Nuclear magnetic resonance plays a central role in elucidating biomolecular mechanisms because the technique permits monitoring systems in a non-invasive manner and with atomic resolution. This information is conveyed by multidimensional correlation maps reporting structural, thermodynamic, kinetic, and dynamic parameters impacting nuclei in molecules. However, spectral complexity challenges data analysis for sever al classes of macromolecules. Here, I will describe how we use NMR spectra as matrices or multi-dimensional arrays and employ mathematical operations to produce novel correlation maps conveying the information otherwise provided separately by these spectra. I will showcase applications for a variety of proteins, with an emphasis on NMR resonance assignments, while describing the procedures necessary to minimize artifacts in correlation maps. The method provides alternative NMR readouts from existing spectra to facilitate biomolecular NMR investigations and help overcome spectral complexity while minimizing user intervention.
