Gaussian accelerated MD (GaMD)

GROMACS version:
GROMACS modification:
Hi all,

After a quick search, I see that GaMD is not implemented in GROMACS. Is there any plan to include it in future releases? I found this branch in Github https://github.com/emptyewer/gromacs-gamd (it has 5 years old, though). Does anyone used it and/or recommended it?

Thanks in advance.

Best,
-Yasser

Hi Yasser,

In a way it’s already implemented in GROMACS under the name “essential dynamics sampling” - though I have to warn that it is not tested extensively and the code is a bit dated, it might be just what you want! Have a look here

and a corresponding publication here:

Best,
Christian

Thanks! I will have a look.

Best.
-Yasser

Yes, it is pretty outdated, but I will give it a try. In any case, I think that, if possible, it would be very useful for the community to fully implement an updated version of the method like in NAMD and AMBER (http://miao.compbio.ku.edu/GaMD/).

Best,
-Yasser

Hi Yasser,

Rather than implement lots of types of enhanced sampling methods, we are currently focusing on better interaction with packages that provide this functionality, e.g., colvars (https://gitlab.com/gromacs/gromacs/-/issues/3357) and plumed.

The reason behind this is that we would like to make sure that GROMACS is very stable and tested and that becomes harder the more features we incorporate - so we try to split responsibility and provide all hooks and base-layers others can then build upon.

Hello Christian, it is worth noting that GaMD is implemented by changing individual potential energy terms, and is not a collective variable-based method. So it would be very difficult to support it in GROMACS only by applying external forces, like Colvars or PLUMED do.

Yasser, why don’t you get in touch with the authors of the GaMD papers and ask them for suggestions?

Giacomo

Dear Christian and Giacomo,

Thanks for your replies.

I contacted the author of GaMD, Prof. Yinglong Miao about the method. It is based on a similar mathematical formalism of the bias potential of conformational flooding. However, GaMD smooths the potential energy surface without the requirement of predefined collective variables. Also, it provides rigorous ways to re-weight both thermodynamics and kinetics from the simulations. All the information is on the website in my previous post.

I don’t know about the intricacies of the development process, which is great, btw. But, I do think that the implementation of this method could be useful for the community. Especially to avoid jumping to other software (NAMD, AMBER) and keep the reproducibility. That’s why my question about gromacs-gamd in Github because it looked like an independent development branch.

Best,
-Yasser