In crystalography we are often faced with the question of how well a part of our model fits the data. Now crystalography has well developed probability models for the reflection amplitudes given then entire fitted model, but these do not provide a metric for “how much of the ligand is inside the blob”. This is because the reflection based models are inherently global.
Continue readingProCare [1] is a package developed at the University of Strasbourg which is able to align and score the similarity of protein cavities. The aim is to find ligand binding sites between different proteins that are similar enough to bind the same ligand. The method used in ProCare is designed to look particularly at fragment (~⅓ size of a druglike ligand) binding sites. The aim is to predict potential fragment hits by comparing the cavities of the targets.
Continue readingIf 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 readingWe are happy to announce the release of CoV-AbDab, our database tracking all coronavirus binding antibodies and nanobodies with molecular-level metadata. The database can be searched and downloaded here: http://opig.stats.ox.ac.uk/webapps/coronavirus
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Hello friends of OPIG,
From my last blopig blog post [link: https://www.blopig.com/blog/2019/10/comparative-analysis-of-the-cdr-loops-of-antigen-receptors/], I summarised our findings that TCR CDRs are more flexible than their antibody counterparts. Because of this observation, we believe that it is more appropriate to represent TCR binding sites using an ensemble of conformations.
Continue readingA zoonosis is an infectious disease that has jumped from a non-human animal to humans.
The coronavirus disease 2019 (COVID-19) is one such zoonosis, and is caused by severe acute respiratory syndrome coronavirus 2 (SARS coronavirus 2, SARS-CoV-2, or 2019-nCoV). This is very similar to the SARS virus that emerged in 2003. Its recent emergence has resulted in a WHO-declared public health emergency of international concern.
Continue readingSlightly belated, these are our thoughts on the MABRA workshop at the University of Surrey, which five OPIGlegts attended in January 2020.
I’m so pleased to be able to write about our work on The evolution of contact prediction: evidence that contact selection in statistical contact prediction is changing (Bioinformatics btz816). Contact prediction – the prediction of parts of the amino-acid chain that are close together – has been critical to improving the ability of scientists to predict protein structures over the last decade. Here we look at the properties of these predictions, and what that might mean for their use.
The paper begins with a question. If contact prediction methods are based on statistical properties of sequence alignments, and those alignments are generated in the presence of ecological and physical constraints, what effect do the physical constraints have on the statistical properties of real sequence alignments? More concisely: when we predict contacts, do we predict particularly important contacts?
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