The solution space is large enough that even proteins sampling it’s points at a rate of trillions per second couldn’t really fold if they were just searching randomly through all possible configurations, that would be NP complete. They don’t actually do this of course. Instead they fold piece by piece as they are produced, with local interactions forming domains which tend to retain their approximate structure once they come together to form a whole protein. They don’t enter the lowest possible energy state therefore. Prion diseases are an example of what can happen when proteins enter a normally inaccessible local energy minimum, which in that case happens to have a snowballing effect on other proteins.
The result is that they follow grooves in the energy landscape towards an energy well which is robust enough to withstand all sorts of variation, including the horrific inaccuracies of our attempts at modeling. Our energy functions are just very crude approximations to the real one, which is dependent on quantum level effects and therefore intractable.
Another issue is that proteins don’t fold in isolation—they interact with chaperone proteins and all sorts of other crap. So simulating their folding might require one to simulate a LOT of other things besides just the protein in question.
Even our ridiculously poor attempts at in silico folding are not completely useless though. They can even be improved with the help of the human brain (see Foldit). I think an A.I. should make good progress on the proteins that exist. Even if it can’t design arbitrary new ones from scratch, intelligent modification of existing ones would likely be enough to get almost anything done. Note also that an A.I. with that much power wouldn’t be limited to what already exists, technology is already in the works to produce arbitrary non-protein polymers using ribosome like systems, stuff like that would open up an unimaginably large space of solutions that existing biology doesn’t have access to.
Right. I’m actually not sure how relevant it all is to discussions of an A.I. trying to get arbitrary things done with proteins. Folding an existing protein may be a great deal easier than finding a protein which folds into an arbitrary shape. Probably not all shapes are allowed by the physics of the problem. Evolution can’t really be said to solve that problem either. It just produces small increments in fitness. Otherwise organisms’ proteomes would be a lot more efficient.
Although on second thought, an A.I. would probably just be able to design a protein with a massively low energy funnel, so that even if it couldn’t simulate folding perfectly, it could still get things done.
Regardless, an imperfect solution would probably suffice for world domination...
The solution space is large enough that even proteins sampling it’s points at a rate of trillions per second couldn’t really fold if they were just searching randomly through all possible configurations, that would be NP complete. They don’t actually do this of course. Instead they fold piece by piece as they are produced, with local interactions forming domains which tend to retain their approximate structure once they come together to form a whole protein. They don’t enter the lowest possible energy state therefore. Prion diseases are an example of what can happen when proteins enter a normally inaccessible local energy minimum, which in that case happens to have a snowballing effect on other proteins.
The result is that they follow grooves in the energy landscape towards an energy well which is robust enough to withstand all sorts of variation, including the horrific inaccuracies of our attempts at modeling. Our energy functions are just very crude approximations to the real one, which is dependent on quantum level effects and therefore intractable. Another issue is that proteins don’t fold in isolation—they interact with chaperone proteins and all sorts of other crap. So simulating their folding might require one to simulate a LOT of other things besides just the protein in question.
Even our ridiculously poor attempts at in silico folding are not completely useless though. They can even be improved with the help of the human brain (see Foldit). I think an A.I. should make good progress on the proteins that exist. Even if it can’t design arbitrary new ones from scratch, intelligent modification of existing ones would likely be enough to get almost anything done. Note also that an A.I. with that much power wouldn’t be limited to what already exists, technology is already in the works to produce arbitrary non-protein polymers using ribosome like systems, stuff like that would open up an unimaginably large space of solutions that existing biology doesn’t have access to.
Right, this gets called Levinthal’s Paradox.
Right. I’m actually not sure how relevant it all is to discussions of an A.I. trying to get arbitrary things done with proteins. Folding an existing protein may be a great deal easier than finding a protein which folds into an arbitrary shape. Probably not all shapes are allowed by the physics of the problem. Evolution can’t really be said to solve that problem either. It just produces small increments in fitness. Otherwise organisms’ proteomes would be a lot more efficient.
Although on second thought, an A.I. would probably just be able to design a protein with a massively low energy funnel, so that even if it couldn’t simulate folding perfectly, it could still get things done.
Regardless, an imperfect solution would probably suffice for world domination...