Evolutionary drivers of thermoadaptation in enzyme catalysis

Insights into the low-temperature adaptation of an enzyme as studied through ancestral sequence reconstruction

For billions of years, enzymes have evolved in response to the changing environments in which their host organisms lived. Various lines of evidence suggest the earliest primitive organisms inhabited high-temperature environments and possessed enzymes adapted to such conditions. Consequently, extant mesophilic and psychrophilic enzymes are believed to have adapted to lower temperatures during the evolutionary process. Herein, we analyzed this low-temperature adaptation using ancestral sequence reconstruction. Previously, we generated the phylogenetic tree of 3-isopropylmalate dehydrogenases (IPMDHs) and reconstructed the sequence of the last bacterial common ancestor. The corresponding ancestral enzyme displayed high thermostability and catalytic activity at elevated temperatures but moderate activity at low temperatures (Furukawa et al., Sci. Rep., 2020;10:15493). Here, to identify amino acid residues that are responsible for the low-temperature adaptation, we reconstructed and characterized all 11 evolutionary intermediates that sequentially connect the last bacterial common ancestor with extant mesophilic IPMDH from Escherichia coli. A remarkable change in catalytic properties, from those suited for high reaction temperatures to those adapted for low temperatures, occurred between two consecutive evolutionary intermediates. Using a combination of sequence comparisons between ancestral proteins and site-directed mutagenesis analyses, three key amino acid substitutions were identified that enhance low-temperature catalytic activity. Intriguingly, amino acid substitutions that had the most significant impact on activity at low temperatures displayed no discernable effect on thermostability. However, these substitutions markedly reduced the activation energy for catalysis, thereby improving low-temperature activity. The results were further investigated by molecular dynamics simulations of the predicted structures of the ancestral enzymes. Our findings exemplify how ancestral sequence reconstruction can identify residues crucial for adaptation to low temperatures.

Structural and Dynamic Impacts of Single-atom Disruptions to Guide RNA Interactions within the Recognition Lobe of Geobacillus stearothermophilus Cas9

The intuitive manipulation of specific amino acids to alter the activity or specificity of CRISPR-Cas9 has been a topic of great interest. As a large multi-domain RNA-guided endonuclease, the intricate molecular crosstalk within the Cas9 protein hinges on its conformational dynamics, but a comprehensive understanding of the extent and timescale of the motions that drive its allosteric function and association with nucleic acids remains elusive. Here, we investigated the structure and multi-timescale molecular motions of the recognition (Rec) lobe of GeoCas9, a thermophilic Cas9 from Geobacillus stearothermophilus. Our results provide new atomic details about the GeoRec subdomains (GeoRec1, GeoRec2) and the full-length domain in solution. Two rationally designed mutants, K267E and R332A, enhanced and redistributed micro-millisecond flexibility throughout GeoRec, and NMR studies of the interaction between GeoRec and its guide RNA showed that mutations reduced this affinity and the stability of the ribonucleoprotein complex. Despite measured biophysical differences due to the mutations, DNA cleavage assays reveal no functional differences in on-target activity, and similar specificity. These data suggest that guide RNA interactions can be tuned at the biophysical level in the absence of major functional losses, but also raise questions about the underlying mechanism of GeoCas9, since analogous single-point mutations have significantly impacted on- and off-target DNA editing in mesophilic S. pyogenes Cas9. A K267E/R332A double mutant did also did not enhance GeoCas9 specificity, highlighting the robust tolerance of mutations to the Rec lobe of GeoCas9 and species-dependent complexity of Rec across Cas9 paralogs. Ultimately, this work provides an avenue by which to modulate the structure, motion, and guide RNA interactions at the level of the Rec lobe of GeoCas9, setting the stage for future studies of GeoCas9 variants and their effect on its allosteric mechanism.

A Practical Guide to Computational Tools for Engineering Biocatalytic Properties

The growing demand for efficient, selective, and stable enzymes has fueled advancements in computational enzyme engineering, a field that complements experimental methods to accelerate enzyme discovery. With a plethora of software and tools available, researchers from different disciplines often face challenges in selecting the most suitable method that meets their requirements and available starting data. This review categorizes the computational tools available for enzyme engineering based on their capacity to enhance the following specific biocatalytic properties of biotechnological interest: (i) protein-ligand affinity/selectivity, (ii) catalytic efficiency, (iii) thermostability, and (iv) solubility for recombinant enzyme production. By aligning tools with their respective scoring functions, we aim to guide researchers, particularly those new to computational methods, in selecting the appropriate software for the design of protein engineering campaigns. De novo enzyme design, involving the creation of novel proteins, is beyond this review's scope. Instead, we focus on practical strategies for fine-tuning enzymatic performance within an established reference framework of natural proteins.

Polyesterase activity is widespread in the family IV carboxylesterases from bacteria

Enzyme-based depolymerization of plastics, including polyesters, has emerged as a promising approach for plastic waste recycling and reducing environmental plastic pollution. Currently, most of the known polyester-degrading enzymes are represented by a few natural and engineered PETases from the carboxylesterase family V. To identify novel groups of polyesterases, we selected 25 proteins from the carboxylesterase family IV, which share 22 % to 80 % sequence identity to the metagenomic thermophilic polyesterase IS12. All purified proteins were found to be active against chromogenic para-nitrophenyl esters with a preference for short acyl chains. Screening for polyesterase activity using emulsified polyesters demonstrated the presence of hydrolytic activity against bis(benzoyloxyethyl) terephthalate (3PET), polycaprolactone (PCL), and polylactic acid (PLA) in all tested proteins. Biochemical characterization of four selected polyesterases revealed high thermostability in CBA10055, whereas the mesophilic GEN0105 exhibited higher polyesterase activity. Two ancestral variants of GEN0105 showed higher thermostability and activity against PCL and PLA, but reduced activity with amorphous PET. Furthermore, six established PETases were found to be highly active against PCL and PLA. Thus, our results indicate that polyesterase activity is widespread in the family IV carboxylesterases, and that most polyesterases are promiscuous being able to degrade different polyesters.

Metabolic rearrangement enables adaptation of microbial growth rate to temperature shifts

Temperature is a key determinant of microbial behaviour and survival in the environment and within hosts. At intermediate temperatures, growth rate varies according to the Arrhenius law of thermodynamics, which describes the effect of temperature on the rate of a chemical reaction. However, the mechanistic basis for this behaviour remains unclear. Here we use single-cell microscopy to show that Escherichia coli exhibits a gradual response to temperature upshifts with a timescale of ~1.5 doublings at the higher temperature. The response was largely independent of initial or final temperature and nutrient source. Proteomic and genomic approaches demonstrated that adaptation to temperature is independent of transcriptional, translational or membrane fluidity changes. Instead, an autocatalytic enzyme network model incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, resulting in a transient temperature memory. The model successfully predicts alterations in the temperature response across nutrient conditions, diverse E. coli strains from hosts with different body temperatures, soil-dwelling Bacillus subtilis and fission yeast. In sum, our model provides a mechanistic framework for Arrhenius-dependent growth.

