deepchain.bio | Prediction AlphaFold pLDDT score
From the root of this repo, create a virtual environment:
conda create --name pLDDT python=3.7 -y
conda activate pLDDT
you will need to manually install Bio-transformers by running:
pip install bio-transformers
Follow this tutorial to make neptune logger works
Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. To frame that importance, they would fold into complex three-dimensional shapes. Thus, knowing how proteins fold is both difficult and absolutely costly, and time-consuming. Thanks to AlphaFold, we now have 3-D structures for virtually all (98.5%) of the proteome. Alphafold produces a per-residue estimate of its confidence on a scale from 0 to 100 "pLDDT" corresponds to the model’s predicted score on the IDDT-C alpha metric. To put this in perspective, we plan to predict pLDDT scores for a given protein sequence and, therefore we can estimate how mutagenesis can be confident in terms of pLDDT score.
Datasets are freely available throught AlphaFold web server FTP