Enhancing Ferroptosis-Related Protein Prediction Through Multimodal Feature Integration and Pre-Trained Language Model Embeddings
Year: 2025
Type: article
Abstract: Ferroptosis, an iron-dependent form of regulated cell death, plays a critical role in various diseases. Accurate identification of ferroptosis-related proteins (FRPs) is essential for understanding their underlying mechanisms and developing targeted therapeutic strategies. Existing computational methods for FRP prediction often exhibit limited accuracy and suboptimal performance. In this study, we harnessed the power of pre-trained protein language models (PLMs) to develop a novel machine learning framework, termed PLM-FRP, which utilizes deep learning-derived features for FRP identification. By integrating ESM2 embeddings with traditional sequence-based features, PLM-FRP effectively captures complex evolutionary relationships and structural patterns within protein sequences, achieving a remarkable accuracy of 96.09% on the benchmark dataset and significantly outperforming previous state-of-the-art methods. We anticipate that PLM-FRP will serve as a powerful computational tool for FRP annotation and facilitate deeper insights into ferroptosis mechanisms, ultimately advancing the development of ferroptosis-targeted therapeutics.
Source: Algorithms
DOI: 10.3390/a18080465
Authors: Jie Zhou, Chunhua Wang
Institution: Shanghai Ocean University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4412915710