De novo active sites for resurrected Precambrian enzymes

Enzyme Enhancement Through Computational Stability Design Targeting NMR-Determined Catalytic Hotspots

Enzymes are the quintessential green catalysts, but realizing their full potential for biotechnology typically requires improvement of their biomolecular properties. Catalysis enhancement, however, is often accompanied by impaired stability. Here, we show how the interplay between activity and stability in enzyme optimization can be efficiently addressed by coupling two recently proposed methodologies for guiding directed evolution. We first identify catalytic hotspots from chemical shift perturbations induced by transition-state-analogue binding and then use computational/phylogenetic design (FuncLib) to predict stabilizing combinations of mutations at sets of such hotspots. We test this approach on a previously designed de novo Kemp eliminase, which is already highly optimized in terms of both activity and stability. Most tested variants displayed substantially increased denaturation temperatures and purification yields. Notably, our most efficient engineered variant shows a ∼3-fold enhancement in activity (kcat ∼ 1700 s-1, kcat/KM ∼ 4.3 × 105 M-1 s-1) from an already heavily optimized starting variant, resulting in the most proficient proton-abstraction Kemp eliminase designed to date, with a catalytic efficiency on a par with naturally occurring enzymes. Molecular simulations pinpoint the origin of this catalytic enhancement as being due to the progressive elimination of a catalytically inefficient substrate conformation that is present in the original design. Remarkably, interaction network analysis identifies a significant fraction of catalytic hotspots, thus providing a computational tool which we show to be useful even for natural-enzyme engineering. Overall, our work showcases the power of dynamically guided enzyme engineering as a design principle for obtaining novel biocatalysts with tailored physicochemical properties, toward even anthropogenic reactions.

Emergence of specific binding and catalysis from a designed generalist binding protein

The evolution of binding and catalysis played a central role in the emergence of life. While natural proteins have finely tuned affinities for their primary ligands, they also bind weakly and promiscuously to other molecules, which serve as starting points for stepwise, incremental evolution of entirely new specificities. Thus, modern proteins emerged from the joint exploration of sequence and structural space. The ability of natural proteins to bind promiscuously to small molecule fragments has been widely evaluated using methods including crystallographic fragment screening. However, this approach had not been applied to de novo proteins. Here, we apply this method to explore the promiscuity of a de novo small molecule-binding protein ABLE. As in Nature, we found ABLE was capable of forming weak complexes, which were found to be excellent starting points for evolving entirely new functions, including a binder of a turn-on fluorophore and a highly efficient and specific Kemp eliminase enzyme. This work shows how Nature and protein designers can take advantage of promiscuous binding interactions to evolve new proteins with specialized functions.

Evolution Enhances Kemp Eliminase Activity by Optimizing Oxyanion Stabilization and Conformational Flexibility

The base-promoted Kemp elimination reaction has been used as a model system for enzyme design. Among the multiple computationally designed and evolved Kemp eliminases generated along the years, the HG3-to-HG3.17 evolutionary trajectory is particularly interesting due to the high catalytic efficiency of HG3.17 and the debated role of glutamine 50 (Gln50) as potential oxyanion stabilizer. This study aims to elucidate the structural and dynamic changes along the evolutionary pathway from HG3 to HG3.17 that contribute to improved catalytic efficiency. In particular, we evaluate key variants along the HG3 evolutionary trajectory via molecular dynamics simulations coupled to non-covalent interactions and water analysis. Our computational study indicates that HG3.17 can adopt a catalytically competent conformation promoted by a water-mediated network of non-covalent interactions, in which aspartate 127 (Asp127) is properly positioned for proton abstraction and Gln50 and to some extent mutation cysteine 84 (Cys84) contribute to oxyanion stabilization. We find that HG3.17 exhibits a rather high flexibility of Gln50, which is regulated by the conformation adopted by the active site residue tryptophan 44 (Trp44). This interplay between Gln50 and Trp44 positioning induced by distal active site mutations affects the water-mediated network of non-covalent interactions, Gln50 preorganization, and water content of the active site pocket.

Transition Path Sampling Based Free Energy Calculations of Evolution's Effect on Rates in β-Lactamase: The Contributions of Rapid Protein Dynamics to Rate

β-Lactamases are one of the primary enzymes responsible for antibiotic resistance and have existed for billions of years. The structural differences between a modern class A TEM-1 β-lactamase compared to a sequentially reconstructed Gram-negative bacteria β-lactamase are minor. Despite the similar structures and mechanisms, there are different functions between the two enzymes. We recently identified differences in dynamics effects that result from evolutionary changes that could potentially account for the increase in substrate specificity and catalytic rate. In this study, we used transition path sampling-based calculations of free energies to identify how evolutionary changes found between an ancestral β-lactamase, and its extant counterpart TEM-1 β-lactamase affect rate.

