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Generative modeling of protein ensembles guided by crystallographic electron densities
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Proteins are dynamic, adopting ensembles of conformations. The nature of this conformational heterogenity is imprinted in the raw electron density measurements obtained from X-ray crystallography experiments. Fitting an ensemble of protein structures to these measurements is a challenging, ill-posed inverse problem. We propose a non-i.i.d. ensemble guidance approach to solve this problem using existing protein structure generative models and demonstrate that it accurately recovers complicated multi-modal alternate protein backbone conformations observed in certain single crystal measurements.
Forward citations
Cited by 2 Pith papers
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Inverse problems with experiment-guided AlphaFold
Experiment-guided AlphaFold3 samples structural ensembles consistent with electron density and NOE restraints, improving heterogeneity modeling and reducing NMR structure determination time.
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Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors
Adam-PnP guides a pre-trained protein diffusion model with multiple experimental data types, using online noise estimation and precision-based weighting, and reports a backbone RMSD of 0.65 Å on one protein.
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