ArticleProceedings of the National Academy of Sciences of the United States of America2022
Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model.ACS catalysis · 2026Article
- What Determines the Directionality of Kinesins? Studying the Anomalous Directionality of Nonclaret Disjunctional Motor Mutants.Journal of the American Chemical Society · 2026Article
- Exploring evolutionary trajectories of drug resistance.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Amino acid substitutions at rheostat positions in the NaProtein science : a publication of the Protein Society · 2025Article
- Approaches for regulating enzyme activities: Recent advances in experiment and computation.Current opinion in structural biology · 2025Review
- Ancestral Protein-Based Lighting.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Bright Thermo-resilient and Promiscuous Zombie Protein for Lighting Applications.ACS materials letters · 2025Article
- Dissecting the effects of single amino acid substitutions in SARS-CoV-2 Mpro.Protein science : a publication of the Protein Society · 2025Article
- SubTuner leverages physics-based modeling to complement AI in enzyme engineering toward non-native substrates.Chem catalysis · 2025Article
- Rheostatic contributions to protein stability can obscure a position's functional role.Protein science : a publication of the Protein Society · 2024Article
- Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function.The Journal of biological chemistry · 2024Review
- Substrate Positioning Dynamics Involves a Non-Electrostatic Component to Mediate Catalysis.The journal of physical chemistry letters · 2023Article
- Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.National science review · 2023Review
- Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Harnessing Generative AI to Decode Enzyme Catalysis and Evolution for Enhanced Engineering.bioRxiv : the preprint server for biology · 2023Article
- EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.Journal of chemical information and modeling · 2023Article
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2 authors.
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Abstract
Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.
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