In this study, computational analysis had done on the Intel core TM 2, 160 GB hard disk, and Linux enterprise version 5.0 as OS. In this study, Uniprot, protein BLAST, structure analysis, and verification server; sitemap, GROMACS, virtual screening workflow, and glide module; and ADME prediction was carried out to find the potential inhibitor for leishmaniasis.
Sequence analysis
Trypanothione syntheses were compared in the blast search using the BLASTp algorithm, which is used for aligning the target sequence against the PDB [8]. The BLAST result shows high identity and query coverage with the protein sequence database in the PDB database. Thus, we performed protein homology modeling in prime (Schrodinger suite) using trypanothione synthetase.
Assessment of the model
For assessing the overall stereochemical quality of the modeled protein, SAVES [9] was used for structure analysis. PROCHECK program was used for stereochemical excellence of the modeled protein structure and overall structural geometry, and the structure was refined by the GALAXY [10] server. The simulated 3D model was evaluated for its stereochemical quality by Ramachandran plot using PROCHECK, and root-mean-square deviation (RMSD) value was noted. Prosa was utilized to obtain a z-score of Ramachandran plot; the verify 3D was used to determine the compatibility of an atomic model (3D) with its location and environment and comparing the result to the good structure.
Molecular dynamics of trypanothione synthetase putative
Molecular dynamic (MD) simulations were done to analyze the stability of the modeled protein. MD is the computational simulation of the physical movement of atoms and molecules. GROMACS (GROningen Machine for Chemical Simulation) is a molecular dynamics package, which is assigned for simulations of protein, lipids, and nucleic acid. MD simulations were carried out using the GROMACS 4.6.3 package, GROMOS96 43a1 force field. Trypanothione synthetase solvation was done in a cubic box and the SPC12 water model. Energy minimized complexes were subjected to 50 ns position restraining simulation to relieve close contact which included NVT and NPT equilibration phases also. During these equilibration phases, leap-frog integrator was used for 50 ns simulations. Coordinates, velocities, and energies were updated every 0.2 Ps with a LINCS algorithm to constrain bond lengths. Finally, 50 ns production phase MD was performed at the NPT canonical ensemble.
Protein preparation
The planning wizard technique was used to design the protein structure. It involved a preparatory process and its refinements. During this event, parent carbon particles were put in with hydrogen (H2) molecules, whereas unnecessary water molecules were avoided and removed. Impact refinement module OPLS-2005 power field was applied to minimize, and it closed when RMSD reached out of 0.30 Å, due to it ensuring quality, vitality, and dependability for using further analysis [11].
Virtual screening
This is a computational-based screening approach that helps to frame or design novel drugs using screening of a large number of chemical compounds, which is obtained from different databases. It is also helpful to find the active site of target receptors. The finding of active sites in target protein is a prime task and initiative point of virtual screening. Development of grid at the active site pocket was determined by the site map module [12]. Screening of chemical compounds was initiated using XP (extra precision) docking by employing a slide module. A computational search was performed to find possible conformations. Conjugate gradient (CG) minimization with steepest descent minimization along with a default value was found to be about 0.05 A at the initial, followed by 1.0 Å for reaching maximum extent. Based on the convergence grouping, energy charge and gradient criteria altogether were determined, and the default value was recorded to be 107 and 0.001 kcal/mol respectively. The abovementioned criteria were accounted for proceeding docking techniques, and glide score was considered to prioritize the best conformations to the selected ligands.
Preparation of ligand
Ligands were filtered from three different databases (Enamine, Maybridge, Specs). Based on the highest glide score, glide energy, and some essential criteria, some of the ligands were selected, and those ligands were prepared using LigPrep modules using Merck molecular force field [13]. Ligand preparation using MMFF involved the development or conversion of 2D structure to 3D of ligand molecules in the optimized potential for liquid simulation for a field. By admitting hydrogen atoms, ligand bond orders were measured to neutralize. Minimization was also affected.
Protein active site prediction
CASTp
To carry out design, identifying location spot and describing and assessing concave surface regions of the 3D structure of the selected proteins, the searching pockets were detected, and it was conceded or hidden in the inner side of the proteins. Surface accessibility of the pockets and unapproachable cavities were detected and determined using the CAST p server [14].
Prediction of MM-GBSA energy/binding energy
To compute the prime binding energy of the searched chemical compounds, it was performed by employing MM-GBSA. This method was carried out by adapting the OPLS-2005 force field along with the GBSA solvent model sourced in Schrodinger. To analyze solvent accessible surface area, we used surface generalized born model with Gaussian surface, alternate to the Vander walls surface which was admitted [15].
The binding energy (Δ G bind) was derived from
$$\Delta\ \mathrm{G}\ \mathrm{Bind}=\Delta \mathrm{E}+\Delta \mathrm{G}\ \mathrm{solv}+\Delta \mathrm{G}\mathrm{SA}$$
(1)
$$\Delta \mathrm{E}=E\ \mathrm{complex}-\mathrm{E}\ \mathrm{protein}-\mathrm{E}\ \mathrm{ligand}$$
(2)
Here, E denotes minimized energy value for protein-ligand complex, similarly
$$\Delta\ \mathrm{G}\ \mathrm{solv}=\Delta \mathrm{G}\ \mathrm{solv}\ \left(\mathrm{complex}\right)-\mathrm{G}\ \mathrm{solv}\ \left(\mathrm{ligand}\right)$$
(3)
where G solv indicates salvation free energy of the complex protein inhibitor.
$$\Delta \mathrm{GSA}=\mathrm{GSA}\ \left(\mathrm{complex}\right)-\mathrm{GSA}\ \left(\mathrm{protein}\right)-\mathrm{GSA}\ \left(\mathrm{ligand}\right)$$
(4)
where Δ GSA (complex) and Δ GSA (ligand) indicated surface area energies for the complex.
MD simulation
Selected target protein and lead compounds were subjected to examine molecular dynamic simulation measure, using GROMACs at 50 ns. The minimization of energy to the test complex was determined by the deepest descent algorithm and neutralized using position restrained dynamic simulation. At 50 ns, dynamic simulation of the complex was tried. The determinant characteristics such as hydrogen bond numerical, root-mean-square deviation (RMSD), and fluctuation (RMSF) were also measured by employing GROMACS [16].
ADME prediction
ADME prediction is the panel for finding the physicochemical and pharmacokinetic properties of the chemical compounds. The Qikprop module is used for finding the ADME properties of the lead selected compounds [17].