ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers

Intrinsically disordered proteins (IDP) are important in a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop drugs against IDPs. Experimental characterization of IDPs are expensive and less efficient and demand the development of computational tools. Here, we present ADOPT, a new predictor of protein disorder. ADOPT is a deep bidirectional transformer, which extracts dense residue level representations from Facebook’s Evolutionary Scale Modeling (ESM) library. Using the experimentally designed CheZod database as a training and test dataset for protein disorder, it predicts Z scores and protein disorder with new state-of-the-art performance in a few seconds. We show that ADOPT offers substantial improvement in comparison to previous predictors with a Spearman correlation coefficient between experimental and computational Z scores of 0.69. We identify the coordinates which are relevant for the prediction performance and show that good performance can already gained with less than 100 features. We believe that ADOPT will be a useful tool for all experimental scientists working with intrinsically disordered proteins. It is available as a standalone package at https://github.com/PeptoneInc/ADOPT.git.