Abstract
Protein complex structure prediction is an important problem in computational biology. While significant progress has been made for protein monomers, accurate evaluation of protein complexes remains challenging. Existing assessment methods in CASP, lack dedicated metrics for evaluating complexes. DockQ, a widely used metric, has some limitations. In this study, we propose a novel metric called BDM (Based on Distance difference Matrix) for assessing protein complex prediction structures. Our approach utilizes a distance difference matrix derived from comparing real and predicted protein structures, establishing a linear correlation with Root Mean Square Deviation (RMSD). BDM overcomes limitations associated with receptor-ligand differentiation and eliminates the requirement for structure alignment, making it a more effective and efficient metric. Evaluation of BDM using CASP14 and CASP15 test sets demonstrates superior performance compared to the official CASP scoring. BDM provides accurate and reasonable assessments of predicted protein complexes, wide adoption of BDM has the potential to advance protein complex structure prediction and facilitate related researches across scientific domains. Code is available at http://mialab.ruc.edu.cn/BDMServer/.
Graphical Abstract
Similar content being viewed by others
Data availability
The source code and server are available at http://mialab.ruc.edu.cn/BDMServer/. The datasets can be found on the websites mentioned in the “Datasets” section of the main text.
References
You X, Zhang X, Cheng J et al (2023) In situ structure of the red algal phycobilisome-PSII-PSI-LHC megacomplex. Nature 616:199–206. https://doi.org/10.1038/s41586-023-05831-0
Altmann M, Altmann S, Rodriguez PA et al (2020) Extensive signal integration by the phytohormone protein network. Nature 583:271–276. https://doi.org/10.1038/s41586-020-2460-0
Liu J, Cao X (2023) RBP-RNA interactions in the control of autoimmunity and autoinflammation. Cell Res 33:97–115. https://doi.org/10.1038/s41422-022-00752-5
Pihl R, Zheng Q, David Y (2023) Nature-inspired protein ligation and its applications. Nat Rev Chem 7:234–255. https://doi.org/10.1038/s41570-023-00468-z
Berggård T, Linse S, James P (2007) Methods for the detection and analysis of protein-protein interactions. Proteomics 7:2833–2842. https://doi.org/10.1002/pmic.200700131
Lee MS, Dennis C, Naqvi I et al (2023) Ornithine aminotransferase supports polyamine synthesis in pancreatic cancer. Nature 616:339–347. https://doi.org/10.1038/s41586-023-05891-2
Mosca R, Ceol A, Stein A et al (2014) 3did: a catalog of domain-based interactions of known three-dimensional structure. Nucleic Acids Res 42:D374–D379. https://doi.org/10.1093/nar/gkt887
Vyas VK, Ukawala RD, Ghate M et al (2012) Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci 74:1. https://doi.org/10.4103/0250-474X.102537
Villegas-Morcillo A, Sanchez V, Gomez AM (2021) FoldHSphere: deep hyperspherical embeddings for protein fold recognition. BMC Bioinform 22:1–21. https://doi.org/10.1186/s12859-021-04419-7
Yan Y, Tao H, He J et al (2020) The HDOCK server for integrated protein–protein docking. Nat Protoc 15:1829–1852. https://doi.org/10.1038/s41596-020-0312-x
Lyskov S, Gray JJ (2008) The RosettaDock server for local protein–protein docking. Nucleic Acids Res 36:W233–W238. https://doi.org/10.1093/nar/gkn216
Gao M, Nakajima An D, Parks JM et al (2022) AF2Complex predicts direct physical interactions in multimeric proteins with deep learning. Nat Commun 13:1744. https://doi.org/10.1038/s41467-022-29394-2
Gainza P, Wehrle S, Van Hall-Beauvais A et al (2023) De novo design of protein interactions with learned surface fingerprints. Nature 617:176–184. https://doi.org/10.1038/s41586-023-05993-x
Kabsch W (1976) A solution for the best rotation to relate two sets of vectors. Acta Crystallogr Sect A 32:922–923. https://doi.org/10.1107/S0567739476001873
Li SC (2013) The difficulty of protein structure alignment under the RMSD. Algorithms Mol Biol 8:1–9. https://doi.org/10.1186/1748-7188-8-1
Coutsias EA, Wester MJ (2019) RMSD and symmetry. J Comput Chem 40:1496–1508. https://doi.org/10.1002/jcc.25802
Zemla A, Venclovas Č, Moult J et al (2001) Processing and evaluation of predictions in CASP4. Proteins 45:13–21. https://doi.org/10.1002/prot.10052
Best RB, Hummer G, Eaton WA (2013) Native contacts determine protein folding mechanisms in atomistic simulations. Proc Natl Acad Sci U S A 110:17874–17879. https://doi.org/10.1073/pnas.1311599110
Méndez R, Leplae R, De Maria L et al (2003) Assessment of blind predictions of protein–protein interactions: current status of docking methods. Proteins 52:51–67. https://doi.org/10.1002/prot.10393
Basu S, Wallner B (2016) DockQ: a quality measure for protein–protein docking models. PLoS One 11:e0161879. https://doi.org/10.1371/journal.pone.0161879
Lensink MF, Méndez R, Wodak SJ (2007) Docking and scoring protein complexes: CAPRI 3rd edition. Proteins 69:704–718. https://doi.org/10.1002/prot.21804
Moult J, Fidelis K, Kryshtafovych A et al (2007) Critical assessment of methods of protein structure prediction-Round VII. Proteins 69:3–9. https://doi.org/10.1002/prot.21767
Jones S, Thornton JM (1997) Analysis of protein-protein interaction sites using surface patches. J Mol Biol 272:121–132. https://doi.org/10.1016/j.jmps.2020.103968
Kryshtafovych A, Schwede T, Topf M et al (2021) Critical assessment of methods of protein structure prediction (CASP)-Round XIV. Proteins 89:1607–1617. https://doi.org/10.1002/prot.26237
Ozden B, Kryshtafovych A, Karaca E (2021) Assessment of the CASP14 assembly predictions. Proteins 89:1787–1799. https://doi.org/10.1002/prot.26199
De Bleser P, Hooghe B, Vlieghe D et al (2007) A distance difference matrix approach to identifying transcription factors that regulate differential gene expression. Genome Biol 8:1–13. https://doi.org/10.1186/gb-2007-8-5-r83
Acknowledgements
The authors thank the anonymous reviewers for their valuable suggestions. This research was supported by Public Computing Cloud and School of Interdisciplinary Studies, Renmin University of China.
Author information
Authors and Affiliations
Corresponding author
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhai, J., Wang, W., Zhao, R. et al. BDM: An Assessment Metric for Protein Complex Structure Models Based on Distance Difference Matrix. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00622-1
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12539-024-00622-1