Ancestral Sequence Reconstruction for Designing Biocatalysts and Investigating their Functional Mechanisms

Biocatalysis has emerged as a green approach for efficient and sustainable production in various industries. In recent decades, numerous advancements in computational and predictive approaches, including ancestral sequence reconstruction (ASR) have sparked a new wave for protein engineers to improve and expand biocatalyst capabilities. ASR is an evolution-based strategy that uses phylogenetic relationships among homologous extant sequences to probabilistically infer the most likely ancestral sequences. It has proven to be a powerful tool with applications ranging from creating highly stable enzymes for direct applications to preparing moderately active robust protein scaffolds for further enzyme engineering. Intriguingly, it can also provide insights into fundamental aspects that are challenging to study with extant (current) enzymes. This Perspective discusses a practical strategy for guiding enzyme engineers on how to embrace ASR as a practical or associated protocol for protein engineering and highlights recent examples of using ASR in various applications, including increasing thermostability, expanding promiscuity, fine-tuning selectivity and function, and investigating mechanistic and evolution aspects. We believe that the use of the ASR approach will continue to contribute to the ongoing development of the biocatalysis field. We have been in a "golden era" for biocatalysis in which numerous useful enzymes have been developed through many waves of enzyme engineering via advancements in computational methodologies.

The Characterization of Ancient Methanococcales Malate Dehydrogenases Reveals That Strong Thermal Stability Prevents Unfolding Under Intense γ-Irradiation

Malate dehydrogenases (MalDHs) (EC.1.1.1.37), which are involved in the conversion of oxaloacetate to pyruvate in the tricarboxylic acid cycle, are a relevant model for the study of enzyme evolution and adaptation. Likewise, a recent study showed that Methanococcales, a major lineage of Archaea, is a good model to study the molecular processes of proteome thermoadaptation in prokaryotes. Here, we use ancestral sequence reconstruction and paleoenzymology to characterize both ancient and extant MalDHs. We observe a good correlation between inferred optimal growth temperatures and experimental optimal temperatures for activity (A-Topt). In particular, we show that the MalDH present in the ancestor of Methanococcales was hyperthermostable and had an A-Topt of 80 °C, consistent with a hyperthermophilic lifestyle. This ancestor gave rise to two lineages with different thermal constraints: one remained hyperthermophilic, while the other underwent several independent adaptations to colder environments. Surprisingly, the enzymes of the first lineage have retained a thermoresistant behavior (i.e. strong thermostability and high A-Topt), whereas the ancestor of the second lineage shows a strong thermostability, but a reduced A-Topt. Using mutants, we mimic the adaptation trajectory toward mesophily and show that it is possible to significantly reduce the A-Topt without altering the thermostability of the enzyme by introducing a few mutations. Finally, we reveal an unexpected link between thermostability and the ability to resist γ-irradiation-induced unfolding.

Evolutionary-Scale Enzymology Enables Biochemical Constant Prediction Across a Multi-Peaked Catalytic Landscape

Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. Here, we expand an emerging microfluidic platform to measure catalytic constants-k cat and K M-for hundreds of diverse naturally occurring sequences and mutants of the model enzyme Adenylate Kinase (ADK). This enables us to dissect the sequence-catalysis landscape's topology, navigability, and mechanistic underpinnings, revealing distinct catalytic peaks organized by structural motifs. These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not slower than their mesophilic counterparts. Combining the rich representations of protein sequences provided by deep-learning models with our custom high-throughput kinetic data yields semi-supervised models that significantly outperform existing models at predicting catalytic parameters of naturally occurring ADK sequences. Our work demonstrates a promising strategy for dissecting sequence-catalysis landscapes across enzymatic evolution and building family-specific models capable of accurately predicting catalytic constants, opening new avenues for enzyme engineering and functional prediction.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

Hotspot Wizard-informed engineering of a hyperthermophilic β-glucosidase for enhanced enzyme activity at low temperatures

Hyperthermophilic enzymes serve as an important source of industrial enzymes due to their high thermostability. Unfortunately, most hyperthermophilic enzymes suffer from reduced activity at low temperatures (e.g., ambient temperature), limiting their applicability. In addition, evolving hyperthermophilic enzymes to increase low temperature activity without compromising other desired properties is generally difficult. In the current study, a variant of β-glucosidase from Pyrococcus furiosus (PfBGL) was engineered to enhance enzyme activity at low temperatures through the construction of a saturation mutagenesis library guided by the HotSpot Wizard analysis, followed by its screening for activity and thermostability. From this library construction and screening, one PfBGL mutant, PfBGL-A4 containing Q214S/A264S/F344I mutations, showed an over twofold increase in β-glucosidase activity at 25 and 50°C compared to the wild type, without compromising high-temperature activity, thermostability and substrate specificity. Our experimental and computational characterizations suggest that the findings with PfBGL-A4 may be due to the elevation of local conformational flexibility around the active site, while slightly compacting the global protein structure. This study showcases the potential of HotSpot Wizard-informed engineering of hyperthermophilic enzymes and underscores the interplays among temperature, enzyme activity, and conformational flexibility in these enzymes.

Evidence that the cold- and menthol-sensing functions of the human TRPM8 channel evolved separately

Transient receptor potential melastatin 8 (TRPM8) is a temperature- and menthol-sensitive ion channel that contributes to diverse physiological roles, including cold sensing and pain perception. Clinical trials targeting TRPM8 have faced repeated setbacks predominantly due to the knowledge gap in unraveling the molecular underpinnings governing polymodal activation. A better understanding of the molecular foundations between the TRPM8 activation modes may aid the development of mode-specific, thermal-neutral therapies. Ancestral sequence reconstruction was used to explore the origins of TRPM8 activation modes. By resurrecting key TRPM8 nodes along the human evolutionary trajectory, we gained valuable insights into the trafficking, stability, and function of these ancestral forms. Notably, this approach unveiled the differential emergence of cold and menthol sensitivity over evolutionary time, providing a fresh perspective on complex polymodal behavior. These studies provide a paradigm for understanding polymodal behavior in TRPM8 and other proteins with the potential to enhance our understanding of sensory receptor biology and pave the way for innovative therapeutic interventions.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Ancestral terpene cyclases: From fundamental science to applications in biosynthesis

