Intense neutral drifts yield robust and evolvable consensus proteins

Carbohydrate-active enzyme (CAZyme) discovery and engineering via (Ultra)high-throughput screening

Carbohydrate-active enzymes (CAZymes) constitute a diverse set of enzymes that catalyze the assembly, degradation, and modification of carbohydrates. These enzymes have been fashioned into potent, selective catalysts by millennia of evolution, and yet are also highly adaptable and readily evolved in the laboratory. To identify and engineer CAZymes for different purposes, (ultra)high-throughput screening campaigns have been frequently utilized with great success. This review provides an overview of the different approaches taken in screening for CAZymes and how mechanistic understandings of CAZymes can enable new approaches to screening. Within, we also cover how cutting-edge techniques such as microfluidics, advances in computational approaches and synthetic biology, as well as novel assay designs are leading the field towards more informative and effective screening approaches.

GMMA Can Stabilize Proteins Across Different Functional Constraints

Stabilizing proteins without otherwise hampering their function is a central task in protein engineering and design. PYR1 is a plant hormone receptor that has been engineered to bind diverse small molecule ligands. We sought a set of generalized mutations that would provide stability without affecting functionality for PYR1 variants with diverse ligand-binding capabilities. To do this we used a global multi-mutant analysis (GMMA) approach, which can identify substitutions that have stabilizing effects and do not lower function. GMMA has the added benefit of finding substitutions that are stabilizing in different sequence contexts and we hypothesized that applying GMMA to PYR1 with different functionalities would identify this set of generalized mutations. Indeed, conducting FACS and deep sequencing of libraries for PYR1 variants with two different functionalities and applying a GMMA analysis identified 5 substitutions that, when inserted into four PYR1 variants that each bind a unique ligand, provided an increase of 2-6 °C in thermal inactivation temperature and no decrease in functionality.

Designing cytochrome P450 enzymes for use in cancer gene therapy

Cancer is a significant global socioeconomic burden, as millions of new cases and deaths occur annually. In 2020, almost 10 million cancer deaths were recorded worldwide. Advancements in cancer gene therapy have revolutionized the landscape of cancer treatment. An approach with promising potential for cancer gene therapy is introducing genes to cancer cells that encode for chemotherapy prodrug metabolizing enzymes, such as Cytochrome P450 (CYP) enzymes, which can contribute to the effective elimination of cancer cells. This can be achieved through gene-directed enzyme prodrug therapy (GDEPT). CYP enzymes can be genetically engineered to improve anticancer prodrug conversion to its active metabolites and to minimize chemotherapy side effects by reducing the prodrug dosage. Rational design, directed evolution, and phylogenetic methods are some approaches to developing tailored CYP enzymes for cancer therapy. Here, we provide a compilation of genetic modifications performed on CYP enzymes aiming to build highly efficient therapeutic genes capable of bio-activating different chemotherapeutic prodrugs. Additionally, this review summarizes promising preclinical and clinical trials highlighting engineered CYP enzymes' potential in GDEPT. Finally, the challenges, limitations, and future directions of using CYP enzymes for GDEPT in cancer gene therapy are discussed.

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.

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.

Molecular determinants of protein evolvability

The plethora of biological functions that sustain life is rooted in the remarkable evolvability of proteins. An emerging view highlights the importance of a protein's initial state in dictating evolutionary success. A deeper comprehension of the mechanisms that govern the evolvability of these initial states can provide invaluable insights into protein evolution. In this review, we describe several molecular determinants of protein evolvability, unveiled by experimental evolution and ancestral sequence reconstruction studies. We further discuss how genetic variation and epistasis can promote or constrain functional innovation and suggest putative underlying mechanisms. By establishing a clear framework for these determinants, we provide potential indicators enabling the forecast of suitable evolutionary starting points and delineate molecular mechanisms in need of deeper exploration.

A dual gene-specific mutator system installs all transition mutations at similar frequencies in vivo

Targeted in vivo hypermutation accelerates directed evolution of proteins through concurrent DNA diversification and selection. Although systems employing a fusion protein of a nucleobase deaminase and T7 RNA polymerase present gene-specific targeting, their mutational spectra have been limited to exclusive or dominant C:G→T:A mutations. Here we describe eMutaT7transition, a new gene-specific hypermutation system, that installs all transition mutations (C:G→T:A and A:T→G:C) at comparable frequencies. By using two mutator proteins in which two efficient deaminases, PmCDA1 and TadA-8e, are separately fused to T7 RNA polymerase, we obtained similar numbers of C:G→T:A and A:T→G:C substitutions at a sufficiently high frequency (∼6.7 substitutions in 1.3 kb gene during 80-h in vivo mutagenesis). Through eMutaT7transition-mediated TEM-1 evolution for antibiotic resistance, we generated many mutations found in clinical isolates. Overall, with a high mutation frequency and wider mutational spectrum, eMutaT7transition is a potential first-line method for gene-specific in vivo hypermutation.

