Molecular and stochastic dynamics of proteins

Area of Science:

• Computational Biophysics
• Protein Dynamics
• Spectroscopy

Background:

• Molecular dynamics (MD) simulations capture rapid protein atom fluctuations.
• Extrapolating these short-time motions to longer timescales is crucial for understanding protein function.
• Existing models may not fully capture the complex dynamics influencing protein behavior.

Purpose of the Study:

• To demonstrate the utility of effective single-particle stochastic models for extrapolating protein atom motions.
• To analyze atomic motions in the active site of Ribonuclease A (RNase A) and the heme group in myoglobin.
• To provide a theoretical explanation for experimental spectroscopic data, specifically 57Fe Mössbauer spectroscopy.

Main Methods:

• Utilized molecular dynamics (MD) simulations to obtain atomic fluctuation data.
• Employed effective single-particle stochastic models, fitting friction and noise parameters from MD data.
• Analyzed velocity autocorrelation functions for lysine side chains in RNase A.
• Applied the model to describe the quasistochastic motion of the heme group in myoglobin.

Main Results:

• The bounded stochastic model accurately describes atomic motions in RNase A.
• Low-frequency relaxation behavior is attributed to collisional damping, not dephasing.
• The model successfully explains experimental 57Fe Mössbauer spectroscopic data for myoglobin.

Conclusions:

• Effective single-particle stochastic models provide a robust method for extending MD-derived protein dynamics to longer timescales.
• Collisional damping is identified as the key mechanism for low-frequency protein motion.
• This modeling approach offers a powerful tool for interpreting complex biomolecular dynamics and spectroscopic observations.