Learning millisecond protein dynamics from what is missing in NMR spectra

TL;DR - A Nature paper (10 Aug 2026) reporting a machine-learning approach that infers millisecond-timescale protein dynamics from information "missing" in NMR spectra — i.e., signal loss/line broadening caused by conformational exchange. Only the title and DOI metadata were available, so the following is inferred from the title rather than reported results.

• Targets millisecond conformational exchange, the timescale linked to enzyme catalysis, allostery and folding intermediates, which is hard to characterize by conventional structure determination.
• The stated novelty is learning from absent NMR observables (e.g. broadened or undetected peaks) instead of only from measured chemical shifts and relaxation dispersion data.
• Positions ML as a complement to static structure prediction: dynamics, not just folded coordinates — relevant to drug discovery and mechanism studies.
• No quantitative accuracy, dataset size, model architecture, or benchmark claims can be stated; the provided content contains only the title and publication metadata.