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Abstract MotivationMillions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently. ResultsWe developed TransFun—a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy. Availability and implementationThe source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.
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This content will become publicly available on January 1, 2026
A synthetic biomolecular condensate from plant proteins with controlled colloidal properties
A synthetic biomolecular condensate (sBC) was developed using zein, a hydrophobic protein derived from plants. These particles can be used as an artifiical platform to understand the structure and function of natural protein-rich condensates.
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- Award ID(s):
- 2317111
- PAR ID:
- 10615093
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Journal of Materials Chemistry B
- ISSN:
- 2050-750X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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