bioRxiv2026
High-resolution multidimensional NMR spectroscopy of proteins remains limited by long acquisition times, sensitivity constraints, and severe peak overlap, particularly for larger systems. Conventional 3D and higher-dimensional experiments trade experimental efficiency for resolution, while post-acquisition analysis often becomes the dominant bottleneck. Here, we present a new framework that redefines both how NMR experiments are constructed and how they are executed and analyzed, by treating an AI agent-controllable series of 2D spectra as a spatiotemporal dataset analogous to a video. Our approach is based on temperature-dependent series of reduced-dimensionality 2D HSQC and novel RDL-TROSY experiments, in which each 2D [1H,15N] cross-peak is controllably shifted and split in proportion to the 13C chemical shift of the J-coupled carbons. We propose treating a variable-temperature (VT) series as a pseudo-temporal video sequence in which each cross-peak traces a physically motivated trajectory through frequency space. The proportionality coefficient () of this reduced-dimensionality encoding is systematically and programmatically varied together with the temperature providing full control for constructing optimal cross-peak trajectories. As a result, individual resonances follow predictable, spectral acquisition time-controllable trajectories in the 2D spectral plane across the series, which can be executed by an autonomous AI agent directly interacting with the NMR GUI layer. Each spectrum represents a single "frame," while temperature and RD controls serves as the temporal dimension. We describe two complementary super-resolution strategies: a cross-peak model-independent approach based on the deep-learning video super-resolution that leverages temporal redundancy to sharpen per-frame peak shapes, and a model-based approach that derives the exact mathematical form of the peak trajectories and uses it to design acquisition schedules that render individual peak paths maximally distinct and amenable for algorithmic deconvolution. As a result, we obtained full backbone resonance assignment in the wide temperature range (279-315 K) with one degree Kelvin resolution in a test protein in an automatic manner in the time frame typically required for collection of a single 3D NMR dataset.