WTop: A Wavelet-Driven Framework for Comprehensive Proteoform Characterization in Top-Down Mass Spectrometry

Top-down mass spectrometry enables the direct analysis of intact protein molecules, thereby preserving information on proteoforms, including terminal truncations, post-translational modifications, and amino acid sequence variations. The characterization of proteoforms constitutes a complex computational task, as the theoretical search space is typically extensive. Existing methods frequently yield inconsistent results and face difficulties in accurately identifying complex combinatorial modifications. In this study, we propose a framework termed WTop for proteoform characterization based on top-down mass spectrometry. WTop aligns experimental and theoretical mass spectra through a continuous wavelet dynamic time warping algorithm, represents proteoform spectrum matches via a mass spectrum graph, and ultimately scores identification results with a random forest model. WTop was evaluated against state-of-the-art top-down mass spectrometry identification methods across multiple data sets from diverse species. Comparative analyses of the overlap of proteoform spectrum matches, the number of identified mass spectra, and matching fragment distribution patterns revealed that WTop yields confident identifications that effectively complement existing tools, such as TopPIC. Furthermore, case studies demonstrated that WTop is capable of detecting complex proteoforms with combinatorial modifications, thereby providing a novel technical support for mass spectrometry-based identification and enhancing the accuracy of proteoform characterization.