Terpenes constitute one of the largest family of natural products with potent applications as renewable platform chemicals and medicines. The low activity, selectivity and stability displayed by terpene biosynthetic machineries can constitute an obstacle towards achieving expedient biosynthesis of terpenoids in processes that adhere to the 12 principles of green chemistry. Accordingly, engineering of terpene synthase enzymes is a prerequisite for industrial biotechnology applications, but obstructed by their complex catalysis that depend on reactive carbocationic intermediates that are prone to undergo bifurcation mechanisms. Rational redesign of terpene synthases can be tedious and requires high-resolution structural information, which is not always available. Furthermore, it has proven difficult to link sequence space of terpene synthase enzymes to specific product profiles. Herein, the author shows how ancestral sequence reconstruction (ASR) can favorably be used as a protein engineering tool in the redesign of terpene synthases without the need of a structure, and without excessive screening. A detailed workflow of ASR is presented along with associated limitations, with a focus on applying this methodology on terpene synthases. From selected examples of both class I and II enzymes, the author advocates that ancestral terpene cyclases constitute valuable assets to shed light on terpene-synthase catalysis and in enabling accelerated biosynthesis.

Hot springs viruses at Yellowstone National Park have ancient origins and are adapted to thermophilic hosts

Geothermal springs house unicellular red algae in the class Cyanidiophyceae that dominate the microbial biomass at these sites. Little is known about host-virus interactions in these environments. We analyzed the virus community associated with red algal mats in three neighboring habitats (creek, endolithic, soil) at Lemonade Creek, Yellowstone National Park (YNP), USA. We find that despite proximity, each habitat houses a unique collection of viruses, with the giant viruses, Megaviricetes, dominant in all three. The early branching phylogenetic position of genes encoded on metagenome assembled virus genomes (vMAGs) suggests that the YNP lineages are of ancient origin and not due to multiple invasions from mesophilic habitats. The existence of genomic footprints of adaptation to thermophily in the vMAGs is consistent with this idea. The Cyanidiophyceae at geothermal sites originated ca. 1.5 Bya and are therefore relevant to understanding biotic interactions on the early Earth.

Extant Sequence Reconstruction: The Accuracy of Ancestral Sequence Reconstructions Evaluated by Extant Sequence Cross-Validation

Ancestral sequence reconstruction (ASR) is a phylogenetic method widely used to analyze the properties of ancient biomolecules and to elucidate mechanisms of molecular evolution. Despite its increasingly widespread application, the accuracy of ASR is currently unknown, as it is generally impossible to compare resurrected proteins to the true ancestors. Which evolutionary models are best for ASR? How accurate are the resulting inferences? Here we answer these questions using a cross-validation method to reconstruct each extant sequence in an alignment with ASR methodology, a method we term "extant sequence reconstruction" (ESR). We thus can evaluate the accuracy of ASR methodology by comparing ESR reconstructions to the corresponding known true sequences. We find that a common measure of the quality of a reconstructed sequence, the average probability, is indeed a good estimate of the fraction of correct amino acids when the evolutionary model is accurate or overparameterized. However, the average probability is a poor measure for comparing reconstructions from different models, because, surprisingly, a more accurate phylogenetic model often results in reconstructions with lower probability. While better (more predictive) models may produce reconstructions with lower sequence identity to the true sequences, better models nevertheless produce reconstructions that are more biophysically similar to true ancestors. In addition, we find that a large fraction of sequences sampled from the reconstruction distribution may have fewer errors than the single most probable (SMP) sequence reconstruction, despite the fact that the SMP has the lowest expected error of all possible sequences. Our results emphasize the importance of model selection for ASR and the usefulness of sampling sequence reconstructions for analyzing ancestral protein properties. ESR is a powerful method for validating the evolutionary models used for ASR and can be applied in practice to any phylogenetic analysis of real biological sequences. Most significantly, ESR uses ASR methodology to provide a general method by which the biophysical properties of resurrected proteins can be compared to the properties of the true protein.

High temperature delays and low temperature accelerates evolution of a new protein phenotype

Since the origin of life, temperatures on earth have fluctuated both on short and long time scales. How such changes affect the rate at which Darwinian evolution can bring forth new phenotypes remains unclear. On the one hand, high temperature may accelerate phenotypic evolution because it accelerates most biological processes. On the other hand, it may slow phenotypic evolution, because proteins are usually less stable at high temperatures and therefore less evolvable. Here, to test these hypotheses experimentally, we evolved a green fluorescent protein in E. coli towards the new phenotype of yellow fluorescence at different temperatures. Yellow fluorescence evolved most slowly at high temperature and most rapidly at low temperature, in contradiction to the first hypothesis. Using high-throughput population sequencing, protein engineering, and biochemical assays, we determined that this is due to the protein-destabilizing effect of neofunctionalizing mutations. Destabilization is highly detrimental at high temperature, where neofunctionalizing mutations cannot be tolerated. Their detrimental effects can be mitigated through excess stability at low temperature, leading to accelerated adaptive evolution. By modifying protein folding stability, temperature alters the accessibility of mutational paths towards high-fitness genotypes. Our observations have broad implications for our understanding of how temperature changes affect evolutionary adaptations and innovations.

The evolutionary origin of naturally occurring intermolecular Diels-Alderases from Morus alba

Biosynthetic enzymes evolutionarily gain novel functions, thereby expanding the structural diversity of natural products to the benefit of host organisms. Diels-Alderases (DAs), functionally unique enzymes catalysing [4 + 2] cycloaddition reactions, have received considerable research interest. However, their evolutionary mechanisms remain obscure. Here, we investigate the evolutionary origins of the intermolecular DAs in the biosynthesis of Moraceae plant-derived Diels-Alder-type secondary metabolites. Our findings suggest that these DAs have evolved from an ancestor functioning as a flavin adenine dinucleotide (FAD)-dependent oxidocyclase (OC), which catalyses the oxidative cyclisation reactions of isoprenoid-substituted phenolic compounds. Through crystal structure determination, computational calculations, and site-directed mutagenesis experiments, we identified several critical substitutions, including S348L, A357L, D389E and H418R that alter the substrate-binding mode and enable the OCs to gain intermolecular DA activity during evolution. This work provides mechanistic insights into the evolutionary rationale of DAs and paves the way for mining and engineering new DAs from other protein families.

