In my work, I mainly look at antigen-bound antibodies and this means a lot of analysing interfaces. Specifically, I spend a lot of my time examining the contributions of complementarity-determining regions (CDRs) to antigen binding, but what about antibodies where the framework (FW) region also contributes to binding? Such structures do exist, and these interactions are rarely trivial. As such, a recent preprint I came across where the authors examined the DE loops of antibodies was a great motivator to broaden my horizons!
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What can you do with the OPIG Antibody Suite? v2.0
Since my last blogpost on this topic back in 2018, OPIG has expanded its range of tools for antibody/BCR analysis. Here is an updated summary of the OPIG antibody databases and immunoinformatics tools.
Continue readingAntibody-protein binding and conformational changes
I came across a recent paper on the antibody-protein binding and conformational changes. As I work mainly on the binding site/Fv regions of antibodies, I am intrigued to see the role of the constant domains in the overall antibody function.
Continue readingReal Space Correlation Coefficient
Introduction
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: cavity similarity searching and its applications to fragment-based drug design
ProCare [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 readingVisualising macromolecules and grids in Jupyter Notebooks with nglview
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 readingThe Coronavirus Antibody Database (CoV-AbDab)
We 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
Continue readingMore tales of 3D printing
TCRBuilder: Multi-state T-cell receptor structure prediction
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 readingCoronavirus
A 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.
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