Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to 'peak shift' to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.

Characterization and catalytic mechanism of a direct demethylsulfide hydrolase for catabolism of the methylthiol-s-triazine prometryn

Demethylthio is one of the most important ways for microorganisms to metabolize triazine herbicides. Previous studies have found that the initial reaction of prometryn catabolism in Leucobacter triazinivorans JW-1 was the hydroxylation of its methylthio group, however, the corresponding functional enzyme was not yet clear. In this study, the gene proA was responsible for the initial step of prometryn catabolism from the strain JW-1 was cloned and expressed, and the purified amidohydrolases ProA have the ability to transform prometryn to 2-hydroxypropazine and methanethiol. The optimized reaction temperature and pH of ProA were 45 °C and 7.0, respectively, and the kinetic constants K_m and V_max of ProA for the catalysis of prometryn were 32.6 μM and 0.09 μmol/min/mg, respectively. Molecular docking analyses revealed that different catalysis efficiency of ProA and TrzN (Nocardioides sp. C190) for prometryn and atrazine was due to non-covalent changes in amino acid residues. Our findings provide new insights into the understanding of s-triazine catabolism at the molecular level.

Engineering indel and substitution variants of diverse and ancient enzymes using Graphical Representation of Ancestral Sequence Predictions (GRASP)

Ancestral sequence reconstruction is a technique that is gaining widespread use in molecular evolution studies and protein engineering. Accurate reconstruction requires the ability to handle appropriately large numbers of sequences, as well as insertion and deletion (indel) events, but available approaches exhibit limitations. To address these limitations, we developed Graphical Representation of Ancestral Sequence Predictions (GRASP), which efficiently implements maximum likelihood methods to enable the inference of ancestors of families with more than 10,000 members. GRASP implements partial order graphs (POGs) to represent and infer insertion and deletion events across ancestors, enabling the identification of building blocks for protein engineering. To validate the capacity to engineer novel proteins from realistic data, we predicted ancestor sequences across three distinct enzyme families: glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD). All tested ancestors demonstrated enzymatic activity. Our study demonstrates the ability of GRASP (1) to support large data sets over 10,000 sequences and (2) to employ insertions and deletions to identify building blocks for engineering biologically active ancestors, by exploring variation over evolutionary time.

Substrate Analogues for the Enzyme-Catalyzed Detoxification of the Organophosphate Nerve Agents-Sarin, Soman, and Cyclosarin

The G-type nerve agents, sarin (GB), soman (GD), and cyclosarin (GF), are among the most toxic compounds known. Much progress has been made in evolving the enzyme phosphotriesterase (PTE) from Pseudomonas diminuta for the decontamination of the G-agents; however, the extreme toxicity of the G-agents makes the use of substrate analogues necessary. Typical analogues utilize a chromogenic leaving group to facilitate high-throughput screening, and substitution of an O-methyl for the P-methyl group found in the G-agents, in an effort to reduce toxicity. Till date, there has been no systematic evaluation of the effects of these substitutions on catalytic activity, and the presumed reduction in toxicity has not been tested. A series of 21 G-agent analogues, including all combinations of O-methyl, p-nitrophenyl, and thiophosphate substitutions, have been synthesized and evaluated for their ability to unveil the stereoselectivity and catalytic activity of PTE variants against the authentic G-type nerve agents. The potential toxicity of these analogues was evaluated by measuring the rate of inactivation of acetylcholinesterase (AChE). All of the substitutions reduced inactivation of AChE by more than 100-fold, with the most effective being the thiophosphate analogues, which reduced the rate of inactivation by about 4-5 orders of magnitude. The analogues were found to reliably predict changes in catalytic activity and stereoselectivity of the PTE variants and led to the identification of the BHR-30 variant, which has no apparent stereoselectivity against GD and a k_cat/K_m of 1.4 × 10⁶, making it the most efficient enzyme for GD decontamination reported till date.