A resurrected ancestor of Cas12a expands target access and substrate recognition for nucleic acid editing and detection

The properties of Cas12a nucleases constrict the range of accessible targets and their applications. In this study, we applied ancestral sequence reconstruction (ASR) to a set of Cas12a orthologs from hydrobacteria to reconstruct a common ancestor, ReChb, characterized by near-PAMless targeting and the recognition of diverse nucleic acid activators and collateral substrates. ReChb shares 53% sequence identity with the closest Cas12a ortholog but no longer requires a T-rich PAM and can achieve genome editing in human cells at sites inaccessible to the natural FnCas12a or the engineered and PAM-flexible enAsCas12a. Furthermore, ReChb can be triggered not only by double-stranded DNA but also by single-stranded RNA and DNA targets, leading to non-specific collateral cleavage of all three nucleic acid substrates with similar efficiencies. Finally, tertiary and quaternary structures of ReChb obtained by cryogenic electron microscopy reveal the molecular details underlying its expanded biophysical activities. Overall, ReChb expands the application space of Cas12a nucleases and underscores the potential of ASR for enhancing CRISPR technologies.

Performance prediction of polypeptide derivatives as efficient potential microbial inhibitors: a computational approach

OBJECTIVE

Lately, various scientists have been paying a lot of consideration to the design of operational antimicrobial agents due to the rise of multiple drug-resistant strains. Therefore, this work is aimed at discovering the biochemical behavior of the analyzed polypeptides in relation to glutamine amidotransferase GatD (pdb id: 5n9m) for gram positive bacteria and beta-lactamase class A (pdb id: 5fqq) for gram negative bacteria. Additionally, this study aims to identify the specific atoms involved in the observed biochemical interactions between the studied complexes using computational methods.

METHODS

In this work, five polypeptides were studied using insilico approach via Spartan 14 software, molecular operating environment, ADMETSar, and Gromacs.

RESULTS

The descriptors obtained revealed the activities of the studied compounds, the molecular interaction between the studied ligands as well as glutamine amidotransferase GatD (pdb id: 5n9m) and beta-lactamase class A (pdb id: 5fqq) which thereby exposed compound 1 and 5 to be the compounds with greatest ability to inhibit the studied targets among other studied compounds.

CONCLUSION

Our discoveries may open door for the design of collection of proficient polypeptide-based drug-like compounds as potential anti-microbial agents.

The Evolution of the Acylation Mechanism in β-Lactamase and Rapid Protein Dynamics

β-Lactamases are a class of well-studied enzymes that are known to have existed since billions of years ago, starting as a defense mechanism to stave off competitors and are now enzymes responsible for antibiotic resistance. Using ancestral sequence reconstruction, it is possible to study the crystal structure of a laboratory resurrected 2-3 billion year-old β-lactamase. Comparing the ancestral enzyme to its modern counterpart, a TEM-1 β-lactamase, the structural changes are minor, and it is probable that dynamic effects play an important role in the evolution of function. We used molecular dynamics simulations and employed transition path sampling methods to identify the presence of rate-enhancing dynamics at the femtosecond level in both systems, found that these fast motions are more efficiently coordinated in the modern enzyme, and examined how specific dynamics can pinpoint evolutionary effects that are essential for improving enzymatic catalysis.

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

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.

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.

Computational design of highly efficient thermostable MHET hydrolases and dual enzyme system for PET recycling

Recently developed enzymes for the depolymerization of polyethylene terephthalate (PET) such as FAST-PETase and LCC-ICCG are inhibited by the intermediate PET product mono(2-hydroxyethyl) terephthalate (MHET). Consequently, the conversion of PET enzymatically into its constituent monomers terephthalic acid (TPA) and ethylene glycol (EG) is inefficient. In this study, a protein scaffold (1TQH) corresponding to a thermophilic carboxylesterase (Est30) was selected from the structural database and redesigned in silico. Among designs, a double variant KL-MHETase (I171K/G130L) with a similar protein melting temperature (67.58 °C) to that of the PET hydrolase FAST-PETase (67.80 °C) exhibited a 67-fold higher activity for MHET hydrolysis than FAST-PETase. A fused dual enzyme system comprising KL-MHETase and FAST-PETase exhibited a 2.6-fold faster PET depolymerization rate than FAST-PETase alone. Synergy increased the yield of TPA by 1.64 fold, and its purity in the released aromatic products reached 99.5%. In large reaction systems with 100 g/L substrate concentrations, the dual enzyme system KL36F achieved over 90% PET depolymerization into monomers, demonstrating its potential applicability in the industrial recycling of PET plastics. Therefore, a dual enzyme system can greatly reduce the reaction and separation cost for sustainable enzymatic PET recycling.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

