2024Feng DeepQs

Citation

Feng, Ming-Feng / Chen, Yu-Xuan / Shen, Hong-Bin. DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score. 2024. J. Structural Biology, Vol. 216, No. 1, p. 108059

Abstract

Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality for 3D cryo-EM density maps, using a deep-learning algorithm based on mapmodel fit score. DeepQs is a parameter-free method for users and incorporates structural information between map and its related atomic model into well-trained models by deep learning. More specifically, the DeepQs approach leverages the interplay between map and atomic model through predefined map-model fit score, Qscore. DeepQs can get close results to the ground truth map-model fit scores with only cryo-EM map as input. In experiments, DeepQs demonstrates the lowest root mean square error with standard method Fourier shell correlation metric and high correlation with map-model fit score, Q-score, when compared with other local quality estimation methods in high-resolution dataset (<=5 Å). DeepQs can also be applied to evaluate the quality of the post-processed maps. In both cases, DeepQs runs faster by using GPU acceleration. Our program is available at htt p://www.csbio.sjtu.edu.cn/bioinf/DeepQs for academic use.

Links

https://www.sciencedirect.com/science/article/pii/S1047847723001223