Improved in Silico Identification of Protein-Protein Interactions Using Deep Learning Approach
Issued Date
2025-01-01
Resource Type
ISSN
17518849
eISSN
17518857
Scopus ID
2-s2.0-105003534659
Journal Title
IET Systems Biology
Volume
19
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
IET Systems Biology Vol.19 No.1 (2025)
Suggested Citation
Khan I., Arif M., Ghulam A., Albaradei S., Thafar M.A., Worachartcheewan A. Improved in Silico Identification of Protein-Protein Interactions Using Deep Learning Approach. IET Systems Biology Vol.19 No.1 (2025). doi:10.1049/syb2.70008 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/109936
Title
Improved in Silico Identification of Protein-Protein Interactions Using Deep Learning Approach
Corresponding Author(s)
Other Contributor(s)
Abstract
Protein–protein interactions (PPIs) perform significant functions in many biological activities likewise gene regulation, metabolic pathways and signal transduction. The deregulation of PPIs may cause deadly diseases, such as cancer, autoimmune, pernicious anaemia etc. Detecting PPIs can aid in elucidating the cellular process's underlying molecular mechanisms and contribute to facilitating the discovery of new proteins for the development of novel drugs. Although high-throughput wet-lab technologies have been matured to identify large scale PPI identification; however, the traditional experimental methods are costly and slow and resource intensive. To support experimental techniques, numerous computational approaches have been emerged for identifying PPIs solely from protein sequences. However, the performance of available PPI tools are unsatisfactory and gaps remain for further improvement. In this study, a novel deep learning-based model, Deep_PPI, was developed for predicting multiple species PPIs. To extract the biological features, the authors used 21D vector representing 20 kinds' native and one special amino acid residue and implemented the Keras binary profile encoding technique to formulate each residue in proteins. The binary profile use the PaddVal strategy to equalise the length of positive and negative PPIs. After extracting the features, the authors fed them into one dimension convolutional neural network to build the final prediction model. The proposed Deep_PPI model, which consider the protein pairs into two convolutional heads. Finally, the authors concatenated the two outputs were concatenated from two branches concatenated by fully connected layer. The efficiency of the proposed predictor was demonstrated both on the cross validation and tested on various species datasets, for example, that is (Human, C. elegans, E. coli, and H. sapiens). The proposed model surpassed both the machine-learning models and existing state-of-the-art PPI methods. The proposed Deep_PPI will serve as valuable tool in the discovery of large-scale PPIs in particular and provide insights for drugs development in general.