A Hybrid Hamiltonian for the Accelerated Sampling along Experimental Restraints

Area of Science:

• Computational chemistry
• Molecular dynamics simulations
• Biophysics

Background:

• Enhanced sampling methods are crucial for exploring complex molecular systems.
• Traditional methods often require prior knowledge of reaction coordinates, limiting their applicability.
• Integrating experimental data into simulations can improve accuracy and efficiency.

Purpose of the Study:

• To develop an enhanced sampling method using a hybrid Hamiltonian.
• To enable adaptive incorporation of experimental distance restraints.
• To avoid the need for pre-defined reaction coordinates.

Main Methods:

• A hybrid Hamiltonian combining experimental distance restraints with path-dependent bias variables.
• On-the-fly determination of bias-coordinates.
• Validation on water models, dialanine, TrpCage mini-protein, and NKR-P1A.

Main Results:

• The method successfully accelerates protein sampling.
• Experimental restraints are adaptively incorporated based on system dynamics.
• Accurate exploration of conformational landscapes using NMR and crosslinking data.

Conclusions:

• The new methodology enhances molecular simulations by adaptively integrating experimental data.
• It offers broad applicability and potential for combination with coarse-graining methods.
• The approach allows exploration of conformational space without compromising ergodicity.