Heme-binding enables allosteric modulation in an ancient TIM-barrel glycosidase

Ancestral Nitrilase Mining and Semi-Rational Engineering for Enhanced Thermal Stability in Rapeseed Meals-Derived Nitriles Degradation

Rapeseed meal (RSM), a protein-rich byproduct, holds potential as a high-quality animal feed, but nitrile compounds derived from glucosinolates (GSLs) in RSM pose a toxicity risk. Nitrilases, enzymes that hydrolyze toxic nitriles to carboxylic acids, offer a potential solution for detoxification. However, the low thermal stability of nitrilases restricts their industrial applicability. We herein identified eight ancestral nitrilases through sequence-based mining using 6803NIT as a probe enzyme. Among these, ancestral enzyme A1 exhibited the highest specific activity (58.3 U/mg) and half-life (t1/2 = 3.5 h at 40 °C). To enhance thermal stability, we engineered a quadruple mutant A1M_4C, which exhibited a 4.7-fold increase in half-life (t1/2 = 16.3 h) and a 2-fold increase in specific activity (118.5 U/mg). Kinetic analysis revealed a reduction in Km from 14.9 to 10.5 mM and an increase in kcat/Km from 1.9 to 4.37 s-1·mM-1. Mechanistic studies indicated that enhanced stability in A1M_4C was due to increased hydrogen bonding and stronger amino acid interactions. Simulated feed pelletization at 90 °C for 2 min showed that A1M_4C acquired a 22.2-fold improvement toward nitriles degradation over wild-type A1. These findings demonstrate the potential of ancestral enzyme mining to develop thermostable nitrilases for industrial feed applications.

Structural determinants of cold activity and glucose tolerance of a family 1 glycoside hydrolase (GH1) from Antarctic Marinomonas sp. ef1

Cold-active enzymes support life at low temperatures due to their ability to maintain high activity in the cold and can be useful in several biotechnological applications. Although information on the mechanisms of enzyme cold adaptation is still too limited to devise general rules, it appears that very diverse structural and functional changes are exploited in different protein families and within the same family. In this context, we studied the cold adaptation mechanism and the functional properties of a member of the glycoside hydrolase family 1 (GH1) from the Antarctic bacterium Marinomonas sp. ef1. This enzyme exhibits all typical functional hallmarks of cold adaptation, including high catalytic activity at 5 °C, broad substrate specificity, low thermal stability, and higher lability of the active site compared to the overall structure. Analysis of the here-reported crystal structure (1.8 Å resolution) and molecular dynamics simulations suggest that cold activity and thermolability may be due to a flexible region around the active site (residues 298-331), whereas the dynamic behavior of loops flanking the active site (residues 47-61 and 407-413) may favor enzyme-substrate interactions at the optimal temperature of catalysis (Topt) by tethering together protein regions lining the active site. Stapling of the N-terminus onto the surface of the β-barrel is suggested to partly counterbalance protein flexibility, thus providing a stabilizing effect. The tolerance of the enzyme to glucose and galactose is accounted for by the presence of a "gatekeeping" hydrophobic residue (Leu178), located at the entrance of the active site.

Expression of a thermostable glucose-stimulated β-glucosidase from a hot-spring metagenome and its promising application to produce gardenia blue

This study reports a thermostable glucose-stimulated β-glucosidase, BglY442, from hot-spring metagenomic data that was cloned and expressed in Escherichia coli BL21 (DE3). The molecular mass of recombinant BglY442 was 69.9 kDa and was used in the production of gardenia blue. The recombinant BglY442 showed its maximum activity at pH 6.0 and 75 °C, maintained 50 % activity at 70 °C for 36 h, presented over 90 % activity in a broad pH range and a wide range of pH stability. Moreover, BglY442 exhibited excellent tolerance toward methanol and ethanol. The specific activity of BglY442 was 235 U/mg at pH 6.0 and 75 °C with 10 mM pNPG as substrate. BglY442 activity increased by over fourfold with 2 M glucose or xylose. Specifically, the enzyme kinetics of BglY442 seem to be non-Michaelis-Menten kinetics or atypical kinetics because the Michaelis-Menten saturation kinetics were not observed with pNPG, oNPG or geniposide as substrates. Under optimum conditions, geniposide was dehydrated by BglY442 and reacted with nine amino acids respectively by the one-pot method. Only the Arg or Met derived pigments showed bright blue, and these two pigments had similar ultraviolet absorption spectra. The OD590 nm of GB was detected to be 1.06 after 24 h with the addition of Arg and 1.61 after 36 h with the addition of Met. The intermediate was elucidated and identified as ginipin. Molecular docking analysis indicated that the enzyme had a similar catalytic mechanism to the reported GH1 Bgls. BglY442 exhibited potential for gardenia blue production by the one-pot method. With outstanding thermostability and glucose tolerance, BglY442 should be considered a potential β-glucosidase in biotechnology applications.