A billion years of evolution manifest in nanosecond protein dynamics

Protein dynamics form a critical bridge between protein structure and function, yet the impact of evolution on ultrafast processes inside proteins remains enigmatic. This study delves deep into nanosecond-scale protein dynamics of a structurally and functionally conserved protein across species separated by almost a billion years, investigating ten homologs in complex with their ligand. By inducing a photo-triggered destabilization of the ligand inside the binding pocket, we resolved distinct kinetic footprints for each homolog via transient infrared spectroscopy. Strikingly, we found a cascade of rearrangements within the protein complex which manifest in time points of increased dynamic activity conserved over hundreds of millions of years within a narrow window. Among these processes, one displays a subtle temporal shift correlating with evolutionary divergence, suggesting reduced selective pressure in the past. Our study not only uncovers the impact of evolution on molecular processes in a specific case, but has also the potential to initiate a field of scientific inquiry within molecular paleontology, where species are compared and classified based on the rapid pace of protein dynamic processes; a field which connects the shortest conceivable time scale in living matter (10[Formula: see text] s) with the largest ones (10[Formula: see text] s).

Snapshots of the Reaction Coordinate of a Thermophilic 2'-Deoxyribonucleoside/ribonucleoside Transferase

Nucleosides are ubiquitous to life and are required for the synthesis of DNA, RNA, and other molecules crucial for cell survival. Despite the notoriously difficult organic synthesis of nucleosides, 2'-deoxynucleoside analogues can interfere with natural DNA replication and repair and are successfully employed as anticancer, antiviral, and antimicrobial compounds. Nucleoside 2'-deoxyribosyltransferase (dNDT) enzymes catalyze transglycosylation via a covalent 2'-deoxyribosylated enzyme intermediate with retention of configuration, having applications in the biocatalytic synthesis of 2'-deoxynucleoside analogues in a single step. Here, we characterize the structure and function of a thermophilic dNDT, the protein from Chroococcidiopsis thermalis (CtNDT). We combined enzyme kinetics with structural and biophysical studies to dissect mechanistic features in the reaction coordinate, leading to product formation. Bell-shaped pH-rate profiles demonstrate activity in a broad pH range of 5.5-9.5, with two very distinct pKa values. A pronounced viscosity effect on the turnover rate indicates a diffusional step, likely product (nucleobase1) release, to be rate-limiting. Temperature studies revealed an extremely curved profile, suggesting a large negative activation heat capacity. We trapped a 2'-fluoro-2'-deoxyarabinosyl-enzyme intermediate by mass spectrometry and determined high-resolution structures of the protein in its unliganded, substrate-bound, ribosylated, 2'-difluoro-2'-deoxyribosylated, and in complex with probable transition-state analogues. We reveal key features underlying (2'-deoxy)ribonucleoside selection, as CtNDT can also use ribonucleosides as substrates, albeit with a lower efficiency. Ribonucleosides are the building blocks of RNA and other key intracellular metabolites participating in energy and metabolism, expanding the scope of use of CtNDT in biocatalysis.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

A miniaturized mode-of-action profiling platform enables high throughput characterization of the molecular and cellular dynamics of EZH2 inhibition

The market approval of Tazemetostat (TAZVERIK) for the treatment of follicular lymphoma and epithelioid sarcoma has established "enhancer of zeste homolog 2" (EZH2) as therapeutic target in oncology. Despite their structural similarities and common mode of inhibition, Tazemetostat and other EZH2 inhibitors display differentiated pharmacological profiles based on their target residence time. Here we established high throughput screening methods based on time-resolved fluorescence energy transfer, scintillation proximity and high content analysis microscopy to quantify the biochemical and cellular binding of a chemically diverse collection of EZH2 inhibitors. These assays allowed to further characterize the interplay between EZH2 allosteric modulation by methylated histone tails (H3K27me3) and inhibitor binding, and to evaluate the impact of EZH2's clinically relevant mutant Y641N on drug target residence times. While all compounds in this study exhibited slower off-rates, those with clinical candidate status display significantly slower target residence times in wild type EZH2 and disease-related mutants. These inhibitors interact in a more entropy-driven fashion and show the most persistent effects in cellular washout and antiproliferative efficacy experiments. Our work provides mechanistic insights for the largest cohort of EZH2 inhibitors reported to date, demonstrating that-among several other binding parameters-target residence time is the best predictor of cellular efficacy.

Characterization of a Novel Thermostable 7α-Hydroxysteroid Dehydrogenase

BACKGROUND

7α-Hydroxysteroid dehydrogenase (7α-HSDH) plays a pivotal role in vivo in the biotransformation of secondary bile acids and has great potential in industrial biosynthesis due to its broad substrate specificity. In this study, we expressed and characterized a novel thermostable 7α-HSDH (named Sa 7α-HSDH).

METHODS

The DNA sequence was derived from the black bear gut microbiome metagenomic sequencing data, and the coding sequence of Sa 7α-HSDH was chemically synthesized. The heterologous expression of the enzyme was carried out using the pGEX-6p-1 vector. Subsequently, the activity of the purified enzyme was studied by measuring the absorbance change at 340 nm. Finally, the three-dimensional structure was predicted with AlphaFold2.

RESULTS

Coenzyme screening results confirmed it to be NAD(H) dependent. Substrate specificity test revealed that Sa 7α-HSDH could catalyze taurochenodeoxycholic acid (TCDCA) with catalytic efficiency (kcat/Km) 3.81 S-1 mM-1. The optimum temperature of Sa 7α-HSDH was measured to be 75°C, confirming that it belongs to thermophilic enzymes. Additionally, its thermostability was assessed using an accelerated stability test over 32 hours. The catalytic activity of Sa 7α-HSDH remained largely unchanged for the first 24 hours and retained over 90% of its functionality after 32 hours at 50°C. Sa 7α-HSDH exhibited maximal activity at pH 10. The effect of metal ions-K+, Na+, Mg2+ and Cu2+-on the enzymatic activity of Sa 7α-HSDH was investigated. Only Mg2+ was observed to enhance the enzyme's activity by 27% at a concentration of 300 mM. Neither K+ nor Na+ had a significant influence on activity. Only Cu2+ was found to reduce enzyme activity.

CONCLUSION

We characterized the thermostable 7α-HSDH, which provides a promising biocatalyst for bioconversion of steroids at high reaction temperatures.

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability

Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain. The method accurately identified stabilizing substitutions in three well-studied systems and was validated prospectively by experimentally testing predicted stabilizing substitutions in a polyamine oxidase. In each case, the method outperformed the widely used bioinformatic consensus approach. The method can also provide insight into fundamental aspects of protein structure, for example, by identifying how many sequence positions in a given protein are relevant to temperature adaptation.

