Emergence of drug resistance is major concern for combating against Cutaneous Leishmaniasis, a neglected tropical disease affecting 98 countries including India. Miltefosine is the only oral drug available for the disease and Miltefosine transporter proteins play pivotal role in the emergence of drug resistant Leishmania major. The cause of resistance is less accumulation of drug inside the parasite either by less uptake of drug due to decrease in the activity of P4ATPase-CDC50 complex or by increased efflux of the drug by P-glycoprotein (P-gp, an ABC transporter). In this paper, we are trying to allosterically modulate the behavior of resistant parasite (L. major) towards its sensitivity for the existing drug (Miltefosine, a phosphatidylcholine analog). We have used computational approaches to deal with the conservedness of the proteins and apparently its 3D structure prediction through ab initio modeling. Long scale membrane embedded molecular dynamics simulations were carried out to study the structural interaction and stability. Parasite specific motifs of these proteins were identified based on machine learning technique, against which a peptide library was designed. The protein-peptide docking shows good binding energy of peptides Pg5F, Pg8F and PC2 with specific binding to the motifs. These peptides were tested both in vitro and in vivo, where Pg5F in combination with PC2 showed 50-60% inhibition in resistant L. major’s promastigote and amastigote forms and 80-90% decrease in parasite load in mice. We posit a model system wherein the data provides sufficient impetus for being novel therapeutics in order to counteract the drug resistance phenotype in Leishmania parasites.

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