Welcome to the GitHub repository for the following publication: The energetic and allosteric landscape for KRAS inhibition (Weng C, Faure AJ & Lehner B, 2022)
Here you'll find an R package with all scripts to reproduce the figures and results from the computational analyses described in the paper.
- 1. Required Software
- 2. Required Data
- 3. Installation Instructions
- 4. Usage
To run the krasddpcams pipeline you will need the following software and associated packages:
- R (ggplot2, ggpubr, ggrepel, ROCR, bio3d, GGally, plot3D, Cairo, ggstatsplot, openxlsx, data.table, dplyr, devtools, hexbin)
Fitness scores, inferred free energy changes and required miscellaneous files should be downloaded from here and unzipped in your project directory (see 'base_dir' option) i.e. where output files should be written.
Make sure you have git and conda installed and then run (expected install time <5min):
# Install dependencies (preferably in a fresh conda environment)
conda install -c conda-forge r-base r-ggplot2 r-ggpubr r-ggrepel r-rocr r-bio3d r-ggally r-plot3d r-cairo r-ggstatsplot r-openxlsx r-data.table r-dplyr r-devtools r-hexbin
# Open an R session and install the krasddpcams R package
devtools::install_github("lehner-lab/krasddpcams")
The top-level function krasddpcams() is the recommended entry point to the pipeline and by default reproduces the figures and results from the computational analyses described in the following publication: The energetic and allosteric landscape for KRAS inhibition (Weng C, Faure AJ & Lehner B, 2022). See Required Data for instructions on how to obtain all required data and miscellaneous files before running the pipeline. Expected run time <20min.
library(krasddpcams)
krasddpcams(base_dir = "MY_PROJECT_DIRECTORY")
The following software packages are required for pre-processing of raw FASTQ files and subsequent thermodynamic model fitting:
- DiMSum v1.2.9 (pipeline for pre-processing deep mutational scanning data i.e. FASTQ to fitness)
- MoCHI (tool to fit mechanistic models to deep mutational scanning data i.e. fitness to free energy changes)
Configuration files and additional scripts for running DiMSum and MoCHI are available in the "DiMSum" and "MoCHI" folders here.
Python scripts and required data to reproduce the surface plasmon resonance (SPR) plots (ED Fig. 2h and ED Fig. 6a) are availabe in the "SPR" folder here.