Chemoinformatics uses a curious jumble of terms from thermodynamics, wet-lab techniques and statistical terminology, which is at its most jarring, it could be argued, in machine learning. In some datasets one often sees pIC50, pEC50, pKi and pKD, in discussion sections a medchemist may talk casually of entropy, whereas in the world of molecular mechanics everything is internal energy. Herein I hope to address some common misconceptions and unify these concepts.
Continue readingCategory Archives: Protein-Ligand Docking
A simple criterion can conceal a multitude of chemical and structural sins
We’ve been investigating deep learning-based protein-ligand docking methods which often claim to be able to generate ligand binding modes within 2Å RMSD of the experimental one. We found, however, this simple criterion can conceal a multitude of chemical and structural sins…
DeepDock attempted to generate the ligand binding mode from PDB ID 1t9b
(light blue carbons, left), but gave pretzeled rings instead (white carbons, right).
9th Joint Sheffield Conference on Cheminformatics
Over the next few days, researchers from around the world will be gathering in Sheffield for the 9th Joint Sheffield Conference on Cheminformatics. As one of the organizers (wearing my Molecular Graphics and Modeling Society ‘hat’), I can say we have an exciting array of speakers and sessions:
- De Novo Design
- Open Science
- Chemical Space
- Physics-based Modelling
- Machine Learning
- Property Prediction
- Virtual Screening
- Case Studies
- Molecular Representations
It has traditionally taken place every three years, but despite the global pandemic it is returning this year, once again in person in the excellent conference facilities at The Edge. You can download the full programme in iCal format, and here is the conference calendar:
Continue readingLigands of CASF-2016
CASF-2016 is a commonly used benchmark for docking tools. Unfortunately, some of the provided ligand files cannot be loaded using RDKit (version 2022.09.1) but there is an easy remedy.
Continue readingMeeko: Docking straight from SMILES string
When docking, using software like AutoDock Vina, you must prepare your ligand by protonating the molecule, generating 3D coordinates, and converting it to a specific file format (in the case of Vina, PDBQT). Docking software typically needs the protein and ligand file inputs to be written on disk. This is limiting as generating 10,000s of files for a large virtual screen can be annoying and hinder the speed at which you dock.
Fortunately, the Forli group in Scripps Research have developed a Python package, Meeko, to prepare ligands directly from SMILES or other molecule formats for docking to AutoDock 4 or Vina, without writing any files to disk. This means you can dock directly from a single file containing all the SMILES of the ligands you are investigating!
Continue reading5th Artificial Intelligence in Chemistry Symposium
The lineup for the Royal Society of Chemistry’s 5th “Artificial Intelligence in Chemistry” Symposium (Thursday-Friday, 1st-2nd September 2022) is now complete for both oral and poster presentations. It really is a fantastic selection of topics and speakers and it is clear this event is now a highlight of the scientific calendar. Our very own Prof. Charlotte M. Deane, MBE will be giving a keynote.
It marks a return to in-person meetings: it will be held at Churchill College, Cambridge, with a conference dinner at Trinity Hall.
More details are here: https://www.rscbmcs.org/events/aichem22/.
Registration for in person attendance is open until Monday 29th August 17:00 (BST).
It is also possible to register for virtual attendance; the meeting will be broadcast on Zoom.
Paper review: “EquiBind”
Molecular docking helps us understand how small-molecules interact with proteins. This is especially useful in early drug development stages such as target identification and compound screening. Quick and accurate docking software allows researchers to focus their attention on a smaller set of lead molecules for further testing. Traditionally, docking software has employed first principles from physics and chemistry. Recently, deep learning has become all the rage for molecular docking, maybe motivated by the successful application of deep learning to molecular folding.
Method
EquiBind is a deep learning unconstrained docking method which models a fixed receptor and a ligand with selected rotatable bonds. It predicts the binding pocket and the ligand’s conformation within the pocket in one go. Under the hood, EquiBind employs two great ideas from a recent ICLR 2022 Paper: a SE3-invariant graph neural network based architecture and the idea to generate fixed sets of matching key points to define a rotation and translation between receptor and ligand. In addition, the authors innovate a fast method to project a deformed ligand onto the space spanned by the rotatable bonds of a pre-generated ligand conformation.
Continue readingHow to prepare a molecule for RDKit
RDKit is very fussy when it comes to inputs in SDF format. Using the SDMolSupplier, we get a significant rate of failure even on curated datasets such as the PDBBind refined set. Pymol has no such scruples, and with that, I present a function which has proved invaluable to me over the course of my DPhil. For reasons I have never bothered to explore, using pymol to convert from sdf, into mol2 and back to sdf format again (adding in missing hydrogens along the way) will almost always make a molecule safe to import using RDKit:
from pathlib import Path
from pymol import cmd
def py_mollify(sdf, overwrite=False):
"""Use pymol to sanitise an SDF file for use in RDKit.
