ADOPT: intrinsic protein disorder prediction through deep bidirectional transformers

Abstract

Intrinsically disordered proteins (IDP) are important for a broad range of biological functions and are involved in many diseases. An understanding of intrinsic disorder is key to develop compounds that target IDPs. Experimental characterization of IDPs is hindered by the very fact that they are highly dynamic. Computational methods that predict disorder from the amino acid sequence have been proposed. Here, we present ADOPT, a new predictor of protein disorder. ADOPT is composed of a self-supervised encoder and a supervised disorder predictor. The former is based on a deep bidirectional transformer, which extracts dense residue level representations from Facebooks Evolutionary Scale Modeling (ESM) library. The latter uses a database of NMR chemical shifts, constructed to ensure balanced amounts of disordered and ordered residues, as a training and test dataset for protein disorder. ADOPT predicts whether a protein or a specific region is disordered with better performance than the best existing predictors and faster than most other proposed methods (a few seconds per sequence). We identify the features which are relevant for the prediction performance and show that good performance can already gained with less than 100 features. ADOPT is available as a standalone package at https://github.com/PeptoneLtd/ADOPT.