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bioRxiv · 10.1101/2025.11.27.690854

Making invisible excited state protein structures visible by combining NMR and machine learning

Bonin, J. P.·Lee, J. S.·Liu, Z. H.·Kim, P. M.·Kay, L. E.
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Abstract

NMR relaxation studies of the precursor form of the pro-inflammatory cytokine interleukin-18, pro-IL-18, show that it adopts two sparsely populated (<0.5%) and transiently formed (ms lifetimes) excited state conformations in exchange with a highly populated ground state conformer. Although NMR data localize regions undergoing exchange to a pair of short {beta}-strands that are preserved in at least one of the excited states, additional structural information is not forthcoming. Here we develop a protocol whereby the NMR data is used to select alternative conformers of pro-IL-18 from ensembles predicted by the generative ML model AlphaFlow that are then evaluated through further NMR experiments. The approach identifies distinct excited state conformers and suggests a general method for combining experiment with computation to characterize protein energy landscapes.

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Bonin, J. P., Lee, J. S., Liu, Z. H., Kim, P. M., Kay, L. E.. 2025-12-01. Making invisible excited state protein structures visible by combining NMR and machine learning. https://doi.org/10.1101/2025.11.27.690854

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