A More In-Depth Look at the hbond Action Command in CPPTRAJ

As I have now accrued much more experience with CPPTRAJ and AMBER16 in general, I can now confidently explain more in-depth the hbond Action Command. While I still won’t be talking about every parameter of the hbond command (seeing as they’ve been explained for the most part in a previous blog post), I will be explaining a few of the other parameters that I did not mention in a previous blog post. As a review, the syntax is as follows:

hbond [<dsname>] [out <filename>] [<mask>] [angle <acut>] [dist <dcut>]

[donormask <dmask> [donorhmask <dhmask>]] [avgout <filename>]

[solvout <filename>] [series [uvseries <filename>]]

As I’ve already explained what some of these parameters do in a previous post, I will not explain what they do, rather, I will be explaining the parameters that you, the reader, have yet to see in this series of blog posts.

  • [angle <acut>] refers to the angle cutoff for hydrogen bond formation, meaning whatever the angle is set to, any hydrogen bond that forms at an angle smaller than what was specified, will not be counted as an actual hydrogen bond (CPPTRAJ ignores it). If this is not specified, then the default angle is 135 degrees. One can disable the cutoff by specifying the angle as -1.
  • [dist <dcut>] refers to the distance cutoff for hydrogen bond formation, meaning whatever the distance is set to, a hydrogen bond that forms with a bond length greater than the set distance will not be counted as an actual hydrogen bond (CPPTRAJ ignores it). If this is not specified, then the default distance is 3.0 Angstroms. One cannot disable this cutoff.
  • [avgout <filename>] refers to the name of a file that will be outputted containing information about the average bond length, the average bond angle, and the number of hydrogen bonds that formed throughout the entire production run for residues in which hydrogen bond formation happened.

After learning about these parameters, I utilized them in my next production run, which yielded much more interesting results then the first time I used it. When I first used the hbond command, my results yielded nothing but 1’s and 0’s. That was because of the limiting default cutoffs. After disabling the angle cutoff and setting the distance cutoff to 3.5Å, my new data yielded much higher numbers. However, just because my data showed much better numbers, that doesn’t necessarily mean that many of those bonds can happen in real life. The reason those defaults are what they are is because those numbers are most likely the average lengths and angles in which hydrogen bonds actually form. Below are pictures of excerpts of the raw data, the average data, and a graph of the raw data of one of my production runs that utilized these new parameters. All of this data was obtained by doing a production run on the molecule, Melittin, and then by doing a trajectory run on the tryptophan of said Melittin. The exact syntax I used for the trajectory can be seen in the previous blog post (here is the link: http://williamkennerly.com/blog/a-mistake-with-the-hbond-action-command/)





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A Mistake with the hbond Action Command

In a previous blog post, I talked about how the hbond action command in CPPTRAJ is capable of outputting a file that shows whether or not a hydrogen bond formed across a series of time steps as indicated by a 1 or a 0 (for clarification, click on this link: http://williamkennerly.com/blog/helpful-cpptraj-commands-part-2-action-commands/). I recently found out that this is not entirely true. When I first used CPPTRAJ’s hbond command, the data I obtained only showed 0’s and 1’s in the solute-solvent hydrogen bond column. At the time, I had made the assumption that these 0’s and 1’s simply indicated whether or not hydrogen bonds formed at a certain time step, 0 meaning no hydrogen bonds were formed and 1 meaning one or more hydrogen bonds were formed. The reason my data yielded strange results was because of two things: a default angle cutoff for bond formation set at 135 degrees, and a default distance cutoff set at 3Å. These cutoffs meant that if a bond was to form at an angle smaller than 135 degrees or at a distance greater than 3Å, then CPPTRAJ does not count it and eliminates the data for that bond entirely.

After changing the cutoffs (I eliminated the angle cutoff by specifying the angle to be -1, and changed the distance cutoff to be 3.5Å), I obtained very different data. The following is the exact syntax that I used in my .traj file to get my output:

parm mlt_wat.prmtop

trajin mdcrd/prod.mdcrd

hbond trpHBond :19 out trp.HBvTime2.dat angle -1 dist 3.5 image solventdonor     :WAT solventacceptor :WAT@O solvout trp.HBAvg2.dat



This .traj file uses a .mdcrd file that contains the final result of a production run of a whole Melittin molecule; however, by specifying the residue number (:19) I only extracted information about the hydrogen bonds formed with the respective tryptophan atoms that form them (the two Nitrogens and the Oxygen), which when plotted results in the graph seen at the bottom of this post. The problem with this obtained data is that it is most likely not reliable. While looking at the average angles of the hydrogen bonds, it was discovered that the angles were very acute ranging from 62° to 106°. They also had slightly longer bond lengths then the original cutoff of 3Å, the lengths ranging from 3 to 3.2Å. Therefore, it can be assumed that these bonds are unlikely to happen in nature.

