Fresh off the pages of Therapeutic Advances in Endocrinology and Metabolism comes a warning no self-respecting sweet tooth should ignore.
“Liquorice is not just a candy,” write a team of ten from Chicago. “Life-threatening complications can occur with excess use.” Hold on to your teabags. Liquorice – the Marmite of sweets – is about to become a lot more sinister.
Continue readingHi everyone,
My blogpost this time around is a list of conferences popular with OPIGlets. You are highly likely to see at least one of us attending or presenting at these meetings! I’ve tried to make it as exhaustive as possible (with thanks to Fergus Imrie!), listing conferences in upcoming chronological order.
(Most descriptions are slightly modified snippets taken from the official websites.)
Crystallographic fragment based lead discovery is a now a routine technique, which can sample 1000’s of compounds per week. But how do we identify the most appropriate compounds to screen against our target of interest?
Continue readingThis blog post explores the RDKit function ConstrainedEmbed.
Continue readingI thought I would use this blog to summarise the recent Alchemistry Free Energy workshop in Göttingen, Germany. This event, organised by MPI BPC and BioExcel, brought together academics and industrialists who work with alchemical MM methods to calculate free energies. This was a very successful successor to a similar event organised two year ago in London and now looks to be repeated yearly, alternating between Europe and Boston.
Continue readingIn cheminformatics, there are many possible ways to encode chemical data represented by small molecules and proteins, such as SMILES, fingerprints, chemical descriptors etc. Recently, utilising graph-based methods for machine learning have become more prominent. In this post, we will explore why representing molecules as graphs is a natural and suitable encoding. Continue reading
A brief overview and discussion of: Automatic recognition of ligands in electron density by machine learning .This paper aims to reduce the bias of crystallographers fitting ligands into electron density for protein ligand complexes. The authors train a supervised machine learning model using known ligand sites across the whole protein databank, to produce a classifier that can identify which common ligands could fit to that electron density.
At the last OPIG meeting, I gave a talk about compound selectivity and machine learning approaching to predict whether a compound might be selective. As promised, I hereby provide a list publications I would hand to a beginner in the field of compound selectivity and machine learning. Continue reading
2D projections (t-SNE) of Mol2vec vectors of amino acids (bold arrows). These vectors were obtained by summing the vectors of the Morgan substructures (small arrows) present in the respective molecules (amino acids in the present example). The directions of the vectors provide a visual representation of similarities. Magnitudes reflect importance, i.e. more meaningful words. [Figure from Ref. 1]
A recent publication of one of my former InhibOx-colleagues, Simone Fulle, and her co-workers, Sabrina Jaeger and Samo Turk, shows how we can embed molecular substructures and chemical compounds into a similarly high-dimensional, continuous vectorial representation, which they dubbed “mol2vec“.1 They also released a Python implementation, available on Samo Turk’s GitHub repository.
In early July I attended the the Seventh Joint Sheffield Conference on Cheminformatics. There was a variety of talks with speakers at all stages of their career. I was lucky enough to be invited to speak at the conference, and gave my first conference talk! I have written two blog posts about the conference: part 1 briefly describes a talk that I found interesting and part 2 describes the work I spoke about at the conference.
One of the most interesting parts of the conference was the active twitter presence. #ShefChem16. All of the talks were live tweeted which provided a summary of each talk and also included links to software or references. It also allowed speakers to gain insight and feedback on their talk instantly.
One of the talks I found most interesting presented the Protein-Ligand Interaction Profiler (PLIP). It is a method for the detection of protein-ligand interactions. PLIP is open-source and has a web-based online tool and a command-line tool. Unlike PyMol which only calculates polar contacts, and not the type of interaction, PLIP calculates 8 different types of interactions: hydrogen bonding, hydrophobic, π-π stacking, π-cation interactions, salt bridges, water bridges, halogen bonds, metal complexes. For a given pdb file the interactions are calculated and shown in a publication quality figure shown here.
The display can also be downloaded as a PyMol session so the display can be modified.
This tool is an extremely useful way to calculate protein-ligand interactions and can be used to find the types of interactions formed by the protein-ligand complex.
PLIP can be found here: https://projects.biotec.tu-dresden.de/plip-web/plip/