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QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures
David W Kastner1,2, Weiliang Luo1,3, Wilson Ho1
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Journal of Chemical Information and Modeling
|May 5, 2026
Summary
QuantumPDB automates quantum mechanical (QM) model generation for enzymes, overcoming bottlenecks in computational studies. This Python package enables high-throughput screening by accurately representing complex active sites.
Area of Science:
- Computational chemistry
- Structural biology
- Biophysics
Background:
- Quantum mechanical (QM) calculations offer molecular insights into enzyme catalysis.
- Preparing QM calculations from experimental structures is a bottleneck for high-throughput studies.
- Existing automated tools struggle with diverse active site chemistries and geometries.
Purpose of the Study:
- To develop an automated workflow for generating QM cluster models from raw protein structures.
- To overcome limitations of current automated tools in handling diverse enzyme active sites.
- To enable high-throughput computational screening of enzymes.
Main Methods:
- Developed QuantumPDB, a Python package for automated QM model generation.
- Integrated structure cleaning, protonation state assignment, and QM calculation setup.
- Utilized Voronoi tessellation to create chemically meaningful interaction spheres for QM models.
Main Results:
- Successfully generated 1,673 QM cluster models for 842 holo-enzymes.
- Demonstrated that enzyme environments modulate substrate charge toward neutrality.
- Observed reduction in substrate dipole moment within simulated enzyme active sites.
Conclusions:
- QuantumPDB automates and standardizes multisphere QM model construction.
- Provides a robust platform for large-scale, data-driven protein investigations.
- Facilitates accurate representation of complex active site geometries for QM studies.