On the Unknown Proteins of Eukaryotic Proteomes

To study unknown proteins on a large scale, a reference system has been set up for the three better studied eukaryotic kingdoms, built with 36 proteomes as taxonomically diverse as possible. Proteins from 362 other eukaryotic proteomes with no known homologue in this set were then analyzed, focusing noteworthy on singletons, that is, on such proteins with no known homologue in their own proteome. Consistently, for a given species, no more than 12% of the singletons thus found are known at the protein level, according to Uniprot. In addition, since they rely on the information found in the alignment of homologous sequences, predictions of AlphaFold2 for their tridimensional structure are poor. In the case of metazoan species, the number of singletons rarely exceeds 1000 for the species the closest to the reference system (divergence times below 75 Myr). Interestingly, in the cases of viridiplantae and fungi, larger amounts of singletons are found for such species, as if the timescale on which singletons are added to proteomes were different in metazoa and in other eukaryotic kingdoms. In order to confirm this phenomenon, further studies of proteomes closer to those of the reference system are, however, needed.

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >10⁷-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >10⁷ variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.

Robustness and innovation in synthetic genotype networks

Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct phenotypes in Escherichia coli. Our synthetic GRNs contain three nodes regulating each other by CRISPR interference and governing the expression of fluorescent reporters. The genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.

Inferring protein fitness landscapes from laboratory evolution experiments

Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory. We apply our modeling approach to dihydrofolate reductase (DHFR) laboratory evolution data and the resulting landscape parameters capture important aspects of DHFR structure and function. We use the resulting model to understand the structure of the fitness landscape and find numerous examples of epistasis but an overall global peak that is evolutionarily accessible from most starting sequences. Finally, we use the model to perform an in silico extrapolation of the DHFR laboratory evolution trajectory and computationally design proteins from future evolutionary rounds.

Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design

The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.

Experimental and computational analysis of the ancestry of an evolutionary young enzyme from histidine biosynthesis

The conservation of fold and chemistry of the enzymes associated with histidine biosynthesis suggests that this pathway evolved prior to the diversification of Bacteria, Archaea, and Eukaryotes. The only exception is the histidinol phosphate phosphatase (HolPase). So far, non-homologous HolPases that possess distinct folds and belong to three different protein superfamilies have been identified in various phylogenetic clades. However, their evolution has remained unknown to date. Here, we analyzed the evolutionary history of the HolPase from γ-Proteobacteria (HisB-N). It has been argued that HisB-N and its closest homologue d-glycero-d-manno-heptose-1,7-bisphosphate 7-phosphatase (GmhB) have emerged from the same promiscuous ancestral phosphatase. GmhB variants catalyze the hydrolysis of the anomeric d-glycero-d-manno-heptose-1,7-bisphosphate (αHBP or βHBP) with a strong preference for one anomer (αGmhB or βGmhB). We found that HisB-N from Escherichia coli shows promiscuous activity for βHBP but not αHBP, while βGmhB from Crassaminicella sp. shows promiscuous activity for HolP. Accordingly, a combined phylogenetic tree of αGmhBs, βGmhBs, and HisB-N sequences revealed that HisB-Ns form a compact subcluster derived from βGmhBs. Ancestral sequence reconstruction and in vitro analysis revealed a promiscuous HolPase activity in the resurrected enzymes prior to functional divergence of the successors. The following increase in catalytic efficiency of the HolP turnover is reflected in the shape and electrostatics of the active site predicted by AlphaFold. An analysis of the phylogenetic tree led to a revised evolutionary model that proposes the horizontal gene transfer of a promiscuous βGmhB from δ- to γ-Proteobacteria where it evolved to the modern HisB-N.

Orthogonal glycolytic pathway enables directed evolution of noncanonical cofactor oxidase

Noncanonical cofactor biomimetics (NCBs) such as nicotinamide mononucleotide (NMN+) provide enhanced scalability for biomanufacturing. However, engineering enzymes to accept NCBs is difficult. Here, we establish a growth selection platform to evolve enzymes to utilize NMN+-based reducing power. This is based on an orthogonal, NMN+-dependent glycolytic pathway in Escherichia coli which can be coupled to any reciprocal enzyme to recycle the ensuing reduced NMN+. With a throughput of >106 variants per iteration, the growth selection discovers a Lactobacillus pentosus NADH oxidase variant with ~10-fold increase in NMNH catalytic efficiency and enhanced activity for other NCBs. Molecular modeling and experimental validation suggest that instead of directly contacting NCBs, the mutations optimize the enzyme's global conformational dynamics to resemble the WT with the native cofactor bound. Restoring the enzyme's access to catalytically competent conformation states via deep navigation of protein sequence space with high-throughput evolution provides a universal route to engineer NCB-dependent enzymes.

