Abstract
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. 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. 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. 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. 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. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.
Highlights
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
For NMR peak picking, if the peaks in the training set have dominantly Gaussian lineshape, the resulting deep neural network (DNN) is more likely to fail when applied to Lorentzian peaks and vice versa
We demonstrate the application of DEEP Picker for a 2D 13C–1H HSQC spectrum of mouse urine, which may contain hundreds of different metabolites with various concentrations
Summary
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. 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. Despite many years of progress, the above steps can only be partially automated This applies in particular to spectra of large molecular systems or complex mixtures containing many cross-peaks that tend to overlap, which makes their spectral deconvolution challenging without expert human assistance. Each feature requires criteria that can depend on many parameters, especially when noise and other artifacts are present, which must be fine-tuned manually These peak picking methods have shown steady improvements, much of the NMR community still relies at least in part on manual peak picking, whereby the final result is dependent on the expertise and judgment of the human NMR spectroscopist(s) working on the project. Its performance is demonstrated for different types of 2D NMR spectra of folded and intrinsically disordered proteins in solution and a mouse urine sample containing spectral regions with variable degrees of spectral overlaps
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