Consensus sequence design as a general strategy to create hyperstable, biologically active proteins

Engineered Oxalate Decarboxylase Boosts Activity and Stability for Biological Applications

Oxalate decarboxylase (OxDC) from Bacillus subtilis is a Mn-dependent hexameric enzyme that converts oxalate to carbon dioxide and formate. Recently, OxDC has attracted the interest of the scientific community due to its biotechnological and medical applications for the treatment of hyperoxaluria, a group of pathologic conditions associated with excessive oxalate urinary excretion caused by either increased endogenous production or increased exogenous absorption. The fact that OxDC displays optimum pH in the acidic range represents a big limitation for most biotechnological applications involving processes occurring at neutral pH, where the activity and stability of the enzyme are remarkably reduced. Here, through bioinformatics-guided protein engineering followed by combinatorial mutagenesis and analyses of activity and thermal stability, we identified a double mutant of OxDC endowed with enhanced catalytic efficiency and stability under physiological conditions. The obtained engineered form of OxDC offers a potential tool for improved intestinal oxalate degradation in hyperoxaluria patients.

Rational design strategy for thermostability enhancement of protein-glutaminase and investigation of the underlying mechanisms

Protein-Glutaminase (PG) with efficient deamidation ability has shown vital potential in food fields. Enzymes with high thermostability remain active in high-temperature environments, which can be applied to some steps requiring high temperature treatment in food processing, thereby greatly expanding their industrial application. In this study, an efficient comprehensive strategy based on consensus sequence and computer-aid analysis was proposed to develop a combinatorial mutant mPG-5 M (A79S/T97V/S108P/N154D/L156Y), exhibiting a 55.1-fold increase in t1/2 at 60 °C (1132.75 min) and a Tm value of 75.21 °C without loss of enzyme activity. Molecular dynamics simulation analysis insisted that the reduced flexibility, increased structural rigidity, and enhanced hydrogen bonding network observed in the mPG-5 M all contribute to its superior stability, particularly under thermal stress. This study provided valuable strategy and comprehensive molecular mechanisms insights for protein engineering of the thermostability enhancement of PG, broadening its industrial applicability of food protein deamidation. These mutations collectively contributed to the enhanced thermal stability of the mPG-5 M mutant.

AI-enabled alkaline-resistant evolution of protein to apply in mass production

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.

Engineering thermostability of industrial enzymes for enhanced application performance

Thermostability is a key factor for the industrial application of enzymes. This review categorizes enzymes by their applications and discusses the importance of engineering thermostability for practical use. It summarizes fundamental theories and recent advancements in enzyme thermostability modification, including directed evolution, semi-rational design, and rational design. Directed evolution uses high-throughput screening to generate random mutations, while semi-rational design combines hotspot identification with screening. Rational design focuses on key residues to enhance stability by improving rigidity, foldability, and reducing aggregation. The review also covers rational strategies like engineering folding energy, surface charge, machine learning methods, and consensus design, along with tools that support these approaches. Practical examples are critically assessed to highlight the benefits and limitations of these strategies. Finally, the challenges and potential contributions of artificial intelligence in enzyme thermostability engineering are discussed.

Protein stability is determined by single-site bias rather than pairwise covariance

The biases revealed in protein sequence alignments have been shown to provide information related to protein structure, stability, and function. For example, sequence biases at individual positions can be used to design consensus proteins that are often more stable than naturally occurring counterparts. Likewise, correlations between pairs of residue can be used to predict protein structures. Recent work using Potts models show that together, single-site biases and pair correlations lead to improved predictions of protein fitness, activity, and stability. Here we use a Potts model to design groups of protein sequences with different amounts of single-site biases and pair correlations, and determine the thermodynamic stabilities of a representative set of sequences from each group. Surprisingly, sequences excluding pair correlations maximize stability, whereas sequences that maximize pair correlations are less stable, suggesting that pair correlations contribute to another aspect of protein fitness. Consistent with this interpretation, we find that for adenylate kinase, enzyme activity is greatly increased by maximizing pair correlations. The finding that elimination of covariant residue pairs increases protein stability suggests a route to enhance stability of designed proteins; indeed, this strategy produces hyperstable homeodomain and adenylate kinase proteins that retain significant activity.

Improving the catalytic performance of carbonyl reductase based on the functional loops engineering

Vibegron functions as a potent and selective β3-adrenergic receptor agonist, with its chiral precursor (2S,3R)-aminohydroxy ester (1b) being crucial to its synthesis. In this study, loop engineering was applied to the carbonyl reductase (EaSDR6) from Exiguobacterium algae to achieve an asymmetric reduction of the (rac)-aminoketone ester 1a. The variant M5 (A138L/A190V/S193A/Y201F/N204A) was obtained and demonstrated an 868-fold increase in catalytic efficiency (kcat/Km = 260.3 s-1 mM-1) and a desirable stereoselectivity (>99% enantiomeric excess, e.e.; >99% diastereomeric excess, d.e.) for the target product 1b in contrast to the wild-type EaSDR6 (WT). Structural alignment with WT indicated that loops 137-154 and 182-210 potentially play vital roles in facilitating catalysis and substrate binding. Moreover, molecular dynamics (MD) simulations of WT-1a and M5-1a complex illustrated that M5-1a exhibits a more effective nucleophilic attack distance and more readily adopts a pre-reaction state. The interaction analysis unveiled that M5 enhanced hydrophobic interactions with substrate 1a on cavities A and B while diminishing unfavorable hydrophilic interactions on cavity C. Computational analysis of binding free energies indicated that M5 displayed heightened affinity towards substrate 1a compared to the WT, aligning with its decreased Km value. Under organic-aqueous biphasic conditions, the M5 mutant showed >99% conversion within 12 h with 300 g/L substrate 1a (highest substrate loading as reported). This study enhanced the catalytic performance of carbonyl reductase through functional loops engineering and established a robust framework for the large-scale biosynthesis of the vibegron intermediate.

