CompoMug stands for COMputational Predictions Of MUtations in GPCRs.

CompoMug consists of four modules: knowledge-based, sequence-based, structure-based, and machine-learning-based, taking maximum advantage of accumulated structural and biophysical data. CompoMug provides a computational platform for thermostabilization of GPCRs and can be further evolved with an accumulation of experimental mutation data. Please check our paper for more details: Popov et al., 'Computational design of thermostabilizing point mutations for G protein-coupled receptors', eLife, 2018

Knowledge-based module

requires a .fasta file and .csv file with generic numbering layout of the target GPCR, which can be obtained from the GPCRdb web-server (www.gpcrdb.org). Given that, to get the list of point mutation candidates simply run : CompoMug-knowledge-module target.fasta target.csv

Sequence-based module

requires five multiple sequence alignment (MSA) files, where the first line in a file indicates the number of sequences in the MSA and the length of the MSA (number of positions). Note, that number of positions must be the same in all 5 MSA files. Given that, to get the list of point mutation candidates simply run : CompoMug-sequence-module msa1.txt msa2.txt msa3.txt msa4.txt msa5.txt

Machine-learning-based module

requires high-quality structural model of the target receptor. First, use the CompoMug_descriptor.icm script to calculate the descriptor of a single point mutation. Edit the first four lines of the script to define a point mutaiton in the structure you are interested in. The script models the point mutation, calculates its descriptor, and store the descriptor in the compomug_descriptor.txt file. In order to run the script you will need a separately licensed ICM-Pro package to be able to run the script (molsoft.com).

Given the compomug_descriptor.txt file (you can change the name of the output file in the end of the script), to predict the effect of the point mutation simply run :

CompoMug-machine-learning-module compomug_descriptor.txt

It will give you the score of the point mutation (the higher is better), unless the score is zero.

If you are interested in analyzing several point mutations, run CompoMug_descriptor.icm for each point mutation separately. Then, you can concatenate output descriptor files into a single one, e.g. descriptors.txt, and run CompoMug-machine-learning-module descriptors.txt

Structure-based module

The module is currently in preparation for release in Version v1.1

Each module has an "example" folder, which contains input files in the required format. All modules were tested on the MacOS operating system.