Prediction of amino acid substitution impact using homologs and a directional substitution matrix.
DeMaSk predictions can be easily obtained for any protein sequence using the web tool. This package can be downloaded for customized usage. See the full documentation for more detailed instructions.
To install, clone the repository or download and unzip. To get Python dependencies and be able to run or import the modules from any directory, install with pip (or pip3 if pip = pip2):
pip install -e DeMaSk/
Unless you're supplying your own aligned homologs, you'll need to have
the blastp program. It can be downloaded as part of BLAST+.
The blastp step requires a formatted sequence database. To use
UniRef90, which demask.princeton.edu uses, download the zipped fasta
file from here, unzip, and use
makeblastdb from BLAST+ to format the database.
To avoid having to specify the location of the blastp binary and
the database for every DeMaSk run, put them in a config file, e.g.:
blastp=/usr/local/ncbi/blast/bin/blastp db=/path/to/database/uniref90.fasta
By default, DeMaSk will look for the config file
DeMaSk/config.ini, which can also contain any other command line
arguments, such as nseqs, threads, and matrix.
Run any of the command modules with -h to see all options, e.g.:
python3 -m demask.homologs -h
Once the demask package is installed, you can run run it from anywhere. If you don't have aligned homologs for your query yet, run the demask.homologs module:
python3 -m demask.homologs -s myquery.fa -o myquery_homologs.a2m
The above command will also produce a file myquery.blast.json containing the intermediate blastp output.
Then, generate fitness impact predictions for all single-residue variants of the query sequence:
python3 -m demask.predict -i myquery_homologs.a2m -o myquery_predictions.txt
The output looks like this:
pos WT var score entropy log2f_var matrix 1 M A -0.3019 1.0666 -21.9207 -0.2641 1 M C -0.3074 1.0666 -21.9207 -0.2713 1 M D -0.4134 1.0666 -21.9207 -0.4100 1 M E -0.4036 1.0666 -21.9207 -0.3972 1 M F -0.2183 1.0666 -4.5455 -0.2707 1 M G -0.3828 1.0666 -21.9207 -0.3700 ...
Corresponding functions can be run in Python code by importing
demask.homologs.find_homologs and demask.predict.run_demask.
DeMaSk comes with a directional substitution matrix computed from a collection of deep mutational scanning datasets, as well as corresponding linear model coefficients. Additional commands are included in case you want to fit the model to a custom matrix, or even calculate a matrix from a custom data collection and then fit the linear model to it.
For example, the default matrix was generated like this:
python3 -m demask.matrix -d DeMaSk/data/datasets -o DeMaSk/data/matrix.txt
Then, linear model coefficients were calculated:
python3 -m demask.fit -d DeMaSk/data/datasets -a DeMaSk/data/alignments -m DeMaSk/data/matrix.txt -o DeMaSk/data/coefficients.txt
Corresponding functions can be run in Python code by importing
demask.matrix.prepare_matrix and demask.fit.fit_model.