Antiplasmodial Activity of Angiotensin II Analogs

Freitas, Danieli Melo de; Oliveira, Vani Xavier

doi:10.17952/24aps.2015.067

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…This is also correlated to several pieces of evidence that have reported that the amino acids Ala, Lys, Arg, Ile, Leu, Phe, Pro, Tyr, and Val are frequently included on antimicrobial and antimalarial peptides. In the case of the hydrophobic character of AMPs, Val, Phe, and Pro are considered to be important for collapsing the plasmodium cell membrane. ,− Research observed that deletion of Val could lead to a 23% decrease in the antiplasmodial activity . Since Phe is necessary for proficient antibacterial activity and membrane phospholipid interaction, the substitution of polar residues (Arg or Lys) by Phe could promisingly increase antimalarial activity .…”

Section: Resultsmentioning

confidence: 99%

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides

et al. 2022

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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 . 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.

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