Rapid and accurate prediction of protein homo-oligomer symmetry using Seq2Symm

Meghana Kshirsagar (), Artur Meller, Ian R. Humphreys, Samuel Sledzieski, Yixi Xu, Rahul Dodhia, Eric Horvitz, Bonnie Berger, Gregory R. Bowman, Juan Lavista Ferres, David Baker and Minkyung Baek ()
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Meghana Kshirsagar: Microsoft Corporation
Artur Meller: Washington University in St. Louis
Ian R. Humphreys: University of Washington
Samuel Sledzieski: Microsoft Corporation
Yixi Xu: Microsoft Corporation
Rahul Dodhia: Microsoft Corporation
Eric Horvitz: Microsoft Corp
Bonnie Berger: Massachusetts Institute of Technology
Gregory R. Bowman: University of Pennsylvania
Juan Lavista Ferres: Microsoft Corporation
David Baker: University of Washington
Minkyung Baek: Seoul National University

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract The majority of proteins must form higher-order assemblies to perform their biological functions, yet few machine learning models can accurately and rapidly predict the symmetry of assemblies involving multiple copies of the same protein chain. Here, we address this gap by finetuning several classes of protein foundation models, to predict homo-oligomer symmetry. Our best model named Seq2Symm, which utilizes ESM2, outperforms existing template-based and deep learning methods achieving an average AUC-PR of 0.47, 0.44 and 0.49 across homo-oligomer symmetries on three held-out test sets compared to 0.24, 0.24 and 0.25 with template-based search. Seq2Symm uses a single sequence as input and can predict at the rate of ~80,000 proteins/hour. We apply this method to 5 proteomes and ~3.5 million unlabeled protein sequences, showing its promise to be used in conjunction with downstream computationally intensive all-atom structure generation methods such as RoseTTAFold2 and AlphaFold2-multimer. Code, datasets, model are available at: https://github.com/microsoft/seq2symm .

Date: 2025
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DOI: 10.1038/s41467-025-57148-3

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