Stability and expression of SARS-CoV-2 spike-protein mutations

Protein fold stability likely plays a role in SARS-CoV-2 S-protein evolution, together with ACE2 binding and antibody evasion. While few thermodynamic stability data are available for S-protein mutants, many systematic experimental data exist for their expression. In this paper, we explore whether such expression levels relate to the thermodynamic stability of the mutants. We studied mutation-induced SARS-CoV-2 S-protein fold stability, as computed by three very distinct methods and eight different protein structures to account for method- and structure-dependencies. For all methods and structures used (24 comparisons), computed stability changes correlate significantly (99% confidence level) with experimental yeast expression from the literature, such that higher expression is associated with relatively higher fold stability. Also significant, albeit weaker, correlations were seen between stability and ACE2 binding effects. The effect of thermodynamic fold stability may be direct or a correlate of amino acid or site properties, notably the solvent exposure of the site. Correlation between computed stability and experimental expression and ACE2 binding suggests that functional properties of the SARS-CoV-2 S-protein mutant space are largely determined by a few simple features, due to underlying correlations. Our study lends promise to the development of computational tools that may ideally aid in understanding and predicting SARS-CoV-2 S-protein evolution.

Structural heterogeneity and precision of implications drawn from cryo-electron microscopy structures: SARS-CoV-2 spike-protein mutations as a test case

Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged “ensemble” estimates for groups of residues when multiple structures are available.

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 towards 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 (ASR) 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 ASR and what it can reveal about the determinants of stability in proteins.

A Simulation Analysis and Screening of Deleterious Nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) in Sheep LEP Gene

Leptin is a polypeptide hormone produced in the adipose tissue and governs many processes in the body. Recently, polymorphisms in the LEP gene revealed a significant change in body weight regulation, energy balance, food intake, and reproductive hormone secretion. This study considers its crucial role in the regulation of the economically important traits of sheep. Several computational tools, including SIFT, Predict SNP2, SNAP2, and PROVEAN, have been used to screen out the deleterious nsSNPs. Following the screening of 11 nsSNPs in the sheep genome, 5 nsSNPs, T86M (C → T), D98N (G → A), N136T (A → C), R142Q (G → A), and P157Q (C → A), were predicted to have a significant deleterious effect on the LEP protein function, leading to phenotypic difference. The analysis of proteins’ stability change due to amino acid substitution using the I-stable, SDM, and DynaMut consistently confirmed that three nsSNPs (T86M (C → T), D98N (G → A), and P157Q (C → A)) increased protein stability. It is suggested that these three nsSNPs may enhance the evolvability of LEP protein, which is vital for the evolutionary adaptation of sheep. Our findings demonstrate that the five nsSNPs reported in this study might be responsible for sheep’s structural and functional modifications of LEP protein. This is the first comprehensive report on the sheep LEP gene. It narrow downs the candidate nsSNPs for in vitro experiments to facilitate the development of reliable molecular markers for associated traits.

Mechanistic insights into global suppressors of protein folding defects

Most amino acid substitutions in a protein either lead to partial loss-of-function or are near neutral. Several studies have shown the existence of second-site mutations that can rescue defects caused by diverse loss-of-function mutations. Such global suppressor mutations are key drivers of protein evolution. However, the mechanisms responsible for such suppression remain poorly understood. To address this, we characterized multiple suppressor mutations both in isolation and in combination with inactive mutants. We examined six global suppressors of the bacterial toxin CcdB, the known M182T global suppressor of TEM-1 β-lactamase, the N239Y global suppressor of p53-DBD and three suppressors of the SARS-CoV-2 spike Receptor Binding Domain. When coupled to inactive mutants, they promote increased in-vivo solubilities as well as regain-of-function phenotypes. In the case of CcdB, where novel suppressors were isolated, we determined the crystal structures of three such suppressors to obtain insight into the specific molecular interactions responsible for the observed effects. While most individual suppressors result in small stability enhancements relative to wildtype, which can be combined to yield significant stability increments, thermodynamic stabilisation is neither necessary nor sufficient for suppressor action. Instead, in diverse systems, we observe that individual global suppressors greatly enhance the foldability of buried site mutants, primarily through increase in refolding rate parameters measured in vitro. In the crowded intracellular environment, mutations that slow down folding likely facilitate off-pathway aggregation. We suggest that suppressor mutations that accelerate refolding can counteract this, enhancing the yield of properly folded, functional protein in vivo.

