Protein dynamic communities from elastic network models align closely to the communities defined by molecular...

Area of Science:

• Structural biology
• Computational biophysics

Background:

• Dynamic communities are cohesive protein units exhibiting internal motions essential for function.
• Identifying these communities typically requires extensive molecular dynamics (MD) simulations.
• Previous work highlighted their role in allosteric regulation and the impact of mutations.

Purpose of the Study:

• To investigate if simpler elastic network models (ENMs) can accurately identify protein dynamic communities.
• To compare community structures derived from MD simulations and ENMs across various proteins.
• To assess the utility of ENM-based community analysis for predicting the effects of mutations.

Main Methods:

• Comparative analysis of dynamic communities identified by MD simulations and ENMs for 44 proteins.
• Evaluation of the correspondence between communities and residue cross-correlations from both methods.
• Application of ENMs to compare wild-type T4 Lysozyme with its stable and unstable mutants.

Main Results:

• A strong correspondence was found between protein dynamic communities identified by MD and ENMs.
• ENMs closely reproduced the dynamic communities derived from MD simulations.
• ENM-based community analysis effectively distinguished between stable and unstable T4 Lysozyme mutants.

Conclusions:

• Elastic network models offer a computationally efficient alternative to MD simulations for identifying protein dynamic communities.
• ENM-derived community structures can serve as a rapid screening tool to identify deleterious protein mutants.