Improved Predictions of Phase Behaviour of IDPs by Tuning the Interaction Range

This repository contains Python code, Jupyer Notebooks and simulation data for reproducing the work of the scientific paper Improved Predictions of Phase Behaviour of Intrinsically Disordered Proteins by Tuning the Interaction Range by G. Tesei and K. Lindorff-Larsen. Layout analyses.ipynb: Jupyter Notebook to analyze all the simulation data and generate plots calc_conc.ipynb: Jupyter Notebook to calculate csat and ccon from direct-coexistence molecular simulations prior.ipynb: Jupyter Notebook to carry out the analysis of the hydrophobicity scales collected by Simm et al. (DOI: 10.1186/s40659-016-0092-5) optimization/: Data and Python code related to the optimization of the residue-specific ``stickiness'' parameters SC/: Data and Python code related to single-chain simulations of the CALVADOS model. Simulations are performed using HOOMD-blue v2.9.3 installed with the mphowardlab/azplugins MC/: Data and Python code related to multi-chain simulations of the CALVADOS model in slab geometry. Simulations are performed using openMM] v7.5 Python code and Jupyter notebooks are also available on GitHub at github.com/KULL-Centre/papers/tree/main/2022/CG-cutoffs-Tesei-et-al Further usage examples of the CALVADOS model are available at github.com/KULL-Centre/CALVADOS. Usage To open the Notebooks, install Miniconda and make sure all required packages are installed by issuing the following terminal commands bash conda env create -f environment.yml
source activate calvados
jupyter-notebook