ArticleInternational journal of molecular sciences2024
DeepSub: Utilizing Deep Learning for Predicting the Number of Subunits in Homo-Oligomeric Protein Complexes.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identification of a membrane transporter for dimethylsulfoniopropionate uptake in marine eukaryotic phytoplankton.Nature communications · 2026Article
- When can AlphaFold predict the oligomeric states of proteins?Protein science : a publication of the Protein Society · 2026Article
- 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
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12 authors.
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Abstract
The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, this information is not readily available for most proteins. In this study, we addressed this gap by extracting and curating subunit information from the UniProt database to establish a robust benchmark dataset. Subsequently, we propose a novel model named DeepSub, which leverages the protein language model and Bi-directional Gated Recurrent Unit (GRU), to predict NS in homo-oligomers solely based on protein sequences. DeepSub demonstrates remarkable accuracy, achieving an accuracy rate as high as 0.967, surpassing the performance of QUEEN. To validate the effectiveness of DeepSub, we performed predictions for protein homo-oligomers that have been reported in the literature but are not documented in the UniProt database. Examples include homoserine dehydrogenase from
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