Insights into the stability of engineered mini-proteins from their dynamic electronic properties

Steeves, Adam H.; Kulik, Heather J.

doi:10.1088/2516-1075/ac8c73

“…The study informs the conditions under which different descriptors can be calculated with high fidelity for predicting the impact of mutations on catalytic functions. In addition, as the interplay between protein dynamics and electronic structures emerges as a new direction of study, 81,82 the convergence trend investigated in the current study might inspire the development of new strategies to predict computationally demanding QM properties using MM-derived properties.…”

Section: Introductionmentioning

confidence: 85%

Convergence in Determining Enzyme Functional Descriptors across Kemp Eliminase Variants

Jiang

¹

,

Stull

²

,

Shao

³

et al. 2022

Preprint

0

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Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on eighteen Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field on the breaking C–H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the electric field was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for eighteen Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSDactive_site and SASAratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EFC–H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

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“…The study informs the conditions under which different descriptors can be calculated with high fidelity for predicting the impact of mutations on catalytic functions. In addition, as the interplay between protein dynamics and electronic structures emerges as a new direction of study [81,82], the convergence trend investigated in the current study might inspire the development of new strategies to predict computationally demanding QM properties using MM-derived properties.…”

Section: Introductionmentioning

confidence: 88%

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

Jiang¹,

Stull²,

Shao³

et al. 2022

View full text Add to dashboard Cite

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on eighteen Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field on the breaking C–H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the electric field was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for eighteen Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSDactive_site and SASAratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EFC–H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

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“…45,88 The choice of partial charge scheme did not influence the overall trends, which were in agreement among most methods (Supporting Information Figures S16-S19). 45 For consistency with previous work, we used the Mulliken partial charge scheme for analyses using by-residue summed partial charges, 38,39 and we used the Hirshfeld partial charge scheme for calculations involving the ESP at the metal center (Supporting Information Figure S1). 45 Next, the charge covariance and mutual information (MI) were computed using sklearn and numpy with the nearest neighbors set to 10 for MI using an in-house script following previous work.…”

Section: D Quantum Mechanical Calculationsmentioning

confidence: 99%

“…To quantify charge-coupling interactions, we utilized linear cross-correlation 100 and mutual information 40,101,102 analyses from information theory of the by-residue-summed Mulliken partial charges on each residue, as introduced previously for charge-coupling in proteins. 38,39 Cross correlation captures the linear dependence between the charges in residue pairs, while mutual information captures both linear and nonlinear relationships through the statistical dependence of charges between residues. We computed cross correlation and mutual information scores for all DFT-computed snapshots on SQM trajectories (see Sec.…”

Section: D Charge-coupling Interactions Distinguish Mimochromesmentioning

confidence: 99%

“…While geometric and electronic effects are interconnected, even small differences in the electronic environment have shown to have large effects on heme catalysis through changes in the electrostatic potential, 32 electric fields, [32][33][34][35] and local dielectric constants. 36 Of particular interest are the effects of dynamic partial charge coupling, which has been shown computationally to be important in small proteins including to distinguish wild type and mutant species [37][38][39] and provides an electrostatic corollary to geometric allosteric coupling. 40 Unlike structural differences, interrogating differences in the electronic environment is typically difficult to probe experimentally for enzyme systems and often must rely extensively on computational studies.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Charge Distribution as a Key Driver of Catalytic Reactivity in an Artificial Metalloenzyme

Kastner,

Reinhardt,

Adamji

et al. 2024

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0

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Miniature artificial enzymes such as mimochromes provide a simplified platform to extract design principles for engineering rate enhancements beyond that of natural enzymes, although design optimizations have largely focused on geometric properties, leaving the impact of the electronic environment unexplored. To investigate how the electronic environment influences reactivity, we carry out classical and ab initio molecular dynamics (MD) simulations, supervised machine learning (ML), and statistical analysis of a series of mimochromes, MC6, MC6*, and MC6*a. Our classical MD simulations reveal a correlation between increased protein–heme contact and improved reactivity, confirming the importance of geometry, while ab initio MD simulations provide insight into the electronic environment, showing the electrostatic potential (ESP) at the metal center also correlates with reactivity. Quantum mechanical calculations of sulfoxidation and hydroxylation reactions demonstrate that the negative ESP at the metal center and active site electric field stabilize the highest-energy intermediate. Furthermore, using ML classifiers, we identify critical residues such as Lys12 and Asp18 in MC6*a that demonstrate charge-coupling patterns that explain differences in reactivity. This suggests that the reactivity series in mimochromes is primarily driven by key aspects of partial charge distribution dynamics, which should guide the engineering of next-generation metalloenzymes.

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Insights into the stability of engineered mini-proteins from their dynamic electronic properties

Cited by 4 publications

References 148 publications

Convergence in Determining Enzyme Functional Descriptors across Kemp Eliminase Variants

Convergence in Determining Enzyme Functional Descriptors across Kemp Eliminase Variants

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

Dynamic Charge Distribution as a Key Driver of Catalytic Reactivity in an Artificial Metalloenzyme

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