We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutions to evaluate local atomic environments followed by 2D convolutions to provide their global contexts and outperforms other methods that similarly predict the accuracy of protein structure models. Overall accuracy predictions for X-ray and cryoEM structures in the PDB correlate with their resolution, and the network should be broadly useful for assessing the accuracy of both predicted structure models and experimentally determined structures and identifying specific regions likely to be in error. Incorporation of the accuracy predictions at multiple stages in the Rosetta refinement protocol considerably increased the accuracy of the resulting protein structure models, illustrating how deep learning can improve search for global energy minima of biomolecules.

https://www.nature.com/articles/s41467-021-21511-x

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Deep learning, AI, neural networks, machine learning, etc are really revolutionizing protein simulations.
Interesting also this part of the paper:

Training and evaluation of the networks was performed on RTX2080 GPUs.

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Interesting also this part of the paper:

Training and evaluation of the networks was performed on RTX2080 GPUs.

They were made for AI anyway. I wonder how long it will be before we get them (either CPU or GPU)?

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