Inferring protein-folding mechanisms from natural sequence diversity

Area of Science:

• Biophysics
• Computational Biology
• Protein Science

Background:

• Protein sequences encode evolutionary history, influencing structure, stability, and function.
• Predicting protein folding mechanisms from sequence alone is a significant challenge in biophysics.

Purpose of the Study:

• To infer globular protein folding mechanisms using only amino acid sequence information.
• To investigate the relationship between protein topology, sequence diversity, and folding cooperativity.

Main Methods:

• Mapping one- and two-body evolutionary energy fields to a coarse-grained model of foldons.
• Simulating folding mechanisms using an Ising chain model with foldon energetics derived from amino acid sequences.
• Analyzing folding cooperativity across diverse protein families and topologies.

Main Results:

• Protein sequence information can predict folding mechanisms beyond native structure and stability.
• Protein topology limits folding cooperativity variability within families.
• Alpha-helical (α) topologies exhibit more diverse folding scenarios compared to beta (β) and alpha/beta (α/β) structures.
• Mutation-induced changes in stability and cooperativity are computable from sequence-based evolutionary models.

Conclusions:

• Sequence-based evolutionary models provide a powerful tool for understanding protein folding dynamics.
• The study demonstrates a method to predict folding mechanisms and mutation effects directly from protein sequences.
• Findings offer insights into the evolutionary basis of protein folding diversity and constraints.