DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins
Issued Date
2022-06-01
Resource Type
ISSN
00104825
eISSN
18790534
Scopus ID
2-s2.0-85127362204
Pubmed ID
35378437
Journal Title
Computers in Biology and Medicine
Volume
145
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computers in Biology and Medicine Vol.145 (2022)
Suggested Citation
Hosen M.F., Mahmud S.M.H., Ahmed K., Chen W., Moni M.A., Deng H.W., Shoombuatong W., Hasan M.M. DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins. Computers in Biology and Medicine Vol.145 (2022). doi:10.1016/j.compbiomed.2022.105433 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84275
Title
DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins
Other Contributor(s)
Abstract
Accurate identification of DNA-binding proteins (DBPs) is critical for both understanding protein function and drug design. DBPs also play essential roles in different kinds of biological activities such as DNA replication, repair, transcription, and splicing. As experimental identification of DBPs is time-consuming and sometimes biased toward prediction, constructing an effective DBP model represents an urgent need, and computational methods that can accurately predict potential DBPs based on sequence information are highly desirable. In this paper, a novel predictor called DeepDNAbP has been developed to accurately predict DBPs from sequences using a convolutional neural network (CNN) model. First, we perform three feature extraction methods, namely position-specific scoring matrix (PSSM), pseudo-amino acid composition (PseAAC) and tripeptide composition (TPC), to represent protein sequence patterns. Secondly, SHapley Additive exPlanations (SHAP) are employed to remove the redundant and irrelevant features for predicting DBPs. Finally, the best features are provided to the CNN classifier to construct the DeepDNAbP model for identifying DBPs. The final DeepDNAbP predictor achieves superior prediction performance in K-fold cross-validation tests and outperforms other existing predictors of DNA–protein binding methods. DeepDNAbP is poised to be a powerful computational resource for the prediction of DBPs. The web application and curated datasets in this study are freely available at: http://deepdbp.sblog360.blog/.