Strategies for modulating transglycosylation activity, substrate specificity, and product polymerization degree of engineered transglycosylases

Glycosides are widely used in many fields due to their favorable biological activity. The traditional plant extractions and chemical methods for glycosides production are limited by environmentally unfriendly, laborious protecting group strategies and low yields. Alternatively, enzymatic glycosylation has drawn special attention due to its mild reaction conditions, high catalytic efficiency, and specific stereo-/regioselectivity. Glycosyltransferases (GTs) and retaining glycoside hydrolases (rGHs) are two major enzymes for the formation of glycosidic linkages. Therein GTs generally use nucleotide phosphate activated donors. In contrast, GHs can use broader simple and affordable glycosyl donors, showing great potential in industrial applications. However, most rGHs mainly show hydrolysis activity and only a few rGHs, namely non-Leloir transglycosylases (TGs), innately present strong transglycosylation activities. To address this problem, various strategies have recently been developed to successfully tailor rGHs to alleviate their hydrolysis activity and obtain the engineered TGs. This review summarizes the current modification strategies in TGs engineering, with a special focus on transglycosylation activity enhancement, substrate specificity modulation, and product polymerization degree distribution, which provides a reference for exploiting the transglycosylation potentials of rGHs.

Impact of ancestral sequence reconstruction on mechanistic and structural enzymology

Ancestral sequence reconstruction (ASR) provides insight into the changes within a protein sequence across evolution. More specifically, it can illustrate how specific amino acid changes give rise to different phenotypes within a protein family. Over the last few decades it has established itself as a powerful technique for revealing molecular common denominators that govern enzyme function. Here, we describe the strength of ASR in unveiling catalytic mechanisms and emerging phenotypes for a range of different proteins, also highlighting biotechnological applications the methodology can provide.

Ancestral sequence reconstruction as a tool to study the evolution of wood decaying fungi

The study of evolution is limited by the techniques available to do so. Aside from the use of the fossil record, molecular phylogenetics can provide a detailed characterization of evolutionary histories using genes, genomes and proteins. However, these tools provide scarce biochemical information of the organisms and systems of interest and are therefore very limited when they come to explain protein evolution. In the past decade, this limitation has been overcome by the development of ancestral sequence reconstruction (ASR) methods. ASR allows the subsequent resurrection in the laboratory of inferred proteins from now extinct organisms, becoming an outstanding tool to study enzyme evolution. Here we review the recent advances in ASR methods and their application to study fungal evolution, with special focus on wood-decay fungi as essential organisms in the global carbon cycling.

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.

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.

Functional characterization of Legionella pneumophila Cu+ transport ATPase. The activation by Cu+ and ATP

Cu⁺-ATPases are integral membrane proteins belonging to the IB subfamily of the P-type ATPases that couple Cu⁺ transport to the hydrolysis of ATP. As some structural and functional particularities arise for Cu⁺-ATPases, several authors suggest that some of the reaction steps of the Albers-Post model postulated for other P-ATPases may be different. In this work we describe a functional characterization of Legionella pneumophila Cu⁺-ATPase (LpCopA), the first P_IB-ATPase whose structure was determined by X-ray crystallography. Cu⁺-ATPase activity of the enzyme presents a maximum at ∼37 °C and pH 6.6-6.8. Phospholipids enhance LpCopA Cu⁺-ATPase activity in a non-essential mode where optimal activity is achieved at an asolectin molar fraction of 0.15 and an amphiphile-protein ratio of ~30,000. As described for other P-ATPases, Mg²⁺ acts as an essential activator. Furthermore, Cu⁺-ATPase activity dependence on [Cu⁺] and [ATP] can both be described by a sum of two hyperbolic functions. Based on that, and the [Cu⁺] and [ATP] dependencies of the best fitting parameters of the hyperbolae pointed above, we propose a minimal reaction scheme for the catalytic mechanism that shares the basic reaction steps of the Albers-Post model for P-type ATPases. The reaction scheme postulated contemplates two different binding affinities for a single ATP (apparent affinities of 0.66 and 550 μM at [Cu⁺] → ∞) and binding of at least 2 Cu⁺ with different affinities as well (apparent affinities of 1.4 and 102.5 μM at [ATP] → ∞).