Low protein expression enhances phenotypic evolvability by intensifying selection on folding stability

Protein abundance affects the evolution of protein genotypes, but we do not know how it affects the evolution of protein phenotypes. Here we investigate the role of protein abundance in the evolvability of green fluorescent protein (GFP) towards the novel phenotype of cyan fluorescence. We evolve GFP in E. coli through multiple cycles of mutation and selection and show that low GFP expression facilitates the evolution of cyan fluorescence. A computational model whose predictions we test experimentally helps explain why: lowly expressed proteins are under stronger selection for proper folding, which facilitates their evolvability on short evolutionary time scales. The reason is that high fluorescence can be achieved by either few proteins that fold well or by many proteins that fold less well. In other words, we observe a synergy between a protein's scarcity and its stability. Because many proteins meet the essential requirements for this scarcity-stability synergy, it may be a widespread mechanism by which low expression helps proteins evolve new phenotypes and functions.

Dan Tawfik's Lessons for Protein Engineers about Enzymes Adapting to New Substrates

Natural evolution has been creating new complex systems for billions of years. The process is spontaneous and requires neither intelligence nor moral purpose but is nevertheless difficult to understand. The late Dan Tawfik spent years studying enzymes as they adapted to recognize new substrates. Much of his work focused on gaining fundamental insights, so the practical utility of his experiments may not be obvious even to accomplished protein engineers. Here we focus on two questions fundamental to any directed evolution experiment. Which proteins are the best starting points for such experiments? Which trait(s) of the chosen parental protein should be evolved to achieve the desired outcome? We summarize Tawfik's contributions to our understanding of these problems, to honor his memory and encourage those unfamiliar with his ideas to read his publications.

Genetic and Phenotypic Study of the Pectobacterium versatile Beta-Lactamase, the Enzyme Most Similar to the Plasmid-Encoded TEM-1

Genus Pectobacterium bacteria include important agricultural pathogens. Pectobacterium versatile isolates contain a chromosome-borne beta-lactamase, PEC-1. This enzyme is the closest relative of TEM-1, a plasmid-borne beta-lactamase widespread in the Enterobacterales. We performed bioinformatics and phenotypic analyses to investigate the genetic and phenotypic features of PEC-1 and its frequency and ability to spread within genus Pectobacterium. We also compared the characteristics of PEC-1 and TEM-1 and evaluated the likelihood of transfer. We found that bla_PEC-1 was present principally in a small number of genetic environments in P. versatile. Identical bla_PEC-1 genetic environments were present in closely related species, consistent with the high frequency of genetic exchange within the genus Pectobacterium. Despite the similarities between PEC-1 and TEM-1, their genetic environments displayed no significant identity, suggesting an absence of recent transfer. Phenotypic analyses on clonal constructs revealed similar hydrolysis spectra. Our results suggest that P. versatile is the main reservoir of PEC-1, which seems to transfer to closely related species. The genetic distance between PEC-1 and TEM-1, and the lack of conserved elements in their genetic environments, suggest that any transfer that may have occurred must have taken place well before the antibiotic era. IMPORTANCE This study aimed to compare the chromosomal beta-lactamase from Pectobacterium versatile, PEC-1, with the well-known and globally distributed TEM-1 in terms of genetic and functional properties. Despite the similarities between the enzymes, we obtained no definitive proof of gene transfer for the emergence of bla_PEC-1 from bla_TEM-1. Indeed, given the limited degree of sequence identity and the absence of a common genetic environment, it seems unlikely that any transfer of this gene has occurred recently. However, although bla_PEC-1 was found mostly in one specific clade of the P. versatile species, certain isolates from other closely related species, such as Pectobacterium brasiliense and Pectobacterium polaris, may also carry this gene inserted into common genetic environments. This observation suggests that genetic exchanges are frequent, accounting for the diffusion of bla_PEC-1 between isolates from different Pectobacterium species and, potentially, to exogenous mobile genetic elements.

Local and Global Protein Interactions Contribute to Residue Entrenchment in Beta-Lactamase TEM-1

Due to their rapid evolution and their impact on healthcare, beta-lactamases, protein degrading beta-lactam antibiotics, are used as generic models of protein evolution. Therefore, we investigated the mutation effects in two distant beta-lactamases, TEM-1 and CTX-M-15. Interestingly, we found a site with a complex pattern of genetic interactions. Mutation G251W in TEM-1 inactivates the protein’s function, just as the reciprocal mutation, W251G, does in CTX-M-15. The phylogenetic analysis revealed that mutation G has been entrenched in TEM-1’s background: while rarely observed throughout the phylogeny, it is essential in TEM-1. Using a rescue experiment, in the TEM-1 G251W mutant, we identified sites that alleviate the deviation from G to W. While few of these mutations could potentially involve local interactions, most of them were found on distant residues in the 3D structure. Many well-known mutations that have an impact on protein stability, such as M182T, were recovered. Our results therefore suggest that entrenchment of an amino acid may rely on diffuse interactions among multiple sites, with a major impact on protein stability.

