CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net

Author:

Gyawali Rajan12ORCID,Dhakal Ashwin12,Wang Liguo3,Cheng Jianlin12ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science , University of Missouri, Columbia, MO 65211 , United States

2. NextGen Precision Health , University of Missouri, Columbia, MO 65211 , United States

3. Laboratory for BioMolecular Structure (LBMS) , Brookhaven National Laboratory, Upton, NY 11973 , United States

Abstract

Abstract Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta’s Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Reference44 articles.

1. Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions;Dhakal;Brief Bioinform,2022

2. Improving protein–ligand interaction Modeling with cryo-EM data, templates, and deep learning in 2021 ligand model challenge;Giri;Biomolecules,2023

3. Predicting protein-ligand binding structure using E(n) Equivariant graph neural networks;Dhakal;bioRxiv,2023

4. A large expert-curated cryo-EM image dataset for machine learning protein particle picking;Dhakal;Sci Data,2023

5. CryoPPP: a large expert-labelled Cryo-EM image dataset for machine learning protein particle picking;Dhakal;bioRxiv,2023

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3