target.pdb preprocessing #

Assuming we have a target protein target.pdb, and we want to create a binder to target.pdb. The first step is preprocessing target.pdb. Generally spearking, there are two steps we need to think of.

Truncating target.pdb #

Truncating target protein helps a lot in reducing the stress of computing. For example, we could remove AAs with very low pLDDT or AAs who are far away from the place we want binders to bind with.

def residue_remove(pdbfile, remove_ids, outputName):
    """
    Remove residues based on positions.
    Assumption: pdbfile only has A chain
    """
    residue_to_remove = []

    pdb_io = PDB.PDBIO()
    pdb_parser = PDB.PDBParser()
    structure = pdb_parser.get_structure(" ", pdbfile)

    model = structure[0]
    chain = model["A"] # assuming pdbfile only has A chain

    for residue in chain:
        id = residue.id
        if id[1] in remove_ids: 
            residue_to_remove.append(residue.id)

    for residue in residue_to_remove:
        chain.detach_child(residue)
    pdb_io.set_structure(structure)
    pdb_io.save(outputName)
    return 

# truncating ids
truncating_ids = [1,2,3]

# remove some AAs
residue_remove(pdbfile="target.pdb", remove_ids=truncating_ids, outputName="truncation.pdb")

This step can also be achieved in PyMol.

How many AAs we can remove from our target protein?
At the very beginning, I was thinking removing all useless AAs. For example, I was creating a binder to a transmembrane protein, and I only left the part of protein which is located at the outside of the whole protein. Unfornutately, this part of protein I aim to create binders to bind with is too “thin” to lead design binders correctly.

If the “target” protein you feed into the RFdiffusion model is too small, ppi.hotspot_res argument becomes ineffective. Let’s imagine the “target” protein we feed into the RFdiffusion model is a paper, and ppi.hotspot_res is located somewhere in this paper. Since this “target” protein (the paper) is too thin, RFdiffusion model feels confused about which side (above or below the paper ) the ppi.hotspot_res points to.

My suggestion would be to try some different trunctations and pick one that can approcimately balance the computation stress and correct leading of bidner design.

Reset AAs’ residues numbers in truncation.pdb #

Normally, after removing some AAs, the residues number in truncation.pdb becomes discontinuous, and we want to concatenate them again and update their residue number with continuous numbers.

def update_resnum(pdbfile):
    """
    🌟 CHANGE RESIDUE NUMBER
    Assumption: pdbfile only has A chain
    """
    pdb_io = PDB.PDBIO()
    pdb_parser = PDB.PDBParser()
    structure = pdb_parser.get_structure(" ", pdbfile)

    model = structure[0]
    chain = model["A"]
    print(len([i for i in chain.get_residues()]))
    new_resnums = list(range(1, 1+len([i for i in chain.get_residues()])))

    for i, residue in enumerate(chain.get_residues()):
        res_id = list(residue.id)
        res_id[1] = new_resnums[i]
        residue.id = tuple(res_id)

    pdb_io.set_structure(structure)
    pdb_io.save(pdbfile.split('.pdb')[0] + '_update_resnum.pdb')
    return