## 1 Introduction  
Ferroptosis, a recently discovered form of regulated cell death characterized by iron-dependent lipid peroxidation [1], is implicated in a range of pathologies, including cancer [2], neurodegenerative disorders [3], and ischemia-reperfusion injury [4]. First defined by [PERSON] et al. [5], this process is driven by the accumulation of lipid peroxides, which is executed or inhibited by a class of proteins known as ferroptosis-related proteins (FRPs). Key examples include the suppressor GPX4 (UniProtKB P36969) [6], the driver ACSL4 (UniProtKB O60488) [7], and various iron-regulating proteins [8]. Consequently, the accurate identification of FRPs is a critical step for advancing therapeutic strategies, such as targeting ferroptosis suppressor protein 1 (FSP1, UniProtKB Q96 NN9) to overcome treatment resistance [9].  
However, traditional experimental methods for identifying and characterizing FRPs, such as gene knockout or overexpression studies, are often time-consuming, laborious, and resource-intensive. Computational methods, particularly those based on machine learning, offer a promising alternative for large-scale and efficient FRP prediction. To our knowledge, only one computational tool, FRP-XGBoost [10], has been specifically developed for this purpose. The authors of that study employed four types of sequence-basedfeatures and evaluated six traditional machine learning classifiers. Although FRP-XGBoost achieved satisfactory accuracy in 10-fold cross-validation, its performance on the independent test set was notably lower, indicating a need for more robust predictive models. This performance gap suggests that there is significant room for improvement through the design of more advanced feature descriptors and more powerful classification algorithms.  
Recent years have witnessed remarkable advancements in protein bioinformatics, largely driven by the development of deep learning models, particularly pre-trained protein language models (PLMs). Models like ESM2 [11] have revolutionized various protein prediction tasks, including the identification of disease-related proteins [12]. Trained on vast datasets of protein sequences, these powerful models excel at learning intricate patterns and long-range dependencies, leading to significant improvements in prediction accuracy over traditional computational methods. Despite their immense promise, the application of PLMs to FRP prediction remains largely unexplored. The prior work, FRP-XGBoost, relied on conventional machine learning and did not leverage the rich, contextual embeddings offered by PLMs. This represents a significant opportunity to enhance the accuracy and efficiency of FRP identification.  
In this study, we introduce PLM-FRP, a novel machine learning framework designed for the accurate identification of FRPs by harnessing the power of PLMs. Our framework integrates traditional sequence-based features (dipeptide composition and DDE) with advanced contextual embeddings from the ESM2 model. These comprehensive features are then supplied to an optimized XGBoost classifier for robust prediction. The overall workflow of PLM-FRP is illustrated in Figure 1. By combining these diverse feature sets, PLM-FRP captures a holistic view of protein properties, from local sequence composition to global evolutionary and structural information. We demonstrate that our model significantly outperforms existing methods on a benchmark dataset, achieving state-of-the-art accuracy and representing a 4% improvement over the previous best-performing method [10]. This advance holds immense potential to accelerate ferroptosis research by enabling the rapid annotation of novel FRPs, thereby facilitating a deeper understanding of their signaling pathways and paving the way for new targeted therapies.  
Figure 1: Workflow of the proposed PLM-FRP model.