system
Given the following post, respond as if you're commenting on Reddit:
human
Can we use the AlphaFold database to invent new proteins based on the function we want?

So AlphaFold released the predicted structures of 200 million naturally occurring proteins. Does this mean we can use this data to invent new protein structures that have different functionality?
gpt
It is a helping step in the right direction, but not as transformational as one might hope. AlphaFold like many other folding prediction algorithms helps in the sequence to structure translation. In other words, given a specific peptide sequence, how will the 3-dimnesional shape of the protein look. It is a big step in accuracy over previous iterations on the problem, but is by no means a complete solution to all structures.

First, AF does not do the job it's supposed to do with perfect accuracy. It's still better in many cases than previous best algorithms, but it's not perfect. Like all ML based approaches, AF heavily utilizes a training data set where a sequence and a structure of a protein are already known. This means that AF is (relatively) weak at predicting structures of proteins for which known structures are hard to get (polymerizing proteins, proteins in a complex, membrane proteins, etc.). Until we have methods for attaining accurate crystal (or otherwise high resolution) structures of those types of proteins, we will lack in accuracy of in silico predictions.

Secondly, AF is designed to go from sequence to structure. The question of structure to function is a different one. Obviously, having more accurate structures helps a lot with design and interpretation of the mechanism by which a protein may function, but designing a structure to perform a certain function is not that simple. It has been done with other tools but to limited success. It's easier to take a protein that performs a specific functions and change it a little to accept a different but similar substrate or bind a different but similar molecule, but still very challenging to design a protein that would perform a given function even if you could place every single atom of that proteins with 100% accuracy.

All of this is to say that these problems are by no means solved. However, yes, AF and other ML methods in sequence to structure to function space will enable more things to be possible and move us further along the path of de-novo designed proteins for custom applications.