ArticleBioinformatics (Oxford, England)2023
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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45 citing papers in PubMed.
- M2-PRNet: multi-scale and multi-modal learning for protein-RNA binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- PUFFIN: protein unit discovery with functional supervision.Bioinformatics (Oxford, England) · 2026Article
- A unified multimodal model for generalizable zero-shot and supervised protein function prediction.Bioinformatics (Oxford, England) · 2026Article
- An SE(3)-equivariant and dynamic multi-modal engine advancing from PTM site prediction to network understanding.Communications chemistry · 2026Article
- Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized Proteins.International journal of molecular sciences · 2026Review
- Research Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials.ACS omega · 2026Review
- Molecular Modeling and Gene Ontology Implicate SLC35F4 and SLC35F5 as Golgi-Associated Importers of Flavin-Adenine-Dinucleotide.International journal of molecular sciences · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.Briefings in bioinformatics · 2025Article
- MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs.Bioinformatics (Oxford, England) · 2025Article
- Multimodal deep learning integration of cryo-EM and AlphaFold3 for high-accuracy protein structure determination.Communications chemistry · 2025Article
- SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction.PLoS computational biology · 2025Article
- GOAnnotator: accurate protein function annotation using automatically retrieved literature.Bioinformatics (Oxford, England) · 2025Article
- Precise prediction of hotspot residues in protein-RNA complexes using graph attention networks and pretrained protein language models.Bioinformatics (Oxford, England) · 2025Article
- GOBoost: leveraging long-tail gene ontology terms for accurate protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning.mSystems · 2025Article
- A Labeled Dataset for AI-based Cryo-EM Map Enhancement.bioRxiv : the preprint server for biology · 2025Article
- MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network.Bioinformatics (Oxford, England) · 2025Article
- Deep learning methods for protein function prediction.Proteomics · 2025Review
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3 authors.
Funding
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 IMPLEMENTATION: The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.