Identification of potential inhibitor and enzyme-inhibitor complex on trypanothione reductase to control Chagas disease Mohammad Uzzal Hossain a, *, Arafat Rahman Oany a , Shah Adil Ishtiyaq Ahmad a, *, Md. Anayet Hasan b , Md. Arif Khan a , Md Al Ahad Siddikey a a Department of Biotechnology and Genetic Engineering, Faculty of Life Scisence, Mawlana Bhashani Science and Technology University, Santosh, Tangail, Bangladesh b Department of Genetic Engineering and Biotechnology, Faculty of Biological Sciences, University of Chittagong, Chittagong 4331, Bangladesh A R T I C L E I N F O Article history: Received 22 August 2015 Received in revised form 13 July 2016 Accepted 6 October 2016 Available online 7 October 2016 Keywords: Chagas disease Pharmacoinformatics Molecular docking Enzyme-inhibitor complex A B S T R A C T Chagas is a parasitic disease with major threat to public health due to its resistance against commonly available drugs. Trypanothione reductase (TryR) is the key enzyme to develop this disease. Though this enzyme is well thought-out as potential drug target, the accurate structure of enzyme-inhibitor complex is required to design a potential inhibitor which is less available for TryR. In this research, we aimed to investigate the advanced drug over the available existing drugs by designing inhibitors as well as to identify a new enzyme-inhibitor complex that may act as a template for drug design. A set of analogues were designed from a known inhibitor Quinacrine Mustard (QUM) to identify the effective inhibitor against this enzyme. Further, the pharmacoinformatics elucidation and structural properties of designed inhibitor proposed effective drug candidates against Chagas disease. Molecular docking study suggests that a designed inhibitor has higher binding affinity in both crystal and modeled TryR and also poses similar interacting residues as of crystal TryR-QUM complex structure. The comparative studies based on in silico prediction proposed an enzyme-inhibitor complex which could be effective to control the disease activity. So our in silico analysis based on TryR built model, Pharmacophore and docking analysis might play an important role for the development of novel therapy for Chagas disease. But both animal model experiments and clinical trials must be done to confirm the efficacy of the therapy. ã 2016 Elsevier Ltd. All rights reserved. 1. Introduction Trypanosomiasis is a parasitic disease that affects both humans and animals (Abimbola et al., 2013). It is called American Trypanosomiasis or Chagas disease when it develops in humans. Chagas disease is endemic in Latin America but it is much less explored. Trypanosoma cruzi, a protozoan parasite, is liable for Chagas disease. It is found all over the American continent in a range of wild mammalian reservoirs and spread out by the triatomine bug insect vector. Besides such transmission, humans can also be infected by T. cruzi through food ingestion, contaminated drinks with live parasites, contaminated blood transfusion, familial transmission during pregnancy and organ transplantation (Moraes et al., 2014). It is estimated that 10 million people of the world are infected with T. cruzi, mostly in the Latin America (World Health Organization, 2010) and about 100 million people are at high risk of the disease in the Americas, with a total estimated incidence of 800,000 new cases per year (Moncayo and Ortiz Yanine, 2006). In more recent years, huge migration of Latin Americans introduced a large number of infected persons to non- endemic areas like North America, Europe, Australia and Japan (Gascon et al., 2010; Bern and Montgomery, 2009). Chagas disease has passed around hundred years since its discovery but there are still no suitable therapies that might lead to reliable cure in the chronic phase of the disease. The importance of producing novel inhibitors against this disease is reinforced due to high death rate Abbreviations: TryR, trypanothione reductase; T. cruzi, Trypanosoma cruzi; QUM, quinacrine mustard; ADMET, absorption, distribution, metabolism distribution and toxicity; BBB, blood brain barrier transport; LogBB, Blood Brain Distribution; LogPS, blood brain barrier permeability; DBP, Drug binding to plasma protein; Vd, volume of distribution; Pgp, P-glycoprotein; QSAR, quantititive structure–activity relation- ship; LogS, solubility; TPSA, the polar surface area; cLogP, logarithm of partition coefficient; Mw, molecular weight. * Corresponding authors at: Department of Biotechnology and Genetic Engineer- ing, Mawlana Bhashani Science and Technology University, Santosh, Tangail 1902, Bangladesh. E-mail addresses: uzzalbge10044@gmail.com (M.U. Hossain), arafatr@outlook.com (A.R. Oany), shahadil_07@yahoo.com (S.A.I. Ahmad), anayet_johnny@yahoo.com (M. A. Hasan), arifkhanbge35@gmail.com (M. A. Khan), green_world20@yahoo.com (M.A.A. Siddikey). http://dx.doi.org/10.1016/j.compbiolchem.2016.10.002 1476-9271/ã 2016 Elsevier Ltd. All rights reserved. Computational Biology and Chemistry 65 (2016) 29–36 Contents lists available at ScienceDirect Computational Biology and Chemistry journal home page : www.elsevier.com/loca te/compbiolchem