Learning inverse folding from millions of predicted structures
We consider the problem of predicting a protein sequence from its backbone atom
coordinates. Machine learning approaches to this problem to date have been limited by the
number of available experimentally determined protein structures. We augment training data
by nearly three orders of magnitude by predicting structures for 12M protein sequences
using AlphaFold2. Trained with this additional data, a sequence-to-sequence transformer
with invariant geometric input processing layers achieves 51% native sequence recovery on …
coordinates. Machine learning approaches to this problem to date have been limited by the
number of available experimentally determined protein structures. We augment training data
by nearly three orders of magnitude by predicting structures for 12M protein sequences
using AlphaFold2. Trained with this additional data, a sequence-to-sequence transformer
with invariant geometric input processing layers achieves 51% native sequence recovery on …
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