In a way it is already incorporated. Broadly speaking, chaperones function by restricting the available conformational sampling space for the protein to fold. Some researchers even consider the ribosome as a chaperone of sorts for the nascent protein chain it synthetizes.
Protein structure prediction methods do the same: they find ways of restricting the conformational space to explore, in hopes of finding the global minimum-energy conformation representing the native structure of the protein.
if you want a protien or any other bio molecule to fold properly, a chaperone system must be either designed or elucidated.
the primary sequence is not the only consideration for proper folding.
chaperones allow higher energy folding events to occur and be maintained until subsequent modification stabilizes high energy structural motif.
chaperones also enforce an A before B before C regime of folding so that the sequence doesnt just crumple up according to energy of hydrostatic interactions
Sure, I get the mechanics. My question is, if we can incorporate knowledge about chaperones into the models as explicit or latent variables, so to speak, then why can’t the models predict something like “probability of molecule a given the presence of chaperone b”?
it sure can, given enough computation, chaperones are often protiens themselves but can be otherwise; they are subject to the same forces so twist and turn fold and conform.
they often interact with each other, and must exert influence at proper stage of modification.
other effects beyond foldingoccur, such as addition or elimination of prosthetic groups.
the take home message is fallacy of oversimplifying the process of many molecules plus ionic enironment, interacting to influence a single molecule
Protein structure prediction methods do the same: they find ways of restricting the conformational space to explore, in hopes of finding the global minimum-energy conformation representing the native structure of the protein.