Protein self-entanglement modulates successful folding to the native state: A multi-scale modeling study

Area of Science:

• Computational Biology
• Biophysics
• Protein Folding Dynamics

Background:

• Coarse-grained models offer computational efficiency for studying protein folding, but their accuracy can be limited by complex protein topologies like knots and slipknots.
• These topological features create conformational bottlenecks, hindering the folding process and posing challenges for low-resolution modeling approaches.

Purpose of the Study:

• To investigate the relationship between protein folding probability, folding pathways, and topological complexity in proteins with nontrivial structures.
• To assess the efficacy of the elastic folder model in simulating folding dynamics and predicting folding rates for topologically complex proteins.

Main Methods:

• Utilized the elastic folder model, a coarse-grained approach employing angular potentials optimized through a genetic algorithm, to simulate protein folding.
• Estimated in silico folding probabilities for a set of proteins exhibiting significant topological complexity.

Main Results:

• Demonstrated an inverse correlation between a measure of topological complexity and estimated folding probability.
• Observed a strong positive correlation between the model's predicted folding probabilities and experimental measurements of protein folding rates.

Conclusions:

• The topological complexity of a protein's native state significantly decreases its folding probability.
• The employed force-field optimization in the elastic folder model effectively mimics evolutionary processes that enable proteins to overcome kinetic traps associated with complex topologies.