Recently I was dealing with a set of compounds with known target activities from the ChEMBL database, and I wanted to find out which of them also had PDB crystal structures in complex with that target.
Referencing this manually is very easy for cases where we are interested in 2-3 compounds, but for any larger number, using the ChEMBL and PDB web services greatly reduces the number of clicks.
Continue readingDetecting intermolecular interactions is often one of the first steps when assessing the binding mode of a ligand. This usually involves the human researcher opening up a molecular viewer and checking the orientations of the ligand and protein functional groups, sometimes aided by the viewer’s own interaction detecting functionality. For looking at single digit numbers of structures, this approach works fairly well, especially as more experienced researchers can spot cases where the automated interaction detection has failed. When analysing tens or hundreds of binding sites, however, an automated way of detecting and recording interaction information for downstream processing is needed. When I had to do this recently, I used an open-source Python module called ODDT (Open Drug Discovery Toolkit, its full documentation can be found here).
My use case was fairly standard: starting with a list of holo protein structures as pdb files and their corresponding ligands in .sdf format, I wanted to detect any hydrogen bonds between a ligand and its native protein crystal structure. Specifically, I needed the number and name of the the interacting residue, its chain ID, and the name of the protein atom involved in the interaction. A general example on how to do this can be found in the ODDT documentation. Below, I show how I have used the code on PDB structure 1a9u.
Continue readingHave you ever read a paper just because it had a funny, endearing, or utterly bizarre graphical abstract? Ever since a colleague showed me the ‘Graphical abstracts that I gone and found’ Facebook page, I have definitely come across a few, and I thought I would share some of my favourite ones below. If you enjoy this kind of thing, I strongly suggest visiting their page for more – it makes for a wonderful distraction from pretty much anything. Continue reading
Fragment-based drug discovery (FBDD) is based on the idea that using small (< 300 Da), highly soluble compounds to screen against a target will give higher hit rates and sample chemical space more efficiently compared to screens using larger, drug-like compounds.
If you do most of your work in Jupyter notebooks, it can be convenient to have a quick visualisation tool to view the results of your latest computation from within the notebook, without having to flick between the notebook and your favourite molecule viewer.
I have recently started using NGLview, an IPython/Jupyter widget, to do this. It is based on the NGL viewer, an embeddable webapp for macromolecular visualisation. The nglvew module documentation can be found here, and in addition to handling the usual formats for molecular structure (.pdb, .mol2, .sdf, .pqr, etc.) and map density(.ccp4 and more), it supports visualising trajectories and even making movies.
Continue reading“Hotspot” is one of those extremely versatile words, similar to “model” and “buffer”, which can mean a variety of things depending on context. According to Merriam-Webster, a hotspot is “a place of more than usual interest, activity, or popularity”. This is the most general definition of the concept I could find in a quick search, and the one I find closest in spirit to the way hotspots are perceived in a structural biology context. What this blog post is definitely not about are hotspots as “areas of political, military, or civil unrest” (my experience with them has so far been mostly peaceful), or anything to do with geology, WiFi connections, or forest fires.
However, even within the context of structural biology and structure-based drug design, the word “hotspot” has multiple meanings. In this blog post, I will try to summarise the main ones I have come across, the (sometimes subtle) differences between them, and provide a few useful papers to serve as an entry point for interested readers. Continue reading
In order to know how a protein works, we generally want to know its 3-dimensional structure. We then can either try to solve it ourselves (which requires considerable time, skill, and resources), or look for it in the Protein Data Bank, in case it has already been solved. The vast majority of structures in the Protein Data Bank (PDB) are solved through protein crystallography, and represent a “snapshot” of the conformational space available to our protein of interest. Continue reading
The idea of bacterial superpowers is perhaps most associated with superbugs: the terrifying, drug-resistant bacterial strains that appear ever more frequently in news reports. While the notion of a world where antibiotics no longer work is chilling, this blog post will focus on a more positive aspect of the bacterial domain.
One of the more “niche” bacterial superpowers is magnetotaxis: the ability of certain bacteria to align their motion to the Earth’s magnetic field. This phenomenon was first reported in 1963 by Salvatore Bellini in the University of Pavia. While observing bog sediment under the microscope, he noticed a set of bacteria orienting themselves in the same direction: towards the Earth’s magnetic North pole. He dubbed these gram-negative bacteria “magnetosensitive”, or “batteri magnetosensibili”, but the discovery went largely unnoticed by the international scientific community [1]. The name “magnetotactic bacteria” (MTB) was introduced about a decade later, when Richard Blakemore reported the same phenomenon for bacteria found in marine sediments [2]. Through transmission electron microscopy, Blakemore was also able to capture the cellular feature that gives MTBs their unusual abilities: a rod-like structure of membrane-bound, iron-rich inorganic crystals, called magnetosomes. Later it was revealed that this structure is supported by a dedicated cytoskeletal system, which keeps it rod-shaped and prevents the aggregation of magnetosomes [4]. Magnetotaxis then results from the combination of the passive alignment of the cell to the Earth’s magnetic field, and flagellar motion. Continue reading