Design of ancestral mammalian alkaline phosphatase bearing high stability and productivity

Mammalian alkaline phosphatase (AP) is widely used in diagnostics and molecular biology but its widespread use is impaired because it is difficult to express in Escherichia coli and has low thermostability. To overcome these challenges, we employed sequence-based protein redesign methods, specifically full consensus design (FCD) and ancestral sequence reconstruction (ASR), to create APs with enhanced properties. Biochemical analyses revealed that these newly designed APs exhibited improved levels of expression in their active form and increased thermostability compared to bovine intestinal AP isozyme II (bIAPII), without impeding enzymatic activity. Notably, the activity in culture broth of the designed APs was an order of magnitude higher than that of bIAPII, and their thermal stability increased by 13°C-17°C (measured as T50). We also assessed 28 single-point mutants of bIAPII to identify regions influencing thermostability and expression level; these mutations were common in the engineered APs but not in bIAPII. Specific mutations, such as T413E and G402S, enhanced thermostability, whereas increasing the expression level required multiple mutations. This suggests that a synergistic effect is required to enhance the expression level. Mutations enhancing thermostability were located in the crown domain, while those improving expression were close to the dimer interface, indicating distinct mechanisms underpinning these enhancements.

IMPORTANCE

Sequence-based protein redesign methods, such as full consensus design (FCD) and ancestral sequence reconstruction (ASR), have the potential to construct new enzymes utilizing protein sequence data registered in a rapidly expanding sequence database. The high thermostability of these enzymes would expand their application in diagnostics and molecular biology. These enzymes have also demonstrated a high level of active expression by Escherichia coli. These characteristics make these APs attractive candidates for industrial application. In addition, different amino acid residues are primarily responsible for thermal stability and active expression, suggesting important implications for strategies for designing enzymes by FCD and ASR.

Semirational Design of a UDP-Glycosyltransferase from Nicotiana tomentosiformis for Efficient Biosynthesis of Rebaudioside M2

Rebaudioside M2 (RebM2) is characterized as 13-[(2-O-β-d-glucopyranosyl-3-O-β-d-glucopyranosyl-β-d-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-[(2-O-β-d-glucopyranosyl-6-O-β-d-glucopyranosyl-β-d-glucopyranosyl) ester], an isomer of rebaudioside M with a 1 → 6 sugar linkage. The product was found in the biotransformation of rebaudioside D (RebD) catalyzed by a glycosyltransferase from Nicotiana tomentosiformis (NtUGT). Herein, guided by consensus engineering and molecular dynamics simulations, a variant NtUGTF72L/L123P/L157P with enhanced activity and thermostability was obtained. It exhibits a strikingly reduced Km (22.47 mM to 0.15 mM) toward RebD, and the catalytic efficiency was over 5000-fold higher than that of the wildtype. When an Arabidopsis sucrose synthase AtSuSy was used for UDP-glucose recycling, NtUGTF72L/L123P/L157P effectively converted 80 g/L RebD to 90.14 g/L RebM2. In a one-pot three-enzyme reaction involving an engineered glycosyltransferase UGTSL2N358F, which catalyzed the conversion of RebA into RebD, 78.8 g/L of RebM2 (with a yield of 84.56%) was produced from 70 g/L of RebA, avoiding the use of the naturally rare and poorly soluble RebD as the starting material. This work will provide a promising biocatalyst for RebM2 biosynthesis on a large scale and create an opportunity to accelerate the exploration of the biological activity of RebM2 and its potential as a candidate for superior SG sweeteners.

Ancestral Sequence Reconstruction to Enable Biocatalytic Synthesis of Azaphilones

Biocatalysis can be powerful in organic synthesis but is often limited by enzymes' substrate scope and selectivity. Developing a biocatalytic step involves identifying an initial enzyme for the target reaction followed by optimization through rational design, directed evolution, or both. These steps are time consuming, resource-intensive, and require expertise beyond typical organic chemistry. Thus, an effective strategy for streamlining the process from enzyme identification to implementation is essential to expanding biocatalysis. Here, we present a strategy combining bioinformatics-guided enzyme mining and ancestral sequence reconstruction (ASR) to resurrect enzymes for biocatalytic synthesis. Specifically, we achieve an enantioselective synthesis of azaphilone natural products using two ancestral enzymes: a flavin-dependent monooxygenase (FDMO) for stereodivergent oxidative dearomatization and a substrate-selective acyltransferase (AT) for the acylation of the enzymatically installed hydroxyl group. This cascade, stereocomplementary to established chemoenzymatic routes, expands access to enantiomeric linear tricyclic azaphilones. By leveraging the co-occurrence and coevolution of FDMO and AT in azaphilone biosynthetic pathways, we identified an AT candidate, CazE, and addressed its low solubility and stability through ASR, obtaining a more soluble, stable, promiscuous, and reactive ancestral AT (AncAT). Sequence analysis revealed AncAT as a chimeric composition of its descendants with enhanced reactivity likely due to ancestral promiscuity. Flexible receptor docking and molecular dynamics simulations showed that the most reactive AncAT promotes a reactive geometry between substrates. We anticipate that our bioinformatics-guided, ASR-based approach can be broadly applied in target-oriented synthesis, reducing the time required to develop biocatalytic steps and efficiently access superior biocatalysts.

