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Protein Embedding with last activation layers? #15
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I would precise the question: How can we execute AF2 pipeline to get fixed-length numeric vector which will represent single AA sequence? |
Hi, |
I didn't run the actual model but I was using the jupyter notebook provided by @sokrypton. He suggested to edit class AlphaFold (located in In the jupyter notebook he provided, prediction_result = model_runner.predict(processed_feature_dict) gives 'prediction_result' as a dictionary with a key as 'representations'
this returns a nested dictionary and then
it contains the learned representations, although I am not sure which one to use. Hope it helps |
@tfgg Could you suggest which representation would be a good choice as an protein embedding for downstream tasks? since i get 5 different representations from the prediction result? |
@tfgg Thank you for considering. |
@xinformatics The first section of the article
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@rmeinl Thank you so much. I was thinking on the similar lines. Actually, the problem is my case is that I only need the representations (not the final PDB product) and somehow I am unable to figure out how to run AF2 prediction in a loop. I have 964 sequences and I wish to avoid running AF2 manually on each sequence. The embedding extraction is available on my Github Alphafold |
Ah interesting! I'm looking at a similar task. Two things I'll look at is 1) "turning off" the recycling step (doing a one-pass only) and 2) using only 1 of the models (instead of all 7 and then select the best scoring as they do in the provided AlphaFold.ipynb).
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Hi @xinformatics, I set Best, |
Is it possible to obtain the last activation values using
AlphaFold
?Something like ESM allows with the
model.forward
method.The text was updated successfully, but these errors were encountered: