PLM-ArgMe: Protein language model for arginine methylation prediction for different species.

Abstract

Protein methylation is a crucial post-translational modification (PTM) responsible for many diseases and accurate prediction of the methylation site is important for understanding the molecular mechanism of the disease. The models have been successful in capturing contextual dependencies in protein sequences, with deep learning models, specifically those based on the Transformer architecture and Multi-Head Attention, exhibiting good performance. However, most existing techniques rely on the sequence-only or hand-crafted features and are unable to capture biochemical properties and positional patterns, thereby limiting cross-species generalization and prediction accuracy. To cater for such demands, PLM-ArgMe is presented that is based on a symmetry-sensitive Transformer framework using context-aware ESM-2 residue embeddings, which is mapped through a novel Bio-Symmetric Mirrored Sinusoidal Encoding (BSMSE) strategy to address the biological symmetry hypothesis of arginine methylation. ESM-2 encodes evolutionary and structural context, while biochemical representations are enhanced by physicochemical features. A symmetry-aware positional encoding strategy and bidirectional multi-head self-attention are used to model structural, sequence-level, and feature-level dependencies. The proposed framework, PLM-ArgMe, achieves prediction accuracies of 90.91%, 93%, 87.44%, and 87.22% on Chimpanzee, Rat, Human, and Mouse datasets, respectively. When trained and evaluated on a combined multi-species dataset, the model attains an overall accuracy of 88.41%. The results reveal good generalization on a variety of datasets and suggest that PLM-ArgMe is a robust method for arginine methylation site prediction.