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253rd ACS National Meeting & Expo

The ACS National Meeting in April was one of the most significant trips I have been on, and by far the largest conference I have attended. I presented my poster, attended fascinating research talks, and networked with chemistry professionals.

Presenting my poster yielded some interesting discussions with the people who stopped by — mostly other computational chemistry professionals, in fact. One evaluated me for the poster award, and he even one-upped me by knowing the directions of the transition dipole moments of indole, which I did not. Presenting to him was a good experience, because it was fun to walk through my entire project. Another professional said that if we could find a successful functional with TD-DFT, perhaps we could eventually publish our results. Talking to other students gave me an idea of how vastly different research topics could be within the domain of computational chemistry.

I went to some great talks. I heard Harry Gray present on Solar Fuels, using catalysts inspired by plant photosystems, giving me a paper topic for my bioinorganic chemistry class. Later I got to hear the big talk on CRISPR, the gene-editing process and its potential implications in our lives. There were a lot of other good talks, but some less than inspiring ones — I heard someone present his graduate research on organic dyes for solar cells, a topic I was familiar with, but I was bored by his lack of passion. However, most of the talks were given with enthusiasm and depth.

Networking was an interesting experience. The career fair got me out of my comfort zone and helped me practice speaking with people in a professional environment. At a speed networking workshop, I talked to a lot of retired chemistry professionals, which was not as useful, because they had only generalized advice to give me and no real leads. Nonetheless, I became more confident in myself after the networking experiences I had at the Meeting.

A cool thing that happened was that I got to speak with the tech support at Gaussian about our research group’s problem with the Gaussian software — the confusion issue with excited state optimizations — and he gave me a few ideas to try. They haven’t worked so far, but they at least gave us something to move us forward on the issue and identify what does not work. I remain hopeful that we will resolve this issue with the software.

Other than that, Kristine and I got to explore the city a bit and walk on the Golden Gate Bridge. Can’t beat the view from there! I hope I get to go on a similar trip in the future.

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Helpful CPPTRAJ Commands Part 2: Action Commands

The next three commands that I found to be helpful are known as Action Commands. Unlike the Topology Commands from the previous post, these Action Commands allow for a file to be created that contains the output. These commands can be used both in CPPTRAJ’s interactive mode, and in .ptraj files that can be called as inputs when starting CPPTRAJ.


distance [<name>] mask1 mask2 [out <filename>]

This command outputs a file that gives the distance from the center of mass of mask1 to the center of mass of mask2 at each time frame (mask1 and mask2 can either be residues or atoms). The output file will show two columns, one indicating the time frame number, and the other indicating the distance between the specified masks in Angstroms. The following visual shows a sample output of the command being called on the CA and N specified atoms (Carbon and Nitrogen respectively).

The next visual is a graph of the first 500 distance values in Angstroms in a 5000 time frame production run.

This action command can utilize other key modifiers to get slightly different outputs; however, the ones listed above are the main modifiers that are necessary to get decent data. When running CPPTRAJ on my production data on Melittin, the modifiers listed above were the only ones that I used.

Example Usage: distance sample_name1 :19@CA :19@N out sample_data1.dat 


hbond [<name>] [out <filename>] [<mask>] [donormask <dmask> [donorhmask <dhmask>]] [solvout <filename>] [series [uvseries <filename>]]

This action command outputs a file that shows where there was hydrogen bonding in the molecule (or specified residue or atom). Some of these modifiers are not intuitively obvious and have yet to be seen in previous action commands, so below is the list of the modifiers that we have yet to identify in this post above and what they do.

  • [donormask <dmask> [donorhmask <dhmask]] refers to a specified residue or number of atoms that will be used as solute donor heavy atoms and a specified residue or number of atoms that will be used as solute donor hydrogen atoms respectively. The second mask should only be specified if the first mask is and the two masks should have a 1 to 1 correspondence between the two masks (in my case, one mask was specified as :WAT and the other was specified as :WAT@O, which represents the water box my Melittin was being simulated in).
  • [solvout <filename>] refers to the name of a file that will be outputted containing the averaged information of the solute-solvent hydrogen bonds in the specified [<mask>]. The output file will show the average distances and average angles of the hydrogen bonds formed between the acceptor and donor atoms (both of which are shown in the output file), along with the number of times a hydrogen bond formed and the fraction of the total number of hydrogen bonds that were specified by the [<mask>].
  • [series [uvseries <filename>]] refers to the name of a file that will be outputted containing the solute-solvent hydrogen bond time data in terms of whether a hydrogen bond was formed or not (as specified by a 1 meaning a hydrogen bond was formed, and a 0 meaning a hydrogen bond was not formed).

