Earlier this month the World Health Organisation (WHO) released Proposed International Nonproprietary Name List 125 (PL125), comprising the therapeutics entering clinical trials during the first half of 2021. We have just added this data to our Therapeutic Structural Antibody Database (Thera-SAbDab), bringing the total number of therapeutic antibodies recognised by the WHO to 711.
There are more python libraries than you can shake a stick at, but here are a handful that don’t get much love and may save you some brain power, compute time or both.
Fire is a library which turns your normal python functions into command-line utilities without requiring more than a couple of additional lines of copy-and-paste code. Being able to immediately access your functions from the command line is amazingly helpful when you’re making quick and dirty utilities and saves needing to reach for the nuclear approach of using getopt.
Nature has now released that AlphaFold 2 paper, after eight long months of waiting. The main text reports more or less what we have known for nearly a year, with some added tidbits, although it is accompanied by a painstaking description of the architecture in the supplementary information. Perhaps more importantly, the authors have released the entirety of the code, including all details to run the pipeline, on Github. And there is no small print this time: you can run inference on any protein (I’ve checked!).
Have you not heard the news? Let me refresh your memory. In November 2020, a team of AI scientists from Google DeepMind indisputably won the 14th Critical Assessment of Structural Prediction competition, a biennial blind test where computational biologists try to predict the structure of several proteins whose structure has been determined experimentally but not publicly released. Their results were so astounding, and the problem so central to biology, that it took the entire world by surprise and left an entire discipline, computational biology, wondering what had just happened.
Have you ever worked with a piece of software that is awfully difficult to set up? That legacy code written on FORTRAN 77, that other one that requires significant modifications to compile, or any of those that require a long-winded bash script with a thousand dependencies (which you also have to install!). Would it not be helpful if, when that red-eyed PhD student, that one that just spent three months writing up their thesis, says that they absolutely must use that server where you have installed all your stuff, you could just relocate to another one without trouble? Well, you may be able to do that now. You just need to use containerization.
The idea behind containerization is rather simple. The best way to ensure anyone can reproduce your work is to, well, ship your entire system to whomever needs to use it. You could, for example, pack up your desktop in a box, and ship it to your collaborators anywhere in the world. Unfortunately, this idea is quite unpractical, not only because of tedious logistics (ever had to deal with customs?), but also because suddenly you won’t be able to run your own pipeline. However, it is a good enough thought that at some point made a clever engineer wonder whether there was a way to ship an entire system without physically delivering the computer. And that’s exactly what they designed.
Best way to make sure your collaborators on the other side of the world can run your pipeline — just pack your desktop in one of these, and ship it away!Continue reading →
Back in May 2020, we released the Coronavirus Antibody Database (‘CoV-AbDab’) to capture molecular information on existing coronavirus-binding antibodies, and to track what we anticipated would be a boon of data on antibodies able to bind SARS-CoV-2. At the time, we had found around 300 relevant antibody sequences and a handful of solved crystal structures, most of which were characterised shortly after the SARS-CoV epidemic of 2003. We had no idea just how many SARS-CoV-2 binding antibody sequences would come to be released into the public domain…
10 months later (2nd March 2021), we now have tracked 2,673 coronavirus-binding antibodies, ~95% with full Fv sequence information and ~5% with solved structures. These datapoints originate from 100s of independent studies reported in either the academic literature or patent filings.
The entire contents CoV-AbDab database as of 2nd March 2021.Continue reading →
Last Wednesday, I was fortunate enough to be invited as a guest lecturer to the 3rd BioDataScience101 workshop, an initiative spearheaded by Paolo Marcatili, Professor of Bioinformatics at the Technical University of Denmark (DTU). This session, on amino acid sequence analysis applied to both proteomics and antibody drug discovery, was designed and organised by OPIG’s very own Tobias Olsen.
Seemingly every conference due to take place this year has either been cancelled or will be run virtually due to the COVID-19 pandemic. Many organisers have decided that running entirely live virtual programmes causes more trouble than it’s worth (e.g. due to unforseeable IT and internet issues disrupting the schedule), and so are asking their presenters to prerecord their talks, which are then broadcast “live” on the day.
I recently “presented” two virtual prerecorded talks at the ISMB conference using Open Broadcast Software Studio (OBS Studio), a free open-source software package most commonly used by live-streamers on Twitch and Youtube. It is super simple to use and achieves a professional output, with video overlaying a presentation slide deck/poster PDF. This blog is a “how-to” on getting started with OBS for conference talks/poster presentations.
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
Scientific code is never fast enough. We need the results of that simulation before that pressing deadline, or that meeting with our advisor. Computational resources are scarce, and competition for a spot in the computing nodes (cough, cough) can be tiresome. We need to squeeze every ounce of performance. And we need to do it with as little effort as possible.
