In Silico DataBase

Foreword

All small python scripts require environment.yml to be installed to work.

Prior to fragmentation

Prepare a list of identified SMILES to fragment. For the moment, we take the structures from https://doi.org/10.5281/zenodo.6378223 as starting point. To prepare the structures to fragment, just run lotus2cfm.py:

python scripts/lotus2cfm.py

Prepare CFM on cluster

Install

This installation procedure works on the UniGE HPC. This does not mean it will work on another.

First, create a cmf-4 directory:

mkdir cfm-4

Then load requested modules:

module load GCC/6.3.0-2.27 Singularity/2.4.2

Build cfm-4 from its last Docker image:

singularity build cfm-4/cfm.sif docker://wishartlab/cfmid

Test

Pull the previsouly generated smiles list (this command is not generic, it needs to be adapted):

scp smiles4cfm.txt rutza@login2.baobab.hpc.unige.ch:smiles.txt

Also pull last bash commands:

scp scripts/run_cfm_test.sh rutza@login2.baobab.hpc.unige.ch:run_cfm_test.sh
scp scripts/run_cfm.sh rutza@login2.baobab.hpc.unige.ch:run_cfm.sh
scp scripts/run_cfm_neg.sh rutza@login2.baobab.hpc.unige.ch:run_cfm_neg.sh

Create a test file with 10 structures to check if everything works fine:

head smiles.txt -n 10 > test.txt

Create a test directory:

mkdir test

Split the test file asit would be split if real:

split --lines=1 --numeric-suffixes=1 --suffix-length=4 --additional-suffix=.txt test.txt test/test-

Create a testout directory:

mkdir testout

Run run_cfm_test.sh in a sbatch array:

sbatch --array=1-10 run_cfm_test.sh

Run CFM on cluster (full)

Split in subfiles

Depending on the length of your SMILES list, you may want to split it:

First, create a smiles directory:

mkdir smiles

Split the big file:

split --lines=100 --numeric-suffixes=1 --suffix-length=4 --additional-suffix=.txt smiles.txt smiles/smiles-

Count how many entries it generated for the next step

ls smiles/ | wc -l

Positive

Create the posout directory:

mkdir posout

Run run_cfm.sh in a sbatch array (adapt the array length):

sbatch --array=1-2875 run_cfm.sh

Negative

Create the negout directory:

mkdir negout

Run run_cfm_neg.sh in a sbatch array (adapt the array length):

sbatch --array=1-2875 run_cfm_neg.sh

Fetch CFM results

Download CFM fragmentation results from the baobab server (this command is not generic, it needs to be adapted): (We first zip them before for an efficient transfer)

zip -r results.zip ./posout
zip -r results_neg.zip ./negout

scp rutza@login2.baobab.hpc.unige.ch:results.zip ./results.zip
scp rutza@login2.baobab.hpc.unige.ch:results_neg.zip ./results_neg.zip 

unzip results.zip 
unzip results_neg.zip

Treating the raw log files

The output of cfm-predict consist of .log file containing mass spectra, where each fragments are individually labelled and eventually linked to a substrcture. Such information might be usefull later but for now we only want to keep the raw ms data: If you want to merge the three different energies you can choose between 'max','mean', and 'sum' for the moment.

python scripts/log2mgf.py results/ log sum
python scripts/log2mgf.py results_neg/ log sum

Populating the mgf headers

Preparation of the headers

We need to prepare and adducted table containing the protonated and deprotonated masses, run prepare_headers.py:

python scripts/prepare_headers.py smiles4cfm.txt s+ adducted.tsv YOUR_SMILES_COLUMN_NAME YOUR_SHORT_INCHIKEY_COLUMN_NAME

Addition of the metadata to the individual mgf headers

We can now populate each mgf with its corresponding metadata, run populate_headers.py:

python scripts/populate_headers.py adducted.tsv results/ positive
python scripts/populate_headers.py adducted.tsv results_neg/ negative

Generating the final spectral file

We concatenate each documented mgf files to a single spectral mgf file, run concat.sh:

bash scripts/concat.sh ./results isdb_pos.mgf
bash scripts/concat.sh ./results_neg isdb_neg.mgf

Listing fragmented entries

For multiple reasons, some entries might not be fragmented. We here list the ones correctly fragmented to facilitate further investigation.

find ./results -type f -name '*.mgf' | sed 's!.*/!!' | sed 's!.mgf!!' > list_fragmented_pos.txt
find ./results_neg -type f -name '*.mgf' | sed 's!.*/!!' | sed 's!.mgf!!' > list_fragmented_neg.txt

Results

Can be found at:

https://zenodo.org/record/6939173