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 The Essential Functional Interplay of the Catalytic Groups in Acid Phosphatase

2021 ◽  
Author(s):  
Martin Pfeiffer ◽  
Bernd Nidetzky ◽  
Rory Crean ◽  
Cátia Moreira ◽  
Antonietta Parracino ◽  
...  
Keyword(s):  
Acid Phosphatase ◽  
Active Site ◽  
Enzymatic Reaction ◽  
Valence Bond ◽  
Phosphoryl Transfer ◽  
Enzyme Design ◽  
Linear Free Energy ◽  
Mechanistic Pathway ◽  
Empirical Valence Bond ◽  
Energy Relationships

Cooperative interplay between the functional devices of a preorganized active site is fundamental to enzyme catalysis. A deepened understanding of this phenomenon is central to elucidating the remarkable efficiency of natural enzymes, and provides an essential benchmark for enzyme design and engineering. Here, we study the functional interconnectedness of the catalytic nucleophile (His18) in an acid phosphatase by analyzing the consequences of its replacement with aspartate. We present crystallographic, biochemical and computational evidence for a conserved mechanistic pathway via a phospho-enzyme intermediate on Asp18. Linear free-energy relationships for phosphoryl transfer from phosphomonoester substrates to His18/Asp18 provide evidence for cooperative interplay between the nucleophilic and general-acid catalytic groups in the wildtype enzyme, and its substantial loss in the H18D variant. As an isolated factor of phosphatase efficiency, the advantage of a histidine compared to an aspartate nucleophile is around 10^4-fold. Cooperativity with the catalytic acid adds ≥10^2-fold to that advantage. Empirical valence bond simulations of phosphoryl transfer from glucose 1-phosphate to His and Asp in the enzyme explain the loss of activity of the Asp18 enzyme through a combination of impaired substrate positioning in the Michaelis complex, as well as a shift from early to late protonation of the leaving group in the H18D variant. The evidence presented furthermore suggests that the cooperative nature of catalysis distinguishes the enzymatic reaction from the corresponding reaction in solution and is enabled by the electrostatic preorganization of the active site. Our results reveal sophisticated discrimination in multifunctional catalysis of a highly proficient phosphatase active site.

scholarly journals Ensemble-based enzyme design can recapitulate the effects of laboratory directed evolution in silico

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Aron Broom ◽  
Rojo V. Rakotoharisoa ◽  
Michael C. Thompson ◽  
Niayesh Zarifi ◽  
Erin Nguyen ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Protein Design ◽  
Active Site ◽  
Room Temperature ◽  
De Novo ◽  
Conformational Ensemble ◽  
Enzyme Design ◽  
X Ray ◽  
Conformational Ensembles ◽  
X Ray Crystallography

Abstract The creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we use room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 (kcat/KM 146 M−1s−1). We observe that catalytic residues are increasingly rigidified, the active site becomes better pre-organized, and its entrance is widened. Based on these observations, we engineer HG4, an efficient biocatalyst (kcat/KM 103,000 M−1s−1) containing key first and second-shell mutations found during evolution. HG4 structures reveal that its active site is pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data.

scholarly journals Evolution of an enzyme conformational ensemble guides design of an efficient biocatalyst

2020 ◽  
Author(s):  
Aron Broom ◽  
Rojo V. Rakotoharisoa ◽  
Michael C. Thompson ◽  
Niayesh Zarifi ◽  
Erin Nguyen ◽  
...  
Keyword(s):  
Directed Evolution ◽  
Protein Design ◽  
Active Site ◽  
Room Temperature ◽  
De Novo ◽  
Conformational Ensemble ◽  
Enzyme Design ◽  
X Ray ◽  
Conformational Ensembles ◽  
X Ray Crystallography

AbstractThe creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we used room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 (kcat/KM 160 M−1s−1). We observed that catalytic residues were increasingly rigidified, the active site became better pre-organized, and its entrance was widened. Based on these observations, we engineered HG4, an efficient biocatalyst (kcat/KM 120,000 M−1s−1) containing active-site mutations found during evolution but not distal ones. HG4 structures revealed that its active site was pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data.

scholarly journals Adaptive landscape flattening allows the design of both enzyme:substrate binding and catalytic power

2019 ◽  
Author(s):  
Vaitea Opuu ◽  
Giuliano Nigro ◽  
Emmanuelle Schmitt ◽  
Yves Mechulam ◽  
Thomas Simonson
Keyword(s):  
Transition State ◽  
Directed Evolution ◽  
Bound State ◽  
Reaction Rates ◽  
Trna Synthetase ◽  
Adaptive Landscape ◽  
Enzyme Design ◽  
Computational Enzyme Design ◽  
Genetic Code Expansion ◽  
Catalytic Power

AbstractDesigned enzymes are of fundamental and technological interest. Experimental directed evolution still has significant limitations, and computational approaches are complementary. A designed enzyme should satisfy multiple criteria: stability, substrate binding, transition state binding. Such multi-objective design is computationally challenging. Two recent studies used adaptive importance sampling Monte Carlo to redesign proteins for ligand binding. By first flattening the energy landscape of the apo protein, they obtained positive design for the bound state and negative design for the unbound. We extend the method to the design of an enzyme for specific transition state binding, i.e., for catalytic power. We consider methionyl-tRNA synthetase (MetRS), which attaches methionine (Met) to its cognate tRNA, establishing codon identity. MetRS and other synthetases have been extensively redesigned by experimental directed evolution to accept noncanonical amino acids as substrates, leading to genetic code expansion. We redesigned MetRS computationally to bind several ligands: the Met analog azidonorleucine, methionyl-adenylate (MetAMP), and the activated ligands that form the transition state for MetAMP production. Enzyme mutants known to have azidonorleucine activity were recovered, and mutants predicted to bind MetAMP were characterized experimentally and found to be active. Mutants predicted to have low activation free energies for MetAMP production were found to be active and the predicted reaction rates agreed well with the experimental values. We expect the present method will become the paradigm for computational enzyme design.

scholarly journals Computational Enzyme Design at Zymvol

2021 ◽  
pp. 249-259
Author(s):  
Emanuele Monza ◽  
Victor Gil ◽  
Maria Fatima Lucas
Keyword(s):  
Directed Evolution ◽  
Target Function ◽  
Enzyme Engineering ◽  
Current Development ◽  
Enzyme Design ◽  
Future Directions ◽  
Computational Enzyme Design ◽  
Random Nature ◽  
Limited Sequence

AbstractDirected evolution is the most recognized methodology for enzyme engineering. The main drawback resides in its random nature and in the limited sequence exploration; both require screening of thousands (if not millions) of variants to achieve a target function. Computer-driven approaches can limit laboratorial screening to a few hundred candidates, enabling and accelerating the development of industrial enzymes. In this book chapter, the technology adopted at Zymvol is described. An overview of the current development and future directions in the company is also provided.

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.

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