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

A distal regulatory strategy of enzymes: from local to global conformational dynamics

Modulating the distribution of various states in protein ensembles through distal sites may be promising in the evolution of enzymes in desired directions. However, the prediction of distal mutation hotspots that stabilize the favoured states from a computational perspective remains challenging. Here, we presented a strategy based on molecular dynamics (MD) and Markov state models (MSM) to predict distal mutation sites. Extensive MD combined with MSM was applied to determine the principally distributed metastable states interconverting at a slow timescale. Then, molecular docking was used to classify these states into active states and inactive ones. Distal mutation hotspots were targeted based on comparing the conformational features between active and inactive states, where mutations destabilize the inactive states and show little influence on the active state. The proposed strategy was used to explore the highly dynamic MHETase, which shows a potential application in the biodegradation of poly(ethylene terephthalate) (PET). Seven principally populated interrelated metastable states were identified, and the atomistic picture of their conformational changes was unveiled. Several residues at distal positions were found to adopt more H-bond occupancies in inactive states than active states, making them potential mutation hotspots for stabilizing the favoured conformations. In addition, the detailed mechanism revealed the significance of calcium ions at a distance from the catalytic centre in reshaping the free energy landscape. This study deepens the understanding of the conformational dynamics of α/β hydrolases containing a lid domain and advances the study of enzymatic plastic degradation.

Exploiting enzyme evolution for computational protein design

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Interplay between membrane active host defense peptides and heme modulates their assemblies and in vitro activity

In the emerging era of antimicrobial resistance, the susceptibility to co-infections of patients suffering from either acquired or inherited hemolytic disorders can lead to dramatic increase in mortality rates. Closely related, heme liberated during hemolysis is one of the major sources of iron, which is vital for both host and invading microorganisms. While recent intensive research in the field has demonstrated that heme exerts diverse local effects including impairment of immune cells functions, it is almost completely unknown how it may compromise key molecules of our innate immune system, such as antimicrobial host defense peptides (HDPs). Since HDPs hold great promise as natural therapeutic agents against antibiotic-resistant microbes, understanding the effects that may modulate their action in microbial infection is crucial. Here we explore how hemin can interact directly with selected HDPs and influence their structure and membrane activity. It is revealed that induced helical folding, large assembly formation, and altered membrane activity is promoted by hemin. However, these effects showed variations depending mainly on peptide selectivity toward charged lipids, and the affinity of the peptide and hemin to lipid bilayers. Hemin-peptide complexes are sought to form semi-folded co-assemblies, which are present even with model membranes resembling mammalian or bacterial lipid compositions. In vitro cell-based toxicity assays supported that toxic effects of HDPs could be attenuated due to their assembly formation. These results are in line with our previous findings on peptide-lipid-small molecule systems suggesting that small molecules present in the complex in vivo milieu can regulate HDP function. Inversely, diverse effects of endogenous compounds could also be manipulated by HDPs.

Ancestral sequence reconstruction for protein engineers

In addition to its value in the study of molecular evolution, ancestral sequence reconstruction (ASR) has emerged as a useful methodology for engineering proteins with enhanced properties. Proteins generated by ASR often exhibit unique or improved activity, stability, and/or promiscuity, all of which are properties that are valued by protein engineers. Comparison between extant proteins and evolutionary intermediates generated by ASR also allows protein engineers to identify substitutions that have contributed to functional innovation or diversification within protein families. As ASR becomes more widely adopted as a protein engineering approach, it is important to understand the applications, limitations, and recent developments of this technique. This review highlights recent exemplifications of ASR, as well as technical aspects of the reconstruction process that are relevant to protein engineering.

Ancestral sequence reconstruction - An underused approach to understand the evolution of gene function in plants?

Whilst substantial research effort has been placed on understanding the interactions of plant proteins with their molecular partners, relatively few studies in plants - by contrast to work in other organisms - address how these interactions evolve. It is thought that ancestral proteins were more promiscuous than modern proteins and that specificity often evolved following gene duplication and subsequent functional refining. However, ancestral protein resurrection studies have found that some modern proteins have evolved de novo from ancestors lacking those functions. Intriguingly, the new interactions evolved as a consequence of just a few mutations and, as such, acquisition of new functions appears to be neither difficult nor rare, however, only a few of them are incorporated into biological processes before they are lost to subsequent mutations. Here, we detail the approach of ancestral sequence reconstruction (ASR), providing a primer to reconstruct the sequence of an ancestral gene. We will present case studies from a range of different eukaryotes before discussing the few instances where ancestral reconstructions have been used in plants. As ASR is used to dig into the remote evolutionary past, we will also present some alternative genetic approaches to investigate molecular evolution on shorter timescales. We argue that the study of plant secondary metabolism is particularly well suited for ancestral reconstruction studies. Indeed, its ancient evolutionary roots and highly diverse landscape provide an ideal context in which to address the focal issue around the emergence of evolutionary novelties and how this affects the chemical diversification of plant metabolism.