README.md

January 31, 2025 · View on GitHub

Internal Coordinate Net (ICoN) for Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning

ICoN is trained on ~10000 of fully atomistic and highly flexible conformations
It uses vector internal coordinate representation as input features- vBAT
It can train ~10K conformations in a few mins, and generate ~100K conformations in less than a min.

Dependancies

python 3.8 >
- pytorch - for Deep Learning
- MDAnalysis - for trajectory I/O
- pytraj - for analysis

Installation

Clone the repository: git clone https://github.com/chang-group/ICoN.git
Navigate to corresponding folder for further instruction

Instuctions for users

If users want to use this package for their system, they can download the aB-crystallin57-69 folder containing three different folders (output, src, and visual). Please follow these steps:

Make a folder (i.e., ICON_Model) and copy the downloaded folder (aB-crystallin57-69) into it. (you can change the name of both folders accordingly)
Make a folder (i.e., TRAJ) within the ICON_Model folder to copy the trajectory .dcd and topology files .prmtop. Please go to the aB-crystallin57-69 folder for further details of each step.