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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Predicting Carbonic Anhydrase Binding Affinity: Insights from QM Cluster Models.

Mackenzie Taylor¹, Haedam Mun¹, Junming Ho¹

¹School of Chemistry, The University of New South Wales, Sydney, NSW 2052, Australia.

The Journal of Physical Chemistry. B

|January 28, 2025

Summary

This summary is machine-generated.

Developing accurate quantum mechanics (QM) cluster models improves predictions of carbonic anhydrase binding affinity. Larger models enhance predictive power, offering insights for hybrid QM/molecular mechanics (QM/MM) approaches.

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Area of Science:

Computational chemistry
Biophysical chemistry
Drug discovery

Background:

Carbonic anhydrase is a key enzyme target for various diseases.
Accurate prediction of ligand binding affinity is crucial for drug development.
Quantum mechanical (QM) methods are essential for modeling molecular interactions but computationally expensive.

Purpose of the Study:

To develop and validate QM cluster models for predicting carbonic anhydrase binding affinity.
To assess the performance of various density functional theory (DFT) methods within these models.
To provide insights for optimizing QM region definitions in QM/MM simulations.

Main Methods:

Systematic development of QM cluster models of varying sizes.
Generation of reference binding energies using high-level DLPNO-CCSD(T)/CBS calculations.
Evaluation of DFT methods, including r2SCAN-3c and ωB97X-3c, against reference data.
Comparison of QM cluster model performance with traditional docking methods.

Main Results:

Predictive accuracy of QM cluster models systematically improves with increasing model size.
The r2SCAN-3c DFT composite method demonstrates good performance on larger models.
QM cluster models show significant improvement over docking for predicting binding trends.
Key interactions requiring QM treatment were identified.

Conclusions:

Validated QM cluster models offer a reliable approach for predicting binding affinity trends.
r2SCAN-3c on larger cluster models provides an economical yet accurate method for binding affinity prediction.
Findings inform the strategic definition of QM regions in QM/MM models for improved efficiency and accuracy.