Engineered odorant receptors illuminate the basis of odour discrimination

How the olfactory system detects and distinguishes odorants with diverse physicochemical properties and molecular configurations remains poorly understood. Vertebrate animals perceive odours through G protein-coupled odorant receptors (ORs)1. In humans, around 400 ORs enable the sense of smell. The OR family comprises two main classes: class I ORs are tuned to carboxylic acids whereas class II ORs, which represent most of the human repertoire, respond to a wide variety of odorants2. A fundamental challenge in understanding olfaction is the inability to visualize odorant binding to ORs. Here we uncover molecular properties of odorant-OR interactions by using engineered ORs crafted using a consensus protein design strategy3. Because such consensus ORs (consORs) are derived from the 17 major subfamilies of human ORs, they provide a template for modelling individual native ORs with high sequence and structural homology. The biochemical tractability of consORs enabled the determination of four cryogenic electron microscopy structures of distinct consORs with specific ligand recognition properties. The structure of a class I consOR, consOR51, showed high structural similarity to the native human receptor OR51E2 and generated a homology model of a related member of the human OR51 family with high predictive power. Structures of three class II consORs revealed distinct modes of odorant-binding and activation mechanisms between class I and class II ORs. Thus, the structures of consORs lay the groundwork for understanding molecular recognition of odorants by the OR superfamily.

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

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

Thermostability and activity improvement in l-threonine aldolase through targeted mutations in V-shaped subunit

l-threonine aldolase (LTA) catalyzes the synthesis of β-hydroxy-α-amino acids, which are important chiral intermediates widely used in the fields of pharmaceuticals and pesticides. However, the limited thermostability of LTA hinders its industrial application. Furthermore, the trade-off between thermostability and activity presents a challenge in the thermostability engineering of this enzyme. This study proposes a strategy to regulate the rigidity of LTA's V-shaped subunit by modifying its opening and hinge regions, distant from the active center, aiming to mitigate the trade-off. With LTA from Bacillus nealsonii as targeted enzyme, a total of 25 residues in these two regions were investigated by directed evolution. Finally, mutant G85A/M207L/A12C was obtained, showing significantly enhanced thermostability with a 20 °C increase in T5060 to 66 °C, and specific activity elevated by 34 % at the optimum temperature. Molecular dynamics simulations showed that the newly formed hydrophobicity and hydrogen bonds improved the thermostability and boosted proton transfer efficiency. This work enhances the thermostability of LTA while preventing the loss of activity. It opens new avenues for the thermostability engineering of other industrially relevant enzymes with active center located at the interface of subunits or domains.

Characterization and rational engineering of an alkaline-tolerant azoreductase derived from Roseibium sp. H3510 for enhanced decolorization of azo dyes

rAzoR2326, an azoreductase derived from Roseibium sp. H3510, functions as an FMN-dependent homodimer utilizing NADH as cofactor. It demonstrated maximum activity at 45 °C and retained moderate activity above 50 °C, exhibiting stability from pH 7-10. Evolution and structure guided rational design of wild-type rAzoR2326 (WT) efficiently yielded 6 single-point mutants with improved thermostability and activity from a 22-variant library. Further combinatorial mutation led to mutant M20 with substantially enhanced thermostability (15-fold longer half-life at 50 °C) and activity (3.24-fold higher kcat/Km). M20 exhibited superior catalytic properties for decolorizing Allura Red compared to WT. Specifically, its decolorization capacity at pH 10.0 was 4.26-fold higher than WT. Additionally, M20 demonstrated remarkable thermostability, retaining 76.83 % decolorization activity for Allura Red after 120 min at 50 °C, whereas WT nearly lost all catalytic activity under the same conditions. Molecular dynamics simulations revealed the structural changes in M20, such as improved hydrogen bonding and a new C-H···π interaction, led to a more compact and rigid enzyme structure. This resulted in a more stable FMN-binding pocket and substrate tunnel, thereby improving the catalytic stability and activity of M20. Given its enhanced dye decolorization ability and alkaline tolerance, M20 shows promise as a biocatalyst for treating azo dye effluents.

Computation-guided engineering of distal mutations in an artificial enzyme

Artificial enzymes are valuable biocatalysts able to perform new-to-nature transformations with the precision and (enantio-)selectivity of natural enzymes. Although they are highly engineered biocatalysts, they often cannot reach catalytic rates akin those of their natural counterparts, slowing down their application in real-world industrial processes. Typically, their designs only optimise the chemistry inside the active site, while overlooking the role of protein dynamics on catalysis. In this work, we show how the catalytic performance of an already engineered artificial enzyme can be further improved by distal mutations that affect the conformational equilibrium of the protein. To this end, we subjected a specialised artificial enzyme based on the lactococcal multidrug resistance regulator (LmrR) to an innovative algorithm that quickly inspects the whole protein sequence space for hotpots which affect the protein dynamics. From an initial predicted selection of 73 variants, two variants with mutations distant by more than 11 Å from the catalytic pAF residue showed increased catalytic activity towards the new-to-nature hydrazone formation reaction. Their recombination displayed a 66% higher turnover number and 14 °C higher thermostability. Microsecond time scale molecular dynamics simulations evidenced a shift in the distribution of productive enzyme conformations, which are the result of a cascade of interactions initiated by the introduced mutations.

Enhancing the catalytic efficiency of M32 carboxypeptidase by semi-rational design and its applications in food taste improvement

BACKGROUND

Carboxypeptidase is an exopeptidase that hydrolyzes amino acids at the C-terminal end of the peptide chain and has a wide range of applications in food. However, in industrial applications, the relatively low catalytic efficiency of carboxypeptidases is one of the main limiting factors for industrialization.

