16:30 BST 27/06/2023 Oxford, UK. A large number of scientists were spotting riding bicycles across town, to the consternation of onlookers. The event was the Oxford Protein Informatics Group (OPIG) “tour de farce” 2023. A circular bike ride from the Department of Statistics, to The Up in Arms (Marston), The Trout Inn (Godstow), The Perch (Port Meadow) and The Holly Bush (Osney Island). This spurred great bystander-anxiety due to one of a multitude of factors: the impressive size of the jovial horde, the erraticism of the cycling, the deplorable maintenance of certain bikes, and the unchained bizarrerie of the overheard dialogue.
For several years now, we have distributed the SAbDab database and SAbPred tools as a virtual machine, SAbBox, via Oxford University Innovation. This virtual machine allows a user to utilise the tools and database locally, allowing for high-throughput analysis and keeping confidential data within a local network. Initially distributed under a commercial licence, the platform proved popular and, in 2020, we introduced a free academic licence to enable our academic colleagues to use our tools and database locally.
Following requests from users, in 2021 we released a new version of the platform packaged as a Singularity container. This included all of the features of SAbBox, allowing Linux users to take advantage of the near bare-metal performance of Singularity when running SAbPred tools. Over the past year, we have made lots of improvements to both SAbBox platforms, and have more work planned for the coming year. I’ll briefly outline these developments below.
pMHCs are set to become a major target class in drug discovery; unusual peptide fragments presented by MHC can be used to distinguish infected/cancerous cells from healthy cells more precisely than over-expressed biomarkers. In this blog post, I will highlight a prototype resource: Dr. Chris Thorpe’s new database of pMHC structures, histo.fyi.
histo.fyi provides a one-stop shop for data on (currently) around 1400 pMHC complexes. Similar to our dedicated databases for antibody/nanobody structures (SAbDab) and T-cell receptor (TCR) structures (STCRDab), histo.fyi will scrape the PDB on a weekly basis for any new pMHC data and process these structures in a way that facilitates their analysis.
When I started my PhD in late 2018, AI hadn’t really entered the field of de novo protein design yet – at least not in a big way. Rosetta’s approach of continually ranking new side chain rotamers on a fixed backbone was still the gold standard for the ‘structure-to-sequence’ problem. And of course before long we had AI making waves in the structure prediction field, eventually culminating in the AlphaFold2 we all know and love.
Now, towards the end of my PhD, we are seeing the emergence of new generative models that learn from existing pdb structures to produce sequences that will (or at least should) fold into viable, sensible and crucially natural-looking shapes. ProtGPT2 is a good example (https://www.nature.com/articles/s41467-022-32007-7), but there are several more. How long before these models start reliably generating not only shapes but functions too? Jury’s out, but it’s looking more and more feasible. Safe to say the field as a whole has evolved massively during my time as a graduate student.
Last year, the Structural Antibody Database (SAbDab) listed a record-breaking 894 new antibody structures, driven in no small part by the continued efforts of the researchers to understand SARS-CoV-2.
Fig. 1: The aggregate growth in antibody structure data (all methods) over time. Taken from http://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab/stats/ on 25th May 2022.
In this blog post I wanted to highlight the major driving force behind this curve – the huge increase in cryo electron microscopy (cryoEM) data – and the implications of this for the field of structure-based antibody informatics.
My first post of the year is about another major change to the way the World Health Organisation will be assigning “International Non-proprietary Name”s (INNs) to antibody-based therapeutics. I haven’t seen this publicised widely, so I thought I’d share it here as it is an important consideration for anyone mining or exploiting this data.
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.
Image of a punched card, showing 80 columns and 12 rows, with particular rectangular holes representing the 1 bits of binary numbers. The upper right corner is cut at an angle, to facilitate feeding the card into a punched card reader. The column numbers are printed along the bottom. The words “IBM UNITED KINGDOM LIMITED” are printed along the very bottom. This card is line 12 from a Fortran program, “12 PIFRA=(A(JB,37)-A(JB,99))/A(JB,47) PUX 0430”. Image Credit: Pete Birkinshaw, Manchester, U.K. CC BY 2.0
Tales of carrying stacks of punched cards to the computer centre with a line drawn diagonally on the side of the stack, to help put them back in order should you trip and fall—seem like another universe—but this is what passed for the human-computer interface in much of the mid-20th century.
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
Websites store cookies to enhance functionality and personalise your experience. You can manage your preferences, but blocking some cookies may impact site performance and services.
Essential cookies enable basic functions and are necessary for the proper function of the website.
Name
Description
Duration
Cookie Preferences
This cookie is used to store the user's cookie consent preferences.
30 days
These cookies are used for managing login functionality on this website.
Name
Description
Duration
wordpress_logged_in
Used to store logged-in users.
Persistent
wordpress_sec
Used to track the user across multiple sessions.
15 days
wordpress_test_cookie
Used to determine if cookies are enabled.
Session
Statistics cookies collect information anonymously. This information helps us understand how visitors use our website.
Google Analytics is a powerful tool that tracks and analyzes website traffic for informed marketing decisions.
Contains information related to marketing campaigns of the user. These are shared with Google AdWords / Google Ads when the Google Ads and Google Analytics accounts are linked together.
90 days
__utma
ID used to identify users and sessions
2 years after last activity
__utmt
Used to monitor number of Google Analytics server requests
10 minutes
__utmb
Used to distinguish new sessions and visits. This cookie is set when the GA.js javascript library is loaded and there is no existing __utmb cookie. The cookie is updated every time data is sent to the Google Analytics server.
30 minutes after last activity
__utmc
Used only with old Urchin versions of Google Analytics and not with GA.js. Was used to distinguish between new sessions and visits at the end of a session.
End of session (browser)
__utmz
Contains information about the traffic source or campaign that directed user to the website. The cookie is set when the GA.js javascript is loaded and updated when data is sent to the Google Anaytics server
6 months after last activity
__utmv
Contains custom information set by the web developer via the _setCustomVar method in Google Analytics. This cookie is updated every time new data is sent to the Google Analytics server.
2 years after last activity
__utmx
Used to determine whether a user is included in an A / B or Multivariate test.
18 months
_ga
ID used to identify users
2 years
_gali
Used by Google Analytics to determine which links on a page are being clicked
30 seconds
_ga_
ID used to identify users
2 years
_gid
ID used to identify users for 24 hours after last activity
24 hours
_gat
Used to monitor number of Google Analytics server requests when using Google Tag Manager
1 minute
Google reCAPTCHA helps protect websites from spam and abuse by verifying user interactions through challenges.
Name
Description
Duration
_GRECAPTCHA
Google reCAPTCHA sets a necessary cookie (_GRECAPTCHA) when executed for the purpose of providing its risk analysis.
.jpg){kind=link}