**1. Introduction**

Nowadays, information about targeted diseases needed for structure-based drug design can be accessed easily. There are more than 80,000 coordinate entries of macromolecules revealed by X-ray, NMR, and Electron Microscopy techniques available in the Brookhaven Protein Data Bank. Many new target proteins whose 3D structures have not been solved experimentally can also be easily predicted by homology modelling with adequate reliability. Similarly, thousands of compounds including their drug-like properties are provided in free chemical databases such as the NCI Diversity set, ZINC, and the Drug Bank. Therefore, it is crucial to applied suitable tools to take advantages of these promising resources for rational drug design. Molecular dynamics (MD) simulation has proven itself as a complement to lab experiments to fill in the gaps in our knowledge between 3D structures of biological targets and their potential inhibitors (Rognan et al., 1998 & Jacob et al., 2011). MD can be simply understood as a simulation of physical movements of particles like atoms in atomistic simulation or a group of atoms in coarse-grained simulation. These particles are allowed to move and interact with each others. Their motions are described by Newton's equations, in which forces between the particles and potential energy are obtained from empirical data or quantum mechanical calculations. There are quite several MD simulation packages that are free for academics such as NAMD (Phillips et al., 2005), AMBER (Case et al., 2005), GROSMOS (Christen et al., 2005) and CHARMM (Brooks et al., 1983). The method was introduced by Alder and Wainwright in the late 1950's (Alder and Wainwright, 1957) for the study of the behaviour of simple liquids and the first protein simulation, bovine pancreatic trypsin inhibitor was done successfully twenty years later (McCammon et al., 1977). After that, MD simulation has been used wildly for investigating, interpreting and discovering the dynamics of biological macromolecules, not only fast internal motions but

© 2012 Le, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 Le, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

also slow conformational changes and even whole folding processes of some small proteins. MD simulation has become important complement to experimental procedures in structurefunction determination of targeted diseases and rational design of their inhibitors. In the lead discovery stage, thousands of compounds from free chemical databases are docked against the rigid structures of receptors. Such screening processes are fast but are associated with a high number of false positives and false negatives. Flexible docking has improved the outcome of this process, but to a very limited degree. A recent method that significantly increases the accuracy of the lead identification process is called ensemble-based docking. This method combines the docking algorithm (Morris et al., 1998) with the dynamic structure of proteins taken from MD simulations as well as statistical analysis of binding energy (Amaro et al., 2008). Another important aspect of rational drug design is to understand all drug-protein interactions at an atomistic level and how these interactions are ruptured by certain mutations, leading to drug resistance. Thorough understanding of drug resistant mechanisms is crucial for the design of new agents effective against current drug-resistant strains. MD simulation is also an important tool in identifying drug binding pathway. However, this could be one of the most challenging tasks in computer-aided drug design due to the limitation of MD simulation time scale. One of the solutions is to combine electrostatic surface potential analysis with steered MD (SMD) simulation (Isralewitz et al., 2001). Forces can be applied to drug molecules in order to pull it along possible pathways predicted from electrostatic surface potential. The findings of drug binding pathway are highly important for investigation on non-active site mutations which possibly prevent drugs from entering binding site by rupture the structure of binding pathway as well as for the rational design of new drugs with good binding kinetics.

Incorporating Molecular Dynamics Simulations

into Rational Drug Design: A Case Study on Influenza a Neuraminidases 161

GenBank Locus ID CY041156. All simulations were performed using NAMD 2.720 and the CHARMM31 force field with the CMAP correction. The ionized systems were minimized for 10,000 integration steps and equilibrated for 20 ns with a 1 fs time step. Following this, a 20 ns unconstrained equilibration production run was performed for subsequent trajectory analysis, with frames stored after each picosecond (every 1000 time steps). Constant temperature (T = 300 K) was enforced using Langevin dynamics with a damping coefficient of 1 ps−1. Constant pressure (p = 1 atm) was enforced using the Nosé-Hoover Langevin piston method. Van der Waals interaction cut-off distances were set at 12 Å (smooth switching function beginning at 10 Å) and long-range electrostatic forces were computed using the particle-mesh Ewald (PME) summation method. The trajectories from our 20ns production run of EQ simulation of H1N1pdm neuraminidase with oseltamivir bound, were

**Figure 1.** Schematic representations of drug-bound simulation systems. Shown here as a representative example of simulated systems is H1N1pdm bound to oselvamivir. In A), the simulation system is shown in the full explicit solvation box with oseltamivir and the active-site calcium ion labelled. In B),

oseltamivir is shown buried in the SA binding pocket of H1N1pdm rendered in surface view

*2.1.2. RMSD clustering to extract receptor ensembles from an all-atom MD simulation* 

The holo system with oseltamivir removed was used for RMSD clustering and the docking experiments. Clustering analyses were performed on 20 ns MD trajectories using the g\_cluster tool in the Gromacs package. In brief, snapshots at every 10 ps over the 20 ns simulation were recorded. 2000 resulting structures were superimposed, using all Cα atoms to remove possible rotational and translational movements of the whole system. We have visually verified that the binding-site residues of avian H5N1 neuraminidase, which cover the active site and are responsible for interaction with putative inhibitors, can also be used for the swine flu H1N1pdm neuraminidase. The four most populated clusters are shown in Figure 2. The RMSD clustering analysis was performed on this subset (117-119, 133-138, 146-152, 156, 179, 180, 196-200, 223-228, 243-247, 277, 278, 293, 295, 344-347, 368, 401, 402, and 426-441) using allatoms (including side chains and hydrogen atoms) with the cut-off of 1.5 Å. A total of 13

clustered as shown in Figure 1, and then used for ensemble based docking.

In this chapter we highlight the successes in using MD simulations to speed up the rational design of antiviral drugs. Due to their ability to mutate quickly, the viruses can easily survive people's immune systems and resist current drugs. Currently, there are two pathogenic viruses, the 2003 avian flu (H5N1) virus( Ford Stephen et al., 2006) and the 2009 swine flu (H1N1pdm) virus (Butler et al., 2009), which share the same subtype 1 (N1) of neuraminidase. Neuraminidase, a glycoprotein component on a flu virus' surface that cleaves the alpha-ketosidic linkage of sialic acid (SA) located on human cells to release new virions that then infect nearby healthy cells, is the most important target for antiviral drug design. Oseltamivir functions as a neuraminidase inhibitor that binds competitively to the Sialic Acid (SA) binding site (Laver et al., 2006). Unfortunately, oseltamivir resistance has been a big concern to public health since 2005 and thus continues effort to gain insight of drug resistant mechanism and designing new effective medicine become highly significant.
