  We outline recent developments in artificial intelligence (AI) and machine
learning (ML) techniques for integrative structural biology of intrinsically
disordered proteins (IDP) ensembles. IDPs challenge the traditional protein
structure-function paradigm by adapting their conformations in response to
specific binding partners leading them to mediate diverse, and often complex
cellular functions such as biological signaling, self organization and
compartmentalization. Obtaining mechanistic insights into their function can
therefore be challenging for traditional structural determination techniques.
Often, scientists have to rely on piecemeal evidence drawn from diverse
experimental techniques to characterize their functional mechanisms. Multiscale
simulations can help bridge critical knowledge gaps about IDP structure
function relationships - however, these techniques also face challenges in
resolving emergent phenomena within IDP conformational ensembles. We posit that
scalable statistical inference techniques can effectively integrate information
gleaned from multiple experimental techniques as well as from simulations, thus
providing access to atomistic details of these emergent phenomena.