Loop dynamics and the evolution of enzyme activity

In the early 2000s, Tawfik presented his 'New View' on enzyme evolution, highlighting the role of conformational plasticity in expanding the functional diversity of limited repertoires of sequences. This view is gaining increasing traction with increasing evidence of the importance of conformational dynamics in both natural and laboratory evolution of enzymes. The past years have seen several elegant examples of harnessing conformational (particularly loop) dynamics to successfully manipulate protein function. This Review revisits flexible loops as critical participants in regulating enzyme activity. We showcase several systems of particular interest: triosephosphate isomerase barrel proteins, protein tyrosine phosphatases and β-lactamases, while briefly discussing other systems in which loop dynamics are important for selectivity and turnover. We then discuss the implications for engineering, presenting examples of successful loop manipulation in either improving catalytic efficiency, or changing selectivity completely. Overall, it is becoming clearer that mimicking nature by manipulating the conformational dynamics of key protein loops is a powerful method of tailoring enzyme activity, without needing to target active-site residues.

Protection of catalytic cofactors by polypeptides as a driver for the emergence of primordial enzymes

Enzymes catalyze the chemical reactions of life. For nearly half of known enzymes, catalysis requires the binding of small molecules known as cofactors. Polypeptide-cofactor complexes likely formed at a primordial stage and became starting points for the evolution of many efficient enzymes. Yet, evolution has no foresight so the driver for the primordial complex formation is unknown. Here, we use a resurrected ancestral TIM-barrel protein to identify one potential driver. Heme binding at a flexible region of the ancestral structure yields a peroxidation catalyst with enhanced efficiency when compared to free heme. This enhancement, however, does not arise from protein-mediated promotion of catalysis. Rather, it reflects protection of bound heme from common degradation processes and a resulting longer life time and higher effective concentration for the catalyst. Protection of catalytic cofactors by polypeptides emerges as a general mechanism to enhance catalysis and may have plausibly benefited primordial polypeptide-cofactor associations.

Conformational exchange divergence along the evolutionary pathway of eosinophil-associated ribonucleases

The evolutionary role of conformational exchange in the emergence and preservation of function within structural homologs remains elusive. While protein engineering has revealed the importance of flexibility in function, productive modulation of atomic-scale dynamics has only been achieved on a finite number of distinct folds. Allosteric control of unique members within dynamically diverse structural families requires a better appreciation of exchange phenomena. Here, we examined the functional and structural role of conformational exchange within eosinophil-associated ribonucleases. Biological and catalytic activity of various EARs was performed in parallel to mapping their conformational behavior on multiple timescales using NMR and computational analyses. Despite functional conservation and conformational seclusion to a specific domain, we show that EARs can display similar or distinct motional profiles, implying divergence rather than conservation of flexibility. Comparing progressively more distant enzymes should unravel how this subfamily has evolved new functions and/or altered their behavior at the molecular level.

Evolution of CRISPR-associated endonucleases as inferred from resurrected proteins

Clustered regularly interspaced short palindromic repeats (CRISPR)-associated Cas9 is an effector protein that targets invading DNA and plays a major role in the prokaryotic adaptive immune system. Although Streptococcus pyogenes CRISPR-Cas9 has been widely studied and repurposed for applications including genome editing, its origin and evolution are poorly understood. Here, we investigate the evolution of Cas9 from resurrected ancient nucleases (anCas) in extinct firmicutes species that last lived 2.6 billion years before the present. We demonstrate that these ancient forms were much more flexible in their guide RNA and protospacer-adjacent motif requirements compared with modern-day Cas9 enzymes. Furthermore, anCas portrays a gradual palaeoenzymatic adaptation from nickase to double-strand break activity, exhibits high levels of activity with both single-stranded DNA and single-stranded RNA targets and is capable of editing activity in human cells. Prediction and characterization of anCas with a resurrected protein approach uncovers an evolutionary trajectory leading to functionally flexible ancient enzymes.

