Large Language Model (LLM)-Based Advances in Prediction of Post-translational Modification Sites in Proteins

Abstract

Post-translational modifications (PTMs) are vital regulators of protein function, influencing a myriad of cellular processes and disease mechanisms. Traditional experimental methods for PTM identification are both costly and labor-intensive, underlining the pressing need for efficient computational approaches. Early computational strategies predominantly relied on primary amino acid sequences and handcrafted features, which often lacked the contextual and structural understanding necessary for precise PTM site prediction. The emergence of transformer-based large language models (LLMs), particularly protein language models (pLMs), has revolutionized PTM prediction by producing context-aware embeddings that capture functional and structural intra-sequence dependencies. In this chapter, we provide a comprehensive review of recent advancements in leveraging LLMs (or, pLMs) for PTM site prediction, an important residue-level task in protein research. We identify emerging trends in the field, including the application of fine-tuning techniques, the integration of embeddings from multiple pLMs, and the incorporation of multiple modalities such as codon-aware embeddings, 3D structural data, and conventional representations. Additionally, we discuss tools that employ graph-based representations, the mamba architecture, and contrastive learning paradigms to further refine pLM-powered PTM site prediction models. We finally explore the interpretability and explainability aspects of the embeddings used in various tools. Despite the significant progress made, persistent limitations remain, and we outline these challenges while proposing directions for future research.

Keywords: AlphaFold; Contrastive learning; Explainability; Fine-tuning; GPT; Graph; Large language model; Mamba; Post-translational modification; Protein language model.