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.