The prediction of intrinsic disorder regions has significant implications for
understanding protein function, structure, and dynamics. It can help to
discover novel functions or protein-protein interactions essential to designing
new drugs, therapies, or enzymes. Recently, a new generation of predictors
based on protein language models is emerging. These algorithms reach
state-of-the-art accuracy without calculating time-consuming multiple sequence
alignments (MSAs). The article pre-sents a new protein intrinsic disorder
predictor DisorderUnetLM based on the Attention U-Net convolutional neural
network using features from the protein language model ProtTrans.
DisorderUnetLM shows top results in the direct comparison with flDPnn and
IDP-CRF predictors using MSAs and with the SETH predictor using features from
the same ProtTrans model. Moreover, among 41 predictors from the latest
Critical Assessment of Protein Intrinsic Disorder Prediction (CAID-2)
benchmark, it ranks 9th for the Disorder-PDB subset (with ROC-AUC of 0.924) and
1st for the Disorder-NOX subset (with ROC-AUC of 0.844) which confirms its
potential to perform well in the upcoming CAID-3 challenge for which
Disor-derUnetLM was submitted.

使用蛋白质语言模型 ProtTrans 构建的基于 Attention U-Net 卷积神经网络的蛋白质内在无序预测器 DisorderUnetLM 在预测蛋白质内在无序区域方面达到了最好的结果，排名 CAIP-2 基准的 9th（ROC-AUC 为 0.924）和 Disorder-NOX 子集的 1st（ROC-AUC 为 0.844），显示出在即将到来的 CAID-3 挑战中具有良好的性能。