Having solved nearly all protein structures known to biology,
As a biologist, “hahaha……no”.
The AI might have solved structures according to the rules it’s been told, but are they accurate with what happens in reality?
No.
As a biologist, “hahaha……no”.
The AI might have solved structures according to the rules it’s been told, but are they accurate with what happens in reality?
No.
Protein structures are immensely complex. Think of stringing a 50 magnets on a piece of string, then trying to predict the structure it would take on if in zero gravity?
We have a pretty big set of protein structures that are known. AI is takes those known structures, plus a set of rules about how amino acids might bind when a part of a protein chain, then tries to predict conformation.
If you ask AI to predict a known structure, it will do well, because, that's the training data.
Ask it to predict a structure that isn't known, and it will do a good job get it's mostly right, but the small differences make all the difference.
It's not a useless tool by any means, but the predictive power is still quite limited.
It’s actually even more complex than that due to interaction with the local environment at the time of adding the magnets onto the string. Both the aqueous and transcription machinery.
It's like trying to solve an equation with 1000's of unknown variables and only 200 rules on variable interactions that are correct 90% of the time.
Even just modeling a 100% pure aqueous environment and the interactions of water molecules is a massively complex problem.
I've been testing out the multimer (protein complex) mode of Alphafold recently, to see if could predict interactions for a family of proteins where some members in the family are known to form complexes, but others previously were found to not form complexes at least when expressed in vitro rather than in vivo. So far I've found that if you try to throw two completely unrelated proteins together, they won't be modeled with any contacts, but for the ones in the family I'm interested in, there's always at least one (of the five models per run) that has them interacting such that there's something that looks like a real DNA-binding domain. For the latter case, it's presently hard to know based just on Alphafold output if it's a structure that could actually form, or if it's just due to bias in the training data, with perhaps the rest of the structured regions of the protein being conformed in unrealistic ways due to less training information for those parts.
TL;DR Alphafold results are biased by existing experimentally resolved structures, and not based on simulating physics, so proteins- or parts of proteins- that don't have good coverage in existing experimental data are not going to be predicted with high confidence.
Wanna know how I know you don't know how that stuff works?
You don't tell these things rules. At all. Nothing even slightly resembling that happens at any point in the process. That's kind of the whole point of machine learning.
> but are they accurate with what happens in reality?
For any given protein, AlphaFold usually predicts a structure very close to what you get for the isolated protein from crystallography or cryo-EM or whatever. It massively outperforms computational chemistry or any other "rules-based" system.
Does it predict every conformation a protein might adopt in vivo? No. But those are not "protein structures known to biology", because guess where biology gets its "known" structures? It does predict the known structures. Yes, including ones that weren't in its training data.
The AI models were basically trained on those.
Learn to use the AI tools or get replaced by Gen-Z interns that can.