IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-45861-4.html
   My bibliography  Save this article

Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features

Author

Listed:
  • Bintao He

    (Shandong University)

  • Fa Zhang

    (Beijing Institute of Technology)

  • Chenjie Feng

    (Ningxia Medical University)

  • Jianyi Yang

    (Shandong University)

  • Xin Gao

    (King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division)

  • Renmin Han

    (Shandong University)

Abstract

Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we present a fast and accurate global and local cryo-EM density map alignment method called CryoAlign, that leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is a feature-based cryo-EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in terms of both alignment accuracy and speed.

Suggested Citation

  • Bintao He & Fa Zhang & Chenjie Feng & Jianyi Yang & Xin Gao & Renmin Han, 2024. "Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45861-4
    DOI: 10.1038/s41467-024-45861-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-45861-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-45861-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kai Sheng & Ning Li & Jessica N. Rabuck-Gibbons & Xiyu Dong & Dmitry Lyumkis & James R. Williamson, 2023. "Assembly landscape for the bacterial large ribosomal subunit," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Ali Dashti & Ghoncheh Mashayekhi & Mrinal Shekhar & Danya Ben Hail & Salah Salah & Peter Schwander & Amedee des Georges & Abhishek Singharoy & Joachim Frank & Abbas Ourmazd, 2020. "Retrieving functional pathways of biomolecules from single-particle snapshots," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
    3. Genki Terashi & Daisuke Kihara, 2018. "De novo main-chain modeling for EM maps using MAINMAST," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    4. David Haselbach & Jil Schrader & Felix Lambrecht & Fabian Henneberg & Ashwin Chari & Holger Stark, 2017. "Long-range allosteric regulation of the human 26S proteasome by 20S proteasome-targeting cancer drugs," Nature Communications, Nature, vol. 8(1), pages 1-8, August.
    5. Jiahua He & Peicong Lin & Ji Chen & Hong Cao & Sheng-You Huang, 2022. "Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    6. D. Herreros & R. R. Lederman & J. M. Krieger & A. Jiménez-Moreno & M. Martínez & D. Myška & D. Strelak & J. Filipovic & C. O. S. Sorzano & J. M. Carazo, 2023. "Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xintao Song & Lei Bao & Chenjie Feng & Qiang Huang & Fa Zhang & Xin Gao & Renmin Han, 2024. "Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Jiahua He & Tao Li & Sheng-You Huang, 2023. "Improvement of cryo-EM maps by simultaneous local and non-local deep learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Jiahua He & Peicong Lin & Ji Chen & Hong Cao & Sheng-You Huang, 2022. "Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    4. Andrew Muenks & Samantha Zepeda & Guangfeng Zhou & David Veesler & Frank DiMaio, 2023. "Automatic and accurate ligand structure determination guided by cryo-electron microscopy maps," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Fanindra Kumar Deshmukh & Gili Ben-Nissan & Maya A. Olshina & Maria G. Füzesi-Levi & Caley Polkinghorn & Galina Arkind & Yegor Leushkin & Irit Fainer & Sarel J. Fleishman & Dan Tawfik & Michal Sharon, 2023. "Allosteric regulation of the 20S proteasome by the Catalytic Core Regulators (CCRs) family," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    6. Marco C. Miotto & Steven Reiken & Anetta Wronska & Qi Yuan & Haikel Dridi & Yang Liu & Gunnar Weninger & Carl Tchagou & Andrew R. Marks, 2024. "Structural basis for ryanodine receptor type 2 leak in heart failure and arrhythmogenic disorders," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45861-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.