AI & Antibodies miniseries | Nanobody thermostability prediction and the great data challenge
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Why is data scarcity a major obstacle for AI in antibody therapeutics?
Today, we're addressing a significant challenge in the use of AI in antibody therapeutic development data availability. You're listening to the Talking Techniques Podcast, and this is the fifth episode of our series covering the ongoing article collection on artificial intelligence and machine learning in antibody development in the journal MAPS. I'm your host, Biotonique Senior Editor Tristan Free, and today I'm joined by Aubin Ramon, a postdoctoral research associate in the Solmani lab at Imperial College London. Orban's paper in the collection, Prediction of Protein Biophysical Traits from Limited Data, a case study on nanobody thermostability through nanomelt, presents a potential solution to a perennial limitation of AR modelling.
Your model is only ever as good as the data you train it on. Or Ben, it's great to have you on the podcast.
Hi Tristan. Thank you so much for having me.
Yeah, you're very welcome. So first off, please can you uh introduce yourself and tell us a little bit more about your institution and your work in the Solmani lab.
Yeah, sure. So I'm um postdoctoral research associate in the Petersomani lab. uh which is now based at Imperio College London. We just moved there a few months ago, back in September uh 2025, yeah, uh from Cambridge, where um Pietro uh Somani started his lab. So I I did my PhD with him and it's during my PhD that I focus a lot on antibody and more specifically nanobody um developability. So prediction, assessment, optimization, Um all of these things. Um yeah, now I'm working on uh multi-objective pipeline optimization, merging all my PhD work together during my postloc.
Fantastic. Um and so in your paper you addressed the limitation of AI that reaches far beyond the life sciences and and antibodies, um, which is the quality and size of the data it's trained on. So as those listening to this podcast likely work in highly specialized fields, this is an even more pronounced problem, right? You have less data availability than you would for sort of more general applications. Um Can you uh can you tell us how you set out to develop a useful model with a relatively small data set? So how did you kind of try approaching this situation?
Yeah, so the problem of small data set for training is particularly true for protein engineering, um, because we have this biophysical characterization issue where um we need for most of this biophysical property, we need uh a good amount of purified protein. And so you have this expression purification bottleneck to get enough data. Um and we're lucky with the most stability in my case because you don't need that big amount of uh purifying nanobody. Basically like how it works is that you just put um your nanobody into a buffer and then you load that uh into a capillary, so you just need a few microliters, and you put this into a a spectrophotometer. Um that is gonna record the fluorescence while heating the laser your capillary.
So you're gonna measure the intrinsic fluorescence of your proteins, that's gonna change upon heating and upon upon unfolding. Um and so you don't need much of a protein to do so. So it's relatively like middle throughput to get data. But then if you want something else like solubility and if you want the real thermodynamic solubility, you will need a lot of proteins uh to measure the crash out and uh and solution. So yeah, so um this is the issue for protein engineering. And because we have small data, then we're gonna have Um A lack of diversity in into the representation of the data. And a lack of also standardized and high quality data points. Because only like a few people from different labs, from different research institutions are gonna measure their protein in their own buff environment with their own um properties and uh condition, experimental setup conditions.
Uh and then when you try to train a model among this limited data set, so small amount. um small quality, uh little quality and uh poor diversity is gonna struggle. So how we did to overcome this is um we um started by characterizing more nanobodies because when I started this project, so at the beginning of my PhD four five years ago, we only had less than a hundred data points of nanobody femor stability, which is
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Chapters
6 chapters
1
Why is data scarcity a major obstacle for AI in antibody therapeutics?
0:03–4:18
2
How did the NanoMelt team build a useful model with only 600‑700 nanobody sequences?
4:18–9:24
3
What validation strategies were used to ensure NanoMelt’s predictions are reliable?
9:24–15:52
4
Why do general protein stability models outperform antibody‑specific models in this study?
15:52–23:29
5
Can the NanoMelt approach be extended to full‑length antibodies and multi‑domain formats?
23:29–28:41
6
What practical advice does Aubin give for applying NanoMelt to my own projects?
28:41–29:20
Speakers
2 identifiedMore from Talking Techniques
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