GlobalChem Protein

GlobalChem has the concept of a protein into a 1-Dimensional format that can be used later on for protein-smiles to protein-ligand interaction of lexical strings. This way we can perhaps make casual inference between sets of ligands and sets of proteins.

Import the Package

from global_chem_extensions import GlobalChemExtensions

bioinformatics = GlobalChemExtensions().bioinformatics()

Initialize a GlobalChem Protein

You can initialize a GlobalChem Protein with a variety of ways, fetching a pdb or passing in a pdb file as as available in the Biopandas package or just passing in a peptide sequence.

http://rasbt.github.io/biopandasrasbt.github.io

Code

gc_protein = bioinformatics.initialize_globalchem_protein(
        peptide_sequence='AAAA',
)

Output

Convert to SMILES

Convert a protein to a smiles string where you can mark the carbonyl carbon or the nitrogen backbone as ionizable centers atom class of 1 or 2.

Code

smiles_protein = gc_protein.convert_to_smiles(
    mark_nitrogen_backbone=False,
    mark_carbonyl_carbon_backbone = False,
)
print (smiles_protein)

Output

Convert to SMARTS

Convert a protein to a smarts string where you can mark the carbonyl carbon or the nitrogen backbone as atom class of 1 or 2.

Code

smarts_protein = gc_protein.convert_to_smarts(
    mark_nitrogen_backbone=False,
    mark_carbonyl_carbon_backbone = False,
)
print (smarts_protein)

Output

Convert Ligand to SMILES

If the PDB has a ligand attached we convert the ligand to SMILES:

Code

smiles_molecule = gc_protein.convert_ligand_to_smiles()

Output

Filter Criteria to Bostrom's Algorithm

A useful criteria for medicinal chemists is to separate ligands out of pdb files and determine if the ligand is suitable for the drug binding. The criteria and the code can be found below:

1.)  80 < molecular weight (Da) < 750
2.)  10 < number of nonhydrogen atoms < 70
3.)  Must not contain atoms of types other than
     H, C, O, N, F, P, S, Cl, Br, or I
4.)  must contain at least one
     non-carbon/non-hydrogen atom
5.)  must not contain two or more phosphorus atoms
6.)  must not have more than 10 rotatable bonds
7.)  must not be a nucleic acid
8.)  must not be composed only from
     non-lead-like PDB-HET-groupsb
9.)  must not be covalently bound
10.) must not have protein contacts from the crystal
     packing environment in less than 3 Å distance
 11.) must have contacts with protein in less than
      7Å distance

Code

pdb_ids = ['5tc0', '4EHM',]

for pdb in pdb_ids:
    gc_protein = gce.initialize_globalchem_protein(
        pdb_id='5tc0',
    )

    criteria_met = gc_protein.determine_bostrom_ligand_criteria(verbose=True)
    ligand_smiles = gc_protein.ligand_smiles

    print ("Ligand: %s, Criteria Met: %s" % (ligand_smiles, criteria_met))

Output

Reference

1.) Boström, Jonas, et al. “Do Structurally Similar Ligands Bind in a Similar Fashion?” Journal of Medicinal Chemistry, vol. 49, no. 23, Nov. 2006, pp. 6716–25.