Making invisible excited-state structures of pro-interleukin-18 visible by combining NMR and machine learning

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Detection of conformational states with <0.5% population

• Primary Outcome: Successful identification of distinct conformers corresponding to two excited states of pro-IL-18

• Nnt Or Sensitivity: Millisecond lifetime detection sensitivity for transient conformational states

• Confidence Interval: Not reported

Clinical Application

Requires specialized NMR facilities and computational resources; limited to research settings Relevant to Australian structural biology research groups; potential applications in drug discovery for inflammatory diseases given IL-18's role in immune responses Structural biology researchers studying protein dynamics and conformational diseases

Abstract

NMR spectroscopy is a powerful tool for studies of protein dynamics over timescales extending more than twelve orders of magnitude, with motion queried at many of the backbone and sidechain positions in the molecule of interest. NMR experiments can, in principle, provide atomic resolution descriptions of excited conformational states that are sparsely populated and transiently formed-i.e., invisible, and there are examples of such structures in the literature. However, in other cases, the NMR data are not sufficient for generating structural ensembles of rare and transient higher energy conformers of the protein of interest. One example is provided by the precursor form of the proinflammatory cytokine interleukin-18, pro-IL-18, studied here. Although NMR studies show that pro-IL-18 adopts two sparsely populated (<0.5%) and transiently formed (ms lifetimes) excited-state conformations in exchange with a highly populated ground state conformer and localize regions undergoing exchange to a pair of short β-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 are used to select alternative conformers of pro-IL-18 from ensembles predicted by the generative machine learning model AlphaFlow that are then evaluated through further NMR experiments. The method identifies distinct conformers that correspond to the pair of excited states reported by NMR, combining experiment with computation to characterize the pro-IL-18 energy landscape in ways that are not possible with either approach alone.

References

1. 1.Bonin, J. P., Lee, J. S., Liu, Z. H., Kim, P. M., & Kay, L. E. (2026). Making invisible excited-state structures of pro-interleukin-18 visible by combining NMR and machine learning. Proceedings of the National Academy of Sciences of the United States of America. https://doi.org/10.1073/pnas.2537014123