RESULTS

The study has enhanced the catalytic efficiency of Bacillus megaterium M32 carboxypeptidase (BmeCPM32) through semi-rational design. Firstly, the specific activity of the optimal mutant, BmeCPM32-M2, obtained through single-site mutagenesis and combinatorial mutagenesis, was 2.2-fold higher than that of the wild type (187.9 versus 417.8 U mg-1), and the catalytic efficiency was 2.9-fold higher (110.14 versus 325.75 s-1 mmol-1). Secondly, compared to the wild type, BmeCPM32-M2 exhibited a 1.8-fold increase in half-life at 60 °C, with no significant changes in its enzymatic properties (optimal pH, optimal temperature). Finally, BmeCPM32-M2 significantly increased the umami intensity of soy protein isolate hydrolysate by 55% and reduced bitterness by 83%, indicating its potential in developing tasty protein components.

CONCLUSION

Our research has revealed that the strategy based on protein sequence evolution and computational residue mutation energy led to an improved catalytic efficiency of BmeCPM32. Molecular dynamics simulations have revealed that a smaller substrate binding pocket and increased enzyme-substrate affinity are the reasons for the enhanced catalytic efficiency. Furthermore the number of hydrogen bonds and solvent and surface area may contribute to the improvement of thermostability. Finally, the de-bittering effect of BmeCPM32-M2 in soy protein isolate hydrolysate suggests its potential in developing palatable protein components. © 2024 Society of Chemical Industry.

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

Unleashing the Power of Evolution in Xylanase Engineering: Investigating the Role of Distal Mutation Regulation

The drive to enhance enzyme performance in industrial applications frequently clashes with the practical limitations of exhaustive experimental screening, underscoring the urgency for more refined and strategic methodologies in enzyme engineering. In this study, xylanase Xyl-1 was used as the model, coupling evolutionary insights with energy functions to obtain theoretical potential mutants, which were subsequently validated experimentally. We observed that mutations in the nonloop region primarily aimed at enhancing stability and also encountered selective pressure for activity. Notably, mutations in this region simultaneously boosted the Xyl-1 stability and activity, achieving a 65% success rate. Using a greedy strategy, mutant M4 was developed, achieving a 12 °C higher melting temperature and doubled activity. By integration of spectroscopy, crystallography, and quantum mechanics/molecular mechanics molecular dynamics, the mechanism behind the enhanced thermal stability of M4 was elucidated. It was determined that the activity differences between M4 and the wild type were primarily driven by dynamic factors influenced by distal mutations. In conclusion, the study emphasizes the pivotal role of evolution-based approaches in augmenting the stability and activity of the enzymes. It sheds light on the unique adaptive mechanisms employed by various structural regions of proteins and expands our understanding of the intricate relationship between distant mutations and enzyme dynamics.

Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation

Protein language models trained on the masked language modeling objective learn to predict the identity of hidden amino acid residues within a sequence using the remaining observable sequence as context. They do so by embedding the residues into a high dimensional space that encapsulates the relevant contextual cues. These embedding vectors serve as an informative context-sensitive representation that not only aids with the defined training objective, but can also be used for other tasks by downstream models. We propose a scheme to use the embeddings of an unmasked sequence to estimate the corresponding masked probability vectors for all the positions in a single forward pass through the language model. This One Fell Swoop (OFS) approach allows us to efficiently estimate the pseudo-perplexity of the sequence, a measure of the model's uncertainty in its predictions, that can also serve as a fitness estimate. We find that ESM2 OFS pseudo-perplexity performs nearly as well as the true pseudo-perplexity at fitness estimation, and more notably it defines a new state of the art on the ProteinGym Indels benchmark. The strong performance of the fitness measure prompted us to investigate if it could be used to detect the elevated stability reported in reconstructed ancestral sequences. We find that this measure ranks ancestral reconstructions as more fit than extant sequences. Finally, we show that the computational efficiency of the technique allows for the use of Monte Carlo methods that can rapidly explore functional sequence space.

Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation

Protein language models trained on the masked language modeling objective learn to predict the identity of hidden amino acid residues within a sequence using the remaining observable sequence as context. They do so by embedding the residues into a high dimensional space that encapsulates the relevant contextual cues. These embedding vectors serve as an informative context-sensitive representation that not only aids with the defined training objective, but can also be used for other tasks by downstream models. We propose a scheme to use the embeddings of an unmasked sequence to estimate the corresponding masked probability vectors for all the positions in a single forward pass through the language model. This One Fell Swoop (OFS) approach allows us to efficiently estimate the pseudo-perplexity of the sequence, a measure of the model's uncertainty in its predictions, that can also serve as a fitness estimate. We find that ESM2 OFS pseudo-perplexity performs nearly as well as the true pseudo-perplexity at fitness estimation, and more notably it defines a new state of the art on the ProteinGym Indels benchmark. The strong performance of the fitness measure prompted us to investigate if it could be used to detect the elevated stability reported in reconstructed ancestral sequences. We find that this measure ranks ancestral reconstructions as more fit than extant sequences. Finally, we show that the computational efficiency of the technique allows for the use of Monte Carlo methods that can rapidly explore functional sequence space.

Engineering of a Baeyer-Villiger monooxygenase to Improve Substrate Scope, Stereoselectivity and Regioselectivity

Baeyer-Villiger monooxygenases belong to a family of flavin-binding proteins that catalyze the Baeyer-Villiger (BV) oxidation of ketones to produce lactones or esters, which are important intermediates in pharmaceuticals or sustainable materials. Phenylacetone monooxygenase (PAMO) from Thermobifida fusca with moderate thermostability catalyzes the oxidation of aryl ketone substrates, but is limited by high specificity and narrow substrate scope. In the present study, we applied loop optimization by loop swapping followed by focused saturation mutagenesis in order to evolve PAMO mutants capable of catalyzing the regioselective BV oxidation of cyclohexanone and cyclobutanone derivatives with formation of either normal or abnormal esters or lactones. We further modulated PAMO to increase enantioselectivity. Crystal structure studies indicate that rotation occurs in the NADP-binding domain and that the high B-factor region is predominantly distributed in the catalytic pocket residues. Computational analyses further revealed dynamic character in the catalytic pocket and reshaped hydrogen bond interaction networks, which is more favorable for substrate binding. Our study provides useful insights for studying enzyme-substrate adaptations.

