Affiliation:
1. Key Laboratory of Dental Maxillofacial Reconstruction and Biological Intelligence Manufacturing of Gansu Province, School of Stomatology Lanzhou University Lanzhou Gansu China
2. The Institute of Pharmacology, Key Laboratory of Preclinical Study for New Drugs of Gansu Province, School of Basic Medical Sciences Lanzhou University Lanzhou Gansu China
3. The Affiliated Hospital of Guangdong Medical University Zhanjiang Guangdong China
Abstract
Antimicrobial peptides (AMPs), a crucial part of the innate immune system, have been exploited as promising candidates for antibacterial agents. Many researchers have been devoting their efforts to develop novel AMPs in recent decades. In this term, many computational approaches have been developed to identify potential AMPs accurately. However, finding peptides specific to a particular bacterial species is challenging. Streptococcus mutans is a pathogen with an apparent cariogenic effect, and it is of great significance to study AMP that inhibit S. mutans for the prevention and treatment of caries. In this study, we proposed a sequence‐based machine learning model, namely iASMP, to exactly identify potential anti‐S. mutans peptides (ASMPs). After collecting ASMPs, the performances of models were compared by utilizing multiple feature descriptors and different classification algorithms. Among the baseline predictors, the model integrating the extra trees (ET) algorithm and the hybrid features exhibited optimal results. The feature selection method was utilized to remove redundant feature information to improve the model performance further. Finally, the proposed model achieved the maximum accuracy (ACC) of 0.962 on the training dataset and performed on the testing dataset with an ACC of 0.750. The results demonstrated that iASMP had an excellent predictive performance and was suitable for identifying potential ASMP. Furthermore, we also visualized the selected features and rationally explained the impact of individual features on the model output.
Funder
National Natural Science Foundation of China
Basic and Applied Basic Research Foundation of Guangdong Province
Subject
Organic Chemistry,Drug Discovery,Pharmacology,Molecular Biology,Molecular Medicine,General Medicine,Biochemistry,Structural Biology
Cited by
1 articles.
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