PenSol simulates a protein folding in a penetrating solvent. The solvent
particles are not explicitly defined. Instead, only the random forces that
occur on the surface of the protein to mimic solvent-protein collisions are
simulated. Ten sample transitions of the protein crambin are chosen between
different pairs of intermediate states, and the transition rate (krate) and
the population of the unbonded state at equilibrium (Pu) are calculated for
each.
For more information, see the preprint.
See top level comment in each file for more details as to what each file does.
Some top level comments also contain examples of how to run each file, if
complicated. The pathways given in these examples assume user is currently in
the pensol/crambin directory. Download crambin.zip from
Releases.
-
src: C++ source code for PenSol program -
examples: JSON inputs which specify protein configuration for running PenSol -
tools: Python scripts for running PenSol and extracting data such as the transition rate -
crambin: HybridMC JSON and HDF5 inputs for running specific transitions for crambin between intermediate states; dir inside which all simulations are carried out
-
C++17 compiler with support for C++20 bit library
see bit operations under C++20 compiler support
tested with GCC 9.3.0 on CentOS 7.9
-
Minimum CMake version is 3.13.1
-
HDF5 version 1.10 or later
-
Boost
To compile PenSol for development and testing:
make debug
To perform unit test with verbose output:
cd debug
ctest -V
To compile PenSol for running simulations:
make release
The JSON file needed to run a transition between two intermediate states of
crambin uses inputs from a master JSON file for crambin inside examples
called crambin.json, and the HybridMC JSON file specific to the transition in
crambin. The inputs from these two files must be combined into one JSON file
for each transition, which can be done using update_json.py. This step must
be completed before the program is run.
To run a simulation for one transition on a computing cluster, use
run_diff_eps.py. To run a simulation for one transition on a local computer,
use run.py.
To calculate the krate and Pu for the PenSol model, use a non-zero epsilon
value such as 3.0 when submitting a simulation using run_diff_eps.py. When
all transitions at this epsilon are completed, use run_krate_solvent.py to
get krate and Pu. This will create a csv file in the home directory called
krate_solvent.csv, which stores krate and Pu. Alternatively, to obtain
krate and Pu for a single transition, enter transition specific folder, and
run krate_solvent.py.
To get the krate for the HybridMC model, the biased entropy and MFPT must be
calculated beforehand. This can be done in each crambin/hybridmc_* directory.
To calculate biased entropy, use get_error_s_bias.py for one or more nboot
(for crambin, 5 nboot is sufficient). To calculate MPFT, use mfpt.py. For
crambin, bootstraps are not needed in the MPFT calculation. If there are
several HDF5 files in crambin/hybridmc_* due to nboot of
get_error_s_bias.py being greater than 1, use the first HDF5 file to
calculate MFPT.
To calculate the krate and Pu for the HybridMC model, set epsilon to zero
when submitting a simulation using run_diff_eps.py. When all transitions at
this epsilon are completed, use run_vacf_or_msd.py with msd.py to calculate
the mean squared displacement (MSD) for each transition. To get krate and
Pu for the HybridMC model for all transitions, use run_krate_hybridmc.py.
This will create a csv file in the main directory called krate_hybridmc.csv,
which stores krate and Pu. Alternatively, to obtain krate and Pu for a
single transition, enter transition specific folder and run plot_msd.py,
followed by krate_hybridmc.py.
If the system consists of a single Brownian particle, either plot_msd.py or
plot_vacf.py can be used to obtain the short time diffusion coefficient. In
the latter case, run_vacf_or_msd.py would need to be carried out with
vacf.py to calculate the velocity autocorrelation function (VACF).
To obtain a ratio of hybridmc to solvent krate at different epsilons for a
single transition, run rate_ratios.py.
To calculate the diffusion coefficient based on the values of the parameters
given in crambin.json, use calc_diff.py.