Evaluation of Methods to Detect Shifts in Directional Selection at the Genome Scale

Identifying the footprints of selection in coding sequences can inform about the importance and function of individual sites. Analyses of the ratio of nonsynonymous to synonymous substitutions (dN/dS) have been widely used to pinpoint changes in the intensity of selection, but cannot distinguish them from changes in the direction of selection, that is, changes in the fitness of specific amino acids at a given position. A few methods that rely on amino-acid profiles to detect changes in directional selection have been designed, but their performances have not been well characterized. In this paper, we investigate the performance of six of these methods. We evaluate them on simulations along empirical phylogenies in which transition events have been annotated and compare their ability to detect sites that have undergone changes in the direction or intensity of selection to that of a widely used dN/dS approach, codeml's branch-site model A. We show that all methods have reduced performance in the presence of biased gene conversion but not CpG hypermutability. The best profile method, Pelican, a new implementation of Tamuri AU, Hay AJ, Goldstein RA. (2009. Identifying changes in selective constraints: host shifts in influenza. PLoS Comput Biol. 5(11):e1000564), performs as well as codeml in a range of conditions except for detecting relaxations of selection, and performs better when tree length increases, or in the presence of persistent positive selection. It is fast, enabling genome-scale searches for site-wise changes in the direction of selection associated with phenotypic changes.

NMR-guided directed evolution

Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1-4. Nonetheless, its potential in even small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6-9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

Active site center redesign increases protein stability preserving catalysis in thioredoxin

The stabilization of natural proteins is a long-standing desired goal in protein engineering. Optimizing the hydrophobicity of the protein core often results in extensive stability enhancements. However, the presence of totally or partially buried catalytic charged residues, essential for protein function, has limited the applicability of this strategy. Here, focusing on the thioredoxin, we aimed to augment protein stability by removing buried charged residues in the active site without loss of catalytic activity. To this end, we performed a charged-to-hydrophobic substitution of a buried and functional group, resulting in a significant stability increase yet abolishing catalytic activity. Then, to simulate the catalytic role of the buried ionizable group, we designed a combinatorial library of variants targeting a set of seven surface residues adjacent to the active site. Notably, more than 50% of the library variants restored, to some extent, the catalytic activity. The combination of experimental study of 2% of the library with the prediction of the whole mutational space by partial least squares regression revealed that a single point mutation at the protein surface is sufficient to fully restore the catalytic activity without thermostability cost. As a result, we engineered one of the highest thermal stabilities reported for a protein with a natural occurring fold (137°C). Further, our hyperstable variant preserves the catalytic activity both in vitro and in vivo.

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.

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.

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.

Revenant: a database of resurrected proteins

Revenant is a database of resurrected proteins coming from extinct organisms. Currently, it contains a manually curated collection of 84 resurrected proteins derived from bibliographic data. Each protein is extensively annotated, including structural, biochemical and biophysical information. Revenant contains a browse capability designed as a timeline from where the different proteins can be accessed. The oldest Revenant entries are between 4200 and 3500 million years ago, while the younger entries are between 8.8 and 6.3 million years ago. These proteins have been resurrected using computational tools called ancestral sequence reconstruction techniques combined with wet-laboratory synthesis and expression. Resurrected proteins are commonly used, with a noticeable increase during the past years, to explore and test different evolutionary hypotheses such as protein stability, to explore the origin of new functions, to get biochemical insights into past metabolisms and to explore specificity and promiscuous behaviour of ancient proteins.

Structural-Based Modeling in Protein Engineering. A Must Do

Biotechnological solutions will be a key aspect in our immediate future society, where optimized enzymatic processes through enzyme engineering might be an important solution for waste transformation, clean energy production, biodegradable materials, and green chemistry, for example. Here we advocate the importance of structural-based bioinformatics and molecular modeling tools in such developments. We summarize our recent experiences indicating a great prediction/success ratio, and we suggest that an early in silico phase should be performed in enzyme engineering studies. Moreover, we demonstrate the potential of a new technique combining Rosetta and PELE, which could provide a faster and more automated procedure, an essential aspect for a broader use.

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.

Hinge-shift mechanism as a protein design principle for the evolution of β-lactamases from substrate promiscuity to specificity

TEM-1 β-lactamase degrades β-lactam antibiotics with a strong preference for penicillins. Sequence reconstruction studies indicate that it evolved from ancestral enzymes that degraded a variety of β-lactam antibiotics with moderate efficiency. This generalist to specialist conversion involved more than 100 mutational changes, but conserved fold and catalytic residues, suggesting a role for dynamics in enzyme evolution. Here, we develop a conformational dynamics computational approach to rationally mold a protein flexibility profile on the basis of a hinge-shift mechanism. By deliberately weighting and altering the conformational dynamics of a putative Precambrian β-lactamase, we engineer enzyme specificity that mimics the modern TEM-1 β-lactamase with only 21 amino acid replacements. Our conformational dynamics design thus re-enacts the evolutionary process and provides a rational allosteric approach for manipulating function while conserving the enzyme active site.

