IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-34600-2.html
   My bibliography  Save this article

Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

Author

Listed:
  • Zhiye Guo

    (University of Missouri)

  • Jian Liu

    (University of Missouri)

  • Jeffrey Skolnick

    (Georgia Institute of Technology)

  • Jianlin Cheng

    (University of Missouri)

Abstract

Residue-residue distance information is useful for predicting tertiary structures of protein monomers or quaternary structures of protein complexes. Many deep learning methods have been developed to predict intra-chain residue-residue distances of monomers accurately, but few methods can accurately predict inter-chain residue-residue distances of complexes. We develop a deep learning method CDPred (i.e., Complex Distance Prediction) based on the 2D attention-powered residual network to address the gap. Tested on two homodimer datasets, CDPred achieves the precision of 60.94% and 42.93% for top L/5 inter-chain contact predictions (L: length of the monomer in homodimer), respectively, substantially higher than DeepHomo’s 37.40% and 23.08% and GLINTER’s 48.09% and 36.74%. Tested on the two heterodimer datasets, the top Ls/5 inter-chain contact prediction precision (Ls: length of the shorter monomer in heterodimer) of CDPred is 47.59% and 22.87% respectively, surpassing GLINTER’s 23.24% and 13.49%. Moreover, the prediction of CDPred is complementary with that of AlphaFold2-multimer.

Suggested Citation

  • Zhiye Guo & Jian Liu & Jeffrey Skolnick & Jianlin Cheng, 2022. "Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34600-2
    DOI: 10.1038/s41467-022-34600-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-34600-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-34600-2?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. Sheng Wang & Siqi Sun & Zhen Li & Renyu Zhang & Jinbo Xu, 2017. "Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-34, January.
    2. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    3. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Author Correction: Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-1, December.
    4. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    5. Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
    6. Mu Gao & Davi Nakajima An & Jerry M. Parks & Jeffrey Skolnick, 2022. "AF2Complex predicts direct physical interactions in multimeric proteins with deep learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. Anna G. Green & Hadeer Elhabashy & Kelly P. Brock & Rohan Maddamsetti & Oliver Kohlbacher & Debora S. Marks, 2021. "Large-scale discovery of protein interactions at residue resolution using co-evolution calculated from genomic sequences," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    8. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peicong Lin & Yumeng Yan & Huanyu Tao & Sheng-You Huang, 2023. "Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Jiahua Rao & Jiancong Xie & Qianmu Yuan & Deqin Liu & Zhen Wang & Yutong Lu & Shuangjia Zheng & Yuedong Yang, 2024. "A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

    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. Hélène Bret & Jinmei Gao & Diego Javier Zea & Jessica Andreani & Raphaël Guerois, 2024. "From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Kazutoshi Tani & Ryo Kanno & Xuan-Cheng Ji & Itsusei Satoh & Yuki Kobayashi & Malgorzata Hall & Long-Jiang Yu & Yukihiro Kimura & Akira Mizoguchi & Bruno M. Humbel & Michael T. Madigan & Zheng-Yu Wang, 2023. "Rhodobacter capsulatus forms a compact crescent-shaped LH1–RC photocomplex," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    4. Patrick Bryant & Gabriele Pozzati & Wensi Zhu & Aditi Shenoy & Petras Kundrotas & Arne Elofsson, 2022. "Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    5. Christoph Buhlheller & Theo Sagmeister & Christoph Grininger & Nina Gubensäk & Uwe B. Sleytr & Isabel Usón & Tea Pavkov-Keller, 2024. "SymProFold: Structural prediction of symmetrical biological assemblies," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. Lei Wang & Jiangguo Zhang & Dali Wang & Chen Song, 2022. "Membrane contact probability: An essential and predictive character for the structural and functional studies of membrane proteins," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-27, March.
    7. Tânia F. Custódio & Maxime Killer & Dingquan Yu & Virginia Puente & Daniel P. Teufel & Alexander Pautsch & Gisela Schnapp & Marc Grundl & Jan Kosinski & Christian Löw, 2023. "Molecular basis of TASL recruitment by the peptide/histidine transporter 1, PHT1," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    8. Patrick Bryant & Gabriele Pozzati & Arne Elofsson, 2022. "Improved prediction of protein-protein interactions using AlphaFold2," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    9. Zachary C. Drake & Justin T. Seffernick & Steffen Lindert, 2022. "Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    10. Niklas W. A. Gebauer & Michael Gastegger & Stefaan S. P. Hessmann & Klaus-Robert Müller & Kristof T. Schütt, 2022. "Inverse design of 3d molecular structures with conditional generative neural networks," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    11. Andreas Lackner & Michael Müller & Magdalena Gamperl & Delyana Stoeva & Olivia Langmann & Henrieta Papuchova & Elisabeth Roitinger & Gerhard Dürnberger & Richard Imre & Karl Mechtler & Paulina A. Lato, 2023. "The Fgf/Erf/NCoR1/2 repressive axis controls trophoblast cell fate," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    12. Brooke M. Britton & Remy A. Yovanno & Sara F. Costa & Joshua McCausland & Albert Y. Lau & Jie Xiao & Zach Hensel, 2023. "Conformational changes in the essential E. coli septal cell wall synthesis complex suggest an activation mechanism," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    13. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.
    14. Qiufen Chen & Yuanzhao Guo & Jiuhong Jiang & Jing Qu & Li Zhang & Han Wang, 2023. "The Relative Distance Prediction of Transmembrane Protein Surface Residue Based on Improved Residual Networks," Mathematics, MDPI, vol. 11(3), pages 1-16, January.
    15. Agnese I. Curatolo & Ofer Kimchi & Carl P. Goodrich & Ryan K. Krueger & Michael P. Brenner, 2023. "A computational toolbox for the assembly yield of complex and heterogeneous structures," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    16. Jiao Li & Wan Zheng & Ming Gu & Long Han & Yanmei Luo & Koukou Yu & Mengxin Sun & Yuliang Zong & Xiuxiu Ma & Bing Liu & Ethan P. Lowder & Deanna L. Mendez & Robert G. Kranz & Kai Zhang & Jiapeng Zhu, 2022. "Structures of the CcmABCD heme release complex at multiple states," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    17. Noelia Ferruz & Steffen Schmidt & Birte Höcker, 2022. "ProtGPT2 is a deep unsupervised language model for protein design," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    18. Patrick Bryant & Frank Noé, 2024. "Structure prediction of alternative protein conformations," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    19. Aaron Gupta & Kevin S. Kao & Rachel Yamin & Deena A. Oren & Yehuda Goldgur & Jonathan Du & Pete Lollar & Eric J. Sundberg & Jeffrey V. Ravetch, 2023. "Mechanism of glycoform specificity and in vivo protection by an anti-afucosylated IgG nanobody," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    20. Lucien F. Krapp & Luciano A. Abriata & Fabio Cortés Rodriguez & Matteo Dal Peraro, 2023. "PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces," Nature Communications, Nature, vol. 14(1), pages 1-11, 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:13:y:2022:i:1:d:10.1038_s41467-022-34600-2. 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.