Molecular determinants for cold adaptation in an Antarctic Na+/K+-ATPase

Enzymes from ectotherms living in chronically cold environments have evolved structural innovations to overcome the effects of temperature on catalysis. Cold adaptation of soluble enzymes is driven by changes within their primary structure or the aqueous milieu. For membrane-embedded enzymes, like the Na+/K+-ATPase, the situation is different because changes to the lipid bilayer in which they operate may also be relevant. Although much attention has been focused on thermal adaptation within lipid bilayers, relatively little is known about the contribution of structural changes within membrane-bound enzymes themselves. The identification of specific mutations that confer temperature compensation is complicated by the presence of neutral mutations, which can be more numerous. In the present study, we identified specific amino acids in a Na+/K+-ATPase from an Antarctic octopus that underlie cold resistance. Our approach was to generate chimeras between an Antarctic clone and a temperate ortholog and then study their temperature sensitivities in Xenopus oocytes using an electrophysiological approach. We identified 12 positions in the Antarctic Na+/K+-ATPase that, when transferred to the temperate ortholog, were sufficient to confer cold tolerance. Furthermore, although all 12 Antarctic mutations were required for the full phenotype, a single leucine in the third transmembrane segment (M3) imparted most of it. Mutations that confer cold resistance are mostly in transmembrane segments, at positions that face the lipid bilayer. We propose that the interface between a transmembrane enzyme and the lipid bilayer is a critical determinant of temperature sensitivity and, accordingly, has been a prime evolutionary target for thermal adaptation.

Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe

Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models.

Metabolomic rearrangement controls the intrinsic microbial response to temperature changes

Temperature is one of the key determinants of microbial behavior and survival, whose impact is typically studied under heat- or cold-shock conditions that elicit specific regulation to combat lethal stress. At intermediate temperatures, cellular growth rate varies according to the Arrhenius law of thermodynamics without stress responses, a behavior whose origins have not yet been elucidated. Using single-cell microscopy during temperature perturbations, we show that bacteria exhibit a highly conserved, gradual response to temperature upshifts with a time scale of ~1.5 doublings at the higher temperature, regardless of initial/final temperature or nutrient source. We find that this behavior is coupled to a temperature memory, which we rule out as being neither transcriptional, translational, nor membrane dependent. Instead, we demonstrate that an autocatalytic enzyme network incorporating temperature-sensitive Michaelis-Menten kinetics recapitulates all temperature-shift dynamics through metabolome rearrangement, which encodes a temperature memory and successfully predicts alterations in the upshift response observed under simple-sugar, low-nutrient conditions, and in fungi. This model also provides a mechanistic framework for both Arrhenius-dependent growth and the classical Monod Equation through temperature-dependent metabolite flux.

Analysis of neutral mutational drift in an allosteric enzyme

Neutral mutational drift is an important source of biological diversity that remains underexploited in fundamental studies of protein biophysics. This study uses a synthetic transcriptional circuit to study neutral drift in protein tyrosine phosphatase 1B (PTP1B), a mammalian signaling enzyme for which conformational changes are rate limiting. Kinetic assays of purified mutants indicate that catalytic activity, rather than thermodynamic stability, guides enrichment under neutral drift, where neutral or mildly activating mutations can mitigate the effects of deleterious ones. In general, mutants show a moderate activity-stability tradeoff, an indication that minor improvements in the activity of PTP1B do not require concomitant losses in its stability. Multiplexed sequencing of large mutant pools suggests that substitutions at allosterically influential sites are purged under biological selection, which enriches for mutations located outside of the active site. Findings indicate that the positional dependence of neutral mutations within drifting populations can reveal the presence of allosteric networks and illustrate an approach for using synthetic transcriptional systems to explore these mutations in regulatory enzymes.

Computational design of the temperature optimum of an enzyme reaction

Cold-adapted enzymes are characterized both by a higher catalytic activity at low temperatures and by having their temperature optimum down-shifted, compared to mesophilic orthologs. In several cases, the optimum does not coincide with the onset of protein melting but reflects some other type of inactivation. In the psychrophilic α-amylase from an Antarctic bacterium, the inactivation is thought to originate from a specific enzyme-substrate interaction that breaks around room temperature. Here, we report a computational redesign of this enzyme aimed at shifting its temperature optimum upward. A set of mutations designed to stabilize the enzyme-substrate interaction were predicted by computer simulations of the catalytic reaction at different temperatures. The predictions were verified by kinetic experiments and crystal structures of the redesigned α-amylase, showing that the temperature optimum is indeed markedly shifted upward and that the critical surface loop controlling the temperature dependence approaches the target conformation observed in a mesophilic ortholog.

AsiteDesign: a Semirational Algorithm for an Automated Enzyme Design

With advances in protein structure predictions, the number of available high-quality structures has increased dramatically. In light of these advances, structure-based enzyme engineering is expected to become increasingly important for optimizing biocatalysts for industrial processes. Here, we present AsiteDesign, a Monte Carlo-based protocol for structure-based engineering of active sites. AsiteDesign provides a framework for introducing new catalytic residues in a given binding pocket to either create a new catalytic activity or alter the existing one. AsiteDesign is implemented using pyRosetta and incorporates enhanced sampling techniques to efficiently explore the search space. The protocol was tested by designing an alternative catalytic triad in the active site of Pseudomonas fluorescens esterase (PFE). The designed variant was experimentally verified to be active, demonstrating that AsiteDesign can find alternative catalytic triads. Additionally, the AsiteDesign protocol was employed to enhance the hydrolysis of a bulky chiral substrate (1-phenyl-2-pentyl acetate) by PFE. The experimental verification of the designed variants demonstrated that F158L/F198A and F125A/F158L mutations increased the hydrolysis of 1-phenyl-2-pentyl acetate from 8.9 to 66.7 and 23.4%, respectively, and reversed the enantioselectivity of the enzyme from (R) to (S)-enantiopreference, with 32 and 55% enantiomeric excess (ee), respectively.

Construction of Macroporous β-Glucosidase@MOFs by a Metal Competitive Coordination and Oxidation Strategy for Efficient Cellulose Conversion at 120 °C

Metal-organic frameworks (MOFs) have become promising accommodation for enzyme immobilization in recent years. However, the microporous nature of MOFs affects the accessibility of large molecules, resulting in a significant decline in biocatalysis efficiency. Herein, a novel strategy is reported to construct macroporous MOFs by metal competitive coordination and oxidation with induced defect structure using a transition metal (Fe²⁺) as a functional site. The feasibility of in situ encapsulating β-glucosidase (β-G) within the developed macroporous MOFs endows an enzyme complex (β-G@MOF-Fe) with remarkably enhanced synergistic catalysis ability. The 24 h hydrolysis rate of β-G@MOF-Fe (with respect to cellobiose) is as high as approximately 99.8%, almost 32.2 times that of free β-G (3.1%). Especially, the macromolecular cellulose conversion rate of β-G@MOF-Fe reached 90% at 64 h, while that of β-G@MOFs (most micropores) was only 50%. This improvement resulting from the expansion of pores (significantly increased at 50-100 nm) can provide enough space for the hosted biomacromolecules and accelerate the diffusion rate of reactants. Furthermore, unexpectedly, the constructed β-G@MOF-Fe showed a superior heat resistance of up to 120 °C, attributing to the new strong coordination bond (Fe²⁺-N) formation through the metal competitive coordination. Therefore, this study offers new insights to solve the problem of the high-temperature macromolecular substrate encountered in the actual reaction.

