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(Note: These tutorials are meant to provide illustrative examples of how to use the AMBER software suite to carry out simulations that can be run on a simple workstation in a reasonable period of time. They do not necessarily provide the optimal choice of parameters or methods for the particular application area.)

# Using Accelerated Molecular Dynamics (aMD) to enhance sampling SECTION 2

Formerly known as AMBER Advanced Tutorial 22

By Romelia Salomon, Levi Pierce & Ross Walker

### 2) Running the aMD calculations and data collection.

Now that we have a relaxed starting structure, eq.rst, the next part of the calculation is to run a short conventional MD simulation to collect the necessary information to set the necessary aMD parameters.

From this run we obtain an average total potential energy of -47,128 kcal/mol and an average dihedral energy of 595 kcal/mol. Using this information, and considering BPTI has 58 residues and the whole system 18,226 atoms, we can calculate the aMD parameters as follows

a) EthreshP: E(tot)= -47128 kcal mol-1 + (0.16kcal*mol-1 atom-1 * 18,226 atoms) = -44212 kcal mol-1

b) alphaP: Alpha(tot)= (0.16kcal mol-1 atom-1 * 18,226 atoms) = 2916 kcal mol-1

c) EthreshD: E(dih)=595 kcal mol-1 + (4kcal mol-1 residue-1 * 58 solute residues) = 827 kcal mol-1

d) alphaD: Alpha(dih)=(1/5)*(4kcal mol-1 residues-1 * 58 solute residues) = 46.4 kcal mol-1

We now can run the full 500ns aMD simulation as follows:

 amd.in ```500 ps NVT production NVT &cntrl imin=0,irest=1,ntx=5, nstlim=250000000,dt=0.002, ntc=2,ntf=2,ig=-1, cut=10.0, ntb=1, ntp=0, ntpr=1000, ntwx=1000, ntt=3, gamma_ln=2.0, temp0=300.0,ioutfm=1,iwrap=1, iamd=3, ethreshd=827, alphad=46.4, ethreshp=-44212, alphap=2916, / ```

We can now run this through pmemd. For example, using 1 GPU on a regular desktop:

\$AMBERHOME/bin/pmemd.cuda -O -i amd.in -o amd.out -p ../*.prmtop -c ../6_/eq.rst -r prod.nc

The original trajectory file produced is significantly large, so we can't include it here due to space limitations. You can use the files provided to run the calculation as stated before which would generate a statistically equivalent trajectory. We ran the 500ns trajectory on a single GPU GeForce GTX 690 in 52 hrs. In the following section we will only present the results of our data analysis based on them in the next section. To reproduce the results in this paper you would need to run this simulation to generate your own trajectory file.