Fragmenstein

Fragmenstein: Merging, linking and placing compounds by stitching bound compounds together like a reanimated corpse.

Name	Colab Link	PyRosetta	Description
Pipeline		✔	Given a template and a some hits, merge them and place the most similar purchasable analogues from Enamine REAL
Light		❌	Generate molecules and see how they merge and how a placed compound fairs

For manuscript data see manuscript data repository For authors see Authors

Stitched molecules

Fragmenstein can perform two different tasks.

Combine hits
Place a given followup molecule (SMILES) based on series of hits

Like Frankenstein's creation it may violate the laws of chemistry. Trigonal planar topologies may be tetrahedral, bonds unnaturally long etc. This monstrosity is therefore then energy minimised with strong constraints within the protein.

Classes

There are four main classes —named after characters from the Fragmenstein book and movies:

Monster makes the stitched together molecules indepent of the protein — documentation
Igor uses PyRosetta to minimise in the protein the fragmenstein monster followup — documentation
Victor is a pipeline that calls the parts, with several features, such as warhead switching —documentation
Laboratory does all the combinatorial operations with Victor (specific case)

NB. In the absence of pyrosetta (which requires an academic licence), all bar Igor work.

Additionally, there are a few minor classes.

One of these is mRMSD, a multiple RMSD variant which does not superpose/align and bases which atoms to use on coordinates —documentation

The class Walton performs geometric manipulations of compounds, to set them up to demonstrate features of Fragmenstein (like captain Walton, it does not partake in the plot, but is key to the narration)

There are two module hosted elsewhere:

Rectifier from molecular_rectifier is a class that corrects mistakes in the molecule automatically merged by Monster.
Params from rdkit to params module parameterises the ligands

Combine

It can also merge and link fragment hits by itself and find the best scoring mergers. For details about linking see linking notes. It uses the same overlapping position clustering, but also has a decent amount of impossible/uncommon chemistry prevention.

Monster:

from fragmenstein import Monster
monster = Monster(hits=[hits_a, hit_b])
monster.combine()
monster.positioned_mol #: RDKit.Chem.Mol

Victor:

from fragmenstein import Victor
import pyrosetta
pyrosetta.init( extra_options='-no_optH false -mute all -ex1 -ex2 -ignore_unrecognized_res false -load_PDB_components false -ignore_waters false')

victor = Victor(hits=[hits_a, hit_b], 
                pdb_filename='foo.pdb',  # or pdb_block='ATOM 1 MET ...'
                covalent_resi=1) # if not covalent, just put the first residue or something.
victor.combine()
victor.minimized_mol

The PyRosetta init step can be done with the helper function:

Igor.init_pyrosetta()

The two seem similar, but Victor places with Monster and minimises with Igor. As a result it has energy scores

victor.ddG

Fragmenstein is not really a docking algorithm as it does not find the pose with the lowest energy within a given volume. Consequently, it is a method to find how faithful is a given followup to the hits provided. Hence the minimised pose should be assessed by the RMSD metric or similar and the ∆∆G score used solely as a cutoff —lower than zero.

For a large number of combination:

from fragmenstein import Laboratory

lab = Laboratory(pdbblock=pdbblock, covalent_resi=None)
combinations:pd.DataFrame = lab.combine(hits, n_cores=28)

Place

Here is an interactive example of placed molecules.

It is rather tolerant to erroneous/excessive submissions (by automatically excluding them) and can energy minimise strained conformations.

Three mapping approaches were tested, but the key is that hits are pairwise mapped to each other by means of one-to-one atom matching based upon position as opposed to similarity which is easily led astray. For example, note here that the benzene and the pyridine rings overlap, not the two pyridine rings:

Examples

Monster:

from fragmenstein import Monster
monster = Monster(hits=[hits_a, hit_b])
monster.place_smiles('CCO')
monster.positioned_mol

Victor:

from fragmenstein import Victor, Igor
    Igor.init_pyrosetta()
    victor = Victor(hits=[hits_a, hit_b], pdb_filename='foo.pdb')
    victor.place('CCO')
    victor.minimized_mol

For a lengthier example see example notes or documentation.

