Deconvolution of 1D NMR spectra: A deep learning-based approach

The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and characterize their parameters, often referred to as deconvolution, is a crucial step in the quantification, elucidation, and verification of the structure of molecular systems. However, deconvolution of 1D NMR spectra is a challenge for both experts and machines. We propose a robust, expert-level quality deep learning-based deconvolution algorithm for 1D experimental NMR spectra. The algorithm is based on a neural network trained on synthetic spectra. Our customized pre-processing and labeling of the synthetic spectra enable the estimation of critical peak parameters. Furthermore, the neural network model transfers well to the experimental spectra and demonstrates low fitting errors and sparse peak lists in challenging scenarios such as crowded, high dynamic range, shoulder peak regions as well as broad peaks. We demonstrate in challenging spectra that the proposed algorithm is superior to expert results.

Influence of pH and Mg(ii) on the catalytic core domain 5 of a bacterial group II intron

RNA molecules fold into complex structures that allow them to perform specific functions. To compensate the relative lack of diversity of functional groups within nucleotides, metal ions work as crucial co-factors. In addition, shifted pK_as are observed in RNA, enabling acid-base reactions at ambient pH. The central catalytic domain 5 (D5) hairpin of the Azotobacter vinelandii group II intron undergoes both metal ion binding and pH dependence, presumably playing an important functional role in the ribozyme's reaction. By NMR spectroscopy we have here characterized the metal ion binding sites and affinities for the hairpin's internal G-A mismatch, bulge, and pentaloop. The influence of Mg(ii) and pH on the local conformation of the catalytically crucial region is also explored by fluorescence spectroscopy.