Normally, I would include sample data; however, the outputted data yielded very odd results that may or may not be completely useless, so I would much rather not give false data until I’m able to completely figure out how this command works. There are many other modifiers for this action command, but they turned out to be useless in my case.

Example Usage: hbond sample_name2 out sample_data2.dat solventdonor :WAT solventacceptor :WAT@O solvout sample_data3.dat series uvseries sample_data4.dat


rms/rmsd [<name>] <mask> [out <filename>]

This action command outputs a file that contains the RMS Deviation values of a specified <mask> at each time frame. The following visual shows a small part of a sample output of the RMS Deviation values of the backbone of Tryptophan in Melittin.

The next visual is a histogram of the 5000 RMS Deviation values that were outputted from using this command on the backbone of the Tryptophan in Melittin. The y-axis represents the fraction of RMS Deviation values that made up the data, while the x-axis represents the RMS Deviation values themselves.

Example Usage: rms sample_rms :19@C,CA,N out sample_rms.dat

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Helpful CPPTRAJ Commands Part 1: Topology Commands

CPPTRAJ has a variety of commands for analyzing MD Simulation outputs, but because there are so many commands it would be very difficult to describe them all in detail in a single blogpost. As such, this first post will consist of the descriptions of three Topology Commands that I found to be useful for my research of the Tryptophan residue in Melittin. These Topology Commands print out Molecular Topology related information in CPPTRAJ’s interactive mode. For all intents and purposes, the visuals used in this blogpost will strictly be regarding Melittin.

Note: [<mask>] indicates a single residue/atom or range of residues/atoms


atominfo [<mask>]

This command prints out general information on each atom specified by the given [<mask>] modifier. The information is outputted into 12 columns. The following visual is an example output of the command being called on the 19th residue of a Melittin .mdcrd file.

  • #Atom refers to the atom’s index as given by Amber16
  • Name refers to the atom’s name identifier as given by Amber16
  • #Res refers to the residue number in which the atom is located
  • 2nd Name refers to the shorthand name of the residue in which the atom is located
  • #Mol refers to the atom’s molecule number
  • Type refers to the type of atom in the residue (i.e. alpha, beta, etc)
  • Charge refers to the electron charge of the atom
  • Mass refers to the mass of the atom
  • GBradius refers to the generalized Born radius of the atom
  • E1 refers to the element symbol
  • rVDW refers to the Van der Waal’s force radius of the atom
  • eVDW refers to the epsilon Van der Waal’s force of the atom

Example Usage: atominfo :19


bondinfo [<mask>]

This command prints out general information in the form of 6 columns about each bond between each atom as specified by the [<mask>] modifier. The following visual is an example output of the command being called on a specific carbon atom in the 19th residue of a Melittin .mdcrd file.

  • Bond refers to the bond index as specified by Amber16
  • Kb refers to the bond force constant
  • Req refers to the bond equilibrium value in Angstroms
  • atom names refers to the names of the bonded atoms as specified by Amber16
  • (numbers) refers to the atom indexes as specified by Amber16 as well as the types of atoms that are bonded together

Example Usage: bondinfo :19@CA


resinfo [<mask>]

This command prints out general information in 7 columns about a single residue or range of residues as specified by the given [<mask>] modifier. The following visual is an example output of this command being called on the range of residues that make up Melittin. The reason I had to indicate all the residues was because the .mdcrd file that I used had Melittin simulated in a water box. If the command was called without specifying the [<mask>] modifier, the output will include the residue info for all the water molecules in the water box as well. This goes for the atominfo and bondinfo commands too.

  • #Res refers to the residue index as specified by Amber16
  • Name refers to the shorthand name for each residue
  • First refers to the atom index of the first atom in the residue
  • Last refers to the atom index of the last atom in the residue
  • Natom refers to the number of atoms in the residue
  • #Orig refers to the original residue number in the original PDB file of which the date comes from
  • #Mol refers to the molecule number

One can also add the modifier, [short], to display the residues in the FASTA code sequence form. For Melittin, the sequence would look like this: GIGAVLKVLTTGLPALISWIKRKRQQ.

Example Usage: resinfo :1-26


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Interesting talks and workshops at the ACS National Meeting

In addition to presenting my work at the poster session as Dr. Kennerly already mentioned, I attended a wide variety of talks and events at the ACS National Meeting. Some of the talks were related to my field of research, such as one by Dr. Joseph Subotnik. That said, one of my favorite things to do at ACS is always learning about new areas of chemistry, so I went to quite a few talks out of sheer curiosity. One I particularly enjoyed discussed modifying the surface coatings of solar cells to allow dust to be washed off more easily, making them more useful in desert areas. I had never realized how significant of a problem dust was for solar technology, so it was fascinating listening to someone who had devoted significant amounts of time to fixing this problem.

