Toward the identification of molecular cogs

Area of Science:

• Computational Chemistry
• Molecular Dynamics
• Biophysics

Background:

• Molecular simulations provide insights into atomic interactions and motions.
• Understanding mesoscopic structural transformations and their causal relationships is challenging.
• Free energy changes are crucial for comprehending molecular structural and functional properties.

Purpose of the Study:

• To develop a method for quantifying atomic energetic contributions to free energy changes during molecular transformations.
• To identify and categorize atoms ('molecular cogs') that influence these transformations.
• To enhance the understanding of structure-function relationships in molecular systems.

Main Methods:

• Quantifying the energetic contribution of atom pairs to total free energy change along a collective variable.
• Employing a genetic clustering algorithm to partition atoms into 'forward' and 'reverse' molecular cogs.
• Testing the procedure on small molecules to validate the algorithm's partitioning capabilities.

Main Results:

• The method successfully quantifies individual atomic energetic contributions to free energy changes.
• A genetic clustering algorithm effectively identifies 'molecular cogs' (forward and reverse) in molecular systems.
• The primary output is a plot illustrating the energetic contributions of molecular cogs to the Potential of Mean Force (PMF) change.

Conclusions:

• The developed method provides a novel way to dissect free energy changes in molecular systems.
• Identifying molecular cogs offers deeper insights into the mechanisms driving structural transformations.
• This approach lays the groundwork for future implementations to better understand molecular behavior.