Engineering of Ancestors as a Tool to Elucidate Structure, Mechanism, and Specificity of Extant Terpene Cyclase

Structural information is crucial for understanding catalytic mechanisms and to guide enzyme engineering efforts of biocatalysts, such as terpene cyclases. However, low sequence similarity can impede homology modeling, and inherent protein instability presents challenges for structural studies. We hypothesized that X-ray crystallography of engineered thermostable ancestral enzymes can enable access to reliable homology models of extant biocatalysts. We have applied this concept in concert with molecular modeling and enzymatic assays to understand the structure activity relationship of spiroviolene synthase, a class I terpene cyclase, aiming to engineer its specificity. Engineering a surface patch in the reconstructed ancestor afforded a template structure for generation of a high-confidence homology model of the extant enzyme. On the basis of structural considerations, we designed and crystallized ancestral variants with single residue exchanges that exhibited tailored substrate specificity and preserved thermostability. We show how the two single amino acid alterations identified in the ancestral scaffold can be transferred to the extant enzyme, conferring a specificity switch that impacts the extant enzyme's specificity for formation of the diterpene spiroviolene over formation of sesquiterpenes hedycaryol and farnesol by up to 25-fold. This study emphasizes the value of ancestral sequence reconstruction combined with enzyme engineering as a versatile tool in chemical biology.

Ancient Antibiotics, Ancient Resistance

As the spread of antibiotic resistance threatens our ability to treat infections, avoiding the return of a preantibiotic era requires the discovery of new drugs. While therapeutic use of antibiotics followed by the inevitable selection of resistance is a modern phenomenon, these molecules and the genetic determinants of resistance were in use by environmental microbes long before humans discovered them. In this review, we discuss evidence that antibiotics and resistance were present in the environment before anthropogenic use, describing techniques including direct sampling of ancient DNA and phylogenetic analyses that are used to reconstruct the past. We also pay special attention to the ecological and evolutionary forces that have shaped the natural history of antibiotic biosynthesis, including a discussion of competitive versus signaling roles for antibiotics, proto-resistance, and substrate promiscuity of biosynthetic and resistance enzymes. Finally, by applying an evolutionary lens, we describe concepts governing the origins and evolution of biosynthetic gene clusters and cluster-associated resistance determinants. These insights into microbes' use of antibiotics in nature, a game they have been playing for millennia, can provide inspiration for discovery technologies and management strategies to combat the growing resistance crisis.

The evolution and engineering of enzyme activity through tuning conformational landscapes

Proteins are dynamic molecules whose structures consist of an ensemble of conformational states. Dynamics contribute to protein function and a link to protein evolution has begun to emerge. This increased appreciation for the evolutionary impact of conformational sampling has grown from our developing structural biology capabilities and the exploration of directed evolution approaches, which have allowed evolutionary trajectories to be mapped. Recent studies have provided empirical examples of how proteins can evolve via conformational landscape alterations. Moreover, minor conformational substates have been shown to be involved in the emergence of new enzyme functions as they can become enriched through evolution. The role of remote mutations in stabilizing new active site geometries has also granted insight into the molecular basis underpinning poorly understood epistatic effects that guide protein evolution. Finally, we discuss how the growth of our understanding of remote mutations is beginning to refine our approach to engineering enzymes.

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

Glycosidases are phylogenetically widely distributed enzymes that are crucial for the cleavage of glycosidic bonds. Here, we present the exceptional properties of a putative ancestor of bacterial and eukaryotic family-1 glycosidases. The ancestral protein shares the TIM-barrel fold with its modern descendants but displays large regions with greatly enhanced conformational flexibility. Yet, the barrel core remains comparatively rigid and the ancestral glycosidase activity is stable, with an optimum temperature within the experimental range for thermophilic family-1 glycosidases. None of the ∼5500 reported crystallographic structures of ∼1400 modern glycosidases show a bound porphyrin. Remarkably, the ancestral glycosidase binds heme tightly and stoichiometrically at a well-defined buried site. Heme binding rigidifies this TIM-barrel and allosterically enhances catalysis. Our work demonstrates the capability of ancestral protein reconstructions to reveal valuable but unexpected biomolecular features when sampling distant sequence space. The potential of the ancestral glycosidase as a scaffold for custom catalysis and biosensor engineering is discussed.

