AmelieSchreiber commited on
Commit
1ef383f
1 Parent(s): 7e76b37

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +1 -1
README.md CHANGED
@@ -34,7 +34,7 @@ there are three protein sequence examples. The first is a DNA binding protein tr
34
  ([see UniProt entry here](https://www.uniprot.org/uniprotkb/D3ZG52/entry)).
35
 
36
  The second and third were obtained using [EvoProtGrad](https://github.com/Amelie-Schreiber/sampling_protein_language_models/blob/main/EvoProtGrad_copy.ipynb)
37
- a Markov Chain Monte Carlo method of (in silico) directed evolution of proteins based on a form of Gibbs sampling. The mutatant-type
38
  protein sequences in theory should have similar binding sites to the wild-type protein sequence, but perhaps with higher binding affinity.
39
  Testing this out on the model, we see the two proteins indeed have the same binding sites, which validates to some degree that the model
40
  has learned to predict binding sites well (and that EvoProtGrad works as intended).
 
34
  ([see UniProt entry here](https://www.uniprot.org/uniprotkb/D3ZG52/entry)).
35
 
36
  The second and third were obtained using [EvoProtGrad](https://github.com/Amelie-Schreiber/sampling_protein_language_models/blob/main/EvoProtGrad_copy.ipynb)
37
+ a Markov Chain Monte Carlo method of (*in silico*) directed evolution of proteins based on a form of Gibbs sampling. The mutatant-type
38
  protein sequences in theory should have similar binding sites to the wild-type protein sequence, but perhaps with higher binding affinity.
39
  Testing this out on the model, we see the two proteins indeed have the same binding sites, which validates to some degree that the model
40
  has learned to predict binding sites well (and that EvoProtGrad works as intended).