Developing Artificial Intelligence and Deep Learning for the analysis of correlation spectroscopy data

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Nuclear magnetic resonance (NMR) spectroscopy is an unprecedented technique to obtain detailed information - at atomic resolution - about macromolecular machines in an environment similar to the cell. NMR spectroscopy has therefore become an imperative tool for the characterisation of large proteins, for the discovery of new molecular interactions, and also for the discovery of new drug-leads. Large proteins and macromolecular machines contain thousands of atoms. High-dimensional (3D, 4D, ..) NMR spectra are therefore required in order to separate the NMR signals for the individual atoms and to facilitate a characterisation large proteins. A common hurdle with high-dimensional NMR spectra is the time required to obtain these, because essentially a 1D NMR spectrum is required for each point within a 100x100 square (3D spectra) or within a 100x100x100 cube (4D spectra). This makes it very time-consuming and nearly impossible to obtain high-dimensional (> 3D) spectra for large proteins. During the proposed project we will leverage the immense power and strength of Artificial Intelligence (AI) and Deep Learning to allow for fast acquisition of high-dimensional NMR spectra to characterise macromolecular machines. We will develop a new tool, where a new deep neural network will be designed and trained so that the required information about the macromolecule can be extracted orders of magnitude faster than using the traditional workflow because only a fraction of the points are recorded. For 4D spectra only about 1% of the points within the 100x100x100 cube need to be sampled. This is possible because the theoretical background of NMR spectroscopy is so well defied that sufficient training data easily can be generated to train the neural networks. The new tools, anchored in deep learning and AI, will not only allow for fast and accurate characterisation of molecular interactions but will also facilitate ultra-high-dimensional NMR that will allow for completely new NMR ventures and for even larger molecular machines to be characterised.