Each cross-peak is characterized by the position of its center (i.e. Identification and quantitative characterization of cross-peaks critically affect all downstream analyses and can have a major impact on data interpretation. A spectrum can consist of several hundred to thousands of cross-peaks manifested as localized multidimensional spectral features that in the case of 2D NMR belong to individual pairs of atoms that possess a nuclear spin. Multidimensional NMR spectroscopy is a powerful and versatile method for the quantitative characterization of a wide range of molecular systems ranging from small molecules to large biomacromolecules and their complexes 1, 2. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness.

Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems.

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