IMPLICIT AND EXPLICIT SOLVENTS IN GAS PHASE, ECHISTATIN CONFORMATION DYNAMICS

The folding of the Echistatin protein and thermodynamics both depend heavily on the discrete character of water. The time frame and duration of the Echistatin molecular modeling simulation are constrained by explicit all-atom of the models. In order to assess the correctness and efficiency of the computation, this research evaluates the gas phase, explicit, and implicit solvents of the echistatin molecule. The gas phase of the Echistatin structure will next be compared to the explicit and implicit solvent simulation. Echistatin is an anticoagulant that inhibits blood clotting by interfering with the blood coagulation process. It has the Arg24-Gly25-Asp triad, which interacts to integrin receptors. The paper will include the molecular modeling trajectory of Echistatin using CHARMM model to analyze sample in gas phase, explicit and implicit solvent.











































Introduction

Echistatin is a small peptide having 49 amino acids found in the venom of the saw-scaled viper, Echis carinatus (Chakrabarty et al., 2017). Echistatin is a member of the disintegrin family of natural peptides which block the platelet-fibrinogen binding inhibiting blood coagulation. The binding sequences Arg24-Gly25-Asp26 help the compound to be a potent inhibitor of the platelet aggregation. The secondary structure also is a factor which determines the high-affinity interaction of platelet with Echistatin. "Echistatin is a single-chain polypeptide with a molecular weight of 5400 and an isoelectric point of 8.3"(Bartsch et al., 2017). Cysteine is the most abundant amino acid accounting 8 out of 49 residues of the protein.

Molecular modeling is essential techniques for studying properties of biological macromolecules such as Echistatin. Availability of efficiency programs and advancement in the computer architecture has led to an increase in the molecular dynamics to simulate the properties of Echistatin which is biological macromolecules. In contrast, time and lengths are one of the challenges in studying the molecular dynamics simulations. To minimize the computation requirement, approaches such as developing computational and efficient force field is essential. It is worth noting that, water constitutes the environment for interaction of protein (Miao et al., 2013). Several studies of MD simulations have been done to examine the importance of water in protein folding and its properties. It has been found that water-induced effects are pronounced in the folding of the protein and its thermodynamics; it essential to the physically and computationally solvent models in the Echistatin MD simulations of Echistatin (Chakrabarty et al., 2017).

Methodology

1. The model of the Echistatin was built using CHARMM22 protein force field starting from the PDB coordinated deposited in the protein data bank.

2. Minimizing the initial protein structure using steepest descent for 200 steps, followed by addition of Adopted Basis Newton-Raphson method for 2000 steps

3. Minimization of protein structure

4. Equilibration of extended dynamics trajectories using steepest descent algorithm

5. Analysis: heating the system to remove the constraints from 100 to 300k for 2 PS using temperature increments of 5k.

6. Equilibration at 300k for another 2ps, finally the production runs of molecular dynamic for 16 ps.

7. Analyzing the production trajectory and extraction of backbone dihedral of the RGD triad







Results

Analysis of the gas Phase

The probability distribution of the Phi2 and psi2 is higher than the phi3 and psi3 from the 50ps high gas phase molecular dynamics. Clustering of the conformations during the MD run of phi2 and psi2 has pronounced clinging comparing to the Phi3 and psi3.

Implicit solvent

The probability distribution of the phi2 and psi2 is lower than phi3 and psi3 in Echistatin from the 50 PS extended molecular dynamics in the implicit solution. Therefore, clustering of the conformations during MD runs, phi3 and psi3 have more clinging of the molecules than phi2 and psi2.

Explicit solvent

The clustering conformations sampled during the MD runs in phi3 and psi3 is higher than phi2 and psi2 in explicit solution. Hence, the probability distribution of the phi2 and psi2 is lower than for phi3 and psi3.

Gas phase, explicit and implicit solvent

Echistatin in explicit solvent shows higher probability distributions in both phi2 and psi2, and phi3 and psi3 than in implicit solution. Hence, explicit solvent shows a high level of clinging of particles while implicit shows scattering of the molecules. Gas phase has higher distribution probability comparing with the explicit solvent modeling run, thus higher sticking of the molecules than explicit.

