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
Science in the Time of COVID-19
If you are reading this blog, I am sure you will agree that science and research are essential, and even more in the context of a pandemic. Concepts such as PCR, antibody, and herd immunity are slowly getting into people’s vocabulary. This fact makes me quite happy but puts in evidence the lack of scientific knowledge among the population.
Continue readingHERO proteins are here to save you (assuming you’re another protein or a fruit fly)
For one of OPIG’s short talks, I recently introduced the work done by Kotaro Tsuboyama et al. found in the paper A widespread family of heat-resistant obscure (Hero) proteins protect against protein instability and aggregation. As the name implies, HERO proteins have been found to retain function even after being boiled at 95C and have been found both in Drosophila and human HEK293T cell lines. Whilst it’s not impossible to find proteins which can “survive” 90+ Celsius, these are expected to be the reserve of extremophiles, not found in humans or fruit flies.
Continue readingDon’t drink bleach, don’t share canards!
Beyond the thousands of deaths and the millions of infected people, the COVID-19 pandemic has quickly transformed Western society. This virus has provoked the confinement of millions of people in their houses, the closure of bars, restaurants, and pubs, schools, museums, and theatres. However, for this post, I will focus on another side effect of the pandemic: the spread of canards, which flow even faster than the virus itself.
Continue readingNon-specialist intro: Convalescent sera and some thoughts on its relevance to structural biology
A couple of weeks ago, I gave a group meeting talk on my current research. Interestingly most of the questions I received were not directly related to my research methods, but rather, on the broader application of antibody-related therapies, as I used the example of convalescent sera as a potential ‘quick fix’ in the current COVID-19 pandemic, to motivate why antibody research is important! So I thought in this blog post, I would give a quick introduction to convalescent sera. (Disclaimer: This does not contain any clinical information.)
Continue readingUnderstanding the synthesizability of molecules proposed by generative models
De novo molecular design is a computational technique to generate molecules with desired properties from scratch. Classical generative algorithms are based on Genetic Algorithms (GA) and the iterative construction of molecules from molecular fragments. Recently, Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs) have been developed for this task, however, the synthesizability of the proposed molecular structures remains an issue. Gao and Coley[1] provided an analysis of the synthesizability of the molecules proposed by these de novo generative algorithms, and discuss their strengths and weaknesses.
Continue readingDeLinker – Deep Generative Models for 3D Linker Design
*** Disclaimer: This blog post represents some shameless self-promotion. ***
I am delighted to announce that our most recent work, DeLinker, was recently published in the Journal of Chemical Information and Modeling (link).
GEMMI: A Python Cookbook
General MacroMocelecular I/O, or GEMMI, is a C++ 11 header only library for low level crystalographic .
Because its header only it is certainly the easiest to access and use low level crystalographic C++ library, however GEMMI comes with python binding via Pybind11, making it arguably the easiest low level crystalographic library to access and use in python as well!
What follows is a cookbook of useful Python code that uses GEMMI to accomplish macromolecular crystalographic tasks.
Continue readingIdentifying shared antibodies using deep learning
Antibody convergence is the presence of similar antibodies in different individuals – suggesting that the individuals have had exposure to a common antigen, which has stimulated the production of similar, antigen-specific antibodies. We want to be able to identify these shared antibodies, sometimes referred to as ‘public clones’, as it could lead to development of immunodiagnostic tests against the shared antibodies, and potentially assist in the design of vaccines and therapeutic antibodies. A recent paper on bioRxiv by Sai Reddy’s group[i] has applied deep learning techniques – variational autoencoders (VAE) and support vector machines (SVM) – to the problem of how to identify shared antibodies.
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