Faculty Opinions recommendation of The challenge of predicting distal active site mutations in computational enzyme design.

2021 ◽  
Author(s):  
Adrian Mulholland
Keyword(s):  
Active Site ◽  
Enzyme Design ◽  
Computational Enzyme Design ◽  
Active Site Mutations

scholarly journals Enhancing a De Novo Enzyme Activity by Computationally-Focused, Ultra-Low-Throughput Sequence Screening

2020 ◽  
Author(s):  
Valeria A. Risso ◽  
Adrian Romero-Rivera ◽  
Luis I. Gutierrez-Rus ◽  
Mariano Ortega-Muñoz ◽  
Francisco Santoyo-Gonzalez ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Active Site ◽  
De Novo ◽  
Enzymatic Reaction ◽  
Valence Bond ◽  
Computational Procedure ◽  
Enzyme Design ◽  
Computational Enzyme Design ◽  
Stability Ranking ◽  
Random Library

<div> <div> <div> <p>Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains a sluggish process, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib (funclib-weizmann.ac.il) is a novel automated computational procedure which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of a stability metric. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this a particularly challenging system to optimize. Yet, experimental screening of a very small number of active-site, multi-point variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (~2·104 M-1 s-1 and ~102 s-1) that compare well with modern natural enzymes, and that approach the catalysis levels for the best Kemp eliminases derived from extensive screening. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ~2 kcal·mol-1 and indicate that the improvements in activity are linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by EVB-based computational predictions of catalytic activity, as a generalized approach for computational enzyme design. </p> </div> </div> </div>

Enhancing a De Novo Enzyme Activity by Computationally-Focused, Ultra-Low-Throughput Sequence Screening

2020 ◽  
Author(s):  
Valeria A. Risso ◽  
Adrian Romero-Rivera ◽  
Luis I. Gutierrez-Rus ◽  
Mariano Ortega-Muñoz ◽  
Francisco Santoyo-Gonzalez ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Active Site ◽  
De Novo ◽  
Enzymatic Reaction ◽  
Valence Bond ◽  
Computational Procedure ◽  
Enzyme Design ◽  
Computational Enzyme Design ◽  
Stability Ranking ◽  
Random Library

<div> <div> <div> <p>Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains a sluggish process, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib (funclib-weizmann.ac.il) is a novel automated computational procedure which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of a stability metric. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this a particularly challenging system to optimize. Yet, experimental screening of a very small number of active-site, multi-point variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (~2·104 M-1 s-1 and ~102 s-1) that compare well with modern natural enzymes, and that approach the catalysis levels for the best Kemp eliminases derived from extensive screening. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ~2 kcal·mol-1 and indicate that the improvements in activity are linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by EVB-based computational predictions of catalytic activity, as a generalized approach for computational enzyme design. </p> </div> </div> </div>

Enhancing a De Novo Enzyme Activity by Computationally-Focused, Ultra-Low-Throughput Sequence Screening

2020 ◽  
Author(s):  
Valeria A. Risso ◽  
Adrian Romero-Rivera ◽  
Luis I. Gutierrez-Rus ◽  
Mariano Ortega-Muñoz ◽  
Francisco Santoyo-Gonzalez ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Active Site ◽  
De Novo ◽  
Enzymatic Reaction ◽  
Valence Bond ◽  
Computational Procedure ◽  
Enzyme Design ◽  
Computational Enzyme Design ◽  
Stability Ranking ◽  
Random Library

<div> <div> <div> <p>Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains a sluggish process, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib (funclib-weizmann.ac.il) is a novel automated computational procedure which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of a stability metric. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this a particularly challenging system to optimize. Yet, experimental screening of a very small number of active-site, multi-point variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (~2·104 M-1 s-1 and ~102 s-1) that compare well with modern natural enzymes, and that approach the catalysis levels for the best Kemp eliminases derived from extensive screening. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ~2 kcal·mol-1 and indicate that the improvements in activity are linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by EVB-based computational predictions of catalytic activity, as a generalized approach for computational enzyme design. </p> </div> </div> </div>

scholarly journals Exploring the challenges of computational enzyme design by rebuilding the active site of a dehalogenase

