Category Archives: Immunoinformatics

What is a plantibody?

Plants can be genetically engineered to express non-native proteins, for example, crops can be engineered to produce insect toxins in order to improve disease-resistance. However, I was not aware of their ability to express antibodies until, inspired by my expanding collection of house plants, I googled ‘plant immune systems’. 

Plants don’t naturally produce antibodies – they do not possess an adaptive immune system or any circulating immune defence cells. Despite this, plants can be made to express and assemble full length antibody heavy chains and light chains. This was first published back in 1989, when Hiatt et al. [1] successfully introduced mouse immunoglobulin genes to tobacco plants and produced functional antibodies with reasonable efficiency. The excellent term ‘plantibody‘ was coined soon after, to refer to antibodies and fragments of antibodies produced by plants transformed with antibody-coding genes. 

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New Antibody Therapeutic INNs will no longer end in “-mab”!

Happy 2022, Blopiggers!

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.

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Antibody Engineering and Therapeutics Conference

I was invited to speak at the Antibody Engineering and Therapeutics Conference (presenting mine and Matt’s recently published epitope profiling paper), in San Diego (December 12th – 16th). Unfortunately, the pandemic had other ideas so I decided not to travel but luckily the conference was hybrid. 

The conference included 1 day of pre-conference workshops and 4 days of presentations from academic and industry, with livestreaming of the initial keynotes (including one from Charlotte). Remaining talks were recorded and made available after the conference. I’ve highlighted a few of my favourite talks and conference themes, with links to papers where available.

Naturally, a lot of the presented research related to covid-19. I was speaking in the ‘Antibody Repertoires and Covid-19’ session, where there were interesting presentations from Professor Eline Luning Prak from the University of Pennsylvania and Elaine Chen from Vanderbilt University analysing antibody responses in covid-recovered individuals, and comparing vaccine responses in covid-recovered vs covid-naiive individuals. Other talks around SARS-CoV-2 vaccines included Dr Laura Walker from Adimab/Adagio Therapeutics comparing BCR repertoire responses to different types of vaccinations, and the effect of using different booster types.

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New review on BCR/antibody repertoire analysis out in MAbs!

In our latest immunoinformatics review, OPIG has teamed up with experienced antibody consultant Dr. Anthony Rees to outline the evidence for BCR/antibody repertoire convergence on common epitopes post-pathogen exposure, and all the ways we can go about detecting it from repertoire gene sequencing data. We highlight the new advances in the repertoire functional analysis field, including the role for OPIG’s latest tools for structure-aware antibody analytics: Structural Annotation of AntiBody repertoires+ (SAAB+), Paratyping, Ab-Ligity, Repertoire Structural Profiling & Structural Profiling of Antibodies to Cluster by Epitope (‘SPACE’).

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Antibodies for gut or bad

Over the last two decades, there has been mounting evidence of the role of the gut microbiome (the collection of microorganisms in the GI tract) in metabolic disorder (Fan and Pedersen 2021) and more recently, in psychiatric illness (Morais, Schreiber, and Mazmanian 2021). The maintenance of the equilibrium of commensal bacteria and their proper compartmentalization and stratification in the gut is critical for health.

There are diverse factors regulating microbiota composition (microbiota homeostasis) (Macpherson and McCoy 2013). I am principally interested in the role of antibodies – the idea that antibodies participate in this process is controversial (Kubinak and Round 2016) because of the difficulty of controlling for the multiple confounding environmental variables that influence the microbiome, but there are theories as to how this happens. The process of the shaping of the microbiota by antibodies was dubbed “antibody-mediated immunoselection” (AMIS) by (Kubinak and Round 2016).

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2021 likely to be a bumper year for therapeutic antibodies entering clinical trials; massive increase in new targets

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.

This is up from 651 at the end of 2020, a year which saw 89 new therapeutic antibodies introduced to the clinic. This rise of 60 in just the first half of 2021 bodes well for a record-breaking year of therapeutics entering trials.

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ISMB 2021: epitope prediction tools

I recently had the opportunity to present my work on antibody virtual screening at the 2021 ISMB/ECCB virtual conference. In this blogpost, I want to summarise two research projects presented in the 3DSIG immunoinformatics session (in which I also presented my work) highlighting two different avenues of approaching epitope prediction (and immunoinformatics questions in general): Structure-based (Epitope3D) and sequence-based (SeRenDIP-CE).

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A to Z of Alternative Antibody Formats: Next-Generation Therapeutics

Do you know your diabodies from your zybodies?

Antibodies are a highly important class of therapeutics used to treat a range of diseases. Given their success as therapeutics, a wide variety of alternative antibody formats have been developed – these are driving the next generation of antibody therapeutics.

To note, this is not an exhaustive list but rather intended to demonstrate the range of existing antibody formats.

Inspired by this article in The Guardian: “Rachel Roddy’s A-Z of pasta“

Figure 1. Alternative Antibody Formats
Many of these figures were adapted from Spiess et al., 2015. Additionally, some of these formats have multiple variations or further possible forms (e.g., trispecific antibodies) – in these cases, one example is given here.

A – Antibodies

Antibodies – a fitting place to start this post. Antibodies are proteins produced by our immune systems to detect and protect against foreign pathogens. The ability of antibodies to bind molecules strongly and specifically – properties essential to their role in our immune defence – also make them valuable candidates for therapeutics. Antibody therapies have been developed for the treatment of various diseases, including cancers and viruses, and form a market estimated at over $100 billion1.

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Can few-shot language models perform bioinformatics tasks?

In 2019, I tried my hand at using large language models, specifically GPT-2, for text generation. In that blogpost, I used Hansard files to fine-tune the public release of GPT-2 to generate speeches by several speakers in the House of Commons (link).

In 2020, OpenAI released GPT-3, their new and improved text generation model (paper), which uses a whopping 175 billion parameters (as opposed to its predecessor’s 1.5 billion) and not only proved to be capable of state of the art performance on common text prediction benchmarks, but also generated a considerable amount of interest in the news media:

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Do antibodies care about sex?

In a recent OPIG antibody meeting, the topic of immune system differences between men and women came up. I thought this was cool and something I hadn’t read about, so what a brilliant topic for a blog most. This post is a high-level overview – I’ve listed the papers I’ve used at the bottom of this post so please consult them for more details!

Differences between males and females can lead to pretty big disparities in disease prevalence and outcomes. For example, non-reproductive cancers occur predominantly in males, whilst the majority of autoimmune disease occurs in females. Many factors may be impacting this, including environmental, genetic and hormonal influences, and much more research is required to fully understand these processes. Here I focus on sex-based biology, rather than gender, though both can influence the immune response.

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