  The prediction of intrinsic disorder regions has significant implications for
understanding protein functions and dynamics. It can help to discover novel
protein-protein interactions essential for designing new drugs and enzymes.
Recently, a new generation of predictors based on protein language models
(pLMs) is emerging. These algorithms reach state-of-the-art accuracy with-out
calculating time-consuming multiple sequence alignments (MSAs). The article
introduces the new DisorderUnetLM disorder predictor, which builds upon the
idea of ProteinUnet. It uses the Attention U-Net convolutional neural network
and incorporates features from the ProtTrans pLM. DisorderUnetLM achieves top
results in the direct comparison with recent predictors exploiting MSAs and
pLMs. Moreover, among 43 predictors from the latest CAID-2 benchmark, it ranks
1st for the Disorder-NOX subset (ROC-AUC of 0.844) and 10th for the
Disorder-PDB subset (ROC-AUC of 0.924). The code and model are publicly
available and fully reproducible at doi.org/10.24433/CO.7350682.v1.
