victor.md

Victor

Victor is the overarching class. This has many features and can be rather complicated in its setup: but then a point and click solution that works universally without customisation is a bad solution for molecular modelling.

Combine vs. Place

Like Monster Victor has two main calculation methods: combine and place. Whereas in Monster these are independent of protein neighbourhood, in Victor they are not thanks to Igor.

Victor does the following steps:

• Is given a followup to test and the mol objects of its inspiration and the pdb template file.
• Calls Monster class
• Parameterises the mol
• Generates the constraints
• Calls Igor
• Can do some extras

Class attributes

Core settings controlling its behaviour can be set via class attribute. For example, the warhead conversion methods can be controlled via:

• covalent_definitions (currently defined only for cysteine)
• warhead_definitions

While other include:

• error_to_catch
• constraint_function_type
• work_path

Class methods

Two key class methods are Victor.extract_mols and Victor.extract_mol. Also several methods for covalent operations — see covalent notes

Note that Igor has pyrosetta specific class methods, e.g. downloading electron density for template prep etc.

Example

The following code may have changed. And several of these methods are

Here is a real world usage that uses multiple features:

Import pyrosetta and initialised before everything (applies to Igor too):

import pyrosetta
pyrosetta.init(extra_options='-no_optH false -mute all -ignore_unrecognized_res true -load_PDB_components false')
from fragmenstein import Igor, Monster, Victor
import logging, csv, json
from rdkit import Chem
from rdkit.Chem import AllChem

configure whither to save and whence to load:

Victor.work_path = '../Mpro_fragmenstein'
Victor.enable_stdout(logging.WARNING)
mpro_folder = '/Users/matteo/Coding/rosettaOps/Mpro'  

alternatively Victor.enable_logfile('reanimate.log',logging.DEBUG) (see logging notes).

add extra constraints that are warhead & protein specific. note that the warhead definitions contain preferred names for the connecting atoms and their neighbours

for cname, con in [('chloroacetamide', 'AtomPair  H  145A  OY  1B HARMONIC 2.1 0.2\n'),
                  ('nitrile', 'AtomPair  H  145A  NX  1B HARMONIC 2.1 0.2\n'),
                  ('acrylamide', 'AtomPair  H  143A  OZ  1B HARMONIC 2.1 0.2\n'),
                  ('vinylsulfonamide', 'AtomPair  H  143A  OZ1 1B HARMONIC 2.1 0.2\n')
                  ]:
    Victor.add_constraint_to_warhead(name=cname, constraint=con)

Here is the definition of a nitrile warhead, for example:

{'name': 'nitrile',
'covalent': 'C(=N)*',  # zeroth atom is attached to the rest
'covalent_atomnames': ['CX', 'NX', 'CONN1'],
'noncovalent': 'C(#N)',  # zeroth atom is attached to the rest
'noncovalent_atomnames': ['CX', 'NX']
}

This allows warheads to be mixed and matched.

The choice of the protein template is a bit weak. I plan to experiment with minimisation against averaged electron densities.

def get_best(hit_codes):
    return Victor.closest_hit(pdb_filenames=[f'{mpro_folder}/Mpro-{i}_0/Mpro-{i}_0_bound.pdb' for i in hit_codes],
                        target_resi=145,
                        target_chain='A',
                        target_atomname='SG',
                        ligand_resn='LIG')

There is a change I require to the pose

def pose_fx(pose):
    pose2pdb = pose.pdb_info().pdb2pose
    r = pose2pdb(res=41, chain='A')
    MutateResidue = pyrosetta.rosetta.protocols.simple_moves.MutateResidue
    MutateResidue(target=r, new_res='HIS').apply(pose)

Define all the steps

def reanimate(smiles, name, hit_codes):
    hits = [get_mol(i) for i in hit_codes]
    best_hit = get_best(hit_codes)
    Victor.journal.debug(f'{name} - best hit as starting is {best_hit}')
    apo = best_hit.replace('_bound', '_apo-desolv')
    print(f'reanimate(smiles="{smiles}", name="{name}", hit_codes={hit_codes})')
    reanimator = Victor(hits=hits,
                        pdb_filename=apo,
                        ligand_resn='LIG',
                        ligand_resi='1B',
                        covalent_resn='CYS', covalent_resi='145A',
                        pose_fx = pose_fx
                        )
    reanimator.place(smiles=smiles,
                     extra_constraint='AtomPair  SG  145A  NE2  41A HARMONIC 3.5 0.2\n',
                     long_name=name)
    return reanimator

Read the data and do all warhead combinations if covalent. This data is actually from github.com/postera-ai/COVID_moonshot_submissions. For which there is a method in fragmenstein.mpro.

data = csv.DictReader(open('../COVID_moonshot_submissions/covid_submissions_all_info.csv'))
    
issue = []

for row in data:
    if row['covalent_warhead'] == 'False':
        pass
        reanimate(name = row['CID'], hit_codes = row['fragments'].split(','), smiles=row['SMILES'])
    else:
        print(f'Covalent: {row["CID"]}')
        for category in ('acrylamide', 'chloroacetamide', 'vinylsulfonamide', 'nitrile'):
            if row[category] == 'True':
                combinations = Victor.make_all_warhead_combinations(row['SMILES'], category)
                if combinations is None:
                    issue.append(row["CID"])
                    break
                for c in combinations:
                    reanimate(name = row['CID']+'-'+c, hit_codes = row['fragments'].split(','), smiles=combinations[c])
                break
        else:
            print(f'What is {row["CID"]}')
            issue.append(row["CID"])

Methods for inheritance

The above could have been customised further, by making a class that inherits Victor and defining post_params_step, post_fragmenstein_step, pose_mod_step or post_igor_step, which are empty methods intended to make subclassing Victor easier as these are meant to be overridden —NB pose_mod_step is run if not pose_fx is given.

Coordinate constraint

The coordinate constraint generated for Igor, the minimiser can be changed from HARMONIC (x = mean) to FLAT_HARMONIC (tol = max distance of contributing atoms) and BOUNDED (fixed penalty potential well).

Do note that the reference atom for the constraint is the covalent residue, regardless of whether the ligand is covalent.