Family 1 glycosidases (GH1) are present in the three domains of life and share classical TIM-barrel fold. Structural and biochemical analyses of a resurrected ancestral GH1 enzyme reveal heme binding, not known in its modern descendants. Heme rigidifies the TIM-barrel and allosterically enhances catalysis.

Protein folding stability and binding interactions through the lens of evolution: a dynamical perspective

While the function of a protein depends heavily on its ability to fold into a correct 3D structure, billions of years of evolution have tailored proteins from highly stable objects to flexible molecules as they adapted to environmental changes. Nature maintains the fine balance of protein folding and stability while still evolving towards new function through generations of fine-tuning necessary interactions with other proteins and small molecules. Here we focus on recent computational and experimental studies that shed light onto how evolution molds protein folding and the functional landscape from a conformational dynamics' perspective. Particularly, we explore the importance of dynamic allostery throughout protein evolution and discuss how the protein anisotropic network can give rise to allosteric and epistatic interactions.

Enzyme dynamics: Looking beyond a single structure

Conventional understanding of how enzymes function strongly emphasizes the role of structure. However, increasing evidence clearly indicates that enzymes do not remain fixed or operate exclusively in or close to their native structure. Different parts of the enzyme (from individual residues to full domains) undergo concerted motions on a wide range of time-scales, including that of the catalyzed reaction. Information obtained on these internal motions and conformational fluctuations has so far uncovered and explained many aspects of enzyme mechanisms, which could not have been understood from a single structure alone. Although there is wide interest in understanding enzyme dynamics and its role in catalysis, several challenges remain. In addition to technical difficulties, the vast majority of investigations are performed in dilute aqueous solutions, where conditions are significantly different than the cellular milieu where a large number of enzymes operate. In this review, we discuss recent developments, several challenges as well as opportunities related to this topic. The benefits of considering dynamics as an integral part of the enzyme function can also enable new means of biocatalysis, engineering enzymes for industrial and medicinal applications.

Harnessing Conformational Plasticity to Generate Designer Enzymes

Recent years have witnessed an explosion of interest in understanding the role of conformational dynamics both in the evolution of new enzymatic activities from existing enzymes and in facilitating the emergence of enzymatic activity de novo on scaffolds that were previously non-catalytic. There are also an increasing number of examples in the literature of targeted engineering of conformational dynamics being successfully used to alter enzyme selectivity and activity. Despite the obvious importance of conformational dynamics to both enzyme function and evolvability, many (although not all) computational design approaches still focus either on pure sequence-based approaches or on using structures with limited flexibility to guide the design. However, there exist a wide variety of computational approaches that can be (re)purposed to introduce conformational dynamics as a key consideration in the design process. Coupled with laboratory evolution and more conventional existing sequence- and structure-based approaches, these techniques provide powerful tools for greatly expanding the protein engineering toolkit. This Perspective provides an overview of evolutionary studies that have dissected the role of conformational dynamics in facilitating the emergence of novel enzymes, as well as advances in computational approaches that allow one to target conformational dynamics as part of enzyme design. Harnessing conformational dynamics in engineering studies is a powerful paradigm with which to engineer the next generation of designer biocatalysts.

Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening

Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains sluggish, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib is a novel approach which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of predicted stability. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this system particularly challenging to optimize. Yet, experimental screening of a small number of active-site variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (∼2 × 104 M-1 s-1 and ∼102 s-1) for this anthropogenic reaction that compare favorably to those of modern natural enzymes. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, even on scaffolds that were not originally evolved for those functions, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ∼2 kcal mol-1 and indicate that the enhanced activity is linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by computational predictions of catalytic activity, as a generalized approach for computational enzyme design.

Structures of hyperstable ancestral haloalkane dehalogenases show restricted conformational dynamics

Ancestral sequence reconstruction is a powerful method for inferring ancestors of modern enzymes and for studying structure-function relationships of enzymes. We have previously applied this approach to haloalkane dehalogenases (HLDs) from the subfamily HLD-II and obtained thermodynamically highly stabilized enzymes (ΔT m up to 24 °C), showing improved catalytic properties. Here we combined crystallographic structural analysis and computational molecular dynamics simulations to gain insight into the mechanisms by which ancestral HLDs became more robust enzymes with novel catalytic properties. Reconstructed ancestors exhibited similar structure topology as their descendants with the exception of a few loop deviations. Strikingly, molecular dynamics simulations revealed restricted conformational dynamics of ancestral enzymes, which prefer a single state, in contrast to modern enzymes adopting two different conformational states. The restricted dynamics can potentially be linked to their exceptional stabilization. The study provides molecular insights into protein stabilization due to ancestral sequence reconstruction, which is becoming a widely used approach for obtaining robust protein catalysts.