Mimetic Neural Networks: A Unified Framework for Protein Design and Folding

Recent advancements in machine learning techniques for protein structure prediction motivate better results in its inverse problem–protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein backbone design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be met and even improved, given recent architectures for protein folding.

Structure and Mutations of SARS-CoV-2 Spike Protein: A Focused Overview

The spike protein (S-protein) of SARS-CoV-2, the protein that enables the virus to infect human cells, is the basis for many vaccines and a hotspot of concerning virus evolution. Here, we discuss the outstanding progress in structural characterization of the S-protein and how these structures facilitate analysis of virus function and evolution. We emphasize the differences in reported structures and that analysis of structure-function relationships is sensitive to the structure used. We show that the average residue solvent exposure in nearly complete structures is a good descriptor of open vs closed conformation states. Because of structural heterogeneity of functionally important surface-exposed residues, we recommend using averages of a group of high-quality protein structures rather than a single structure before reaching conclusions on specific structure-function relationships. To illustrate these points, we analyze some significant chemical tendencies of prominent S-protein mutations in the context of the available structures. In the discussion of new variants, we emphasize the selectivity of binding to ACE2 vs prominent antibodies rather than simply the antibody escape or ACE2 affinity separately. We note that larger chemical changes, in particular increased electrostatic charge or side-chain volume of exposed surface residues, are recurring in mutations of concern, plausibly related to adaptation to the negative surface potential of human ACE2. We also find indications that the fixated mutations of the S-protein in the main variants are less destabilizing than would be expected on average, possibly pointing toward a selection pressure on the S-protein. The richness of available structures for all of these situations provides an enormously valuable basis for future research into these structure-function relationships.

The Interplay of Electrostatics and Chemical Positioning in the Evolution of Antibiotic Resistance in TEM β-Lactamases

The interplay of enzyme active site electrostatics and chemical positioning is important for understanding the origin(s) of enzyme catalysis and the design of novel catalysts. We reconstruct the evolutionary trajectory of TEM-1 β-lactamase to TEM-52 toward extended-spectrum activity to better understand the emergence of antibiotic resistance and to provide insights into the structure-function paradigm and noncovalent interactions involved in catalysis. Utilizing a detailed kinetic analysis and the vibrational Stark effect, we quantify the changes in rates and electric fields in the Michaelis and acyl-enzyme complexes for penicillin G and cefotaxime to ascertain the evolutionary role of electric fields to modulate function. These data are combined with MD simulations to interpret and quantify the substrate-dependent structural changes during evolution. We observe that this evolutionary trajectory utilizes a large preorganized electric field and substrate-dependent chemical positioning to facilitate catalysis. This governs the evolvability, substrate promiscuity, and protein fitness landscape in TEM β-lactamase antibiotic resistance.

Ancestral sequences of a large promiscuous enzyme family correspond to bridges in sequence space in a network representation

Evolutionary relationships of protein families can be characterized either by networks or by trees. Whereas trees allow for hierarchical grouping and reconstruction of the most likely ancestral sequences, networks lack a time axis but allow for thresholds of pairwise sequence identity to be chosen and, therefore, the clustering of family members with presumably more similar functions. Here, we use the large family of arylsulfatases and phosphonate monoester hydrolases to investigate similarities, strengths and weaknesses in tree and network representations. For varying thresholds of pairwise sequence identity, values of betweenness centrality and clustering coefficients were derived for nodes of the reconstructed ancestors to measure the propensity to act as a bridge in a network. Based on these properties, ancestral protein sequences emerge as bridges in protein sequence networks. Interestingly, many ancestral protein sequences appear close to extant sequences. Therefore, reconstructed ancestor sequences might also be interpreted as yet-to-be-identified homologues. The concept of ancestor reconstruction is compared to consensus sequences, too. It was found that hub sequences in a network, e.g. reconstructed ancestral sequences that are connected to many neighbouring sequences, share closer similarity with derived consensus sequences. Therefore, some reconstructed ancestor sequences can also be interpreted as consensus sequences.

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

Selection enhances protein evolvability by increasing mutational robustness and foldability

Natural selection can promote or hinder a population’s evolvability—the ability to evolve new and adaptive phenotypes—but the underlying mechanisms are poorly understood. To examine how the strength of selection affects evolvability, we subjected populations of yellow fluorescent protein to directed evolution under different selection regimes and then evolved them toward the new phenotype of green fluorescence. Populations under strong selection for the yellow phenotype evolved the green phenotype most rapidly. They did so by accumulating mutations that increase both robustness to mutations and foldability. Under weak selection, neofunctionalizing mutations rose to higher frequency at first, but more frequent deleterious mutations undermined their eventual success. Our experiments show how selection can enhance evolvability by enhancing robustness and create the conditions necessary for evolutionary success.