Increasing the Soluble Expression and Whole-Cell Activity of the Plastic-Degrading Enzyme MHETase through Consensus Design

The mono(2-hydroxyethyl) terephthalate hydrolase (MHETase) from Ideonella sakaiensis carries out the second step in the enzymatic depolymerization of poly(ethylene terephthalate) (PET) plastic into the monomers terephthalic acid (TPA) and ethylene glycol (EG). Despite its potential industrial and environmental applications, poor recombinant expression of MHETase has been an obstacle to its industrial application. To overcome this barrier, we developed an assay allowing for the medium-throughput quantification of MHETase activity in cell lysates and whole-cell suspensions, which allowed us to screen a library of engineered variants. Using consensus design, we generated several improved variants that exhibit over 10-fold greater whole-cell activity than wild-type (WT) MHETase. This is revealed to be largely due to increased soluble expression, which biochemical and structural analysis indicates is due to improved protein folding.

The importance of input sequence set to consensus-derived proteins and their relationship to reconstructed ancestral proteins

A protein sequence encodes its energy landscape-all the accessible conformations, energetics, and dynamics. The evolutionary relationship between sequence and landscape can be probed phylogenetically by compiling a multiple sequence alignment of homologous sequences and generating common ancestors via Ancestral Sequence Reconstruction or a consensus protein containing the most common amino acid at each position. Both ancestral and consensus proteins are often more stable than their extant homologs-questioning the differences between them and suggesting that both approaches serve as general methods to engineer thermostability. We used the Ribonuclease H family to compare these approaches and evaluate how the evolutionary relationship of the input sequences affects the properties of the resulting consensus protein. While the consensus protein derived from our full Ribonuclease H sequence alignment is structured and active, it neither shows properties of a well-folded protein nor has enhanced stability. In contrast, the consensus protein derived from a phylogenetically-restricted set of sequences is significantly more stable and cooperatively folded, suggesting that cooperativity may be encoded by different mechanisms in separate clades and lost when too many diverse clades are combined to generate a consensus protein. To explore this, we compared pairwise covariance scores using a Potts formalism as well as higher-order sequence correlations using singular value decomposition (SVD). We find the SVD coordinates of a stable consensus sequence are close to coordinates of the analogous ancestor sequence and its descendants, whereas the unstable consensus sequences are outliers in SVD space.

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.

Structure-Based Rational and General Strategy for Stabilizing Single-Chain T-Cell Receptors to Enhance Affinity

The T-cell receptor (TCR) is a crucial molecule in cellular immunity. The single-chain T-cell receptor (scTCR) is a potential format in TCR therapeutics because it eliminates the possibility of αβ-TCR mispairing. However, its poor stability and solubility impede the in vitro study and manufacturing of therapeutic applications. In this study, some conserved structural motifs are identified in variable domains regardless of germlines and species. Theoretical analysis helps to identify those unfavored factors and leads to a general strategy for stabilizing scTCRs by substituting residues at exact IMGT positions with beneficial propensities on the consensus sequence of germlines. Several representative scTCRs are displayed to achieve stability optimization and retain comparable binding affinities with the corresponding αβ-TCRs in the range of μM to pM. These results demonstrate that our strategies for scTCR engineering are capable of providing the affinity-enhanced and specificity-retained format, which are of great value in facilitating the development of TCR-related therapeutics.

Simultaneous Enhancement of the Thermostability and Catalytic Activity of D-Allulose 3-Epimerase from Clostridium bolteae ATTC BAA-613 Based on the "Back to Consensus Mutations" Hypothesis

D-Allulose is a high value rare sugar with multiple physiological functions and commercial potential that can be enzymatically synthesized from D-fructose by D-allulose 3-epimerase (DAEase). Poor catalytic activity and thermostability of DAEase prevent the industrial production of D-allulose. In this work, rational design was applied to a previously identified DAEase from Clostridium bolteae ATCC BAA-613 based on the "back to consensus mutations" hypothesis, and the catalytic activity of the Cb-I265 V variant was enhanced 2.5-fold. Furthermore, the Cb-I265 V/E268D double-site variant displayed 2.0-fold higher specific catalytic activity and 1.4-fold higher thermostability than the wild-type enzyme. Molecular docking and kinetic simulation results indicated increased hydrogen bonds between the active pocket and substrate, possibly contributing to the improved thermal stability and catalytic activity of the double-site mutant. The findings outlined a feasible approach for the rational design of multiple preset functions of target enzymes simultaneously.

Semirational Design Based on Consensus Sequences to Balance the Enzyme Activity-Stability Trade-Off

In this study, the phenomenon of the stability-activity trade-off, which is increasingly recognized in enzyme engineering, was explored. Typically, enhanced stability in enzymes correlates with diminished activity. Utilizing Rosa roxburghii copper-zinc superoxide dismutase (RrCuZnSOD) as a model, single-site mutations were introduced based on a semirational design derived from consensus sequences. The initial set of mutants was selected based on activity, followed by combinatorial mutation. This approach yielded two double-site mutants, D25/A115T (18,688 ± 206 U/mg) and A115T/S135P (18,095 ± 1556 U/mg), exhibiting superior enzymatic properties due to additive and synergistic effects. These mutants demonstrated increased half-lives (T1/2) at 80 °C by 1.2- and 1.6-fold, respectively, and their melting temperatures (Tm) rose by 3.4 and 2.5 °C, respectively, without any loss in activity relative to the wild type. Via an integration of structural analysis and molecular dynamics simulations, we elucidated the underlying mechanism facilitating the concurrent enhancement of both thermostability and enzymatic activity.

