A Scoring Function for Monolayer-Protected Gold Nanoparticles Capable of Recognizing Small Organic Molecules in...

Area of Science:

• Nanotechnology
• Chemical Sensing
• Computational Chemistry

Background:

• Ligand-coated gold nanoparticles (AuNPs) function as nanoreceptors for selective analyte recognition.
• Applications include nuclear magnetic resonance (NMR) chemosensing.
• Rational design of AuNP-based nanosensors requires efficient prediction of binding affinities.

Purpose of the Study:

• To develop a data-driven scoring function for rapid estimation of AuNP-analyte binding affinities.
• To enable fast in silico prescreening of ligand-coated AuNP sensors.
• To provide insights into atomistic interactions governing sensor performance.

Main Methods:

• Development of a scoring function using chemical similarity, hydrophobicity, and charge complementarity.
• Validation of the scoring function against experimental data.
• Enhanced sampling molecular dynamics simulations to study binding interactions.

Main Results:

• The scoring function demonstrated excellent predictive accuracy (R² = 0.85, MAE = 0.45 kcal/mol).
• Molecular dynamics revealed critical roles of ligand flexibility, monolayer packing, and hydrogen bonding.
• Insights were gained into factors influencing binding, especially for weak interactions.

Conclusions:

• The data-driven scoring function provides a robust framework for rational design of AuNP nanosensors.
• Atomistic insights enhance understanding and optimization of AuNP-analyte interactions.
• This approach accelerates the development of selective and efficient nanosensing platforms.