Disruption of electrostatic contacts in the HNH nuclease from a thermophilic Cas9 rewires allosteric motions and enhances high-temperature DNA cleavage

Allosteric signaling within multidomain proteins is a driver of communication between spatially distant functional sites. Understanding the mechanism of allosteric coupling in large multidomain proteins is the most promising route to achieving spatial and temporal control of the system. The recent explosion of CRISPR-Cas9 applications in molecular biology and medicine has created a need to understand how the atomic level protein dynamics of Cas9, which are the driving force of its allosteric crosstalk, influence its biophysical characteristics. In this study, we used a synergistic approach of nuclear magnetic resonance (NMR) and computation to pinpoint an allosteric hotspot in the HNH domain of the thermostable GeoCas9. We show that mutation of K597 to alanine disrupts a salt-bridge network, which in turn alters the structure, the timescale of allosteric motions, and the thermostability of the GeoHNH domain. This homologous lysine-to-alanine mutation in the extensively studied mesophilic S. pyogenes Cas9 similarly alters the dynamics of the SpHNH domain. We have previously demonstrated that the alteration of allostery via mutations is a source for the specificity enhancement of SpCas9 (eSpCas9). Hence, this may also be true in GeoCas9.

Learning deep representations of enzyme thermal adaptation

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.

Temperature-sensitive contacts in disordered loops tune enzyme I activity

Homologous enzymes with identical folds often exhibit different thermal and kinetic behaviors. Understanding how an enzyme sequence encodes catalytic activity at functionally optimal temperatures is a fundamental problem in biophysics. Recently it was shown that the residues that tune catalytic activities of thermophilic/mesophilic variants of the C-terminal domain of bacterial enzyme I (EIC) are largely localized within disordered loops, offering a model system with which to investigate this phenomenon. In this work, we use molecular dynamics simulations and mutagenesis experiments to reveal a mechanism of sequence-dependent activity tuning of EIC homologs. We find that a network of contacts in the catalytic loops is particularly sensitive to changes in temperature, with some contacts exhibiting distinct linear or nonlinear temperature-dependent trends. Moreover, these trends define structurally clustered dynamical modes and can distinguish regions that tend toward order or disorder at higher temperatures. Assaying several thermophilic EIC mutants, we show that complementary mesophilic mutations to the most temperature-sensitive positions exhibit the most enhanced activity, while mutations to relatively temperature insensitive positions exhibit the least enhanced activities. These results provide a mechanistic explanation of sequence-dependent temperature tuning and offer a computational method for rational enzyme modification.

Calculation of Heat Capacity Changes in Enzyme Catalysis and Ligand Binding

It has been suggested that heat capacity changes in enzyme catalysis may be the underlying reason for temperature optima that are not related to unfolding of the enzyme. If this were to be a common phenomenon, it would have major implications for our interpretation of enzyme kinetics. In most cases, the support for the possible existence of a nonzero (negative) activation heat capacity, however, only relies on fitting such a kinetic model to experimental data. It is therefore of fundamental interest to try to use computer simulations to address this issue. One way is simply to calculate the temperature dependence of the activation free energy and determine whether the relationship is linear or not. An alternative approach is to calculate the absolute heat capacities of the reactant and transition states from plain molecular dynamics simulations using either the temperature derivative or fluctuation formula for the enthalpy. Here, we examine these different approaches for a designer enzyme with a temperature optimum that is not caused by unfolding. Benchmark calculations for the heat capacity of liquid water are first carried out using different thermostats. It is shown that the derivative formula for the heat capacity is generally the most robust and insensitive to the thermostat used and its parameters. The enzyme calculations using this method give results in agreement with direct calculations of activation free energies and show no sign of a negative activation heat capacity. We also provide a simple scheme for the calculation of binding heat capacity changes, which is of clear interest in ligand design, and demonstrate it for substrate binding to the designer enzyme. Neither in that case do the simulations predict any negative heat capacity change.

Engineering functional thermostable proteins using ancestral sequence reconstruction

Natural proteins are often only slightly more stable in the native state than the denatured state, and an increase in environmental temperature can easily shift the balance toward unfolding. Therefore, the engineering of proteins to improve protein stability is an area of intensive research. Thermostable proteins are required to withstand industrial process conditions, for increased shelf-life of protein therapeutics, for developing robust 'biobricks' for synthetic biology applications, and for research purposes (e.g., structure determination). In addition, thermostability buffers the often destabilizing effects of mutations introduced to improve other properties. Rational design approaches to engineering thermostability require structural information, but even with advanced computational methods, it is challenging to predict or parameterize all the relevant structural factors with sufficient precision to anticipate the results of a given mutation. Directed evolution is an alternative when structures are unavailable but requires extensive screening of mutant libraries. Recently, however, bioinspired approaches based on phylogenetic analyses have shown great promise. Leveraging the rapid expansion in sequence data and bioinformatic tools, ancestral sequence reconstruction can generate highly stable folds for novel applications in industrial chemistry, medicine, and synthetic biology. This review provides an overview of the factors important for successful inference of thermostable proteins by ancestral sequence reconstruction and what it can reveal about the determinants of stability in proteins.

Efficient Base-Catalyzed Kemp Elimination in an Engineered Ancestral Enzyme

The routine generation of enzymes with completely new active sites is a major unsolved problem in protein engineering. Advances in this field have thus far been modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral β-lactamase with a new active site that is capable of efficiently catalyzing Kemp elimination. The engineering of our Kemp eliminase involved minimalist design based on a single function-generating mutation, inclusion of an extra polypeptide segment at a position close to the de novo active site, and sharply focused, low-throughput library screening. Nevertheless, its catalytic parameters (kcat/KM~2·105 M-1 s-1, kcat~635 s-1) compare favorably with the average modern natural enzyme and match the best proton-abstraction de novo Kemp eliminases that are reported in the literature. The general implications of our results for de novo enzyme engineering are discussed.

Engineering and screening of novel β-1,3-xylanases with desired hydrolysate type by optimized ancestor sequence reconstruction and data mining

•

A novel integrative strategy for engineering β-1,3-xylanases with desired products.

•

AncXyl10 is the first successful example of ASR to shift the hydrolysate types.

•

The hydrolysates of AncXyl10 was only β-1,3-xylobiose and β-1,3-xylotriose.

•

The underlying mechanism laid a new groundwork towards hydrolase engineering.

