collisional cross section measured by ion mobility spectrometry, both of which are often used to improve compound identification.

The codes of 3DMolMS are available at https://github.com/JosieHong/3DMolMS and the web service is at https://spectrumprediction.gnps2.org.

 

Rapid palladium (Pd) catalyzed deallylation of an uncoloured reagent within a flowing stream affords a dose dependent colour formation that can be used for convenient online analysis of trace levels of Pd contamination using a modified HPLC instrument. An application to the online sensing of Pd breakthrough from a flow through Pd adsorption cartridge is described. An alternative configuration of the instrumentation allows the rapid (<1 min) and accurate measurement of Pd levels within samples injected via a conventional HPLC autosampler. 

Microfluidic droplet sorting enables the high‐throughput screening and selection of water‐in‐oil microreactors at speeds and volumes unparalleled by traditional well‐plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for high‐throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESI‐MS). Droplets are split, one portion is analyzed by ESI‐MS, and the second portion is sorted based on the MS result. Throughput of 0.7 samples s⁻¹is achieved with 98 % accuracy using a self‐correcting and adaptive sorting algorithm. We use the system to screen ≈15 000 samples in 6 h and demonstrate its utility by sorting 25 nL droplets containing transaminase expressed in vitro. Label‐free ESI‐MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.

 

Microfluidic droplet sorting enables the high‐throughput screening and selection of water‐in‐oil microreactors at speeds and volumes unparalleled by traditional well‐plate approaches. Most such systems sort using fluorescent reporters on modified substrates or reactions that are rarely industrially relevant. We describe a microfluidic system for high‐throughput sorting of nanoliter droplets based on direct detection using electrospray ionization mass spectrometry (ESI‐MS). Droplets are split, one portion is analyzed by ESI‐MS, and the second portion is sorted based on the MS result. Throughput of 0.7 samples s⁻¹is achieved with 98 % accuracy using a self‐correcting and adaptive sorting algorithm. We use the system to screen ≈15 000 samples in 6 h and demonstrate its utility by sorting 25 nL droplets containing transaminase expressed in vitro. Label‐free ESI‐MS droplet screening expands the toolbox for droplet detection and recovery, improving the applicability of droplet sorting to protein engineering, drug discovery, and diagnostic workflows.