物理学报2023，Vol.72Issue(24)：248-263,16.DOI:10.7498/aps.72.20231071
蛋白质结构模型质量评估方法综述
Recent advances in estimating protein structure model accuracy
摘要
Abstract
The quality assessment of protein models is a key technology in protein structure prediction and has become a prominent research focus in the field of structural bioinformatics since advent of CASP7.Model quality assessment method not only guides the refinement of protein structure model but also plays a crucial role in selecting the best model from multiple candidate conformations,offering significant value in biological research and practical applications.This study begins with reviewing the critical assessment of protein structure prediction(CASP)and continuous automated model evaluation(CAMEO),and model evaluation metrics for monomeric and complex proteins.It primarily summarizes the development of model quality assessment methods in the last five years,including consensus methods(multi-model methods),single-model methods,and quasi-single-model methods,and also introduces the evaluation methods for protein complex models in CASP15.Given the remarkable progress of deep learning in protein prediction,the article focuses on the in-depth application of deep learning in single-model methods,including data set generation,protein feature extraction,and network architecture construction.Additionally,it presents the recent efforts of our research group in the field of model quality assessment.Finally,the article analyzes the limitations and challenges of current protein model quality assessment technology,and also looks forward to future development trends.关键词
蛋白质模型质量评估/深度学习/单模型方法/复合物模型评估Key words
protein model quality assessment/deep learning/single-model methods/complex model evaluation引用本文复制引用
刘栋,崔新月,王浩东,张贵军..蛋白质结构模型质量评估方法综述[J].物理学报,2023,72(24):248-263,16.基金项目
科技创新2030—"新一代人工智能"重大项目(批准号:2022ZD0115103)、国家自然科学基金(批准号:62173304)和浙江省自然科学基金重点项目(批准号:LZF030002)资助的课题.Project supported by the Scientific and Technological Innovation 2030—"New Generation Artificial Intelligence",China(Grant No.2022ZD0115103),the National Nature Science Foundation of China(Grant No.62173304),and the Key Project of Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ20F030002). （批准号:2022ZD0115103）
