Sequence Prediction, Structural Modelling and the Challenges of Plasticity in Coiled Coils

Area of Science:

• Protein structure and bioinformatics
• Computational biology
• Structural biology

Background:

• Coiled coils are prevalent protein folding motifs with diverse biological roles.
• Computational tools have significantly advanced the study and design of coiled-coil assemblies.
• Existing methods often present coiled coils as static structures.

Purpose of the Study:

• To review in silico methods for coiled-coil prediction, classification, and modeling.
• To highlight the structural plasticity and dynamic conformational changes of coiled coils.
• To discuss the role of molecular dynamics simulations in understanding coiled-coil behavior.

Main Methods:

• Review of sequence-based algorithms for coiled-coil prediction.
• Analysis of parametric methods for generating atomic-resolution structural models.
• Exploration of molecular dynamics (MD) simulations for atomistic insights.

Main Results:

• Computational tools are essential but can oversimplify coiled coils as static.
• Coiled coils exhibit significant structural plasticity and can adopt multiple conformations.
• MD simulations provide crucial dynamic and mechanistic insights beyond static modeling.

Conclusions:

• The next generation of bioinformatics tools must account for coiled-coil plasticity.
• Integrating AI-predicted structures and large genomic datasets is key for future research.
• Understanding coiled-coil dynamics is vital for fully appreciating their biological functions.