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DeepMind is known to have trained the program on over 170,000 proteins from the Protein Data Bank, a public repository of protein sequences and structures.The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution. [2]
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There has been rapid development in computational ability to determine protein structure with programs such as AlphaFold, [2] and the demand for the corresponding protein-ligand docking predictions is driving implementation of software that can find accurate models. Once the protein folding can be predicted accurately along with how the ligands ...
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AlphaFold's detailed GDT-TS scores are available from CASP at (select group='427 AlphaFold 2', model = '1', and targets = 'TBM' and 'FM'). From it one gets a median score (#46) of 91.72 (not quite sure why that doesn't exactly match what's been quoted elsewhere, which maybe was for all models, but it's very similar).
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Interpretation of PAE values allows scientists to understand the level of confidence in the predicted structure of a protein: Lower PAE values between residue pairs from different domains indicate that the model predicts well-defined relative positions and orientations for those domains.