Scalable continuous evolution for the generation of diverse enzyme variants encompassing promiscuous activities

Enzyme orthologs sharing identical primary functions can have different promiscuous activities. While it is possible to mine this natural diversity to obtain useful biocatalysts, generating comparably rich ortholog diversity is difficult, as it is the product of deep evolutionary processes occurring in a multitude of separate species and populations. Here, we take a first step in recapitulating the depth and scale of natural ortholog evolution on laboratory timescales. Using a continuous directed evolution platform called OrthoRep, we rapidly evolve the Thermotoga maritima tryptophan synthase β-subunit (TmTrpB) through multi-mutation pathways in many independent replicates, selecting only on TmTrpB's primary activity of synthesizing L-tryptophan from indole and L-serine. We find that the resulting sequence-diverse TmTrpB variants span a range of substrate profiles useful in industrial biocatalysis and suggest that the depth and scale of evolution that OrthoRep affords will be generally valuable in enzyme engineering and the evolution of biomolecular functions.

Automated Continuous Evolution of Proteins in Vivo

We present automated continuous evolution (ACE), a platform for the hands-free directed evolution of biomolecules. ACE pairs OrthoRep, a genetic system for continuous targeted mutagenesis of user-selected genes in vivo, with eVOLVER, a scalable and automated continuous culture device for precise, multiparameter regulation of growth conditions. By implementing real-time feedback-controlled tuning of selection stringency with eVOLVER, genes of interest encoded on OrthoRep autonomously traversed multimutation adaptive pathways to reach desired functions, including drug resistance and improved enzyme activity. The durability, scalability, and speed of biomolecular evolution with ACE should be broadly applicable to protein engineering as well as prospective studies on how selection parameters and schedules shape adaptation.

Collateral fitness effects of mutations

The distribution of fitness effects of mutation plays a central role in constraining protein evolution. The underlying mechanisms by which mutations lead to fitness effects are typically attributed to changes in protein specific activity or abundance. Here, we reveal the importance of a mutation's collateral fitness effects, which we define as effects that do not derive from changes in the protein's ability to perform its physiological function. We comprehensively measured the collateral fitness effects of missense mutations in the Escherichia coli TEM-1 β-lactamase antibiotic resistance gene using growth competition experiments in the absence of antibiotic. At least 42% of missense mutations in TEM-1 were deleterious, indicating that for some proteins collateral fitness effects occur as frequently as effects on protein activity and abundance. Deleterious mutations caused improper posttranslational processing, incorrect disulfide-bond formation, protein aggregation, changes in gene expression, and pleiotropic effects on cell phenotype. Deleterious collateral fitness effects occurred more frequently in TEM-1 than deleterious effects on antibiotic resistance in environments with low concentrations of the antibiotic. The surprising prevalence of deleterious collateral fitness effects suggests they may play a role in constraining protein evolution, particularly for highly expressed proteins, for proteins under intermittent selection for their physiological function, and for proteins whose contribution to fitness is buffered against deleterious effects on protein activity and protein abundance.

Directed evolution of bacterial polysialyltransferases

Polysialyltransferases (polySTs) are glycosyltransferases that synthesize polymers of sialic acid found in vertebrates and some bacterial pathogens. Bacterial polySTs have utility in the modification of therapeutic proteins to improve serum half-life, and the potential for tissue engineering. PolySTs are membrane-associated proteins and as recombinant proteins suffer from inherently low solubility, low expression levels and poor thermal stability. To improve their physicochemical and biochemical properties, we applied a directed evolution approach using a FACS-based ultrahigh-throughput assay as a simple, robust and reliable screening method. We were able to enrich a large mutant library and, in combination with plate-based high-throughput secondary screening, we discovered mutants with increased enzymatic activity and improved stability compared to the wildtype enzyme. This work presents a powerful strategy for the screening of directed evolution libraries of bacterial polySTs to identify better catalysts for in vitro polysialylation of therapeutics.

High-Throughput, Lysis-Free Screening for Sulfatase Activity Using Escherichia coli Autodisplay in Microdroplets

Directed evolution of enzymes toward improved catalytic performance has become a powerful tool in protein engineering. To be effective, a directed evolution campaign requires the use of high-throughput screening. In this study we describe the development of an ultra high-throughput lysis-free procedure to screen for improved sulfatase activity by combining microdroplet-based single-variant activity sorting with E. coli autodisplay. For the first step in a 4-step screening procedure, we quantitatively screened >10⁵ variants of the homodimeric arylsulfatase from Silicibacter pomeroyi (SpAS1), displayed on the E. coli cell surface, for improved sulfatase activity using fluorescence activated droplet sorting. Compartmentalization of the fluorescent reaction product with living E. coli cells autodisplaying the sulfatase variants ensured the continuous linkage of genotype and phenotype during droplet sorting and allowed for direct recovery by simple regrowth of the sorted cells. The use of autodisplay on living cells simplified and reduced the degree of liquid handling during all steps in the screening procedure to the single event of simply mixing substrate and cells. The percentage of apparent improved variants was enriched >10-fold as a result of droplet sorting. We ultimately identified 25 SpAS1 variants with improved performance toward 4-nitrophenyl sulfate (up to 6.2-fold) and/or fluorescein disulfate (up to 30-fold). In SpAS1 variants with improved performance toward the bulky fluorescein disulfate, many of the beneficial mutations occur in residues that form hydrogen bonds between α-helices in the C-terminal oligomerization region, suggesting a previously unknown role for the dimer interface in shaping the substrate binding site of SpAS1.

