Fragmenstein 0.10

Merging, linking and placing compounds by stitching them together like a reanimated corpse

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 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 three 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

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 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.

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
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')
victor.place('CCO')
victor.minimized_mol

For a lengthier example see example notes.

MPro example

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

• Covalent hits
• Logging

Installation

Fragmenstein and dependencies

Install from pipy

sudo pip3 install fragmenstein

Requires Pyrosetta

Pyrosetta requires a password to be downloaded (acamedic 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 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.

Authors

Author	Role	Homepage	Department	Badges
Matteo Ferla	main developer	WCHG	Wellcome Centre for Human Genetics, University of Oxford
Rubén Sánchez-Garcia	developer	Stats	Department of Statistics, University of Oxford
Jenny Taylor	PI	WCHG	Wellcome Centre for Human Genetics, University of Oxford
Frank von Delft	PI	CMD	Diamond Lightsource / CMD, Oxford
Brian Marsden	PI	CMD	CMD, Oxford

See Also

• ChemRXiv preprint — TBA
• Steph Wills's fragment network merges repo contains useful filtering algorithms
• Fragmenstein is used in Schuller et. al. 2021
• Figures for the upcoming manuscript are in a separate repo
• The conversion of a rdkit Chem.Mol that cannot be sanitised to an analogue that can is done by the molecular rectifier package
• The conversion of a rdkit Chem.Mol to a PyRosetta residue type (a "params file") is done via the rdkit-to-params package
• The pipeline demo colab notebook uses Brian Shoichet's SmallWorld webapp, interfaced via its API in Python
• The playground demo colab notebook features a JSME widget — JSME is a popular JS only molecular editor