基于蛋白质语言模型的突变效应预测研究进展

Research progress in mutation effect prediction based on protein language models

摘要:

蛋白质突变效应预测是生物信息学和蛋白质工程领域的一个关键挑战。近年来，深度学习，特别是蛋白质语言模型的发展为该领域带来了新的机遇。本文综述了蛋白质语言模型在蛋白质突变效应预测中的应用，重点讨论了3类主要模型：基于序列的模型、基于结构的模型以及结合序列和结构信息的模型，详细分析了这些模型的原理、优势和局限性，并探讨了无监督学习和监督学习在模型训练中的应用。此外，还讨论了当前面临的主要挑战，包括高质量数据集的获取、数据噪声的处理等。最后，展望了未来研究方向，包括多模态融合、少样本学习等新兴技术的应用前景。本综述为研究者提供了一个全面的视角，以推动蛋白质突变效应预测领域的进一步发展。

关键词:蛋白质语言模型;突变效应预测;深度学习;序列模型;结构模型;多模态融合;无监督学习;监督学习

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.

Key words:protein language models;mutation effect prediction;deep learning;sequence models;structure models;multimodal fusion;unsupervised learning;supervised learning