Interactome CM2D3 (CoMparative Modeling and Data Driven Docking) database
- GENERATE A NETWORK WITH BIANA python generate_network_interactomix.py -radius 0 -taxid 9606 -ttype geneid -trans network.geneid.trans -edge network.edges -node network.nodes -format multi-fields We can use "generate_network_interactomix.py" to generate the whole network of an organism (-radius =0). We get the BIANA MAIN NETWORK: Number of edges: 5260924 Number of nodes: 58200
Also generate the network translated to protein sequence so we can get protein protein interactions and their relative fasta sequences.
python generate_network_interactomix.py -radius 0 -taxid 9606 -ttype proteinsequence d -trans network.proteinsequence.trans -edge network.edges -node network.nodes -format multi-fields
- FILTER THE NETWORK Select only the interactions that have been validated by at least methods of this file python filter_network.py -n network.edges -rmethod restrictions.txt
30 cross-linking study 397 two hybrid array 1112 two hybrid prey pooling approach 18 two hybrid 401 biochemical 1356 validated two hybrid 1313 proximity labelling technology 398 two hybrid pooling approach 415 enzymatic study 89 protein array 96 pull down
We obtain a network of 1739568
- SELECT INTERACTIONS VALIDATED BY AT LEAST 3 METHODS: We use the script: python 3filter.py
We obtain 37419 interactions
- TRANSLATE THE NETWORK python ../NetworkAnalysis/translate_network.py -trans network.uniprotentry.trans -in network.3filtered.edges -on network_uniprot_entry.trans -if multi-fields -of multi-fields
We obtain 37241 translated interactions
- GET PPI AND FASTA FILES We can use the script "get_ppi_and_fasta_from_network.py" to get a file of PPIs and a FASTA file from a network. python get_ppi_and_fasta_from_network.py -transps -transhead <translation file from BIANA IDs to another type of ID (optional)> -in <edges input file (not translated)> -if -oppi -ofasta
python get_ppi_and_fasta_from_network.py -transps ../genomewide/network.proteinsequence.trans -transhead ../genomewide/network.uniprotentry.trans -in ../genomewide/network.3filtered.edges -if multi-fields -oppi prova.ppi -ofasta prova.fasta
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COMPARATIVE MODELING on the cluster with MODPIN python scripts/modppi.py -seq example/3filter.trans.nodes.fasta -ppi example/output.txt -o example/TMP -d ./dummy -v --hydrogens -skip -force -3did -n 1 --renumerate
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FoldX scoring for models obtained with ModPIN perl ../get_ddg.v1.pl file_name.pdb
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Generate docking models steps: RUN M4T
nohup sh runm4t.sh > nohup.txt &
RUN VD2OCK
nohup sh run_vdock_nr.sh > nohup.txt &
- AlphaFold Download alphafold database for humans: folder of all pdb + cif files. Get all the failed models by vdock: simply get the pairs for which the folder is empty. sh failed_models.sh
Run phenix to clean the predicted apha fold model from the regions with low LLD score . ../phenix-1.20.1-4487/phenix_env.sh sh run_phenix.sh
Run vd2ock nohup sh run_vdock_af.sh > nohup.txt &
Obtain vdock models rot vdockmodels.sh