AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks.
Methods
; 222: 142-151, 2024 Feb.
Article
in En
| MEDLINE
| ID: mdl-38242383
ABSTRACT
Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https//github.com/fxh1001/AGF-PPIS.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Benchmarking
/
Proton Pump Inhibitors
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Methods
Journal subject:
BIOQUIMICA
Year:
2024
Type:
Article
Affiliation country:
China