# About
This project contains all code and data associated with the Nature Chemistry paper "Supercharging enables organized assembly of synthetic biomolecules".
The files are split into multiple archives to simplify the archiving process.
This README contains the information on all of the files and how to make use of them.
For more details on the contents beyond what is provided in the Deep Blue Data repository metadata, please see the paper itself.

# Files
The data for this paper is split into two distinct but closely related projects that are detailed below:

* candidate\_stability: This project is associated with creating atomistic representations of the candidate protomer structures.
  * candidate\_stability\_workspace.tar.gz: This archive contains the outputs of atomistic simulations and the data associated with them. The data space was managed with signac, and all outputs were generated using GROMACS.
  * candidate\_stability\_project.tar.gz: This archive contains simulation code, analysis notebooks, and input files needed to generate the outputs contained in the candidate\_stability\_workspace.tar.gz archive. The code is written in Python using the signac framework to manage data and the workflow. All simulations were performed using GROMACS, which is interfaced with through Python functions calling the appropriate command line APIs. This archive includes its own, more detailed README.md, along with a LICENSE for the code.
* rigid\_protein: This project is associated with coarse-grained Monte Carlo simulations of the protomers.
  * rigid\_protein\_workspace.tar.gz: This archive contains the outputs of coarse-grained simulations and the data associated with them. All outputs are generated by HOOMD-blue. Each data point is linked to a corresponding atomistic seed structure from the candidate\_stability\_workspace.tar.gz archive.
  * rigid\_protein\_project.tar.gz: This archive contains simulation code, analysis notebooks, and input files needed to generate the outputs contained in the rigid\_protein\_workspace.tar.gz archive. The code is written in Python using the signac framework to manage data and the workflow. All simulations were performed using HOOMD-blue. This archive includes its own, more detailed README.md, along with a LICENSE for the code.

# How to use
To unpack these archives into a usable form, the two \*\_project.tar.gz archives should each be unpacked into separate folders.
Then, the corresponding \*\_workspace.tar.gz archives should be unpacked into the corresponding project folders.
Once unpacked, simulations can be run using the project.py scripts in both folders using the signac-flow interface: `python project.py run`.
For more information, please see the relevant package documentation for HOOMD-blue, GROMACS, and signac.

## Requirements
The following pieces of software are required for running the code.
Note that the version numbers shown indicate the last versions tested; earlier versions may work, but no guarantees are provided.

* signac 0.9.2
* signac-flow 0.6.1
* HOOMD-blue 2.4.0
* GSD 1.5.0
* NumPy 1.14.1
* Pandas 0.21.0
* Intel TBB 2018 Update 5
* Chimera: chimera production version 1.12 (build 41623) 2017-10-24 23:35:37 UTC
* GROMACS: version 5.1.4