SCMRSA: A New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides
Issued Date
2022-09-13
Resource Type
eISSN
24701343
Scopus ID
2-s2.0-85137699080
Journal Title
ACS Omega
Volume
7
Issue
36
Start Page
32653
End Page
32664
Rights Holder(s)
SCOPUS
Bibliographic Citation
ACS Omega Vol.7 No.36 (2022) , 32653-32664
Suggested Citation
Charoenkwan P., Kanthawong S., Schaduangrat N., Li' P., Moni M.A., Shoombuatong W. SCMRSA: A New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides. ACS Omega Vol.7 No.36 (2022) , 32653-32664. 32664. doi:10.1021/acsomega.2c04305 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84052
Title
SCMRSA: A New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides
Other Contributor(s)
Abstract
Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobial peptides have captured attention as novel drug candidates due to their rapid and broad-spectrum antimicrobial activity, anti-MRSA peptides have emerged as potential therapeutics for the treatment of bacterial infections. Although experimental approaches can precisely identify anti-MRSA peptides, they are usually cost-ineffective and labor-intensive. Therefore, computational approaches that are able to identify and characterize anti-MRSA peptides by using sequence information are highly desirable. In this study, we present the first computational approach (termed SCMRSA) for identifying and characterizing anti-MRSA peptides by using sequence information without the use of 3D structural information. In SCMRSA, we employed an interpretable scoring card method (SCM) coupled with the estimated propensity scores of 400 dipeptides. Comparative experiments indicated that SCMRSA was more effective and could outperform several machine learning-based classifiers with an accuracy of 0.960 and Matthews correlation coefficient of 0.848 on the independent test data set. In addition, we employed the SCMRSA-derived propensity scores to provide a more in-depth explanation regarding the functional mechanisms of anti-MRSA peptides. Finally, in order to serve community-wide use of the proposed SCMRSA, we established a user-friendly webserver which can be accessed online at http://pmlabstack.pythonanywhere.com/SCMRSA. SCMRSA is anticipated to be an open-source and useful tool for screening and identifying novel anti-MRSA peptides for follow-up experimental studies.