2018 ◽  
Vol 116 (2) ◽  
pp. 389-394 ◽  
Author(s):  
Garima Jindal ◽  
Katerina Slanska ◽  
Veselin Kolev ◽  
Jiri Damborsky ◽  
Zbynek Prokop ◽  
...  
Keyword(s):  
Catalytic Activity ◽  
Active Site ◽  
Test Case ◽  
Wild Type ◽  
Haloalkane Dehalogenase ◽  
Enzyme Design ◽  
Wild Type Enzyme ◽  
Computational Enzyme Design ◽  
Activity Design ◽  
Computational Predictions

Rational enzyme design presents a major challenge that has not been overcome by computational approaches. One of the key challenges is the difficulty in assessing the magnitude of the maximum possible catalytic activity. In an attempt to overcome this challenge, we introduce a strategy that takes an active enzyme (assuming that its activity is close to the maximum possible activity), design mutations that reduce the catalytic activity, and then try to restore that catalysis by mutating other residues. Here we take as a test case the enzyme haloalkane dehalogenase (DhlA), with a 1,2-dichloroethane substrate. We start by demonstrating our ability to reproduce the results of single mutations. Next, we design mutations that reduce the enzyme activity and finally design double mutations that are aimed at restoring the activity. Using the computational predictions as a guide, we conduct an experimental study that confirms our prediction in one case and leads to inconclusive results in another case with 1,2-dichloroethane as substrate. Interestingly, one of our predicted double mutants catalyzes dehalogenation of 1,2-dibromoethane more efficiently than the wild-type enzyme.

scholarly journals Rational Prediction of Distal Activity-Enhancing Mutations in Tryptophan Synthase

2021 ◽  
Author(s):  
Miguel ÁNgel Maria-Solano ◽  
Thomas Kinateder ◽  
Javier Iglesias-Fernández ◽  
Reinhard Sterner ◽  
Sílvia Osuna
Keyword(s):  
Active Site ◽  
Enzyme Engineering ◽  
Rational Approach ◽  
Conformational Ensemble ◽  
Tryptophan Synthase ◽  
Amino Acid Residues ◽  
Enzyme Design ◽  
Computational Enzyme Design ◽  
Experimental Laboratory ◽  
Mechanistic Basis

Allostery is a central mechanism for the regulation of multi-enzyme complexes. The mechanistic basis that drives allosteric regulation is poorly understood, but harbors key information for enzyme engineering. In the present study, we focus on the tryptophan synthase complex that is composed of TrpA and TrpB subunits, which allosterically activate each other. Specifically, we develop a rational approach for identifying key amino acid residues of TrpB distal from the active site. In particular, we predict positions crucial for shifting the inefficient conformational ensemble of the isolated TrpB to a productive ensemble through intra-subunit allosteric effects. The experimental validation of the new conformationally-driven TrpB design demonstrates its superior stand-alone activity in the absence of TrpA, comparable to those enhancements obtained after multiple rounds of experimental laboratory evolution. Our work evidences that the current challenge of distal active site prediction for enhanced function in computational enzyme design can be ultimately addressed.

scholarly journals Rational Prediction of Distal Activity-Enhancing Mutations in Tryptophan Synthase

2021 ◽  
Author(s):  
Miguel ÁNgel Maria-Solano ◽  
Thomas Kinateder ◽  
Javier Iglesias-Fernández ◽  
Reinhard Sterner ◽  
Sílvia Osuna
Keyword(s):  
Active Site ◽  
Enzyme Engineering ◽  
Rational Approach ◽  
Conformational Ensemble ◽  
Tryptophan Synthase ◽  
Amino Acid Residues ◽  
Enzyme Design ◽  
Computational Enzyme Design ◽  
Experimental Laboratory ◽  
Mechanistic Basis

Allostery is a central mechanism for the regulation of multi-enzyme complexes. The mechanistic basis that drives allosteric regulation is poorly understood, but harbors key information for enzyme engineering. In the present study, we focus on the tryptophan synthase complex that is composed of TrpA and TrpB subunits, which allosterically activate each other. Specifically, we develop a rational approach for identifying key amino acid residues of TrpB distal from the active site. In particular, we predict positions crucial for shifting the inefficient conformational ensemble of the isolated TrpB to a productive ensemble through intra-subunit allosteric effects. The experimental validation of the new conformationally-driven TrpB design demonstrates its superior stand-alone activity in the absence of TrpA, comparable to those enhancements obtained after multiple rounds of experimental laboratory evolution. Our work evidences that the current challenge of distal active site prediction for enhanced function in computational enzyme design can be ultimately addressed.

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