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Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces.

Michael S Jones¹, Kirill Shmilovich¹, Andrew L Ferguson¹

¹Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States.

Journal of Chemical Theory and Computation

|October 31, 2023

Summary

DiAMoNDBack is a new AI model that reconstructs detailed all-atom protein structures from simplified Cα traces. This method enables accurate protein modeling by generating diverse, realistic atomic configurations from coarse-grained data.

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Area of Science:

Computational Biology
Structural Bioinformatics
Artificial Intelligence in Molecular Modeling

Background:

Coarse-grained protein models offer computational efficiency for large-scale simulations but lack atomistic detail.
Restoring all-atom resolution from coarse-grained models (backmapping) is crucial for mechanistic insights.
Existing backmapping methods may struggle with accuracy, bond formation, and generating diverse configurations.

Purpose of the Study:

To introduce DiAMoNDBack, an autoregressive denoising diffusion probability model for protein backmapping.
To enable the generation of realistic, diverse all-atom protein structures from Cα-only representations.
To provide a transferable and accurate method for reconstructing atomistic protein details.

Main Methods:

Developed DiAMoNDBack, an autoregressive denoising diffusion model.
Trained the model on over 65,000 Protein Data Bank structures.
Conditioned residue-by-residue generation on Cα trace and local atomic context.

Main Results:

Achieved state-of-the-art performance in reconstructing all-atom protein structures.
Demonstrated accurate bond formation and avoidance of steric clashes.
Generated diverse ensembles of side-chain configurations consistent with Cα traces.

Conclusions:

DiAMoNDBack effectively restores atomistic detail to coarse-grained protein models.
The model's local and autoregressive nature ensures transferability across different proteins.
Publicly released DiAMoNDBack as an open-source Python package for broader accessibility.