ProtFun: A Protein Function Prediction Model Using Graph Attention Networks with a Protein Large Language Model
Talo, M.; Bozdag, S.
Show abstract
Understanding protein functions facilitates the identification of the underlying causes of many diseases and guides the research for discovering new therapeutic targets and medications. With the advancement of high throughput technologies, obtaining novel protein sequences has been a routine process. However, determining protein functions experimentally is cost- and labor-prohibitive. Therefore, it is crucial to develop computational methods for automatic protein function prediction. In this study, we propose a multi-modal deep learning architecture called ProtFun to predict protein functions. ProtFun integrates protein large language model (LLM) embeddings as node features in a protein family network. Employing graph attention networks (GAT) on this protein family network, ProtFun learns protein embeddings, which are integrated with protein signature representations from InterPro to train a protein function prediction model. We evaluated our architecture using three benchmark datasets. Our results showed that our proposed approach outperformed current state-of-the-art methods for most cases. An ablation study also highlighted the importance of different components of ProtFun. The data and source code of ProtFun is available at https://github.com/bozdaglab/ProtFun under Creative Commons Attribution Non Commercial 4.0 International Public License.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling 96%
- GOBoost: Leveraging Long-Tail Gene Ontology Terms for Accurate Protein Function Prediction 96%
- CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models 96%
Similar papers in this journal
- DeepSS2GO: protein function prediction from secondary structure 98%
- INTREPPPID - An Orthologue-Informed Quintuplet Network for Cross-Species Prediction of Protein-Protein Interaction 95%
- EGRET: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction 95%
Similar papers in this journal
Similar papers in this journal
- Improving protein function prediction by learning and integrating representations of protein sequences and function labels 96%
- LMPred: Predicting Antimicrobial Peptides Using Pre-Trained Language Models and Deep Learning 94%
- SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction 94%
Similar papers in this journal
- Predicting Gene Disease Associations With Knowledge Graph Embeddings For Diseases With Curtailed Information 95%
- Decoding proteome functional information in model organisms using protein language models. 94%
- GeneMark-EP and -EP+: eukaryotic gene prediction with self-training in the space of genes and proteins 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.