Novel analysis and data visualization of molecular dynamics trajectories
Creators: name: Dibyajyoti Maity
name: Debnath Pal
Publisher: name: F1000 Research Limited
Year: 2019

description: Molecular Dynamics (MD) simulations help with understanding the molecular motions important for functioning of protein molecules. With the advent of high-performance computing microsecond long MD simulations for proteins can be easily performed. Since, visual inspection of such huge amount of data is not feasible, statistical analysis and efficient representation of the data is necessary. Proper data visualization helps in understanding complex systems and communication of significant results. In this poster we present two novel visualizations and the python package MD DaVis for creating these visualization. <i>Surface Electrostatic Potential of an Ensemble from MD:</i> To understand the electric environment around a molecule the electrostatic potentials are calculated by solving the Poisson-Boltzmann equation and visualized by coloring the molecular surface according to the electrostatic potential in the vicinity. This is generally done for a single structure. It is difficult to visually compare surfaces from different molecules or from different conformation of the same molecule. Instead, we calculated the electrostatic potential for an ensemble of conformations from the MD trajectory and plotted the total and mean electrostatic potential on the molecular surface per residue. The use of an ensemble of conformations for electrostatics calculations also offered an estimated of the fluctuation in electrostatic potential around the molecule, which would not be available from a single structure. This enabled us to locate residues responsible for significantly perturbing the electric environment around the molecule. Significant differences between surface electrostatic potential of PDB structure and the ensemble from MD was observed. This is despite the fact that backbone structures of the conformations in the ensemble closely resemble the conformation in the PDB. <i>Visualization of Electric Field Dynamics:</i> Static images of electric field lines do not reveal the changes in the electrostatic environment around a protein molecule. These changes can visualized by drawing the electric field lines in 3D for each conformation in the ensemble as consecutive frames in a molecular graphics software like PyMOL. The information from the surface electrostatic potential of the ensemble is incorporated, by setting tube thickness of the cartoon representation of the structure proportional to the standard deviation of the mean surface potential and colored according to the average of mean surface potential. Inspecting such visualizations reveal that the dynamics of the electric field may passively steer a ligand towards the active site of a molecule. <i>Analyzing MD simulation of Acylphosphatase from Different Organisms</i><br>We performed 1 microsecond long all atom MD simulation of acylphosphatases from the following organisms: Human (2VH7), Bovine (2ACY), Horse (1APS), Fruit Fly (1URR), <i>E. coli</i> (2GV1), <i>T. thermophilus</i> (1ULR), <i>B. subtilis</i> (3BR8) and <i>C. burnetii</i> (3TRG). The backbone superposition did not show any major difference and their dynamics was quite similar as well. The RMSF of the conserved loop among common type acylphosphatases between sheet-1 and helix-1 just prior to the active site arginine was comparable among all cases, with the central glycine showing the highest value. The mobility of this loop is possibly important for the functioning of this molecule. The electric field dynamics showed that the Arg/Lys in the conserved loop dictates the direction of the field around the active site due to its superficial location and the charged side chain facing the solvent. Since RMSF of the backbone is low for most residues, the electric field around the molecule is primarily influenced by side chain orientation of charged residues. For most cases the location of largest positive surface potential is at the active site arginine, which would strongly attract the negatively charged ligand for acylphosphatase.<br><br><i>MD DaVis</i><br>We built a package for python programming language called MD DaVis to analyze MD trajectories of proteins. The most informative plot contains the following information pertaining to residues in the protein molecule: Secondary structure calculated using DSSP program. Root mean square fluctuation (RMSF) Circular standard deviation of the backbone dihedral angles Mean and standard deviation for the total surface electrostatic potential per residue Mean and standard deviation for the mean surface electrostatic potential per residue It can overlay the data from different proteins on the same axes for comparison and taking into account gaps in their sequence alignment. It can create interactive plot for time series data: root mean squared deviation (RMSD) and radius of gyration. Interactive plots allow us to quickly make comparisons and draw conclusions from MD trajectories. Moreover, these plots can be easily shared through the web. However, Only inspecting 2D plots is not enough to draw conclusions. Since the proximity of various parts of the molecule in 3D is important as well, it can create a 3D visualization of the dynamics in the electric field lines around a protein molecule. There are many other scripts and functions useful for analysis of MD trajectories. More details and source code for MD DaVis can be obtained from https://github.com/djmaity/md_davis