It’s interesting how Egypt’s efforts to monitor and test for malaria contributed to this accomplishment. It underscores how eradicating many infectious diseases will require a deep understanding not only of the disease itself, but also the cycles of transmission and the complex ecology of different hosts.
Malaria’s complex lifecycle [1] seems like it would be easy to “break” with different interventions, but we’ve seen historically malaria has been difficult to eradicate. Why is this?
For those like myself who design proteins for a living, the open secret is that well before AlphaFold, it was pretty much possible to get a good-enough structure of any particular protein you really cared about (from say 2005) by other means, namely Baker’s Rosetta.
I constantly use AlphaFold structures today [1]. And AlphaFold is fantastic. But it only replaces one small step in solving any real-world problem involving proteins such as designing a safe, therapeutic protein binder to interrupt cancer-associated protein-protein interactions or designing an enzyme to degrade PFAS.
I think the primary achievement is that it gets protein structures in front of a lot more smart eyes, and for a lot more proteins. For “everyone else” who never needed to master computational protein structure prediction workflows before, they now have easy access to the rich, function-determinative structural information they need to understand and solve their problem.
The real tough problem in protein design is how to use these structure predictions to understand and ultimately create proteins we care about.
Agreed, the UX of a magnet timer on the fridge beats using any kind of smart device for the task of setting kitchen timers. Most of them start a simple count up if not programmed for a specific duration, so you can watch the seconds.
Most magnet timers also remember the last duration, so if you use the same timer a lot for the same task (tea, for example), it’s literally a single button press. The same operation on any kind of smart device contains a staggering number of steps, each of which requires cognition and attention.
Magnet timers are also super cheap, so you can get another one if you have two favorite durations. A simple solution meets a simple problem
Agreed on the hopes that these methods lead to novel biocatalysts (but they aren’t quite there yet).
David Baker’s lab has recently published on using their own diffusion model (RFdiffusion) to design novel biocatalysts that perform hydrolysis using a catalytic triad of serine, aspartic acid, and histidine, as well as an oxyanion hole, which is much more complex than the binders designed by AlphaProteo [1].
It gives me hope that we’ll soon be able to design biocatalysts as good as natural ones, but for any problem we care about.
Good point! And a related topic, we call the organism that lives happily in our gut E. coli and we also call the organisms that cause disease the same name. What’s the difference?
It turns out that the Escherichia coli (to spell out its Latin binomial) that cause disease are in some sense “diseased” themselves: the genes that enable them to be pathogenic, or make them pathogenic, I should say, are originally from a phage, a type of virus that infects bacteria [1]. In a manner that is not the same as, but conceptually similar to how HIV inserts its genes into the human’s genome, phages insert their genes (termed the “prophage”) into the bacterial genome.
In addition, most strains of pathogenic Escherichia are also holding on to an entirely separate, small, circular “genome” called a plasmid, also of exogenous origin, that contains additional genes that make them pathogenic.
So in addition to wide genome variation within the “species” (which is not really the same thing for bacteria as for mammals, mind you) of Escherichia coli, many subtypes have additional genetic material from endogenous sources that substantially changes their observed characteristics (phenotype).
The flip side of this is that progress in ML for biology is always going to be _slower_ than progress in ML for natural languages and images [1].
Humans are natural machines capable of sensing and verifying the correctness of a piece of text or an image in milliseconds. So if you have a model that generates text or images, it’s trivial to see if they’re any good. Whereas for biology, the time to validate a model’s output is measured more in weeks. If you generate a new backbone with RFDiffusion, and then generate some protein sequences with LigandMPNN, and then want to see if they fold correctly … that takes a week. Every time. Use ML to solve _that_ problem and you’ll be rich.
TFA mentions the difficulty of performing biological assays at scale, and there are numerous other challenges. Such as the number of different kinds of assays required to get the multimodal data needed to train the latest models like ESM-3 (which is multimodal, in this context meaning primary sequence, secondary structure, tertiary structure, as well as several other tracks). You can’t just scale a fluorescent product plate reader assay to get the data you need. We need sequencing tech, functional assays, protein-protein interaction assays, X-ray crystallography, and a dozen others, all at scale.
What I’d love to see companies like A-Alpha and Gordian and others do is see if they can use the ML to improve the wet lab tech. Make the assays better, faster, cheaper with ML. Like how they use ML to translate the electrical signals of DNA passing through the pore into a sequence in the Nanopore sequencers. So many companies have these sweet assays that are very good. In my opinion, if we want transformative progress in biology, we should spend less time fitting the same data with different models, and spend more time improving and scaling wet lab assays using ML. Can we use ML to make the assay better, make our processes better, to improve the amount and quality of data we generate? The thesis of TFA (and experience) suggests that using the data will be the easy part
Probably worth mentioning that David Baker’s lab released a similar model (predicts protein structure along with bound DNA and ligands), just a couple of months ago, and it is open source [1].
It’s also worth remembering that it was David Baker who originally came up with the idea of extending AlphaFold from predicting just proteins to predicting ligands as well [2].
Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules. While I am certain that neither network is up to the task of designing an enzyme, these are exciting steps.
One of the more exciting aspects of the RosettaFold paper is that they train the model for predicting structures, but then also use the structure predicting model as the denoising model in a diffusion process, enabling them to actually design new functional proteins. Presumably, DeepMind is working on this problem as well.
As cool as this is, the word “microplastics” is a little misleading. There are dozens of types of plastic in common use, each made from a different monomer with a different chemical linkage, of which PET is only one. The engineered protein in TFA will only work on PET and we’ll need to design new proteins for the other types of plastic. (I can help with that.)
The problem with enzymes eating plastic is that enzymes are small Pacman-shaped protein blobs that are maybe 10 nanometers in diameter, whereas things made of plastic like bottles or even microplastics are huge in comparison. How do you get the little Pacman jaws around the bottle to start breaking it down?
The research paper [1] describes the authors’ effective innovation. They make a protein where one end is a pore-forming shape, and the other end is a PET cutting (called a PETase in the jargon of the field). This way, their protein can access nooks and crannies in the macroplastic shapes, allowing tons of copies of this small enzyme to fully degrade a bottle.
Without this, a great deal of physical agitation is required to break down the plastics into small enough chunks that earlier Pacman enzymes could work on, increasing the time and the cost.
I hope we’ll see the idea of linking the enzymatic “scissors” to a protein pore be used to engineer enzymes to degrade other types of plastics in the future, as the general idea of getting the catalytic machinery into physical contact with every bit of the bottle is broadly applicable to all plastics, not just PET (which is great news)
Malaria’s complex lifecycle [1] seems like it would be easy to “break” with different interventions, but we’ve seen historically malaria has been difficult to eradicate. Why is this?
1. https://en.m.wikipedia.org/wiki/Plasmodium#/media/File%3ALif...