Skip to content

satyatifrh1/cv_autoencoding_ligand_binding

Repository files navigation

CV_autoencoding_ligand_binding

Collective Variable Discovery by Deep Encoder-Decoder Model:

The paper is: "A deep encoder–decoder framework for identifying distinct ligand binding pathways" By Satyabrata Bandyopadhyay and Jagannath Mondal
J. Chem. Phys. 158, 194103 (2023) https://doi.org/10.1063/5.0145197

The paper link is: https://pubs.aip.org/aip/jcp/article/158/19/194103/2890463/A-deep-encoder-decoder-framework-for-identifying

Here, in this repository, time-evolution of three different trajectories have been shown with visualization of the snap-shots in vmd along with time-evolution on free energy surface.

(1) a_traj1_4H_6H_vmd_fes.mp4 is for Trajectory-1(a) ---> Ligand Binding Through Helix-4 and Helix-6.

(2) b_traj2_7H_9H_vmd_fes.mp4 is for Trajectory-2(b) ---> Ligand Binding Through Helix-7 and Helix-9.

(3) c_traj3_5H_6H_7H_8H_vmd_fes.mp4 for Trajectory-3(c) ---> Ligand Binding Through Helices-5-6-7-8.

The lig-binding-ae-model.py python file will take the ligand-com-to-C-alpha distances as input vector and after model training and prediction, it will return us the hidden-projected "encoded data". This "encoded data" will be needed for all our future work.

We need to have pre-installed keras and tensorflow library to run our code.

About

Collective Variable Discovery by Deep Encoder-Decoder Model

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages