SUMO wrestling with developability

When engineering antibodies into effective biotherapeutics, ideally, factors such as affinity, specificity, chemical stability and solubility should all be optimised. In practice, we know that it’s often not feasible to co-optimise all of these, and so compromises are made, but identifying these developability issues early on in the antibody drug discovery process could save costs and reduce attrition rates. For example, we could avoid choosing a candidate that expresses poorly, which would make it expensive to manufacture as a drug, or one with a high risk of aggregation that would drive unwanted immunogenicity.

On this theme, I was interested to read recently a paper by the Computational Chemistry & Biologics group at Merck (Evers et al., 2022 https://www.biorxiv.org/content/10.1101/2022.11.19.517175v1). They have developed a pipeline called SUMO (In Silico Sequence Assessment Using Multiple Optimization Parameters), that brings together publicly-available software for in silico developability assessment and creates an overall developability profile as a starting point for antibody or VHH optimisation.

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For each sequence assessed, they report factors such as sequence liabilities (residues liable to chemical modifications that can alter properties such as binding affinity or aggregation propensity), surface hydrophobicity, sequence identity compared to most similar human germline and predicted immunogenicity (based on MHC-II binding). Also provided are an annotated sequence viewer and 3D visualisation of calculated properties. Profiles are annotated with a red-yellow-green colour-coding system to indicate which sequences have favourable properties.

Overall, this approach is a useful way to discriminate between candidates and steer away from those with major developability issues prior to the optimisation stage. Given that the thresholds for their colour-coding system are based on data from marketed therapeutic antibodies, and that the software used has primarily been designed for use on antibody datasets, I would be interested to see whether the particular descriptors chosen for SUMO translate well to VHHs, or whether there are other properties that are stronger indicators of nanobody developability.

Datamining Wikipedia and writing JS with ChatGTP just to swap the colours on university logos…

I am not sure the University of Oxford logo works in the gold from the University of Otago…

A few months back I moved from the Oxford BRC to OPIG, both within the university of Oxford, but like many in academia I have moved across a few universities. As this is my first post here I wanted to do something neat: a JS tool that swapped colours in university logos!
It was a rather laborious task requiring a lot of coding, but once I got it working, I ended up tripping up at the last metre. So for technical reasons, I have resorted to hosting it in my own blog (see post), but nevertheless the path towards it is worth discussing.

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Creating a Personal Website

Personal websites are a great and increasingly important way to build your online presence. Along with professional social media pages, such as on LinkedIn and Twitter, a website can provide a boost to your career and/or job search.

This blog post is based on my recent experience creating a personal website, following guidelines from Lewis’ talk at the OPIG Retreat last year (thank you Lewis!). The method I used and will cover here, based on an HTML5 UP! template and GitHub pages, is free and fast.

Why have a personal website?

  • Improves your online presence and brand
  • Boost for your career, including by allowing potential future employers to find you
  • Share things you have accomplished or are interested in
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Some ponderings on generalisability

Now that machine learning has managed to get its proverbial fingers into just about every pie, people have started to worry about the generalisability of methods used. There are a few reasons for these concerns, but a prominent one is that the pressure and publication biases that have led to reproducibility issues in the past are also very present in ML-based science.

The Center for Statistics and Machine Learning at Princeton University hosted a workshop last July highlighting the scale of this problem. Alongside this, they released a running list of papers highlighting reproducibility issues in ML-based science. Included on this list are 20 papers highlighting errors from 17 distinct fields, collectively affecting a whopping 329 papers.

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Happy 10th Birthday, Blopig!

OPIG recently celebrated its 20th year; and on 10 January 2023 I gave a talk just a day before the 10th anniversary of BLOPIG’s first blog post. It’s worth reflecting on what’s stayed the same and what’s changed since then.

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SAbBox in 2023: ImmuneBuilder and more!

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.

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The Boltzmann Distribution and Gender Stereotypes

Journalist Caitlin Moran recently tweeted the following:

“I feel like every day now, I read/hear something saying “We don’t talk about what’s POSITIVE about masculinity; what’s GOOD about men and boys.” So: what IS the best stuff about boys, and men? Honest, celebratory question.”

What followed was a collection of replies acknowledging and celebrating various traits seen typically as ‘male’, including certain activities, such as knowing about sports or cars, or a desire to do DIY type work, and characteristics such as physical strength, no-nonsense attitudes and a ‘less complicated’ style of friendship between men.

Whilst I condone Moran’s efforts to turn recent discussions surrounding masculinity on their head and frame it in a positive light, to me the the responses offered and discussion that followed felt somewhat stifling. I am biologically male and identify as male, but do not feel like I personally adhere to most of these stereotypes. I am not physically strong, I know very little about cars and sports, and find there be just as much nuance and drama in male-male friendships as there is in friendships between other genders. 

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Three months in industry and returning to my PhD

Being in my third year of my DPhil, I decided that I should try to see what the world of industry looks like. Thankfully, I was lucky enough to be able to complete an industrial internship at Exscientia here in Oxford where I spent most of my time on scientific software engineering. I expected this to not be too different from what my work looks like here at OPIG, but quickly came to realise that this is not the case. What followed were three months of building a software package, getting to know all the new people around me, and getting used to all the new tools and infrastructure. Below are a few things that I am very happy to take back with me. 

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A PhD can often be very solitary work. You are the expert in your project, and also have the highest stake in your project. At times, the freedom of what to explore next this affords is fantastic, but can make things difficult when problems arise. In industry, projects are a lot more collaborative. Your work direction will be aligned heavily with company needs, and depending on company size there might be specialised teams to support you in specific aspects of the project. 

Emphasis placed on code quality is also a stark difference. Internal software written for company use has to be readable and well-documented. The codebase must also be standardised to maintain consistency. This will make life for newcomers easy and ensures that if the author of some software leaves the company the next person can easily take on the task of maintaining their code. Here, academia is catching up. Scientific software engineering is becoming more focused on maintainability (one of the core values of the SABS programme), but sadly Github is still full of legacy code that was written in ways that make maintaining the code difficult after the author stops being involved with it.

Lastly, on a more personal note, it was also fantastic to be surrounded by people in a team who work with the same techniques as me. In my PhD, I am one of two people at OPIG regularly using molecular dynamics simulations but I also spend a lot of my time working in the Biochemistry Department with the Higgins Group which is an experimental structural biology group. This being the case, my internship has been a fantastic way of picking up some additional techniques from people who are already familiar with them. I would highly recommend giving yourself the opportunity to do this if possible, either via something like an industrial internship, or a research visit to a collaborating academic group. 

The past three months have been invaluable. They have given me the opportunity to see what industry is like and given me experience with new skills that I can take back to my PhD. Best of all, I got to meet a fantastic team who were always ready to take time out of their days to help and who made the time I spent at Exscientia as fun as it was!

Quality Stats

Disclaimer – the title is a Quality Street pun only and bears no relation to the quality of the data or analysis presented below. This whole blog post is basically to discredit the personal chocolate preferences of a group member who shall remain nameless. Safe to say though, they Vostly overestimated people’s love for the Toffee Finger. Long live the Orange Creme.

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