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Understanding and Improving Designed Enzymes by Computer Simulations

  • Author(s): Bhowmick, Asmit
  • Advisor(s): Head-Gordon, Teresa
  • et al.
Abstract

Abstract

Understanding and Improving Designed Enzymes by Computer Simulations

By

Asmit Bhowmick

Doctor of Philosophy in Chemical Engineering

University of California, Berkeley

Professor Teresa Head-Gordon, Chair

The ability to control for protein structure, electrostatics and dynamical motions is a fundamental problem that limits our ability to rationally design catalysts for new chemical reactions not known to have a natural biocatalyst. Current computational approaches for de novo enzyme design seek to engineer a small catalytic construct into an accommodating protein scaffold as exemplified by the Rosetta strategy. Here we consider 3 designed enzymes for the Kemp elimination reaction (KE07, KE70 and KE15) that showed minimal catalytic activity. KE07 and KE70 were subsequently improved by 2 orders of magnitude in catalytic efficiency by directed evolution and highlighted the shortcomings of the design process. This work studies two keys issues plaguing the designs – side chain conformational variability and electrostatics.

For the first part, a new Monte Carlo sampling method was developed that uses a physical forcefield and coupled with backbone variability and a backbone dependent rotamer library. Using transition state theory with energies/entropies calculated from Monte Carlo simulations, it is shown that in both KE07 and KE70, the initial design was over-optimized to stabilize the enzyme-substrate complex. Mutations introduced by directed evolutions led to destabilization of the enzyme-substrate complex and stabilization of the transition state. Furthermore, analysis of residue correlations via mutual information yielded hotspots, several of which were mutations during directed evolution. Laboratory mutations of these hotspots in the best variant of KE07 led to a drop in catalytic performance, demonstrating their importance. The metrics identified in KE07/KE70 studies were used to predict mutations to improve enzyme KE15 that had not been improved prior to this study. Several mutants, all predicted through computer simulations have now yielded better catalytic activity in the laboratory with the best one 10-fold better than the starting enzyme.

In order to quantify the role of electrostatics, a new method was developed using the AMOEBA polarizable forcefield that allowed splitting the contribution of electric field at the substrate by residues and solvent. The improvement in KE07 series could be tracked directly through changes in electric field at the substrate. In comparison, KE70 did not show a significant shift in electrostatic field, suggesting other factors like substrate binding may have been the reason for enhancement of activity. However, the common theme in both enzymes was the lack of participation (and in fact detrimental role) of the scaffold in the reaction. Future design efforts would benefit from an expanded theozyme and careful selection of scaffold based on electrostatic properties.

Generating efficient biocatalysts without using laboratory directed evolution would be an inflection point in the field of enzyme design. This work is a step in that direction.

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