Computer-aided design to enhance the stability of aldo-keto reductase KdAKR

The aldo-keto reductase (AKR) KdAKR from Kluyvermyces dobzhanskii can reduce t-butyl 6-chloro-(5S)-hydroxy-3-oxohexanoate ((5S)-CHOH) to t-butyl 6-chloro-(3R,5S)-dihydroxyhexanoate ((3R,5S)-CDHH), which is the key chiral intermediate of rosuvastatin. Herein, a computer-aided design that combined the use of PROSS platform and consensus design was employed to improve the stability of a previously constructed mutant KdAKRM6 . Experimental verification revealed that S196C, T232A, V264I and V45L produced improved thermostability and activity. The "best" mutant KdAKRM10 (KdAKRM6 -S196C/T232A/V264I/V45L) was constructed by combining the four beneficial mutations, which displayed enhanced thermostability. Its T50 15 and Tm values were increased by 10.2 and 10.0°C, respectively, and half-life (t1/2 ) at 40°C was increased by 17.6 h. Additionally, KdAKRM10 demonstrated improved resistance to organic solvents compared to that of KdAKRM6 . Structural analysis revealed that the increased number of hydrogen bonds and stabilized hydrophobic core contributed to the rigidity of KdAKRM10 , thus improving its stability. The results validated the feasibility of the computer-aided design strategy in improving the stability of AKRs.

Engineering styrene biosynthesis: designing a functional trans-cinnamic acid decarboxylase in Pseudomonas

We are interested in converting second generation feedstocks into styrene, a valuable chemical compound, using the solvent-tolerant Pseudomonas putida DOT-T1E as a chassis. Styrene biosynthesis takes place from L-phenylalanine in two steps: firstly, L-phenylalanine is converted into trans-cinnamic acid (tCA) by PAL enzymes and secondly, a decarboxylase yields styrene. This study focuses on designing and synthesizing a functional trans-cinnamic acid decarboxylase in Pseudomonas putida. To achieve this, we utilized the "wholesale" method, involving deriving two consensus sequences from multi-alignments of homologous yeast ferulate decarboxylase FDC1 sequences with > 60% and > 50% identity, respectively. These consensus sequences were used to design Pseudomonas codon-optimized genes named psc1 and psd1 and assays were conducted to test the activity in P. putida. Our results show that the PSC1 enzyme effectively decarboxylates tCA into styrene, whilst the PSD1 enzyme does not. The optimal conditions for the PSC1 enzyme, including pH and temperature were determined. The L-phenylalanine DOT-T1E derivative Pseudomonas putida CM12-5 that overproduces L-phenylalanine was used as the host for expression of pal/psc1 genes to efficiently convert L-phenylalanine into tCA, and the aromatic carboxylic acid into styrene. The highest styrene production was achieved when the pal and psc1 genes were co-expressed as an operon in P. putida CM12-5. This construction yielded styrene production exceeding 220 mg L-1. This study serves as a successful demonstration of our strategy to tailor functional enzymes for novel host organisms, thereby broadening their metabolic capabilities. This breakthrough opens the doors to the synthesis of aromatic hydrocarbons using Pseudomonas putida as a versatile biofactory.

Enzymatic Degradation of Deoxynivalenol with the Engineered Detoxification Enzyme Fhb7

In the era of global climate change, the increasingly severe Fusarium head blight (FHB) and deoxynivalenol (DON) contamination have caused economic losses and brought food and feed safety concerns. Recently, an FHB resistance gene Fhb7 coding a glutathione-S transferase (GST) to degrade DON by opening the critical toxic epoxide moiety was identified and opened a new window for wheat breeding and DON detoxification. However, the poor stability of Fhb7 and the elusiveness of the catalytic mechanism hinder its practical application. Herein, we report the first structure of Fhb7 at 2.41 Å and reveal a unique catalytic mechanism of epoxide opening transformation in GST family proteins. Furthermore, variants V29P and M10 showed that 5.5-fold and 266.7-fold longer half-life time than wild-type, respectively, were identified. These variants offer broad substrate scope, and the engineered biosafe Bacillus subtilis overexpressing the variants shows excellent DON degradation performance, exhibiting potential at bacterium engineering to achieve DON detoxification in the feed and biomedicine industry. This work provides a profound mechanistic insight into the enzymatic activities of Fhb7 and paves the way for further utilizing Fhb7-related enzymes in crop breeding and DON detoxification by synthetic biology.

Thermal Stabilization of a Bacterial Zn(II)-Dependent Phospholipase C through Consensus Sequence Design

Proteins' extraordinary performance in recognition and catalysis has led to their use in a range of applications. However, proteins obtained from natural sources are oftentimes not suitable for direct use in industrial or diagnostic setups. Natural proteins, evolved to optimally perform a task in physiological conditions, usually lack the stability required to be used in harsher conditions. Therefore, the alteration of the stability of proteins is commonly pursued in protein engineering studies. Here, we achieved a substantial thermal stabilization of a bacterial Zn(II)-dependent phospholipase C by consensus sequence design. We retrieved and analyzed sequenced homologues from different sources, selecting a subset of examples for expression and characterization. A non-natural consensus sequence showed the highest stability and activity among those tested. Comparison of the stability parameters of this stabilized mutant and other natural variants bearing similar mutations allows us to pinpoint the sites most likely to be responsible for the enhancement. Point mutations in these sites alter the unfolding process of the consensus sequence. We show that the stabilized version of the protein retains full activity even in harsh oil degumming conditions, making it suitable for industrial applications.

