Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins
This review addresses the challenge of characterizing intrinsically disordered proteins for structural biologists, which are crucial for understanding complex cellular functions.
This paper reviews recent advancements in AI/ML for integrative structural biology of intrinsically disordered proteins (IDPs). IDPs pose challenges for traditional structural determination due to their adaptable conformations, and the paper suggests that scalable statistical inference techniques can integrate diverse experimental and simulation data to provide atomistic details of IDP conformational ensembles.
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.