Current Stage and Future Perspectives for Homology Modeling, Molecular Dynamics Simulations, Machine Learning with...

Area of Science:

• Proteomics
• Computational Biology
• Structural Biology

Background:

• Intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) pose challenges for traditional experimental characterization due to their dynamic and flexible nature.
• Understanding the structural ensembles of these proteins is critical for elucidating their biological functions.

Purpose of the Study:

• To review and discuss computational techniques applicable to the study of intrinsically disordered proteins (IDPs) and hybrid proteins containing intrinsically disordered regions (IDRs).
• To highlight the complementary role of computational approaches to experimental methods in characterizing IDP structural ensembles.

Main Methods:

• Homology modeling
• Molecular dynamics (MD) simulations
• Machine learning integrated with MD simulations
• Quantum computing applications

Main Results:

• Computational techniques provide valuable insights into the structural ensembles of IDPs and proteins with IDRs.
• A range of computational methods, from established to emerging, can be applied to investigate these challenging protein types.
• The discussion covers techniques applicable to both IDPs and hybrid proteins comprising ordered and disordered domains.

Conclusions:

• Computational strategies are indispensable for characterizing the structural ensembles of intrinsically disordered proteins and regions.
• Future perspectives suggest continued advancements in computational techniques will further enhance our ability to study IDPs and hybrid proteins.
• The integration of various computational approaches offers a powerful toolkit for structural and functional investigations of disordered proteins.