Contents lists available at ScienceDirect Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/cbac A Computational workflow for the identification of the potent inhibitor of type II secretion system traffic ATPase of Pseudomonas aeruginosa Md. Arifuzzaman a , Sarmistha Mitra b , Sultana Israt Jahan c , Md. Jakaria d , Tahmina Abeda e , Nurul Absar f , Raju Dash f,g, ⁎ a Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, 7003, Bangladesh b Department of Pharmacy, University of Chittagong, Chittagong, 4331, Bangladesh c Department of Biotechnology and Genetic Engineering, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh d Department of Applied Life Science, Graduate School, Konkuk University, Chungju 27478, Republic of Korea e Department of Pharmacy, Southern University Bangladesh, Chittagong, 4000, Bangladesh f Department of Biochemistry and Biotechnology, University of Science & Technology Chittagong, Chittagong, 4202, Bangladesh g Molecular Modeling & Drug Design Laboratory (MMDDL), Pharmacology Research Division, Bangladesh Council of Scientific & Industrial Research (BCSIR), Chittagong, 4220, Bangladesh ARTICLE INFO Keywords: Type II secretion system P. aeruginosa ATPase GspE R Molecular docking ABSTRACT Bacterial type II secretion system has now become an attractive target for antivirulence drug development. The aim of the present study was to characterize the binding site of the type II secretion system traffic ATPase GspE R of Pseudomonas aeruginosa, and identify potent inhibitors using extensive computational and virtual screening approaches. Initially, the designed platform focused on the evolutionary relationship of ATPase GspE R of P. aeruginosa, followed by protein-protein interaction network analysis to characterize its function. In addition, homology modeling, virtual screening and molecular dynamics simulation have been performed to identify potent hits and understand the ligand binding properties of ATPase GspE R. According to the evolutionary re- lationship, high level of genetic change was observed for P. aeruginosa, bearing orthology relationships with P.alcaligenes and P.otitidis. Concurrently, the binding site analysis represented residue Gly268, Ser267, Thr270, Thr271and Lys269 in Walker A motif directly formed hydrogen bonds with the ATP, which modulates the function of ATPase GspE R in the secretion process, is also validated by the molecular docking analysis and molecular dynamics simulation. Furthermore, ZINC04325133 is one of the most potent hits has been identified from virtual screening approach followed by molecular dynamics simulation and MM-GBSA binding energy analysis. These results may provide new knowledge for the development of novel therapeutic strategies against P. aeruginosa, as well as inhibiting secretion system process of a wide range of gram-negative bacteria. 1. Introduction It is estimated that, globally there are about 1 million cases annually due to pathogenic gram-negative bacteria like Pseudomonas aeruginosa (Health, 2013; Spellberg and Rex, 2013). Multidrug-resistant strains are evolving in a great pace and the public health becoming more vulner- able to infections (Allen et al., 2010; Davies and Davies, 2010; Wellington et al., 2013). The development of new therapeutic target and entering it into the treatment procedure is extremely challenging and time-consuming (Payne et al., 2007; Butler et al., 2013; Schäberle and Hack, 2014; Bassetti et al., 2013). An alternative approach to treat bacterial infection should come to the limelight by targeting virulence systems or the regulatory pathways of the bacteria (Lynch and Wiener- Kronish, 2008). https://doi.org/10.1016/j.compbiolchem.2018.07.012 Received 9 April 2018; Received in revised form 30 June 2018; Accepted 10 July 2018 Abbreviations: kj/mol, kilojoule per mol; MD, molecular dynamics; MM, molecular mechanics; MM-GBSA, molecular mechanics – generalized born and surface area; OPLS, optimized potential for liquid simulations; GBSA, generalized–born surface area; PME, particle mesh ewald; RMSD, root mean square deviation; RMSF, root mean square fluctuation; SGB, surface generalized born; SSE, secondary structure elements; SP, standard precision; T2SS, type 2 secretion system; TIP3P, the transferable intermolecular potential3 points; XP, extra precision; YASARA, yet another scientific artificial reality application; VSGB, variable dielectric surface generalized born ⁎ Corresponding author at: Department of Biochemistry and Biotechnology, University of Science & Technology Chittagong, Chittagong, 4202, Bangladesh. E-mail addresses: arifmilon2016@gmail.com (Md. Arifuzzaman), sarmisthacu@gmail.com (S. Mitra), sultanajahan75@gmail.com (S.I. Jahan), pharmajakaria@rocketmail.com (Md. Jakaria), tahmi209@gmail.com (T. Abeda), nurul_ustc@yahoo.com (N. Absar), rajudash.bgctub@gmail.com (R. Dash). Computational Biology and Chemistry 76 (2018) 191–201 1476-9271/ © 2018 Elsevier Ltd. All rights reserved. T