Bacterial catabolism of s-triazine herbicides: biochemistry, evolution and application

The synthetic s-triazines are abundant, nitrogen-rich, heteroaromatic compounds used in a multitude of applications including, herbicides, plastics and polymers, and explosives. Their presence in the environment has led to the evolution of bacterial catabolic pathways in bacteria that allow use of these anthropogenic chemicals as a nitrogen source that supports growth. Herbicidal s-triazines have been used since the mid-twentieth century and are among the most heavily used herbicides in the world, despite being withdrawn from use in some areas due to concern about their safety and environmental impact. Bacterial catabolism of the herbicidal s-triazines has been studied extensively. Pseudomonas sp. strain ADP, which was isolated more than thirty years after the introduction of the s-triazine herbicides, has been the model system for most of these studies; however, several alternative catabolic pathways have also been identified. Over the last five years, considerable detail about the molecular mode of action of the s-triazine catabolic enzymes has been uncovered through acquisition of their atomic structures. These structural studies have also revealed insights into the evolutionary origins of this newly acquired metabolic capability. In addition, s-triazine-catabolizing bacteria and enzymes have been used in a range of applications, including bioremediation of herbicides and cyanuric acid, introducing metabolic resistance to plants, and as a novel selectable marker in fermentation organisms. In this review, we cover the discovery and characterization of bacterial strains, metabolic pathways and enzymes that catabolize the s-triazines. We also consider the evolution of these new enzymes and pathways and discuss the practical applications that have been considered for these bacteria and enzymes. One Sentence Summary: A detailed understanding of bacterial herbicide catabolic enzymes and pathways offer new evolutionary insights and novel applied tools.

Revealing Unexplored Sequence-Function Space Using Sequence Similarity Networks

The rapidly expanding number of protein sequences found in public databases can improve our understanding of how protein functions evolve. However, our current knowledge of protein function likely represents a small fraction of the diverse repertoire that exists in nature. Integrative computational methods can facilitate the discovery of new protein functions and enzymatic reactions through the observation and investigation of the complex sequence-structure-function relationships within protein superfamilies. Here, we highlight the use of sequence similarity networks (SSNs) to identify previously unexplored sequence and function space. We exemplify this approach using the nitroreductase (NTR) superfamily. We demonstrate that SSN investigations can provide a rapid and effective means to classify groups of proteins, therefore exposing experimentally unexplored sequences that may exhibit novel functionality. Integration of such approaches with systematic experimental characterization will expand our understanding of the functional diversity of enzymes and their associated physiological roles.

Evolutionary repurposing of a sulfatase: A new Michaelis complex leads to efficient transition state charge offset

The recruitment and evolutionary optimization of promiscuous enzymes is key to the rapid adaptation of organisms to changing environments. Our understanding of the precise mechanisms underlying enzyme repurposing is, however, limited: What are the active-site features that enable the molecular recognition of multiple substrates with contrasting catalytic requirements? To gain insights into the molecular determinants of adaptation in promiscuous enzymes, we performed the laboratory evolution of an arylsulfatase to improve its initially weak phenylphosphonate hydrolase activity. The evolutionary trajectory led to a 100,000-fold enhancement of phenylphosphonate hydrolysis, while the native sulfate and promiscuous phosphate mono- and diester hydrolyses were only marginally affected (≤50-fold). Structural, kinetic, and in silico characterizations of the evolutionary intermediates revealed that two key mutations, T50A and M72V, locally reshaped the active site, improving access to the catalytic machinery for the phosphonate. Measured transition state (TS) charge changes along the trajectory suggest the creation of a new Michaelis complex (E•S, enzyme-substrate), with enhanced leaving group stabilization in the TS for the promiscuous phosphonate (β_leavinggroup from -1.08 to -0.42). Rather than altering the catalytic machinery, evolutionary repurposing was achieved by fine-tuning the molecular recognition of the phosphonate in the Michaelis complex, and by extension, also in the TS. This molecular scenario constitutes a mechanistic alternative to adaptation solely based on enzyme flexibility and conformational selection. Instead, rapid functional transitions between distinct chemical reactions rely on the high reactivity of permissive active-site architectures that allow multiple substrate binding modes.