Research progress in mutation effect prediction based on protein language models.
- Author:
Liang ZHANG
1
;
Pan TAN
2
;
Liang HONG
1
Author Information
- Publication Type:English Abstract
- Keywords: deep learning; multimodal fusion; mutation effect prediction; protein language models; sequence models; structure models; supervised learning; unsupervised learning
- MeSH: Mutation; Proteins/chemistry*; Computational Biology/methods*; Deep Learning; Protein Engineering
- From: Chinese Journal of Biotechnology 2025;41(3):934-948
- CountryChina
- Language:Chinese
- Abstract: Predicting protein mutation effects is a key challenge in bioinformatics and protein engineering. Recent advancements in deep learning, particularly the development of protein language models (PLMs), have brought new opportunities to this field. This review summarizes the application of PLMs in predicting protein mutation effects, focusing on three main types of models: sequence-based models, structure-based models, and models that combine sequence and structural information. We analyze in detail the principles, advantages, and limitations of these models and discuss the application of unsupervised and supervised learning in model training. Furthermore, this paper discusses the main challenges currently faced, including the acquisition of high-quality datasets and the handling of data noise. Finally, we look ahead to future research directions, including the application prospects of emerging technologies such as multimodal fusion and few-shot learning. This review aims to provide researchers with a comprehensive perspective to further advance the prediction of protein mutation effects.
