Computing Protein-Protein Association Affinity with Hybrid Steered Molecular Dynamics.

Area of Science:

• Computational Biophysics
• Biochemistry
• Molecular Dynamics

Background:

• Calculating protein-protein association affinities is crucial but computationally challenging due to high-dimensional phase spaces.
• Existing methods struggle to adequately sample these complex systems, even with high-performance computing.

Purpose of the Study:

• To extend the hybrid steered molecular dynamics (hSMD) approach for calculating protein-protein association energies.
• To address the limitations of sampling in high-dimensional phase spaces for protein complex formation.

Main Methods:

• The hSMD method steers multiple centers of mass from two protein protomers (P1 and P2) in opposite directions within a 3(m+n) dimensional phase space.
• It calculates the potential of mean force (PMF) difference and incorporates partial partition functions for rotational and vibrational degrees of freedom.
• The method reduces complex 6D partitions to simpler 1D sampling problems for efficient computation.

Main Results:

• The hSMD approach was applied to the Ras-RalGDS complex (m=n=3).
• The calculated association energy was -9.2 ± 1.9 kcal/mol using CHARMM 36 parameters.
• This result shows good agreement with experimental data (-8.4 ± 0.2 kcal/mol).

Conclusions:

• The extended hSMD method provides an accurate and efficient way to compute protein-protein association affinities.
• This approach overcomes significant computational hurdles in sampling complex molecular interactions.
• The findings have implications for understanding protein complex formation in biophysical and biochemical research.