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Experiment-guided AlphaFold3 resolves accurate protein ensembles

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Abstract AlphaFold3 predicts highly accurate protein structures from sequence, but tends to collapse to a single dominant conformation, even when the underlying structure is inherently heterogeneous. Moreover, its predictions are oblivious to experimental conditions that can alter local sequence conformation. In this work we show that AlphaFold3 can be guided to match data obtained by NMR spectroscopy, X-ray crystallography and cryo-EM experiments. We demonstrate that this methodology can generate ensembles of conformations having less distance restraint violations than traditionally resolved NMR structures and uncover unmodelled alternate conformations detectable in electron density. We show that AlphaFold3 can also be guided by cryo-electron microscopy maps and that doing so in combination with NMR parameters improves model quality. This methodology paves the way for the development of experimentally aware predictive models that capture the ensemble nature of protein structures.
Title: Experiment-guided AlphaFold3 resolves accurate protein ensembles
Description:
Abstract AlphaFold3 predicts highly accurate protein structures from sequence, but tends to collapse to a single dominant conformation, even when the underlying structure is inherently heterogeneous.
Moreover, its predictions are oblivious to experimental conditions that can alter local sequence conformation.
In this work we show that AlphaFold3 can be guided to match data obtained by NMR spectroscopy, X-ray crystallography and cryo-EM experiments.
We demonstrate that this methodology can generate ensembles of conformations having less distance restraint violations than traditionally resolved NMR structures and uncover unmodelled alternate conformations detectable in electron density.
We show that AlphaFold3 can also be guided by cryo-electron microscopy maps and that doing so in combination with NMR parameters improves model quality.
This methodology paves the way for the development of experimentally aware predictive models that capture the ensemble nature of protein structures.

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