ArticleNature communications2025
Rapid and accurate prediction of protein homo-oligomer symmetry using Seq2Symm.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Stoic: fast and accurate protein stoichiometry prediction.Bioinformatics (Oxford, England) · 2026Article
- Protein Self-Interaction in Cellular Function and Network Evolution: Molecular Mechanisms, AI-Driven Insights, and Therapeutic Potentials.International journal of molecular sciences · 2026Review
- GLMYsymm: inferring symmetry categories of protein complexes from single sequences using persistent GLMY homology.Briefings in bioinformatics · 2026Article
- Computational prediction resolves thousands of homooligomeric phage protein structures.bioRxiv : the preprint server for biology · 2026Article
- Supervised fine-tuning enhances unsupervised learning from 45 million amino acids in TCR and peptide sequences.Bioinformatics (Oxford, England) · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Elevation-associated shifts in plasma metabolite abundance and lung gene expression in the Xizang plateau frog, Nanorana parkeri.BMC genomics · 2026Article
- KlebPhaCol: a community-driven resource for Klebsiella research identified a novel phage family.Nucleic acids research · 2025Article
- StoPred: Accurate Stoichiometry Prediction for Protein Complexes Using Protein Language Models and Graph Attention.Research square · 2025Article
- StoPred: Accurate Stoichiometry Prediction for Protein Complexes Using Protein Language Models and Graph Attention.bioRxiv : the preprint server for biology · 2025Article
- mamp-ml: A deep learning approach to epitope immunogenicity in plants.bioRxiv : the preprint server for biology · 2025Article
- Rapid and accurate prediction of protein homo-oligomer symmetry using Seq2Symm.Nature communications · 2025Article
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12 authors.
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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 .
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