This video demonstrates using GaussView and its optional GMMX module to perform a conformational
search and predict a Boltzmann-averaged VCD spectrum.
We will be studying the linear diterpenoid elegandiol.
Diterpenoids are uncommon in nature due to their olefinic bonds being prone to carbocation-olefin
cyclization.
However, elegandiol and other diterpenoid compounds are found in Bifurcaria bifurcate,
a brown seaweed native to the shores of the north Atlantic ocean.
We want to predict the VCD spectrum for elegandiol.
Here are the IR and VCD spectra for this molecule from the original study, along with the experimental
details.
The VCD spectrum is on top.
These researchers found excellent agreement between calculation and experiment.
The researchers also explored a fragment model for the molecule.
These spectra compare the predicted spectra for the fragment and the whole molecule.
The VCD spectrum in again on top.
They indicate that the fragment does account for the majority of the spectral features.
Therefore, we will model the fragment in our demonstration here.
Our study will consist of several phases: First, we will use GMMX to search for conformations
of our molecule.
Next, we will perform low-level ab initio optimizations for all of the located conformations.
Third, we will perform high-level optimizations for the set of unique structures produced
by the second step.
Finally, we will run frequency calculations including VCD for each unique conformation.
We will use the individual results to compute the conformationally-averaged VCD spectrum
in GaussView.
We begin by opening the GMMX Conformer Calculation dialog via the Calculate menu.
This window displays the options available to set up a GMMX Calculation.
The Force Field option specifies the molecular mechanics force field to use.
We retain the default force field of MMFF94, which is a general purpose force field designed
for organic molecules.
The Method dropdown specifies the type of calculation to run.
We keep the default of Conformer Search.
We retain the Energy Window default value of 3.5 kcal/mol.
This field specifies the maximum energy difference for the conformer set.
Conformations that fall outside of this range are discarded.
We leave the Max Search setting at 10,000.
It specifies the maximum number of conformers to examine.
We also keep Exclude Epimers and Exclude Double Bond Stereoisomers checked.
Exclude Epimers says to discard candidate structures which are a different diastereomer
than the current structure.
Exclude Double Bond Stereoisomers says to discard ones that correspond to bond rotations
about double bonds.
Note that these two items apply only to the Cartesian search method, which we consider
next.
The Search Method section contains three options: Bonds, Cartesian, and Both.
In a Cartesian search, each of the heavy atoms is moved by some random distance in each of
the X, Y, and Z dimensions.
It is a good method for exploring the conformational space about a local minima.
In the Bonds method, each of the selected bonds in the list are rotated by a random
amount.
This technique normally provides large changes in the conformation of the molecule, and thus
is a good method for hopping between local minima.
If applicable, the Bonds method also works with ring systems, which can also be individually
selected or excluded.
Since we are interested in finding the maximum number of conformers, we keep the default
of Both for the Search Method.
However, we uncheck the double bonds in the bonds list so that double bond rotations will
not be made with the Bonds method.
Any conformations found by the Cartesian method which involve double bond rotations will be
discarded since Exclude Double Bond Stereoisomers is checked.
Note that we can highlight any bond by clicking on the corresponding button in the bond list.
The bond angle is included as the final column in the list.
After we click Submit, we are prompted to save a GMMX file and then start the job.
When the GMMX calculation is finished, we are prompted to open its output file, which
is an SDF file.
When we do so, we see a window displaying the total number of conformations the program
found: 40 in this case.
We click Select All to select all 40 conformations, and we open them into a single molecule group.
We can view the relative energies of the various conformations using the Results=>Energy Plot
menu item.
In this case, all of the conformations are distinct since all of their predicted energy
values are different.
This table shows our results so far.
We will now perform a low-level optimization on each conformation.
We navigate to the Gaussian Calculation Setup dialog and specify Optimization as the Job
Type.
We will use the APFD DFT functional with the 6-31g(d) basis set.
Next, we go to the Solvation panel and specify chloroform as the solvent.
Before submitting the job, we click the Assign to Molecule Group button.
This will apply the current calculation settings to all of the molecules in the group.
Clicking the Submit button allows us to save the job files.
In the Save dialog, we click Advanced, and the Advanced Save Structure Files dialog opens.
We change the Save Molecule Group option from No to Yes, separate file for each molecule
in order to save each molecule into a separate job file.
We right click inside the list to open the context menu, and select Include molecule
numbers in file names from the All Items submenu.
Each file now has a unique name.
We click Save to save all of the job files and then go on to run all of the jobs in Gaussian.
Most of the optimizations run smoothly, but a few require the CalcFC option to reach a
stationary point.
When all of the jobs have finished, we open all of their formatted checkpoint files at
once, ensuring that Read Intermediate Geometries is not selected and that a single new molecule
group for all files is the target.
Next, we select Results->Molecule Group Table.
When the table opens, we sort the conformations by their energies.
We examine the various entries to locate and remove duplicates.
For example, conformers 1 and 16 have essentially the same energy.
We examine and compare their structures using a second view.
We can see that they are the same.
We remove conformer 16 using the controls in the Molecule Group Table.
We next compare the conformers in frames 5 and 37 and again remove the latter as a duplicate.
However, when we compare frames 26 and 12, we see that the structures are different and
so we keep both of them.
We remove all other duplicates with the molecule group and end up with 37 unique structures.
For the purposes of this video, we discard ones with energies more than 4 kcal/mol from
the lowest energy structure, selecting 16 structures for high level optimizations.
We’ve updated the summary table with our new results.
Then we set up and run high level optimizations.
These jobs all succeed, although one optimization requires restarting with Opt=CalcFC from an
intermediate point.
For the purposes of this demonstration, we select the conformations within 2.5 kcal/mol
of the lowest energy for Frequency=VCD calculation, a total of 7 structures.
We open the Gaussian Calculation Setup dialog and set the Job Type to Frequency.
We select the Compute VCD checkbox.
Next, we specify our standard model chemistry.
We also set Chloroform as the solvent.
As before, we apply these settings to the molecule group, save each molecule to its
own job file, and then run the jobs in Gaussian.
Once all of the jobs complete, we again open the result files into a single molecule group.
We will use GaussView’s Mixture Spectra feature to compute and display the conformationally-averaged
VCD spectrum.
We open the Vibrations dialog.
We scale the frequencies by 0.98 to match the original study.
We then click the Spectra button, and the IR and VCD spectra for the selected molecule
appear.
We then select Mixture Spectra from the menu.
Once it opens, we focus on the VCD spectrum, which contains all of the predicted spectra
superimposed.
We open the plot Properties and modify the direction and range of the X-axis.
We also change the IR Peak Half-Width at Half Height value to 8 inverse centimeters for
comparison with the original publication.
Next, we open the Mixture Editor in order to produce the computationally-averaged curve.
First, we check Use Boltzmann Population as Weights.
This applies weightings based on each conformation’s Free Energy.
We also sort the conformers based on their Boltzmann population.
We can see that the 3 lowest energy conformers will dominate the Boltzmann-averaged spectrum,
so we use the Plot Style fields to display the spectra for the lowest energy conformers
as Curves, and we remove the spectra of the others by selecting None.
Note the data for the undisplayed conformers is still included in the Boltzmann average
as long as the corresponding Active checkbox is selected.
Finally, we add the computationally averaged spectrum using the controls in the Combination
section at the bottom of the dialog.
We specify a thick dotted red line for the curve.
Here is the resulting display.
The combination spectrum generally follows that of the lowest energy conformation, with
adjustments coming from other conformations.
We can now compare our predicted spectra to the experimental data.
There is reasonable agreement between the two.
Here is a summary of our complete study.