β-Lactamase of Mycobacterium tuberculosis Shows Dynamics in the Active Site That Increase upon Inhibitor Binding

The Mycobacterium tuberculosis β-lactamase BlaC is a broad-spectrum β-lactamase that can convert a range of β-lactam antibiotics. Enzymes with low specificity are expected to exhibit active-site flexibility. To probe the motions in BlaC, we studied the dynamic behavior in solution using nuclear magnetic resonance (NMR) spectroscopy. ¹⁵N relaxation experiments show that BlaC is mostly rigid on the pico- to nanosecond timescale. Saturation transfer experiments indicate that also on the high-millisecond timescale BlaC is not dynamic. Using relaxation dispersion experiments, clear evidence was obtained for dynamics in the low-millisecond range, with an exchange rate of ca. 860 s^-1 The dynamic amide groups are localized in the active site. Upon formation of an adduct with the inhibitor avibactam, extensive line broadening occurs, indicating an increase in magnitude of the active-site dynamics. Furthermore, the rate of the motions increases significantly. Upon reaction with the inhibitor clavulanic acid, similar line broadening is accompanied by duplication of NMR signals, indicative of at least one additional, slower exchange process (exchange rate, k _ex, of <100 s^-1), while for this inhibitor also loss of pico- to nanosecond timescale rigidity is observed for some amides in the α domain. Possible sources of the observed dynamics, such as motions in the omega loop and rearrangements of active-site residues, are discussed. The increase in dynamics upon ligand binding argues against a model of inhibitor binding through conformational selection. Rather, the induced dynamics may serve to maximize the likelihood of sampling the optimal conformation for hydrolysis of the bound ligand.

Origin of conformational dynamics in a globular protein

Protein structures are dynamic, undergoing motions that can play a vital role in function. However, the link between primary sequence and conformational dynamics remains poorly understood. Here, we studied how conformational dynamics can arise in a globular protein by evaluating the impact of individual core-residue substitutions in DANCER-3, a streptococcal protein G domain β1 variant that we previously designed to undergo a specific mode of conformational exchange that has never been observed in the wild-type protein. Using a combination of solution NMR experiments and molecular dynamics simulations, we demonstrate that only two mutations are necessary to create this conformational exchange, and that these mutations work synergistically, with one destabilizing the native structure and the other allowing two new conformational states to be accessed on the energy landscape. Overall, our results show how dynamics can appear in a stable globular fold, a critical step in the molecular evolution of dynamics-linked functions.

Towards descriptor of elementary functions for protein design

We review studies of the protein evolution that help to formulate rules for protein design. Acknowledging the fundamental importance of Dayhoff's provision on the emergence of functional proteins from short peptides, we discuss multiple evidences of the omnipresent partitioning of protein globules into structural/functional units, using which greatly facilitates the engineering and design efforts. Closed loops and elementary functional loops, which are descendants of ancient ring-like peptides that formed fist protein domains in agreement with Dayhoff's hypothesis, can be considered as basic units of protein structure and function. We argue that future developments in protein design approaches should consider descriptors of the elementary functions, which will help to complement designed scaffolds with functional signatures and flexibility necessary for their functions.

The Structural Dynamics of Engineered β-Lactamases Vary Broadly on Three Timescales yet Sustain Native Function

Understanding the principles of protein dynamics will help guide engineering of protein function: altering protein motions may be a barrier to success or may be an enabling tool for protein engineering. The impact of dynamics on protein function is typically reported over a fraction of the full scope of motional timescales. If motional patterns vary significantly at different timescales, then only by monitoring motions broadly will we understand the impact of protein dynamics on engineering functional proteins. Using an integrative approach combining experimental and in silico methodologies, we elucidate protein dynamics over the entire span of fast to slow timescales (ps to ms) for a laboratory-engineered system composed of five interrelated β-lactamases: two natural homologs and three laboratory-recombined variants. Fast (ps-ns) and intermediate (ns-µs) dynamics were mostly conserved. However, slow motions (µs-ms) were few and conserved in the natural homologs yet were numerous and widely dispersed in their recombinants. Nonetheless, modified slow dynamics were functionally tolerated. Crystallographic B-factors from high-resolution X-ray structures were partly predictive of the conserved motions but not of the new slow motions captured in our solution studies. Our inspection of protein dynamics over a continuous range of timescales vividly illustrates the complexity of dynamic impacts of protein engineering as well as the functional tolerance of an engineered enzyme system to new slow motions.

