Fast and Accurate Prediction of Intrinsically Disordered Protein by Protein Language Model

MotivationIntrinsically disordered proteins (IDPs) play a vital role in various biological processes and have attracted increasing attention in the last decades. Predicting IDPs from primary structures of proteins provides a very useful tool for protein analysis. However, most of the existing prediction methods heavily rely on multiple sequence alignments (MSAs) of homologous sequences which are formed by evolution over billions of years. Obtaining such information requires searching against the whole protein databases to find similar sequences and since this process becomes increasingly time-consuming, especially in large-scale practical applications, the alternative method is needed. ResultsIn this paper, we proposed a novel IDP prediction method named IDP-PLM, based on the protein language model (PLM). The method does not rely on MSAs or MSA-based profiles but leverages only the protein sequences, thereby achieving state-of-the-art performance even compared with predictors using protein profiles. The proposed IDP-PLM is composed of stacked predictors designed for several different protein-related tasks: secondary structure prediction, linker prediction, and binding predictions. In addition, predictors for the single task also achieved the highest accuracy. All these are based on PLMs thus making IDP-PLM not rely on MSA-based profiles. The ablation study reveals that all these stacked predictors contribute positively to the IDP prediction performance of IDP-PLM. AvailabilityThe method is available at http://github.com/xu-shi-jie. Contactakira.onoda@ees.hokudai.ac.jp Supplementary informationSupplementary data are available at Bioinformatics online.