Limits to Compensatory Mutations: Insights from Temperature-Sensitive Alleles

Previous experiments with temperature-sensitive mutants of the yeast enzyme orotidine 5'-phosphate decarboxylase (encoded in gene URA3) yielded the unexpected result that reversion occurs only through exact reversal of the original mutation (Jakubowska A, Korona R. 2009. Lack of evolutionary conservation at positions important for thermal stability in the yeast ODCase protein. Mol Biol Evol. 26(7):1431-1434.). We recreated a set of these mutations in which the codon had two nucleotide substitutions, making exact reversion much less likely. We screened these double mutants for reversion and obtained a number of compensatory mutations occurring at alternative sites in the molecule. None of these compensatory mutations fully restored protein performance. The mechanism of partial compensation is consistent with a model in which protein stabilization is additive, as the same secondary mutations can compensate different primary alternations. The distance between primary and compensatory residues precludes direct interaction between the sites. Instead, most of the compensatory mutants were clustered in proximity to the catalytic center. All of the second-site compensatory substitutions occurred at relatively conserved sites, and the amino acid replacements were to residues found at these sites in a multispecies alignment of the protein. Based on the estimated distribution of changes in Gibbs free energy among a large number of amino acid replacements, we estimate that, for most proteins, the probability that a second-site mutation would have a sufficiently large stabilizing effect to offset a temperature-sensitive mutation in the order of 10-4 or less. Hence compensation is likely to take place only for slightly destabilizing mutations because highly stabilizing mutations are exceeding rare.

Selection strategies for randomly partitioned genetic replicators

The amplification cycle of many replicators (natural or artificial) involves the usage of a host compartment, inside of which the replicator expresses phenotypic compounds necessary to carry out its genetic replication. For example, viruses infect cells, where they express their own proteins and replicate. In this process, the host cell boundary limits the diffusion of the viral protein products, thereby ensuring that phenotypic compounds, such as proteins, promote the replication of the genes that encoded them. This role of maintaining spatial colocalization, also called genotype-phenotype linkage, is a critical function of compartments in natural selection. In most cases, however, individual replicating elements do not distribute systematically among the hosts, but are randomly partitioned. Depending on the replicator-to-host ratio, more than one variant may thus occupy some compartments, blurring the genotype-phenotype linkage and affecting the effectiveness of natural selection. We derive selection equations for a variety of such random multiple occupancy situations, in particular considering the effect of replicator population polymorphism and internal replication dynamics. We conclude that the deleterious effect of random multiple occupancy on selection is relatively benign, and may even completely vanish is some specific cases. In addition, given that higher mean occupancy allows larger populations to be channeled through the selection process, and thus provide a better exploration of phenotypic diversity, we show that it may represent a valid strategy in both natural and technological cases.

Validation and Stabilization of a Prophage Lysin of Clostridium perfringens by Using Yeast Surface Display and Coevolutionary Models

Bacteriophage lysins are compelling antimicrobial proteins whose biotechnological utility and evolvability would be aided by elevated stability. Lysin catalytic domains, which evolved as modular entities distinct from cell wall binding domains, can be classified into one of several families with highly conserved structure and function, many of which contain thousands of annotated homologous sequences. Motivated by the quality of these evolutionary data, the performance of generative protein models incorporating coevolutionary information was analyzed to predict the stability of variants in a collection of 9,749 multimutants across 10 libraries diversified at different regions of a putative lysin from a prophage region of a Clostridium perfringens genome. Protein stability was assessed via a yeast surface display assay with accompanying high-throughput sequencing. Statistical fitness of mutant sequences, derived from second-order Potts models inferred with different levels of sequence homolog information, was predictive of experimental stability with areas under the curve (AUCs) ranging from 0.78 to 0.85. To extract an experimentally derived model of stability, a logistic model with site-wise score contributions was regressed on the collection of multimutants. This achieved a cross-validated classification performance of 0.95. Using this experimentally derived model, 5 designs incorporating 5 or 6 mutations from multiple libraries were constructed. All designs retained enzymatic activity, with 4 of 5 increasing the melting temperature and with the highest-performing design achieving an improvement of +4°C.IMPORTANCE Bacteriophage lysins exhibit high specificity and activity toward host bacteria with which the phage coevolved. These properties of lysins make them attractive for use as antimicrobials. Although there has been significant effort to develop platforms for rapid lysin engineering, there have been numerous shortcomings when pursuing the ultrahigh throughput necessary for the discovery of rare combinations of mutations to improve performance. In addition to validation of a putative lysin and stabilization thereof, the experimental and computational methods presented here offer a new avenue for improving protein stability and are easily scalable to analysis of tens of millions of mutations in single experiments.

