proteins do more than serve as the building blocks of the body — the ones that serve as mostly static structural components are actually a special case. More generally, proteins are self-assembling nanomachines that do almost everything in the body. Your cellular processes — everything which can be said to make you alive — are tasks carried out by proteins.
Proteins are defined linearly. They are coded by strings of nucleotides in your DNA and RNA. They are formed by chains of amino acids reacting with each other. But despite this simple linear identity, proteins act in time and space. Once produced, atomic forces cause them to self-assemble into messy 3D structures that determine their function. Proteins are fundamental to pharmaceutical research, where scientists are often trying to find a molecule that will activate or inactivate a particular protein. Since we only know the structures of around a quarter of the proteins in the human body, this has often been a trial-and-error effort. By using AlphaFold 2 or its successors to create a catalog of the structures of every protein humans can produce, scientists will be able to reason about which molecules could be good candidate drugs, dramatically reducing the error rate. This, in turn, could turbocharge drug development and enable the discovery of cures for almost every disease. We may even discover that already-approved drugs can be used to treat conditions we hadn’t tried them on yet.
In a future pandemic where we might not have experience with similar kind of a virus in the past, the ability to map the structure of its proteins, we could determine what kind of molecule would be needed to inactivate it. nstead of blindly experimenting with random antimalarials, we could reason about which existing drugs could be a first-wave therapeutic. This could save countless lives.
There are a few caveats though -
1) Protein nanomachinery is dynamic, but AlphaFold only predicts fixed protein structures. This limitation is a consequence of the fact that our existing techniques for empirically determining the structure of a protein — X-ray crystallography and cryo-electron microscopy — capture a static structure only. This static picture is the ground truth against which AlphaFold was trained. While AlphaFold has essentially solved the static structure prediction problem, there is a further rabbit hole of dynamic behavior to understand.
2) The time to actually operationally adapt it might take many years before we see it having any real influence in the world.