iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides
Issued Date
2022-11-15
Resource Type
eISSN
24701343
Scopus ID
2-s2.0-85141586738
Journal Title
ACS Omega
Volume
7
Issue
45
Start Page
41082
End Page
41095
Rights Holder(s)
SCOPUS
Bibliographic Citation
ACS Omega Vol.7 No.45 (2022) , 41082-41095
Suggested Citation
Charoenkwan P., Schaduangrat N., Lio P., Moni M.A., Chumnanpuen P., Shoombuatong W. iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides. ACS Omega Vol.7 No.45 (2022) , 41082-41095. 41095. doi:10.1021/acsomega.2c04465 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84034
Title
iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides
Other Contributor(s)
Abstract
Antimalarial peptides (AMAPs) varying in length, amino acid composition, charge, conformational structure, hydrophobicity, and amphipathicity reflect their diversity in antimalarial mechanisms. Due to the worldwide major health problem concerning antimicrobial resistance, these peptides possess great therapeutic value owing to their low incidences of drug resistance as compared to conventional antibiotics. Although well-known experimental methods are able to precisely determine the antimalarial activity of peptides, these methods are still time-consuming and costly. Thus, machine learning (ML)-based methods that are capable of identifying AMAPs rapidly by using only sequence information would be beneficial for the high-throughput identification of AMAPs. In this study, we propose the first computational model (termed iAMAP-SCM) for the large-scale identification and characterization of peptides with antimalarial activity by using only sequence information. Specifically, we employed an interpretable scoring card method (SCM) to develop iAMAP-SCM and estimate propensities of 20 amino acids and 400 dipeptides to be AMAPs in a supervised manner. Experimental results showed that iAMAP-SCM could achieve a maximum accuracy and Matthew's coefficient correlation of 0.957 and 0.834, respectively, on the independent test dataset. In addition, SCM-derived propensities of 20 amino acids and selected physicochemical properties were used to provide an understanding of the functional mechanisms of AMAPs. Finally, a user-friendly online computational platform of iAMAP-SCM is publicly available at http://pmlabstack.pythonanywhere.com/iAMAP-SCM. The iAMAP-SCM predictor is anticipated to assist experimental scientists in the high-throughput identification of potential AMAP candidates for the treatment of malaria and other clinical applications.