Mapping conformational dynamics of proteins using torsional dynamics simulations

Area of Science:

• Computational biology
• Biophysics
• Molecular dynamics

Background:

• All-atom molecular dynamics (MD) simulations are crucial for studying protein flexibility.
• Simulating millisecond-timescale protein dynamics necessitates enhanced sampling techniques.
• Traditional MD methods often struggle to capture slow conformational changes.

Purpose of the Study:

• To introduce and validate the Generalized Newton-Euler Inverse Mass Operator (GNEIMO) method for enhanced protein conformational sampling.
• To assess GNEIMO's efficiency in simulating millisecond-timescale dynamics across various proteins.
• To compare GNEIMO performance against traditional Cartesian MD simulations.

Main Methods:

• Development and application of the Generalized Newton-Euler Inverse Mass Operator (GNEIMO) torsional dynamics method.
• Simulations of four proteins: fasciculin, calmodulin, crambin, and bovine pancreatic trypsin inhibitor.
• Utilizing GNEIMO with replica exchange and implicit-solvent models.

Main Results:

• GNEIMO simulations successfully sampled conformational substates of fasciculin and transitions in calmodulin.
• For crambin and bovine pancreatic trypsin inhibitor, GNEIMO reproduced structural fluctuations comparable to explicit-solvent Cartesian MD.
• GNEIMO captured conformational transitions that were missed by unconstrained all-atom Cartesian simulations.

Conclusions:

• The GNEIMO method significantly enhances protein conformational sampling, particularly for low-frequency torsional degrees of freedom.
• GNEIMO is a promising technique for efficiently studying long-timescale protein dynamics, including domain motion.
• This method overcomes limitations of traditional MD in capturing rare conformational events.