Engineering the cofactor binding site of 7α-hydroxysteroid dehydrogenase for improvement of catalytic activity, thermostability, and alteration of substrate preference

Hydroxysteroid dehydrogenases (HSDHs) are crucial for bile acid metabolism and influence the size of the bile acid pool and gut microbiota composition. HSDHs with high activity, thermostability, and substrate selectivity are the basis for constructing engineered bacteria for disease treatment. In this study, we designed mutations at the cofactor binding site involving Thr15 and Arg16 residues of HSDH St-2-2. The T15A, R16A, and R16Q mutants exhibited 7.85-, 2.50-, and 4.35-fold higher catalytic activity than the wild type, respectively, while also displaying an altered substrate preference (from taurocholic acid (TCA) to taurochenodeoxycholic acid (TCDCA)). These mutants showed lower Km and higher kcat values, indicating stronger binding to the substrate and resulting in 3190-, 3123-, and 3093-fold higher kcat/Km values for TCDCA oxidation. Furthermore, the Tm values of the T15A, R16A, and R16Q mutants were found to increase by 4.3 °C, 6.0 °C, and 7.0 °C, respectively. Molecular structure analysis indicated that reshaped internal hydrogens and surface mutations could improve catalytic activity and thermostability, and altered interactions among the catalytic triad, cofactor binding sites, and substrates could change substrate preference. This work provides valuable insights into modifying substrate preference as well as enhancing the catalytic activity and thermostability of HSDHs by targeting the cofactor binding site.

Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

Rare HER2 L796P missense mutation promotes the growth and oncogenic signaling in breast cancer cells

PURPOSE

This research aimed to find potential HER2 mutations that would have an impact on breast cancer and investigate the underlying mechanism.

EXPERIMENTAL DESIGN

This study first investigated 238 pairs of breast cancer and para-cancerous tissue samples from patients on the targeted next-generation sequencing (tNGS) platform. CCK-8 and clone formation assay were used to investigate whether the mutation exerts proliferative effects on breast cancer cells. In addition, mass spectrometry-based comparative proteomic and phosphoproteomic analyses of the mutation types and wild types of MCF-7 cell lines were carried out.

RESULTS

Among the identified mutations, a new mutation HER2 L796P promoted the proliferation of breast cancer cells and had resistance to lapatinib using CCK-8 cell proliferation assay and clone formation assay. The bioinformatic analysis showed that RAS family proteins and ERK phosphorylated proteins significantly increased in the L796P mutant cells. The Gene Ontology (GO) analysis revealed that L796P mutation affected the function of breast cancer at the level of upstream genes in the MAPK and PI3K-AKT-TOR pathways.

CONCLUSIONS AND CLINICAL RELEVANCE

This study demonstrated that a rare mutation HER2 L796P could be a potential therapeutic target for the clinical management of breast cancer.

Thermal Stability Enhancement of L-Asparaginase from Corynebacterium glutamicum Based on a Semi-Rational Design and Its Effect on Acrylamide Mitigation Capacity in Biscuits

Acrylamide is present in thermally processed foods, and it possesses toxic and carcinogenic properties. L-asparaginases could effectively regulate the formation of acrylamide at the source. However, current L-asparaginases have drawbacks such as poor thermal stability, low catalytic activity, and poor substrate specificity, thereby restricting their utility in the food industry. To address this issue, this study employed consensus design to predict the crucial residues influencing the thermal stability of Corynebacterium glutamicum L-asparaginase (CgASNase). Subsequently, a combination of site-point saturating mutation and combinatorial mutation techniques was applied to generate the double-mutant enzyme L42T/S213N. Remarkably, L42T/S213N displayed significantly enhanced thermal stability without a substantial impact on its enzymatic activity. Notably, its half-life at 40 °C reached an impressive 13.29 ± 0.91 min, surpassing that of CgASNase (3.24 ± 0.23 min). Moreover, the enhanced thermal stability of L42T/S213N can be attributed to an increased positive surface charge and a more symmetrical positive potential, as revealed by three-dimensional structural simulations and structure comparison analyses. To assess the impact of L42T/S213N on acrylamide removal in biscuits, the optimal treatment conditions for acrylamide removal were determined through a combination of one-way and orthogonal tests, with an enzyme dosage of 300 IU/kg flour, an enzyme reaction temperature of 40 °C, and an enzyme reaction time of 30 min. Under these conditions, compared to the control (464.74 ± 6.68 µg/kg), the acrylamide reduction in double-mutant-enzyme-treated biscuits was 85.31%, while the reduction in wild-type-treated biscuits was 68.78%. These results suggest that L42T/S213N is a promising candidate for industrial applications of L-asparaginase.

Structural-guided design to improve the catalytic performance of aldo-keto reductase KdAKR

Aldo-keto reductases (AKRs) are important biocatalysts that can be used to synthesize chiral pharmaceutical alcohols. In this study, the catalytic activity and stereoselectivity of a NADPH-dependent AKR from Kluyveromyces dobzhanskii (KdAKR) toward t-butyl 6-chloro (5S)-hydroxy-3-oxohexanoate ((5S)-CHOH) were improved by mutating its residues in the loop regions around the substrate-binding pocket. And the thermostability of KdAKR was improved by a consensus sequence method targeted on the flexible regions. The best mutant M6 (Y28A/L58I/I63L/G223P/Y296W/W297H) exhibited a 67-fold higher catalytic efficiency compared to the wild-type (WT) KdAKR, and improved R-selectivity toward (5S)-CHOH (dep value from 47.6% to >99.5%). Moreover, M6 exhibited a 6.3-fold increase in half-life (t1/2 ) at 40°C compared to WT. Under the optimal conditions, M6 completely converted 200 g/L (5S)-CHOH to diastereomeric pure t-butyl 6-chloro-(3R, 5S)-dihydroxyhexanoate ((3R, 5S)-CDHH) within 8.0 h, with a space-time yield of 300.7 g/L/day. Our results deepen the understandings of the structure-function relationship of AKRs, providing a certain guidance for the modification of other AKRs.

