| _version_ | 1867014189036339201 |
|---|---|
| author | Yucong Liu Yijun Liu Zhenhai Li |
| author_facet | Yucong Liu Yijun Liu Zhenhai Li Yucong Liu Yijun Liu Zhenhai Li |
| collection | Wiley Open Access |
| contents | Protein–Protein Interaction Prediction via Structure‐Based Deep Learning Yucong Liu Yijun Liu Zhenhai Li Proteins: Structure, Function, and Bioinformatics ABSTRACT Protein–protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end‐to‐end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three‐dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed‐length requirement. The RSPPI model possessed excellent cross‐species performance and outperformed several state‐of‐the‐art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm. 10.1002/prot.26721 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| doi_str_mv | 10.1002/prot.26721 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_prot_26721 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#vor |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Protein–Protein Interaction Prediction via Structure‐Based Deep Learning Yucong Liu Yijun Liu Zhenhai Li Proteins: Structure, Function, and Bioinformatics Protein–Protein Interaction Prediction via Structure‐Based Deep Learning Yucong Liu Yijun Liu Zhenhai Li Proteins: Structure, Function, and Bioinformatics ABSTRACT Protein–protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end‐to‐end deep learning framework, RSPPI, combining residual neural network (ResNet) and spatial pyramid pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein three‐dimensional structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed‐length requirement. The RSPPI model possessed excellent cross‐species performance and outperformed several state‐of‐the‐art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategy to develop an AI PPI prediction algorithm. 10.1002/prot.26721 http://onlinelibrary.wiley.com/termsAndConditions#vor |
| title | Protein–Protein Interaction Prediction via Structure‐Based Deep Learning |
| topic | Proteins: Structure, Function, and Bioinformatics |
| url | https://onlinelibrary.wiley.com/doi/10.1002/prot.26721 |