Since I am graduating this year and searching for a job, I also participated in some of career building workshops offered at the conference. In particular, I attended one which was designed to allow undergraduates to network with professionals from all areas of chemistry. There were quite a few people representing academia at this event, and I think it would have been a bit more helpful if there were more from industry and perhaps also the government. That said, I met with a representative from ACS who directed me to some helpful resources for recent graduates, so it was a worthwhile event.

Overall the conference was fun and instructive and definitely worth the trip.

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Justin and Kristine present research at national American Chemical Society meeting in San Francisco

On April 3, 2017, senior chemistry majors Justin Gerard and Kristine Vorwerk traveled to the national American Chemical Society meeting in San Francisco to present their research.   They each presented a poster.  Kristine presented on her progress using the software package Newton-X developed by the Barbatti group to calculate absorption spectra of indole, as well as calculate surface-hopping trajectories of excited-state indole.  Justin presented his conclusions about appropriate functionals to use for TD-DFT absorption and fluorescence calculations, both in the gas phase and with a state-specific solvation method.  Great work Kristine and Justin!


Justin Gerard proudly stands by his research poster at the Spring 2017 national ACS meeting in San Francisco.




Kristine Vorwerk gesticulates about science while presenting her poster at the Spring 2017 national ACS meeting in San Francisco.


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Research Group, Spring 2017

The Spring 2017 semester has started, and as appropriate for the coming season, the group has sprouted in size — eight student members!


Kennerly Research Group, Spring 2017. From (left to right):  Will Magee ’19, Justin Tam ’19, Ryan Dohrn ’20, Annabel Pruitt ’20, Ethan Celebuski ’19, Kristine Vorwerk ’17, Justin Gerard ’17 and James McKee ’18

Will, Annabel and James will be working on developing our molecular dynamics simulations and analyses of Trp-containing proteins.  Justin T, Justin G, and Kristine will be working on various aspects of the quantum mechanics/dynamics of indole using Gaussian and NewtonX.   Ethan will be developing an instructional exposition on analyzing hybridization using Gaussian.   Ryan will be python scripting to support our never-ending needs for parsing and analysis.

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Kristine and Justin present research at Chemistry Department Winter Celebration

A bit belated recognition here, but in December at the end of the fall semester, Justin Gerard ’17 and Kristine Vorwerk ’17 each presented a poster at Skidmore College’s joint Chemistry/Biology winter celebration.  Justin presented on his continued efforts to analyze functionals and basis sets for their suitability for calculating indole’s vertical absorption and emission energies, which turns out to be quite tricky business.   Kristine presented on her new focus on using a free research-grade software package called NewtonX to calculate indole’s absorption spectra with more structure than a traditional software like Gaussian typically produces.  Good work this semester!

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Running the NewtonX Tutorial

After I got the tests to run, I started working my way through the tutorial.  Specifically, I am trying part 4.  I managed to get to step 18 before I ran into problems.  In step 18, we are trying to generate the inital conditions for NewtonX to use.  To do this you run a line of code:

$NX/initcond.pl >initcond.log &

This line of code immediately spits out a message which is a number in brackets followed by another number.  The numbers change each time.  It then prints a message saying initcond.pl is dying now.  At this point, the console freezes and does not respond until you abort with ctrl-c.  It then spits out an exit code, “Exit 255.” (Which I have been unable to find more information on).

To try to figure out what was wrong, I examined the initcond.log file.  So far I have gotten two different messages which seem to alternate with no real reason.  The first occurs early in the run and says:

Cheking input files

Cheking geometry lines

Searching for qvector...

qvector does not exist

Creating qvector...

It appears to die while trying to make the missing qvector.  The qvector it is referring to used to be a file in version 1.1 that contained the quantum vibrational number, but it isn’t referenced at all in 1.4.

The second error happens later in the run and says:

Starting run_g09_initcond.pl at Tue Nov 22 08:21:30 PST 2016

Vertical energies with Gaussian 09

Starting .  $g09root/g09/bsd/g09.profile;$g09root/g09/g09 gaussian.com at Tue Nov 22 08:21:30 PST 2016

Finished .  $g09root/g09/bsd/g09.profile;$g09root/g09/g09 gaussian.com with ERROR at Tue Nov 22 08:21:30 PST 2016

Finished run_g09_initcond.pl with ERROR at Tue Nov 22 08:21:30 PST 2016

This error seems to be referring to the same issue with NewtonX inserting an extra /g09/ which we were seeing earlier.  However, when I tried using the same set of variable definitions which worked in the tests, it did not change anything.  Also, I poked around in the file and found the same qvector error as earlier but instead of crashing after creating the qvector, it continued successfully.  So it seems like that error is one that it can sometimes get past.

So far I haven’t been able to figure out when one error occurs versus the other.  There doesn’t seem to be any particular logic to it.




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