Enzymatic Reactivity Modeling Data for Phosphate Hydrolysis

A dataset supporting the Locality Accelerated and Systematically Improvable (LASI) QM/MM protocol for computational enzymology. It contains results from quantum embedding and DFT calculations, including data for enzyme-catalyzed phosphate hydrolysis reactions. The dataset is provided by author Dénes Berta in an XLS file of 144.0 KB.

Use Cases

• Benchmark density functional theory (DFT) models against local natural orbital (LNO)-based CCSD(T) reference energetics for chemical accuracy.
• Apply the local embedded subsystem (LESS) approach to analyze cost reduction for DFT calculations with QM subsystems up to 400 atoms.
• Validate the LASI protocol using data from clinically relevant enzyme-catalyzed phosphate hydrolysis studies.
• Analyze hybrid DFT method selections and their suitability for calculating reaction barriers in large QM subsystems.

Strengths

• Data supports a validated protocol for achieving chemical accuracy in QM/MM simulations at an affordable computational cost.
• Includes results for challenging and clinically relevant enzyme-catalyzed phosphate hydrolysis, providing a representative test case.
• The LASI scheme is designed for systematic improvability, benefiting from converged QM size and quantum embedding affordability.

Limitations

• The dataset is small in scale at 144.0 KB, limiting the scope of analysis or model training.
• Specific row counts and column structures are unknown, which may complicate direct integration into automated pipelines.
• Data is focused on a specific enzymatic reaction (phosphate hydrolysis), limiting generalizability to other biochemical systems without adaptation.

Provenance

Source

Dénes Berta via figshare.

Collection Method

Generated through quantum mechanics/molecular mechanics (QM/MM) simulations, employing the LASI protocol with LNO-CCSD(T), DFT, and LESS quantum embedding.

Freshness

Last updated on 2026-03-25.