Demo data

Some demo data is provided in the demo submodule.

from fragmenstein.demo import MPro, Mac1

pdbblock: str = Mac1.get_template()
for hitname in Mac1.get_hit_list():
    Mac1.get_hit(hitname)
    ...

To use SAR-COV-2 MPro as a test bed, the following may be helpful:

fragmenstein.MProVictor, a derived class (of Victor), with various presents specific for MPro.
fragemenstein.get_mpro_template(), returns the PDB block (str) of MPro
fragemenstein.get_mpro_molblock(xnumber), returns the mol block (str) of a MPro hit from Fragalysis
fragemenstein.get_mpro_mol(xnumber), as above but returns a Chem.Mol instance.

Other features

Installation

Fragmenstein and dependencies

Python 3.6 or above. Install from pipy

python -m pip install fragmenstein

Requires Pyrosetta

⚠️ PyRosetta no longer runs on CentOS 7 due to old kernel headers (cf. blog post).

Pyrosetta requires a password to be downloaded (academic licence) obtained by https://els2.comotion.uw.edu/product/pyrosetta. This is a different licence from the Rosetta one. The username of the Rosetta binaries is formatted variant of "academic user", while the PyRosetta is the name of a researcher whose name bares an important concept in protein folding, like boltzmann + constant (but is not that). Pyrosetta can be downloaded via a browser from http://www.pyrosetta.org/dow. Or in the terminal via:

curl -u 👾👾👾:👾👾👾https://graylab.jhu.edu/download/PyRosetta4/archive/release/PyRosetta4.Release.python38.linux/PyRosetta4.Release.python38.linux.release-NNN.tar.bz2 -o a.tar.bz2
tar -xf a.tar.bz2
cd PyRosetta4.Release.python38.linux
sudo pip3 install .

or using conda

or using install_pyrosetta from the pyrosetta-help package.

pip install pyrosetta-help
PYROSETTA_USERNAME=👾👾👾 PYROSETTA_PASSWORD=👾👾👾 install_pyrosetta

The PYROSETTA_USERNAME and PYROSETTA_PASSWORD are environment variables, which should not be shared publicly (i.e. store them as private environmental variables in your target application).

Origin

See Fragmenstein and COVID moonshot.

Fragmenstein was created to see how reasonable are the molecules of fragment mergers submitted in the COVID moonshot project, because after all the underlying method is fragment based screening. This dataset has some unique peculiarities that potentially are not encountered in other projects.

Command line interface

The strength of Fragmenstein is as a python module, but there is a command line interface.

fragmenstein monster combine -i hit1.mol hit2.mol >> combo.mol
fragmenstein monster place -i hit1.mol hit2.mol -s 'CCO' >> placed.mol
fragmenstein victor combine -i hit1.mol hit2.mol -t protein.pdb -o output >> combo.mol
fragmenstein victor combine -i hit1.mol hit2.mol -s 'NCO' -n molname -t protein.pdb -o output >> placed.mol
fragmenstein laboratory combine -i hits.sdf -o output -d output.csv -s output.sdf -c 24

Authors

Author	Role	Homepage	Department
Matteo Ferla	main developer	WCHG	Wellcome Centre for Human Genetics, University of Oxford
Rubén Sánchez-Garcia	discussion/code	Stats	Department of Statistics, University of Oxford
Rachael Skyner	discussion/editing/code
Stefan Gahbauer	discussion
Jenny Taylor	PI	WCHG	Wellcome Centre for Human Genetics, University of Oxford
Brian Marsden	PI	CMD	CMD, Oxford
Charlotte Deane	PI
Frank von Delft	PI	CMD	Diamond Lightsource / CMD, Oxford

matteoferla/Fragmenstein

matteoferla

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