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WeTICA

WeTICA is a binless weighted ensemble based enhanced sampling method to study rare event kinetics on a predefined linear CV space. It uses WEPY weighted ensemble software & OpenMM MD engine to run and store simulation data.

WEPY (https://adicksonlab.github.io/wepy/index.html)

OpenMM (https://openmm.org)

Input Data Preparation

To start WeTICA simulation, at least 4 major files are required.

  1. An equilibrated, neutrilize and solvated $\bf{initial\ structure\ file}$ (GROMACS .gro format).
  2. $\bf{System\ topology\ file}$ (GROMACS .top format).
  3. $\bf{Target\ structure\ file}$ (GROMACS .gro format).
  4. An $\bf{eigenvector\ file}$ (.txt format). Each column represents one eigenvector.
  5. Pairwise selected atom id file (.txt format, Optional).

Example files are provided in the "Systems" folder of this repository.

Note 1

Amber structure and topology files can be converted to GROMACS format using the following utility of AmberTools.

  amb2gro_top_gro.py -p <name>.prmtop -c <name>.inpcrd -t <name>.top -g <name>.gro

Note 2

Files in GROMACS format can be also generated from CHARMM-GUI.

Note 3

Currently WeTICA framework supports two featurization schemes: 1) CA-CA atom pairwise distances ('allCA'), 2) User selected atom pairwise distances ('sel_pair'). 0 based selected atom ids supported by MDTraj are required for the 'sel_pair' featurization scheme only. $\bf{Caution:}$ Both the eigenvector generation and the simulation should be performed using the same feature.

Usage

Follow the instructions step-by-step.

step-1

  git clone https://github.com/TeamSuman/WeTICA.git

step-2

  cd WeTICA
  conda create --name wepy
  conda activate wepy
  conda install pip==21.2.2 wheel==0.37.1 python==3.7.13
  pip install -r requirements.txt
  conda install -c conda-forge openmm==7.5.1

step-3

  cd Scripts
  
  python WeTICA.py -h

         -dire DIRE          Full PATH of the directory containing system related input files

         -num NUM            Number of walkers [Optional, default=24]

         -rid RID            Run index

         -steps STEPS        Number of MD steps between two consecutive resampling processes [optional, default=10000 -> 20 ps]

         -cyc CYC            Total number of cycles in each run [optional, default=20000]

         -init INIT          Starting structure file (GROMACS .gro format)

         -top TOP            System topology file (GROMACS .top format)

         -ref REF            Target structure file (GROMACS .gro format)

         -vec VEC            Eigenvector file

         -vid VID [VID ...]  Eigenvectors to be used e.g 0-> 1st vector, 1-> 2nd vector, ...

         -feat FEAT          Select feature e.g 'sel_pair' or 'allCA'.'sel_pair'-> selected pairwise atom distances & 'allCA'-> pairwise CA-CA distances

         -pair PAIR          File containing indices of selected atoms [Optional, but required for 'sel_pair' feature]

         -dist1 DIST1        Merge distance cut-off (unitless) [Optional, default=0.5]

         -dist2 DIST2        Warped distance cut-off (unitless) [Optional, default=0.75]

         -tem TEM            Temperature in Kelvin [Optional, default=300]

         -gid GID [GID ...]  GPU ids
Example

This example showcases the use of WeTICA.py script to study the unfolding of Protein G using the first two eigenvectors (specified as: -vid 0 1) as the collective variables (CVs).

python WeTICA.py -dire Systems/protein_G -rid 1 -init folded.gro -top topol.top -ref unfolded.gro -vec eigenvectors.txt -vid 0 1
-feat sel_pair  -pair atom_pairs.txt -gid 0 1 2 3 -tem 350

On-the-fly monitoring of the simulation progress

Open the $\bf{"Info.txt"}$ file generated inside the same folder that contains the system related input files. Gradual decrease of the $\bf{"Clst\ walk. dist"}$ value indicates the progress of the simulation. Is $\bf{Clst\ walk. dist ≈ d_{warp}?}$ If yes, there is posibility that some walkers reach the target. Now its time for convergence check as described below.

Caution: During on-the-fly convergence check, opening and closing of the hdf5 file might cause unexpected termination of the simulation. To avoid this, copy the wepy.results.h5 file to a new location and check convergence.

Data Analysis

A jupyter notebook is provided in the "Analysis notebooks" folder of this repository for the following analyses:

A) Convergence check
B) MFPT calculation
C) Generation of WE productive trajectory

For further information, visit wepy documentation (https://adicksonlab.github.io/wepy/_source/tutorials/data_analysis/index.html).