Engineering-driven biological insights into DNA polymerase mechanism

DNA-dependent DNA polymerases have been extensively studied for over 60 years and lie at the core of multiple biotechnological and diagnostic applications. Nevertheless, these complex molecular machines remain only partially understood. Here we present some evidence on how polymerase engineering for the synthesis and replication of xenobiotic nucleic acids (XNAs) have improved our understanding of these enzymes and how that can be used to gain further insight into their mechanism. Better understanding of the mechanisms of DNA polymerases can accelerate their engineering and we highlight how it is now feasible to use structure-based and function-based approaches to systematically and iteratively develop XNA polymerases for increasingly divergent chemistries.

Thermophilic Proteins as Versatile Scaffolds for Protein Engineering

Literature from the past two decades has outlined the existence of a trade-off between protein stability and function. This trade-off creates a unique challenge for protein engineers who seek to introduce new functionality to proteins. These engineers must carefully balance the mutation-mediated creation and/or optimization of function with the destabilizing effect of those mutations. Subsequent research has shown that protein stability is positively correlated with "evolvability" or the ability to support mutations which bestow new functionality on the protein. Since the ultimate goal of protein engineering is to create and/or optimize a protein's function, highly stable proteins are preferred as potential scaffolds for protein engineering. This review focuses on the application potential for thermophilic proteins as scaffolds for protein engineering. The relatively high inherent thermostability of these proteins grants them a great deal of mutational robustness, making them promising scaffolds for various protein engineering applications. Comparative studies on the evolvability of thermophilic and mesophilic proteins have strongly supported the argument that thermophilic proteins are more evolvable than mesophilic proteins. These findings indicate that thermophilic proteins may represent the scaffold of choice for protein engineering in the future.

Shuffling the Neutral Drift of Unspecific Peroxygenase in Saccharomyces cerevisiae

Fungal peroxygenases resemble the peroxide shunt pathway of cytochrome P450 monoxygenases, performing selective oxyfunctionalizations of unactivated C-H bonds in a broad range of organic compounds. In this study, we combined neutral genetic drift and in vivo DNA shuffling to generate highly functional peroxygenase mutant libraries. The panel of neutrally evolved peroxygenases showed different activity profiles for peroxygenative substrates and improved stability with respect to temperature and the presence of organic cosolvents, making the enzymes valuable blueprints for emerging evolution campaigns. This association of DNA recombination and neutral drift is paving the way for future work in peroxygenase engineering and, from a more general perspective, to any other enzyme system heterologously expressed in S. cerevisiae.

Unspecific peroxygenase (UPO) is a highly promiscuous biocatalyst, and its selective mono(per)oxygenase activity makes it useful for many synthetic chemistry applications. Among the broad repertory of library creation methods for directed enzyme evolution, genetic drift allows neutral mutations to be accumulated gradually within a polymorphic network of variants. In this study, we conducted a campaign of genetic drift with UPO in Saccharomyces cerevisiae, so that neutral mutations were simply added and recombined in vivo. With low mutational loading and an activity threshold of 45% of the parent's native function, mutant libraries enriched in folded active UPO variants were generated. After only eight rounds of genetic drift and DNA shuffling, we identified an ensemble of 25 neutrally evolved variants with changes in peroxidative and peroxygenative activities, kinetic thermostability, and enhanced tolerance to organic solvents. With an average of 4.6 substitutions introduced per clone, neutral mutations covered approximately 10% of the protein sequence. Accordingly, this study opens new avenues for UPO design by bringing together neutral genetic drift and DNA recombination in vivo.

IMPORTANCE Fungal peroxygenases resemble the peroxide shunt pathway of cytochrome P450 monoxygenases, performing selective oxyfunctionalizations of unactivated C-H bonds in a broad range of organic compounds. In this study, we combined neutral genetic drift and in vivo DNA shuffling to generate highly functional peroxygenase mutant libraries. The panel of neutrally evolved peroxygenases showed different activity profiles for peroxygenative substrates and improved stability with respect to temperature and the presence of organic cosolvents, making the enzymes valuable blueprints for emerging evolution campaigns. This association of DNA recombination and neutral drift is paving the way for future work in peroxygenase engineering and, from a more general perspective, to any other enzyme system heterologously expressed in S. cerevisiae.

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

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