ASMC combines (i) homology modeling of family members (MODELLER), (ii) ligand-binding pocket search (P2RANK), (iii) structural alignment of modeled active sites (USalign) and (iv) density-based spatial clustering of obtained alignments (DBSCAN) in a single command line. Clustering step can be carried out on either structural or sequence alignment.

Download the latest GitHub release to obtain the code: https://github.com/labgem/ASMC/releases

• Python ≥ 3.8
• biopython ≥ 1.81
• numpy
• scikit-learn
• pyyaml
• pillow
• weblogo

You can install the python dependencies with pip, conda or mamba with the following commands and the files given in the releases:

pip

pip install -r requirements.txt

conda

conda env create -n env_name -f env.yml

mamba

mamba env create -n env_name -f env.yml

Installation via conda and mamba includes the MODELLER installation, but you still need to request the license key.

• P2RANK - for ligand-binding pocket detection (https://github.com/rdk/p2rank)
• MODELLER - for homology modeling (https://salilab.org/modeller/)
• USalign - for structural alignment (https://zhanggroup.org/US-align/)

Please follow the links above and the instructions given by their authors.

In ASMC/resources, add a file exactly named config_asmc.yml. This file should contain 3 information:

• the path to the ASMC/resources/AA_distances.tsv
• the path of P2RANK executable
• the path of USalign executable

Example:

distances: "/home/User/ASMC/resources/AA_distances.tsv"
usalign: "/home/User/bin/USALIGN/USalign"
p2rank: "/home/User/bin/p2rank_2.4.1/prank"

The keys should be identical to this example.

Run ASMC in a blind way (unknown active site) using a multi fasta file that should contain at least 100 sequences for clustering to be sufficiently relevant.

python ASMC/run_asmc.py --log run_asmc.log --threads 6 -r reference_file -s sequences.fasta

reference_file should contains the path to the reference(s) structure(s), e.g:

<path>/RefA.pdb
<path>/RefB.pdb

NB: For more details, see the wiki