IDP-T5CKNN: Protein Language Models for Prediction of Intrinsically Disordered Proteins
TL;DR
The results of this study demonstrate that embeddings generated by protein language models contain disorder-relevant information and that classification methods based on similarities to other proteins can achieve similar performance levels as deep artificial neural networks, while also improving the ability to understand the meaning of the outputs and reducing the amount of computational resources needed to produce the desired results.
Abstract
Disordered proteins (IDPs) and disordered protein regions (IDRs) have important roles in cellular signalling and regulation and in disease development. However, their flexible conformations create challenges in annotating them through computational methods. Recent advances in IDP disorder predictive methods based on deep learning models (e.g., SPOTDisorder, AUCpreD, IDP-Fusion) have improved the accuracy of IDP disorder prediction; however, all current methods still require heavy computational resources and are lacking in the ability to interpret their results. A new lightweight method for predicting IDRs called IDP-T5CKNN, which uses embeddings produced by ProtT5-XL-UniRef50 and a cosine similarity k-nearest neighbour (KNN) classifier to predict the residue-level disorder in an input protein sequence, is proposed in this study. The residue level embeddings have been normalised and class-balanced, and evaluated using standard binary classification metrics. This study also estimated the computational costs for the IDP-T5CKNN method using formal Big-O notation. For the independent MXD494 dataset, the IDP-T5CKNN method had a maximum correlation coefficient (MCC) of 0.5459 and a balanced accuracy coefficient (BAC) of 0.7906, outperforming all other currently available IDP disorder predictors. For the sample from the fiDPnn Test176 dataset, the IDP-T5CKNN method produced an MCC of 0.3942 and a BAC of 0.7163, with approximately equal sensitivity and specificity, while also achieving a comparable performance to deep neural networks without requiring iterative training. The results of this study demonstrate that embeddings generated by protein language models contain disorder-relevant information and that classification methods based on similarities to other proteins can achieve similar performance levels as deep artificial neural networks, while also improving our ability to understand the meaning of the outputs and reducing the amount of computational resources needed to produce the desired results. Overall, the IDP-T5CKNN method provides a low-cost, scalable, and interpretable method for making residue-level disorder predictions for entire proteomes.