AlphaFold3 versus experimental structures:assessment of the accuracy in ligand-bound G protein-coupled receptors  

作　　者：Xin-heng He Jun-rui Li Shi-yi Shen H.Eric Xu 

机构地区：[1]State Key Laboratory of Drug Research and CAS Key Laboratory of Receptor Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,Shanghai,201203,China [2]University of Chinese Academy of Sciences,Beijing,100049,China

出　　处：《Acta Pharmacologica Sinica》2025年第4期1111-1122,共12页中国药理学报(英文版)

基　　金：supported by the National Key R&D Program of China(2022YFC2703105 to HEX);The National Natural Science Foundation of China(32130022,82121005);CAS Strategic Priority Research Program(XDB37030103 to HEX);Shanghai Municipal Science and Technology Major Project(2019SHZDZX02 to HEX);Shanghai Municipal Science and Technology Major Project(HEX);the Lingang Laboratory,Grant No.LG-GG-202204-01(HEX).

摘　　要：G protein-coupled receptors(GPCRs)are critical drug targets involved in numerous physiological processes,yet many of their structures remain unresolved due to inherent flexibility and diverse ligand interactions.This study systematically evaluates the accuracy of AlphaFold3-predicted GPCR structures compared to experimentally determined structures,with a primary focus on ligand-bound states.Our analysis reveals that while AlphaFold3 shows improved performance over AlphaFold2 in predicting overall GPCR backbone architecture,significant discrepancies persist in ligand-binding poses,particularly for ions,peptides,and proteins.Despite advancements,these limitations constrain the utility of AlphaFold3 models in functional studies and structure-based drug design,where high-resolution details of ligand interactions are crucial.We assess the accuracy of predicted structures across various ligand types,quantifying deviations in binding pocket geometries and ligand orientations.Our findings highlight specific challenges in the computational prediction of ligand-bound GPCR structures,emphasizing areas where further refinement is needed.This study provides valuable insights for researchers using AlphaFold3 in GPCR studies,underscores the ongoing necessity for experimental structure determination,and offers direction for improving protein–ligand interaction predictions in future computational models.

关 键 词：AlphaFold structure-based drug design artificial intelligence GPCR structural biology 

分 类 号：Q61[生物学—生物物理学]