One-shot Cryo-EM Complex Structure Determination with High Accuracy and Ultra-fast Speed.

One-shot Cryo-EM Complex Structure Determination with High Accuracy and Ultra-fast Speed. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article One-shot Cryo-EM Complex Structure Determination with High Accuracy and Ultra-fast Speed. Jue Wang, Cheng Tan, Zhangyang Gao, GuiJun Zhang, Yang Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5776842/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jun, 2025 Read the published version in Nature Machine Intelligence → Version 1 posted You are reading this latest preprint version Abstract While cryo-electron microscopy (Cryo-EM) yields high-resolution density maps for complex structures, accurate determination of the corresponding three-dimensional atomic structures still necessitates significant expertise and labor-intensive manual interpretation. Recently, AI-based methods have emerged to streamline this process in the biological community; however, several challenges persist. First, existing methods typically require multi-stage training and inference, causing inefficiencies and inconsistency between stages. Second, these approaches often encounter bias in aligning predicted atomic coordinates with sequence. Researchers have utilized Hidden Markov Model(HMM) or Traveling Salesman Problem (TSP) algorithms to explore the sequence space, which incurs substantial computational costs. Lastly, due to limitations of available datasets, prior works struggle to generalize effectively to complicated and unseen test data, a common problem in machine learning. In response to these challenges, we introduce End-to-End and Efficient CryoFold, or E3-CryoFold for short, a deep learning method that enables end-to-end training and one-shot inference. E3-CryoFold employs both 3D and sequence Transformers to extract features from density maps and sequences, using cross-attention modules to integrate the two modalities. Additionally, it utilizes an equivariant graph neural network to construct the atomic structure based on the extracted features. Importantly, E3-CryoFold incorporates a pretraining stage, during which models are trained on simulated density maps derived from Protein Data Bank (PDB) structures. Empirical results demonstrate that E3-CryoFold improves the average TM-score of the generated structures by 400% as compared to Cryo2Struct and achieves this huge improvement using merely 1/1000 of the inference time as required by Cryo2Struct. Thus, E3-CryoFold represents a robust, streamlined, and cohesive paradigm for Cryo-EM structure determination. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Structural biology/Electron microscopy/Cryoelectron microscopy Cryo-EM Deep Learning Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 24 Jun, 2025 Read the published version in Nature Machine Intelligence → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5776842","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411116610,"identity":"a345548d-cc0f-4ef8-8201-1546aceaf827","order_by":0,"name":"Jue 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Recently, AI-based methods have emerged to streamline this process in the biological community; however, several challenges persist.\r\nFirst, existing methods typically require multi-stage training and inference, causing inefficiencies and inconsistency between stages. Second, these approaches often encounter bias in aligning predicted atomic coordinates with sequence. Researchers have utilized Hidden Markov Model(HMM) or Traveling Salesman Problem (TSP) algorithms to explore the sequence space, which incurs substantial computational costs. Lastly, due to limitations of available datasets, prior works struggle to generalize effectively to complicated and unseen test data, a common problem in machine learning.\r\nIn response to these challenges, we introduce End-to-End and Efficient CryoFold, or E3-CryoFold for short, a deep learning method that enables end-to-end training and one-shot inference. E3-CryoFold employs both 3D and sequence Transformers to extract features from density maps and sequences, using cross-attention modules to integrate the two modalities. Additionally, it utilizes an equivariant graph neural network to construct the atomic structure based on the extracted features.\r\nImportantly, E3-CryoFold incorporates a pretraining stage, during which models are trained on simulated density maps derived from Protein Data Bank (PDB) structures. Empirical results demonstrate that E3-CryoFold improves the average TM-score of the generated structures by 400\\% as compared to Cryo2Struct and achieves this huge improvement using merely 1/1000 of the inference time as required by Cryo2Struct. 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