TY - JOUR
T1 - In silico Leishmania proteome mining applied to identify drug target potential to be used to treat against visceral and tegumentary leishmaniasis
AU - Chávez-Fumagalli, Miguel A.
AU - Lage, Daniela P.
AU - Tavares, Grasiele S.V.
AU - Mendonça, Débora V.C.
AU - Dias, Daniel S.
AU - Ribeiro, Patrícia A.F.
AU - Ludolf, Fernanda
AU - Costa, Lourena E.
AU - Coelho, Vinicio T.S.
AU - Coelho, Eduardo A.F.
N1 - Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2019/3
Y1 - 2019/3
N2 - New therapeutic strategies against leishmaniasis are desirable, since the treatment against disease presents problems, such as the toxicity, high cost and/or parasite resistance. As consequence, new antileishmanial compounds are necessary to be identified, as presenting high activity against Leishmania, but low toxicity in mammalian hosts. In the present study, a Leishmania proteome mining strategy was developed, in order to select new drug targets with low homology to human proteins, but that are considered relevant for the parasite' survival. Results showed a hypothetical protein, which was functionally annotated as a glucosidase-like protein, as presenting such characteristics. This protein was associated with the metabolic network of the N-Glycan biosynthesis pathway in Leishmania, and two specific inhibitors – acarbose and miglitol – were predicted to be potential targets against it. In this context, miglitol [1-(2-Hydroxyethyl)-2-(hydroxymethyl)piperidine-3,4,5-triol] was tested against stationary promastigotes and axenic amastigotes of the Leishmania amazonensis and L. infantum species, and results showed high values of antileishmanial inhibition against both parasite species. Miglitol showed also efficacy in the treatment of Leishmania-infected macrophages; thus denoting its potential use as an antileishmanial candidate. In conclusion, this work presents a new drug target identified by a proteome mining strategy associated with bioinformatics tools, and suggested its use as a possible candidate to be applied in the treatment against disease.
AB - New therapeutic strategies against leishmaniasis are desirable, since the treatment against disease presents problems, such as the toxicity, high cost and/or parasite resistance. As consequence, new antileishmanial compounds are necessary to be identified, as presenting high activity against Leishmania, but low toxicity in mammalian hosts. In the present study, a Leishmania proteome mining strategy was developed, in order to select new drug targets with low homology to human proteins, but that are considered relevant for the parasite' survival. Results showed a hypothetical protein, which was functionally annotated as a glucosidase-like protein, as presenting such characteristics. This protein was associated with the metabolic network of the N-Glycan biosynthesis pathway in Leishmania, and two specific inhibitors – acarbose and miglitol – were predicted to be potential targets against it. In this context, miglitol [1-(2-Hydroxyethyl)-2-(hydroxymethyl)piperidine-3,4,5-triol] was tested against stationary promastigotes and axenic amastigotes of the Leishmania amazonensis and L. infantum species, and results showed high values of antileishmanial inhibition against both parasite species. Miglitol showed also efficacy in the treatment of Leishmania-infected macrophages; thus denoting its potential use as an antileishmanial candidate. In conclusion, this work presents a new drug target identified by a proteome mining strategy associated with bioinformatics tools, and suggested its use as a possible candidate to be applied in the treatment against disease.
KW - Drug targets
KW - N-Glycan biosynthesis pathway
KW - Proteome mining
KW - Toxicity
KW - Treatment
KW - Visceral leishmaniasis
UR - http://www.scopus.com/inward/record.url?scp=85057831348&partnerID=8YFLogxK
U2 - 10.1016/j.jmgm.2018.11.014
DO - 10.1016/j.jmgm.2018.11.014
M3 - Article
C2 - 30522092
AN - SCOPUS:85057831348
SN - 1093-3263
VL - 87
SP - 89
EP - 97
JO - Journal of Molecular Graphics and Modelling
JF - Journal of Molecular Graphics and Modelling
ER -