Discussion

The potential energy function comprises the non-bonded and bonded potential. The bonded describes the angle bending, torsion along the bond, bond stretching and improper angles. Non-bonded involves Vander Waals interactions and electrostatic interactions (Cochran et al., 2013). Ignoring the interaction between two bonded atoms separated by a distance higher than pre-defined is vital to speed the computation. Minimizing the structure of Echistatin relieve the steric clashes and bad contacts using Steepest Descent (SD) for 200 steps, then Adapted Basis Newton-Rap son (ABNR) for 2000 levels (Bartsch et al., 2017). To check the significance of the changes, calculate the heavy-atom RMSD between the two structures. Use the root mean square deviation (RMSD) calculator to first align and compute the RMSD between the two structures depending on the positions of the heavy atoms (Chakrabarty et al., 2017). The simulations start at 0k and the heating up of the system up to the 300k using 5k increments every 0.1ps( 100fs). The simulation runs for 2000 steps using a 0.001 picosecond time.

Maintaining the structural integrity in Echistatin requires patching of the disulfide bridges and building of missing hydrogen using the HBuild facility in CHARMM force field model (Limam et al., 2010). In minimization of structure, there was the usage of steepest descent algorithms for 200 steps and Basis Newton-Raphson Method for 2000 levels. Boundary potential was significant in keeping all molecules off the sphere through all water molecules simulation (Bartsch et al., 2017). Using the steepest descent algorithms removes the vandal Waals and electrostatic forces then equilibrated to accommodate the protein for 200 steps with 0.002 PS step time (Chakrabarty et al., 2017). There was the performance of clustering of the trajectory of ph2-psi2 and phi3-psi3 conformation space and plotting of the probability distribution of each angle. For the performance evaluation of different solvent models, there was the computation of root mean square deviation (RMSD) and solvent accessible surface area for the Echistatin in each system (Bartsch et al., 2017). One of the measures of the Echistatin behavior is the solvent surface area of Echistatin. Solvent imposes a surface tension near the Echistatin-solvent interface which affects its dynamics and structure.

Conclusion

In the explicit simulation of Echistatin, all-atom force field models represent the water. Among the liquids, investigation of water is crucial due to its importance. Currently, their many water model in use in bio molecular simulations. The physical properties of each model are optimized such that it fit radial distribution function, density anomaly or diffusivity. It is realistic to include the molecules of water explicitly in the simulation of Echistatin. However, the presence of water molecules increases the numbers of the degree of freedom by more than 1000 in the system (Chakrabarty et al., 2017). Though the discrete nature of water plays a significant role in the Echistatin thermodynamics, the main aim is to study the properties of the compound and not the detailed atomistic behaviors of the solvent molecules. Unimportant atomistic features of the solvent decrease the degree of freedom and this improves the computational efficiency. Molecular dynamics simulation which is not implicit or explicit solvent in protein is referred to as gas phase (Bartsch et al., 2017). Gas phase simulation is very cheap but unrealistic. One of the difficulties is that there is no screening of the electrostatic force hence too high. The significance of the electrostatic force makes the monogram to show a rigid like a clenched fist of which it appears less in the solvent. The temperature of water and protein increases a result impact changes. Within few PS, the energy transfer happens very fast to the water droplet then to the protein. For fully dissolvation of the protein, impact energies should be higher than the cohesive strength of water. The protein dehydration depends on the collision speed of the molecules.



















References

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Cochran, Jennifer R., Richard Kimura, and Aron M. Levin. "Engineered integrin binding peptides." U.S. Patent 8,536,301, issued September 17, 2013.

Limam, Inès, Amine Bazaa, Najet Srairi-Abid, Salma Taboubi, Jed Jebali, Raoudha Zouari-Kessentini, Olfa Kallech-Ziri et al. "Leberagin-C, A disintegrin-like/cysteine-rich protein from Macrovipera lebetina transmediterranea venom, inhibits alphavbeta3 integrin-mediated cell adhesion." Matrix Biology 29, no. 2 (2010): 117-126.

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