Engineering of hydrolases to shift their hydrolysate types has not been attempted so far, though computer-assisted enzyme design has been successful. A novel integrative strategy for engineering and screening the β-1,3-xylanase with desired hydrolysate types was proposed, with the purpose to solve problems that the separation and preparation of β-1,3-xylo-oligosaccharides was in high cost yet in low yield as monosaccharides existed in the hydrolysates. By classifying the hydrolysate types and coding them into numerical values, two robust mathematical models with five selected attributes from molecular docking were established based on LogitBoost and partial least squares regression with overall accuracy of 83.3% and 100%, respectively. Then, they were adopted for efficient screening the potential mutagenesis library of β-1,3-xylanases that only product oligosaccharides. The virtually designed AncXyl10 was selected and experimentally verified to produce only β-1,3-xylobiose (60.38%) and β-1,3-xylotriose (39.62%), which facilitated the preparation of oligosaccharides with high purity. The underlying mechanism of AncXyl10 may associated with the gap processing and ancestral amino acid substitution in the process of ancestral sequence reconstruction. Since many carbohydrate-active enzymes have highly conserved active sites, the strategy and their biomolecular basis will shield a new light for engineering carbohydrates hydrolase to produce specific oligosaccharides.

Thermoadaptation in an Ancestral Diterpene Cyclase by Altered Loop Stability

Thermostability is the key to maintain the structural integrity and catalytic activity of enzymes in industrial biotechnological processes, such as terpene cyclase-mediated generation of medicines, chiral synthons, and fine chemicals. However, affording a large increase in the thermostability of enzymes through site-directed protein engineering techniques can constitute a challenge. In this paper, we used ancestral sequence reconstruction to create a hyperstable variant of the ent-copalyl diphosphate synthase PtmT2, a terpene cyclase involved in the assembly of antibiotics. Molecular dynamics simulations on the μs timescale were performed to shed light on possible molecular mechanisms contributing to activity at an elevated temperature and the large 40 °C increase in melting temperature observed for an ancestral variant of PtmT2. In silico analysis revealed key differences in the flexibility of a loop capping the active site, between extant and ancestral proteins. For the modern enzyme, the loop collapses into the active site at elevated temperatures, thus preventing biocatalysis, whereas the loop remains in a productive conformation both at ambient and high temperatures in the ancestral variant. Restoring a Pro loop residue introduced in the ancestral variant to the corresponding Gly observed in the extant protein led to reduced catalytic activity at high temperatures, with only moderate effects on the melting temperature, supporting the importance of the flexibility of the capping loop in thermoadaptation. Conversely, the inverse Gly to Pro loop mutation in the modern enzyme resulted in a 3-fold increase in the catalytic rate. Despite an overall decrease in maximal activity of ancestor compared to wild type, its increased thermostability provides a robust backbone amenable for further enzyme engineering. Our work cements the importance of loops in enzyme catalysis and provides a molecular mechanism contributing to thermoadaptation in an ancestral enzyme.

The Effects of Temperature on Cellular Physiology

Temperature impacts biological systems across all length and timescales. Cells and the enzymes that comprise them respond to temperature fluctuations on short timescales, and temperature can affect protein folding, the molecular composition of cells, and volume expansion. Entire ecosystems exhibit temperature-dependent behaviors, and global warming threatens to disrupt thermal homeostasis in microbes that are important for human and planetary health. Intriguingly, the growth rate of most species follows the Arrhenius law of equilibrium thermodynamics, with an activation energy similar to that of individual enzymes but with maximal growth rates and over temperature ranges that are species specific. In this review, we discuss how the temperature dependence of critical cellular processes, such as the central dogma and membrane fluidity, contributes to the temperature dependence of growth. We conclude with a discussion of adaptation to temperature shifts and the effects of temperature on evolution and on the properties of microbial ecosystems.

The stability and dynamics of computationally designed proteins

Protein stability, dynamics and function are intricately linked. Accordingly, protein designers leverage dynamics in their designs and gain insight to their successes and failures by analyzing their proteins' dynamics. Molecular dynamics (MD) simulations are a powerful computational tool for quantifying both local and global protein dynamics. This review highlights studies where MD simulations were applied to characterize the stability and dynamics of designed proteins and where dynamics were incorporated into computational protein design. First, we discuss the structural basis underlying the extreme stability and thermostability frequently observed in computationally designed proteins. Next, we discuss examples of designed proteins, where dynamics were not explicitly accounted for in the design process, whose coordinated motions or active site dynamics, as observed by MD simulation, enhanced or detracted from their function. Many protein functions depend on sizeable or subtle conformational changes, so we finally discuss the computational design of proteins to perform a specific function that requires consideration of motion by multi-state design.

In Vivo Temperature Dependency of Molybdenum and Vanadium Nitrogenase Activity in the Heterocystous Cyanobacteria Anabaena variabilis

The reduction of atmospheric dinitrogen by nitrogenase is a key component of terrestrial nitrogen cycling. Nitrogenases exist in several isoforms named after the metal present within their active center: the molybdenum (Mo), the vanadium (V), and the iron (Fe)-only nitrogenase. While earlier in vitro studies hint that the relative contribution of V nitrogenase to total BNF could be temperature-dependent, the effect of temperature on in vivo activity remains to be investigated. In this study, we characterize the in vivo effect of temperature (3-42 °C) on the activities of Mo nitrogenase and V nitrogenase in the heterocystous cyanobacteria Anabaena variabilis ATTC 29413 using the acetylene reduction assay by cavity ring-down absorption spectroscopy. We demonstrate that V nitrogenase becomes as efficient as Mo nitrogenase at temperatures below 10-15 °C. At temperatures above 22 °C, BNF seems to be limited by O₂ availability to respiration in both enzymes. Furthermore, Anabaena variabilis cultures grown in Mo or V media achieved similar growth rates at temperatures below 20 °C. Considering the average temperature on earth is 15 °C, our findings further support the role of V nitrogenase as a viable backup enzymatic system for BNF in natural ecosystems.

