Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• Intrinsically disordered proteins and regions (IDP/IDRs) are prevalent in all life domains.
• IDP/IDRs lack stable tertiary structures, existing in diverse transient states adaptable to binding partners and environmental changes.
• This conformational flexibility is crucial for IDP/IDRs' roles as interaction hubs in signaling, transcription, and translation.

Purpose of the Study:

• To review experimental and computational methods for analyzing IDP/IDR conformational landscapes.
• To highlight recent computational advancements enabling proteome-wide IDP/IDR conformation analysis.
• To discuss challenges in predicting IDP/IDR ensembles.

Main Methods:

• Review of established and novel experimental techniques for IDP/IDR structure determination.
• Evaluation of computational approaches, including molecular dynamics and machine learning, for predicting IDP/IDR conformational ensembles.
• Case study applying selected computational methods to the disordered N-terminal region of human copper transporter 1.

Main Results:

• IDP/IDRs' conformational heterogeneity presents significant analytical challenges.
• Recent computational advances offer powerful tools for large-scale IDP/IDR conformational analysis.
• Experimental and computational methods are complementary in characterizing IDP/IDR dynamics.

Conclusions:

• Understanding IDP/IDR conformational ensembles is key to elucidating their cellular functions.
• Computational methods are increasingly vital for exploring the vast conformational space of IDP/IDRs.
• Further development is needed to overcome challenges in accurate and comprehensive IDP/IDR ensemble prediction.