A Fast Neural Network for Isotopic Charge State Assignment

This is the authors' abstract. We don't add key points for this paper.

Electrospray ionization (ESI) mass spectrometry is an essential technique for chemical analysis in a range of fields. In ESI, analytes can produce multiple charge states, which must be correctly assigned for identification. Existing approaches to charge state assignment can suffer from limited accuracy or poor speed. Here, we developed a fast neural network to perform isotopic envelope charge assignment. The performance of our algorithm, IsoDec, was demonstrated on top-down proteomics spectra collected on diverse instruments. On these highly complex individual spectra, we found that IsoDec correctly assigns more features compared to existing software tools while simultaneously providing improved speed and accuracy. Importantly, this performance enhancement stems directly from the neural network charge assignment approach and not simply from improved scoring and filtering of isotopic envelopes. Finally, when applied to large top-down proteomics data sets, we discovered that database searching of the IsoDec deconvolution output produces proteoform-spectrum matches with a better combination of coverage and accuracy. Overall, IsoDec provides a compelling demonstration of the potential of lightweight neural networks in mass spectrometry data analysis for diverse applications.