Recreating the natural evolutionary trend in key microdomains provides an effective strategy for engineering of a thermomicrobial N-demethylase

N-demethylases have been reported to remove the methyl groups on primary or secondary amines, which could further affect the properties and functions of biomacromolecules or chemical compounds; however, the substrate scope and the robustness of N-demethylases have not been systematically investigated. Here we report the recreation of natural evolution in key microdomains of the Thermomicrobium roseum sarcosine oxidase (TrSOX), an N-demethylase with marked stability (melting temperature over 100 °C) and enantioselectivity, for enhanced substrate scope and catalytic efficiency on -C-N- bonds. We obtained the structure of TrSOX by crystallization and X-ray diffraction (XRD) for the initial framework. The natural evolution in the nonconserved residues of key microdomains—including the catalytic loop, coenzyme pocket, substrate pocket, and entrance site—was then identified using ancestral sequence reconstruction (ASR), and the substitutions that accrued during natural evolution were recreated by site-directed mutagenesis. The single and double substitution variants catalyzed the N-demethylation of N-methyl-L-amino acids up to 1800- and 6000-fold faster than the wild type, respectively. Additionally, these single substitution variants catalyzed the terminal N-demethylation of non-amino-acid compounds and the oxidation of the main chain -C-N- bond to a -C=N- bond in the nitrogen-containing heterocycle. Notably, these variants retained the enantioselectivity and stability of the initial framework. We conclude that the variants of TrSOX are of great potential use in N-methyl enantiomer resolution, main-chain Schiff base synthesis, and alkaloid modification or degradation.

Thermostabilization mechanisms in thermophilic versus mesophilic three-helix bundle proteins

The engineered three‐helix bundle, UVF, is thermostabilized entropically due to heightened, native‐state dynamics. However, it is unclear whether this thermostabilization strategy is observed in natural proteins from thermophiles. We performed all‐atom, explicit solvent molecular dynamics simulations of two three‐helix bundles from thermophilic H. butylicus (2lvsN and 2lvsC) and compared their dynamics to a mesophilic three‐helix bundle, the Engrailed homeodomain (EnHD). Like UVF, 2lvsC had heightened native dynamics, which it maintained without unfolding at 100°C. Shortening and rigidification of loops in 2lvsN and 2lvsC and increased surface hydrogen bonds in 2lvsN were observed, as is common in thermophilic proteins. A buried disulfide and salt bridge in 2lvsN and 2lvsC, respectively, provided some stabilization, and addition of a homologous disulfide bond in EnHD slowed unfolding. The transferability and commonality of stabilization strategies among members of the three‐helix bundle fold suggest that these strategies may be general and deployable in designing thermostable proteins.

Systematic investigation of the link between enzyme catalysis and cold adaptation

Cold temperature is prevalent across the biosphere and slows the rates of chemical reactions. Increased catalysis has been predicted to be a dominant adaptive trait of enzymes to reduced temperature, and this expectation has informed physical models for enzyme catalysis and influenced bioprospecting strategies. To systematically test rate enhancement as an adaptive trait to cold, we paired kinetic constants of 2223 enzyme reactions with their organism's optimal growth temperature (T_Growth) and analyzed trends of rate constants as a function of T_Growth. These data do not support a general increase in rate enhancement in cold adaptation. In the model enzyme ketosteroid isomerase (KSI), there is prior evidence for temperature adaptation from a change in an active site residue that results in a tradeoff between activity and stability. Nevertheless, we found that little of the rate constant variation for 20 KSI variants was accounted for by T_Growth. In contrast, and consistent with prior expectations, we observed a correlation between stability and T_Growth across 433 proteins. These results suggest that temperature exerts a weaker selection pressure on enzyme rate constants than stability and that evolutionary forces other than temperature are responsible for the majority of enzymatic rate constant variation.

Ancestral Sequence Reconstruction and Alternate Amino Acid States Guide Protein Library Design for Directed Evolution

Engineered proteins possess nearly limitless possibilities in medical and industrial applications but finding a precise amino acid sequence for these applications is challenging. A robust approach for discovering protein sequences with a desired functionality uses a library design method in which combinations of mutations are applied to a robust starting point. Determining useful mutations can be tortuous, yet rewarding; in this chapter, we present a novel library design method that uses information provided by ancestral sequence reconstruction (ASR) to create a library likely to have stable proteins with diverse function. ASR computational tools use a multi-sequence alignment of homologous proteins and an evolutionary model to estimate the protein sequences of the numerous common ancestors. For all ancestors, these tools calculate the probability of every amino acid occurring at each position within the sequence alignment. The alternate amino acid states at individual positions corelate to a region of stability in sequence space around the ancestral sequence which can inform site-wise diversification within a combinatorial library. The method presented in this chapter balances the quality of results, the computational resources needed, and ease of use.

Highly Efficient Synergistic Biocatalysis Driven by Stably Loaded Enzymes within Hierarchically Porous Iron/Cobalt Metal-Organic Framework via Biomimetic Mineralization

The integration of multimodal chemo-/bio-catalysis for efficient cascade reactions has long provided broad prospects in the field of biotechnology for ages. In this work, we describe the synthesis of a...

Temperature effect on polymerase fidelity

The discovery of extremophiles helped enable the development of groundbreaking technology such as PCR. Temperature variation is often an essential step of these technology platforms, but the effect of temperature on the error rate of polymerases from different origins is underexplored. Here, we applied high-throughput sequencing to profile the error rates of DNA polymerases from psychrophilic, mesophilic, and thermophilic origins with single-molecule resolution. We found that the reaction temperature substantially increases substitution and deletion error rates of psychrophilic and mesophilic DNA polymerases. Our motif analysis shows that the substitution error profiles cluster according to phylogenetic similarity of polymerases, not the reaction temperature, thus suggesting that the reaction temperature increases the global error rate of polymerases independent of the sequence context. Intriguingly, we also found that the DNA polymerase I of psychrophilic bacteria exhibits higher polymerization activity than its mesophilic ortholog across all temperature ranges, including down to -19 °C, which is well below the freezing temperature of water. Our results provide a useful reference for how the reaction temperature, a crucial parameter of biochemistry, can affect DNA polymerase fidelity in organisms adapted to a wide range of thermal environments.

Adaptations for Pressure and Temperature in Dihydrofolate Reductases

Enzymes from extremophilic microbes that live in extreme conditions are generally adapted so that they function under those conditions, although adaptations for extreme temperatures and pressures can be difficult to unravel. Previous studies have shown mutation of Asp27 in Escherichia coli dihydrofolate reductase (DHFR) to Glu27 in Moritella profunda (Mp). DHFR enhances activity at higher pressures, although this may be an adaptation for cold. Interestingly, MpDHFR unfolds at ~70 MPa, while Moritella yayanosii (My) was isolated at depths corresponding to ~110 MPa, indicating that MyDHFR might be adapted for higher pressures. Here, these adaptations are examined using molecular dynamics simulations of DHFR from different microbes in the context of not only experimental studies of activity and stability of the protein but also the evolutionary history of the microbe. Results suggest Tyr103 of MyDHFR may be an adaptation for high pressure since Cys103 in helix F of MpDHFR forms an intra-helix hydrogen bond with Ile99 while Tyr103 in helix F of MyDHFR forms a hydrogen bond with Leu78 in helix E. This suggests the hydrogen bond between helices F and E in MyDHFR might prevent distortion at higher pressures.