Arguments:
sdf: location of faulty sdf file
overwrite: whether or not to overwrite the original sdf. If False,
a new file will be written in the form <sdf_fname>_pymol.sdf
Returns:
Original sdf filename if overwrite == False, else the filename of the
sanitised output.
"""
sdf = Path(sdf).expanduser().resolve()
mol2_fname = str(sdf).replace('.sdf', '_pymol.mol2')
new_sdf_fname = sdf if overwrite else str(sdf).replace('.sdf', '_pymol.sdf')
cmd.load(str(sdf))
cmd.h_add('all')
cmd.save(mol2_fname)
cmd.reinitialize()
cmd.load(mol2_fname)
cmd.save(str(new_sdf_fname))
return new_sdf_fname
Post-processing for molecular docking: Assigning the correct bond order using RDKit.
AutoDock4 and AutoDock Vina are the most commonly used open-source software for protein-ligand docking. However, they both rely on a derivative of the “PDB” (Protein Data Base) file format: the “PDBQT” file (Protein Data Bank, Partial Charge (Q), & Atom Type (T)). In addition to the information contained in normal PDB files, PDBQT files have an additional column that lists the partial charge (Q) and the assigned AutoDock atom type (T) for each atom in the molecule. AutoDock atom types offer a more granular differentiation between atoms such as listing aliphatic carbons and aromatic carbons as separate AutoDock atom types.
The biggest drawback about the PDBQT format is that it does not encode for the bond order in molecules explicitly. Instead, the bond order is inferred based on the atom type, distance and angle to nearby atoms in the molecule. For normal sp3 carbons and molecules with mostly single bonds this system works fine, however, for more complex structures containing for example aromatic rings, conjugated systems and hypervalent atoms such as sulphur, the bond order is often not displayed correctly. This leads to issues downstream in the screening pipeline when molecules suddenly change their bond order or have to be discarded after docking because of impossible bond orders.
The solution to this problem is included in RDKit: The AssignBondOrdersFromTemplate function. All you need to do is load the original molecule used for docking as a template molecule and the docked pose PDBQT file into RDKIT as a PDB, without the bond order information. Then assign the original bond order from your template molecule. The following code snippet covers the necessary functions and should help you build a more accurate and reproducible protein-ligand docking pipeline:
#import RDKit AllChem from rdkit import Chem from rdkit.Chem import AllChem #load original molecule from smiles SMILES_STRING = "CCCCCCCCC" #the smiles string of your ligand template = Chem.MolFromSmiles(SMILES_STRING) #load the docked pose as a PDB file loc_of_docked_pose = "docked_pose_mol.pdb" #file location of the docked pose converted to PDB file docked_pose = AllChem.MolFromPDBFile(loc_of_docked_pose) #Assign the bond order to force correct valence newMol = AllChem.AssignBondOrdersFromTemplate(template, docked_pose) #Add Hydrogens if desired. "addCoords = True" makes sure the hydrogens are added in 3D. This does not take pH/pKa into account. newMol_H = Chem.AddHs(newMol, addCoords=True) #save your new correct molecule as a sdf file that encodes for bond orders correctly output_loc = "docked_pose_assigned_bond_order.sdf" #output file name Chem.MolToMolFile(newMol_H, output_loc)
Using normalized SuCOS scores.
If you are working in cheminformatics or utilise protein-ligand docking, then you should be aware of the SuCOS score, an open-source shape and chemical feature overlap metric designed by a former member of OPIG: Susan Leung.
The metric compares the 3D conformers of two ligands based on their shape overlap as well as their chemical feature overlap using the RDKit toolkit. Leung et al. show that SuCOS is able to select fewer false positives and false negatives when doing re-docking studies than other scoring metrics such as RMSD or Protein Ligand Interaction Fingerprints (PLIF) similarity scores and performs better at differentiating actives from decoys when tested on the DUD-E dataset.
Most importantly, SuCOS was designed with fragment based drug discovery in focus, where a smaller fragment ligand is elaborated or combined with other fragments to create a larger molecule, with hopefully stronger binding affinity. Unlike for example RMSD, SuCOS is able to quickly calculate an overlap score between a small fragment and a larger molecule, giving chemists an idea on how the fragment elaboration might interact with the protein. However, the original SuCOS algorithm was not normalized and could create scores of > 1 for some cases.
I’ve uploaded a normalised version of the original SuCOS algorithm as a GitHub fork of Susan’s original repository. You can find the normalised SuCOS algorithm here.
Hopefully this is helpful for anyone using the SuCOS algorithm and for all docking enthusiasts who are interested in an alternative way to evaluate their docked poses.