Fragmenstein 0.9.4

Scaffold hopping between bound compounds by stitching them together like a reanimated corpse

Fragmenstein

Scaffold hopping between bound compounds by stitching them together like a reanimated corpse.

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

Requires RDKit

To install for system Python3 on Linux:

sudo apt-get install python3-rdkit librdkit1 rdkit-data

To install for system Python3 on MacOS via Brew:

brew install rdkit --with-python3

To install for Conda Python3

conda install -c conda-forge rdkit

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 latter is formatted variant of "academic user", while the former is the name of a researcher whose names bares an important concept in protein folding. Pyrosetta can be downloaded via a browser from http://www.pyrosetta.org/dow. Or with a variant of the following:

curl -u 👾👾👾:👾👾👾https://graylab.jhu.edu/download/PyRosetta4/archive/release/PyRosetta4.Release.python38.linux/PyRosetta4.Release.python38.linux.release-273.tar.bz2 -o a.tar.bz2
tar -xf a.tar.bz2
cd PyRosetta4.Release.python38.linux
sudo pip3 install .

Fragmenstein and dependencies

Install from pipy

sudo pip3 install fragmenstein

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.

Autogenerated documentations

For more see the source code or the Sphinx converted documentation in read the docs.

Changes

Some changes to the algorithm may happen, see changelog and wip.md for more.

Publications

Fragmenstein is not published, but is used in

• 

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