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Predictive methods for computational metalloenzyme redesign - a test case with carboxypeptidase A.

Crystal E Valdez¹, Amanda Morgenstern², Mark E Eberhart²

¹Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA. ana@chem.ucla.edu.

Physical Chemistry Chemical Physics : PCCP

|November 15, 2016

Summary

Computational metalloenzyme design uses a multi-scale approach. New methods accurately predict enzyme activity, validating computational approaches for designing novel catalysts like carboxypeptidase A (CPA).

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Area of Science:

Biochemistry and Computational Chemistry
Enzyme Catalysis and Design

Background:

Metalloenzyme design is complex, requiring quantum mechanical treatment of metal centers, protein dynamics, and electrostatic preorganization.
Accurate computational modeling is crucial for understanding and engineering enzyme function.

Purpose of the Study:

To present a multi-scale computational framework for metalloenzyme design.
To validate the proposed methods using the well-studied Zn2+-dependent carboxypeptidase A (CPA).

Main Methods:

Quantum Mechanics/Drifting Dynamics (QM/DMD) for mixed quantum-classical dynamic sampling.
Quantum Theory of Atoms in Molecules (QTAIM) for electrostatic preorganization assessment.
Density Functional Theory (DFT) for mechanistic studies.

Main Results:

The computational methods accurately reproduced the catalytic mechanism and rate of native CPA.
Predictions were made for the activity of CPA mutants with modified substrates.
One specific CPA mutant and substrate combination showed predicted reasonable activity.

Conclusions:

The integrated computational approach is effective for predicting metalloenzyme activity and guiding enzyme design.
The study validates the use of QM/DMD, QTAIM, and DFT in concert for enzyme research.
Carboxypeptidase A serves as a robust test case for these advanced computational tools.