Taming the complexity of protein folding

Area of Science:

• Structural biology
• Computational biology
• Biophysics

Background:

• Protein folding is a fundamental problem in structural biology with implications for diseases such as Alzheimer's.
• Solving the protein folding problem requires integrating theoretical models with experimental data.
• Progress over the last decade has been driven by theoretical advancements.

Purpose of the Study:

• To review recent progress in theoretical models of protein folding.
• To highlight the impact of chemically detailed models on understanding folding pathways.
• To discuss new insights from Markov state models (MSMs) in protein folding.

Main Methods:

• Review of theoretical advances in protein folding over the past decade.
• Application and analysis of Markov state models (MSMs) for protein folding simulations.
• Integration of computational modeling with experimental data.

Main Results:

• Recent theoretical advances are enabling more chemically detailed models of protein folding.
• Markov state models (MSMs) provide new insights into the protein folding process.
• Key findings include the role of non-native contacts and the hub-like nature of folded protein states.

Conclusions:

• Theoretical models, especially MSMs, are significantly advancing our understanding of protein folding.
• These models offer a comprehensive view of folding dynamics and pathways.
• Further integration of theory and experiment is essential for solving the protein folding problem and addressing misfolding disorders.