10 Applications of Molecular Dynamics Simulations …
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10.5.8 Establishing Production Dynamics
A well-equilibrated system from the previous step run in the final production MD
simulation.
10.5.9 Analyzing Trajectory
The trajectory file obtained from the production run needed to compute the structural,
thermodynamics, and dynamic nature of the protein or protein complex by calculating
the RMSD, RMSF, radius of gyration, extract a representative structure, and more.
The quality of the representative structure from the trajectory can be verified using
Ramachandran plots. Visualizing trajectory files using various molecular graphics
software packages yields insight into atomic-level protein conformational changes
based on the time interval. In addition, detailed quantitative structural information
such as hydrogen bonds, inter- and intra-molecular interactions, radius of gyration,
bond angles, distance and geometrical quantities is calculated. RMSD and RMSF
show the deviation of the protein structures from the initial structure based on the
time interval and different flexible region of the protein, respectively. The free energy
decomposition can be calculated from the trajectory files using the MM-PB(GB)SA
method [66].
10.6 Applications of MD Simulation
Predicting compound toxicity by leveraging computational methods is an emerging
yet important field [67]. MD simulation has been applied to predict toxicity. Below are
a few examples of recent MD simulation applications in computational toxicology.
10.6.1 Binding Interactions Between Chemicals and Human
Nicotinic Acetylcholine Receptor α4β2
Tobacco product addiction is a major global health concern. Among various tobacco
constituents, nicotine plays a major role in tobacco addiction by binding to the neuronal nicotinic acetylcholine receptors (nAChRs). Among different types of nAChRs,
α4β2 mediates nicotine addiction. Hence, pinpointing detailed interactions between
nicotine and the human α4β2 receptor will reveal the mechanism of nicotine addiction. In addition, the interaction can inform a predictive model to screen tobacco
constituents. To address these questions, homology modeling, molecular docking,
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10.5.8 Establishing Production Dynamics
A well-equilibrated system from the previous step run in the final production MD
simulation.
10.5.9 Analyzing Trajectory
The trajectory file obtained from the production run needed to compute the structural,
thermodynamics, and dynamic nature of the protein or protein complex by calculating
the RMSD, RMSF, radius of gyration, extract a representative structure, and more.
The quality of the representative structure from the trajectory can be verified using
Ramachandran plots. Visualizing trajectory files using various molecular graphics
software packages yields insight into atomic-level protein conformational changes
based on the time interval. In addition, detailed quantitative structural information
such as hydrogen bonds, inter- and intra-molecular interactions, radius of gyration,
bond angles, distance and geometrical quantities is calculated. RMSD and RMSF
show the deviation of the protein structures from the initial structure based on the
time interval and different flexible region of the protein, respectively. The free energy
decomposition can be calculated from the trajectory files using the MM-PB(GB)SA
method [66].
10.6 Applications of MD Simulation
Predicting compound toxicity by leveraging computational methods is an emerging
yet important field [67]. MD simulation has been applied to predict toxicity. Below are
a few examples of recent MD simulation applications in computational toxicology.
10.6.1 Binding Interactions Between Chemicals and Human
Nicotinic Acetylcholine Receptor α4β2
Tobacco product addiction is a major global health concern. Among various tobacco
constituents, nicotine plays a major role in tobacco addiction by binding to the neuronal nicotinic acetylcholine receptors (nAChRs). Among different types of nAChRs,
α4β2 mediates nicotine addiction. Hence, pinpointing detailed interactions between
nicotine and the human α4β2 receptor will reveal the mechanism of nicotine addiction. In addition, the interaction can inform a predictive model to screen tobacco
constituents. To address these questions, homology modeling, molecular docking,