AI & Antibodies mini-series | Balancing binding affinity and therapeutic practicality
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What is the background of the host and guest and why are they discussing antibody design?
Today we're exploring a new AI model that could change the way we approach antibody design. Hello and welcome to Talking Techniques, the show that brings you the latest from the life sciences, strength from the people exploring them. I'm your host, Biotechnique's senior editor Tristan Free, and today as part of a special series of podcasts covering the ongoing article collection on artificial intelligence and machine learning in antibody development in the journal MABS, I'm joined by Ryan Emerson, Senior Vice President of Data Science at A Alpha Bio. Ryan's contribution to the collection, a paper titled Alpha Bind, a domain-specific model to predict and optimize antibody antigen binding affinity, presents a model that can optimize antibody sequences to strengthen the binding affinity of novel antibody candidates.
And we're delighted to have him with us today to take us through the key concepts that run through that paper and that underlie this model. Ryan, it's lovely to have you on the show.
Likewise, thank you for having me.
So first off, Ryan, can you just tell us a bit more about yourself um and what it is you work on at A Alpha Bio?
I'd be happy to. Um as you mentioned, I'm the head of data science at A Alpha Bio. Um we are a spinout from the University of Washington in Seattle, and so so I am here in Seattle. Uh we really work at the intersection of synthetic biology and machine learning. So my own training is in computational biology. Um I've done a lot of work on how we can build and optimize assays to get big biological data sets. And then for about the last eight years, more and more of my work has been on the ML and AI applications we can do with big data sets in biology. Uh so at A Alpha, we're really based around uh a wet lab platform that allows us to measure protein protein affinities at very high scale, so so up to tens of thousands or or millions of measurements at a time.
Uh and we do a lot of work here with uh with ML research groups, internally and externally, building those data sets and figuring out how we can use them to develop new and interesting models. And we also do uh applied protein engineering, so so using using all of this to make uh new and better biologics candidates um with with partners across the industry.
Fantastic. Uh and so your paper in this collection addresses the challenges in the design of novel antibody sequences. So to to sort of outline that process, can you give us a uh an understanding or a brief outline of the process behind um sorry, the current process behind the design of novel antibody sequences and the challenges that are associated with that field?
Yeah, absolutely. So uh I will I will try and get as close as I can to uh regurgitating Janeway here, kind of the the standard way one would think about this process, right? So we want a biologic drug. Uh antibodies are fantastic drugs. Uh and so usually what that's going to do is go through uh first a discovery uh and then what we'd call a lead optimization phase. So discovery is finding an antibody that biologically does what we want it. to do. It you know, binds a particular target is the classic case.
What are the current challenges in designing novel antibody sequences?
Uh and for that there are a number of technologies at play, right? So we can immunize an animal and then get an get uh an antibody from that. That's kind of classic monoclonal technology. Uh we can screen from an in vitro library, so so uh panning from phage is kind of a a classic example there. Uh increasingly, right, we can use an in silico model to simply design an antibody. But what that gets us is a very early candidate. Uh, which is still quite some ways from what we'd want as an ideal drug. Uh and so then the process that it goes through, which we'll call lead optimization, is turning that raw sequence uh into uh a finished preclinical candidate that we think could potentially be a drug. And again, this is mostly done by changing the primary sequence, right?
So these are proteins. So we just we'd edit the protein sequence to be a better drug. And we're looking at things like optimizing the binding affinity, but also getting something that's going to be thermostable so it doesn't need special storage conditions, something uh that's very soluble so that we can have it uh at high concentration.
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Chapters
8 chapters
1
What is the background of the host and guest and why are they discussing antibody design?
0:10–3:12
2
What are the current challenges in designing novel antibody sequences?
3:12–6:01
3
How does AlphaBind predict and optimize antibody‑antigen binding affinity?
6:01–9:41
4
What data and training steps were used to build the AlphaBind model?
9:41–14:05
5
How did the team demonstrate AlphaBind’s effectiveness on real antibodies?
14:05–17:35
6
What practical tips help users get the most out of AlphaBind?
17:35–22:02
7
What are the predicted impacts of AI on antibody engineering over the next five years?
22:02–25:55
8
What data would most improve AI‑driven antibody design and developability predictions?
25:55–31:07
Speakers
1 identifiedMore from Talking Techniques
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AI & Antibodies mini-series | An artificial approach to humanization