i was reading this In our opinion the precise function of AE
In our opinion, the precise function of AE of S. mansoni (Sm32) remains unclear, and accurate knowledge of the three dimensional structure of this enzyme would be valuable for a better understanding the molecular basis of many of its properties, including its role in the host-parasite interaction, and its potential use in the development of new therapeutic drugs. Due to the importance of Sm32 as a vaccine candidate and its potential use for the development of diagnostic methods (Figueiredo et al., 2015), the three dimensional structure can also be exploited, since several peptides of this protein were chemically synthesized and, when administrated, showed relative immunogenicity in rabbits and mice (Noya et al., 2003; Chacón et al., 2003).
To date, only the crystal structures of human (Dall and Brandstetter, 2013) and mouse (World Health Organization, 2018) AEs have been reported. These studies describe the structures of the proenzyme and the mature forms of the enzymes. Here, we present the first three-dimensional structure for the proenzyme form of Sm32, an AE of S. mansoni (Sm32), a cysteine protease, determined by homology modeling and molecular dynamics (MD) refinement. Comparison of the experimental 3D model of human and M. musculus AEs with the theoretical one of Sm32 showed that there are structural differences that can be exploited for the development of new therapeutic drugs, vaccines and diagnostic methods. Our model could also be useful to understand the role of this enzyme in host-parasite interaction.
Computational methods The amino i was reading this sequence of S. mansoni was retrieved from NCBI database (locus CAB71158). The primary sequence (429 aa) was submitted to the Signal IP 4.1 Server (Petersen et al., 2011) to predict the cleavage site of the signal peptide. Predictions showed the highest probability in Cys19 and Gln20. This peptide fragment was not included in the 3D model. Multiple sequence alignments were performed using Clustal Omega (Sievers et al., 2011) with default parameters. The secondary structure prediction was obtained with the web-based Psipred (McGuffin et al., 2000). The homology model for Sm32 was generated using the crystallographic structure of the M. musculus AE (Chacón et al., 2003) (PDB: 4NOK) as template (Berman et al., 2000), based on sequence identity (41%), high similarity in conserved regions and high agreement with the predicted secondary structure. An initial model was obtained with the SWISS-MODEL modeling server (Biasini et al., 2014). Energy minimizations and molecular dynamics simulations were performed with NAMD 2.9 (Phillips et al., 2005) using CHARMM force field and Gasteiger’s atomic charges in VEGA ZZ 3.0.5 (Pedretti et al., 2004). Hydrogen atoms were added to the model. To remain compatible with physiological pH values, the side chains of Arg, Lys, Glu, and Asp were ionized, whereas His residues were considered neutral. A preliminary energy minimization was performed on the initial structure to avoid high-energy interactions (10,000 steps of conjugate gradients minimization) while applying constraints to the protein backbone with a force constant of 60 Kcal mol−1 Å−2 to preserve global folding. Subsequently, a MD simulation was performed in a water shell. The system was neutralized by adding Na+ counter ions. The 3D model was placed in a box (81 × 81 × 81 Å3; overlap 0.80 Å) of TIP3P water molecules (Jorgensen et al., 1983). The long-range electrostatic potential was treated by the particle mesh Ewald (PME) algorithm (Darden et al., 1993). Periodic boundary conditions were applied to avoid edge effects. Lennard-Jones interactions were calculated with a cutoff of 12 Å, an activation switching distance of 10 Å and a distance for inclusion pair list of 14 Å. 10,000 steps of conjugated gradient minimization were applied to the model system. After this first step, MD simulations were carried out in three phases. First, the system was gradually heated from 0 to 300 K over 9000 iterations (0.009 ns) with constraints of 60 Kcal mol−1 Å−2 applied on the protein backbone atoms under constant volume and normal temperature (NTV). The temperature was controlled using Langevin dynamics for non-hydrogen atoms with a damping coefficient of 5 ps−1. Next, 8.4 ns equilibrations were performed at constant temperature while the constraints were progressively released and the resulting structure was used as a starting point of a 1 ns production run. During the equilibration the energetic and geometric components were employed as indicative that equilibrium had been reached. Having finished the MD simulation, the five lower energy frames of the production phase were selected and further minimized with 20,000 iterations (0.02 ns) of conjugated gradient algorithm. The lowest energy structure was selected for further analysis. MD simulations were carried out on a WorkStation Z420 HP.