Discrete molecular dynamics can predict helical prestructured motifs in disordered proteins

Area of Science:

• Biochemistry and Molecular Biology
• Computational Biology
• Structural Biology

Background:

• Intrinsically disordered proteins (IDPs) lack stable structures but contain functional Pre-Structured Motifs (PreSMos).
• Current experimental and computational tools for studying IDP conformational ensembles and folding initiation are limited.
• Modulating IDP function is crucial for many biological processes.

Purpose of the Study:

• To develop and validate an efficient computational method for characterizing IDP conformational ensembles.
• To identify and analyze transient secondary structures, specifically alpha-helical PreSMos, in IDPs.
• To provide insights into the role of PreSMos in protein binding and function.

Main Methods:

• Utilized discrete molecular dynamics (DMD) simulations combined with the replica exchange (RE) method (RX-DMD).
• Employed an ab initio approach based on physical principles of protein folding and dynamics, distinct from methods relying on protein-protein interactions.
• Validated predictions against experimental Nuclear Magnetic Resonance (NMR) data.

Main Results:

• RX-DMD efficiently sampled conformational space and detected alpha-helical conformational states in disordered protein regions.
• The method accurately predicted alpha-PreSMos with high confidence, confirmed by NMR data.
• RX-DMD could resolve closely located alpha-PreSMos and assess helix stability, revealing their role in binding initiation or as binding elements.

Conclusions:

• RX-DMD offers a breakthrough computational approach for the structural and dynamical characterization of IDPs.
• The method generates reliable structural ensembles for IDPs, even in the absence of experimental data.
• Understanding PreSMo behavior is key to deciphering IDP function and designing therapeutic strategies.