Protein engineers turned evolutionists-the quest for the optimal starting point

The advent of laboratory directed evolution yielded a fruitful crosstalk between the disciplines of molecular evolution and bio-engineering. Here, we outline recent developments in both disciplines with respect to how one can identify the best starting points for directed evolution, such that highly efficient and robust tailor-made enzymes can be obtained with minimal optimization. Directed evolution studies have highlighted essential features of engineer-able enzymes: highly stable, mutationally robust enzymes with the capacity to accept a broad range of substrates. Robust, evolvable enzymes can be inferred from the natural sequence record. Broad substrate spectrum relates to conformational plasticity and can also be predicted by phylogenetic analyses and/or by computational design. Overall, an increasingly powerful toolkit is becoming available for identifying optimal starting points including network analyses of enzyme superfamilies and other bioinformatics methods.

Tracing whale myoglobin evolution by resurrecting ancient proteins

Extant cetaceans, such as sperm whale, acquired the great ability to dive into the ocean depths during the evolution from their terrestrial ancestor that lived about 50 million years ago. Myoglobin (Mb) is highly concentrated in the myocytes of diving animals, in comparison with those of land animals, and is thought to play a crucial role in their adaptation as the molecular aqualung. Here, we resurrected ancestral whale Mbs, which are from the common ancestor between toothed and baleen whales (Basilosaurus), and from a further common quadrupedal ancestor between whale and hippopotamus (Pakicetus). The experimental and theoretical analyses demonstrated that whale Mb adopted two distinguished strategies to increase the protein concentration in vivo along the evolutionary history of deep sea adaptation; gaining precipitant tolerance in the early phase of the evolution, and increase of folding stability in the late phase.

Molecular modeling of conformational dynamics and its role in enzyme evolution

With increasing computational power, biomolecular simulations have become an invaluable tool for understanding enzyme mechanisms and the origins of enzyme catalysis. More recently, computational studies have started to focus on understanding how enzyme activity itself evolves, both in terms of enhancing the native or new activities on existing enzyme scaffolds, or completely de novo on previously non-catalytic scaffolds. In this context, both experiment and molecular modeling provided strong evidence for an important role of conformational dynamics in the evolution of enzyme functions. This contribution will present a brief overview of the current state of the art for computationally exploring enzyme conformational dynamics in enzyme evolution, and, using several showcase studies, illustrate the ways molecular modeling can be used to shed light on how enzyme function evolves, at the most fundamental molecular level.

The ascent of man(made oxidoreductases)

Though established 40 years ago, the field of de novo protein design has recently come of age, with new designs exhibiting an unprecedented level of sophistication in structure and function. With respect to catalysis, de novo enzymes promise to revolutionise the industrial production of useful chemicals and materials, while providing new biomolecules as plug-and-play components in the metabolic pathways of living cells. To this end, there are now de novo metalloenzymes that are assembled in vivo, including the recently reported C45 maquette, which can catalyse a variety of substrate oxidations with efficiencies rivalling those of closely related natural enzymes. Here we explore the successful design of this de novo enzyme, which was designed to minimise the undesirable complexity of natural proteins using a minimalistic bottom-up approach.

Conformational dynamics and enzyme evolution

Enzymes are dynamic entities, and their dynamic properties are clearly linked to their biological function. It follows that dynamics ought to play an essential role in enzyme evolution. Indeed, a link between conformational diversity and the emergence of new enzyme functionalities has been recognized for many years. However, it is only recently that state-of-the-art computational and experimental approaches are revealing the crucial molecular details of this link. Specifically, evolutionary trajectories leading to functional optimization for a given host environment or to the emergence of a new function typically involve enriching catalytically competent conformations and/or the freezing out of non-competent conformations of an enzyme. In some cases, these evolutionary changes are achieved through distant mutations that shift the protein ensemble towards productive conformations. Multifunctional intermediates in evolutionary trajectories are probably multi-conformational, i.e. able to switch between different overall conformations, each competent for a given function. Conformational diversity can assist the emergence of a completely new active site through a single mutation by facilitating transition-state binding. We propose that this mechanism may have played a role in the emergence of enzymes at the primordial, progenote stage, where it was plausibly promoted by high environmental temperatures and the possibility of additional phenotypic mutations.