Recent advances in structure-based enzyme engineering for functional reconstruction

Structural information can help engineer enzymes. Usually, specific amino acids in particular regions are targeted for functional reconstruction to enhance the catalytic performance, including activity, stereoselectivity, and thermostability. Appropriate selection of target sites is the key to structure-based design, which requires elucidation of the structure-function relationships. Here, we summarize the mutations of residues in different specific regions, including active center, access tunnels, and flexible loops, on fine-tuning the catalytic performance of enzymes, and discuss the effects of altering the local structural environment on the functions. In addition, we keep up with the recent progress of structure-based approaches for enzyme engineering, aiming to provide some guidance on how to take advantage of the structural information.

Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences

The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase (RLuc) homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate to improve either luciferase activity or stability of designed single mutants is ~50%. This finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in RLuc toward emitting blue light that holds advantages in terms of water penetration compared to other light spectra. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

Engineered odorant receptors illuminate structural principles of odor discrimination

A central challenge in olfaction is understanding how the olfactory system detects and distinguishes odorants with diverse physicochemical properties and molecular configurations. Vertebrate animals perceive odors via G protein-coupled odorant receptors (ORs). In humans, ~400 ORs enable the sense of smell. The OR family is composed of two major classes: Class I ORs are tuned to carboxylic acids while Class II ORs, representing the vast majority of the human repertoire, respond to a wide variety of odorants. How ORs recognize chemically diverse odorants remains poorly understood. A fundamental bottleneck is the inability to visualize odorant binding to ORs. Here, we uncover fundamental molecular properties of odorant-OR interactions by employing engineered ORs crafted using a consensus protein design strategy. Because such consensus ORs (consORs) are derived from the 17 major subfamilies of human ORs, they provide a template for modeling individual native ORs with high sequence and structural homology. The biochemical tractability of consORs enabled four cryoEM structures of distinct consORs with unique ligand recognition properties. The structure of a Class I consOR, consOR51, showed high structural similarity to the native human receptor OR51E2 and yielded a homology model of a related member of the human OR51 family with high predictive power. Structures of three Class II consORs revealed distinct modes of odorant-binding and activation mechanisms between Class I and Class II ORs. Thus, the structures of consORs lay the groundwork for understanding molecular recognition of odorants by the OR superfamily.

Customized multiple sequence alignment as an effective strategy to improve performance of Taq DNA polymerase

Engineering Taq DNA polymerase (TaqPol) for improved activity, stability and sensitivity was critical for its wide applications. Multiple sequence alignment (MSA) has been widely used in engineering enzymes for improved properties. Here, we first designed TaqPol mutations based on MSA of 2756 sequences from both thermophilic and non-thermophilic organisms. Two double mutations were generated including a variant H676F/R677G showing a decrease in both activity and stability, and a variant Y686R/E687K showing an improved activity, but a decreased stability. Mutations targeted on coevolutionary residues of Arg677 and Tyr686 were then applied to rescue stability or activity loss of the double mutants, which achieved a partial success. Sequence analysis revealed that the two mutations are abundant in non-thermophilic sequences but not in thermophilic homologues. Then, a small-scale MSA containing sequences from only thermophilic organisms was applied to predict 13 single variants and two of them, E507Q and E734N showed a simultaneous increase in both stability and activity, even in sensitivity. A customized MSA was hence more effective in engineering a thermophilic enzyme and could be used in engineering other enzymes. Molecular dynamics simulations revealed the impact of mutations on the protein dynamics and interactions between TaqPol and substrates. KEY POINTS: • The pool of sequence for alignment is critical to engineering Taq DNA polymerase. • The variants with low properties can be rescued by mutations in coevolving network. • Improving binding with DNA can improve DNA polymerase stability and activity.

High-Accuracy Prediction of Stabilizing Surface Mutations to the Three-Helix Bundle, UBA(1), with EmCAST

The accurate modeling of energetic contributions to protein structure is a fundamental challenge in computational approaches to protein analysis and design. We describe a general computational method, EmCAST (empirical Cα stabilization), to score and optimize the sequence to the structure in proteins. The method relies on an empirical potential derived from the database of the Cα dihedral angle preferences for all possible four-residue sequences, using the data available in the Protein Data Bank. Our method produces stability predictions that naturally correlate one-to-one with the experimental results for solvent-exposed mutation sites. EmCAST predicted four mutations that increased the stability of a three-helix bundle, UBA(1), from 2.4 to 4.8 kcal/mol by optimizing residues in both helices and turns. For a set of eight variants, the predicted and experimental stabilizations correlate very well (R2 = 0.97) with a slope near 1 and with a 0.16 kcal/mol standard error for EmCAST predictions. Tests against literature data for the stability effects of surface-exposed mutations show that EmCAST outperforms the existing stability prediction methods. UBA(1) variants were crystallized to verify and analyze their structures at an atomic resolution. Thermodynamic and kinetic folding experiments were performed to determine the magnitude and mechanism of stabilization. Our method has the potential to enable the rapid, rational optimization of natural proteins, expand the analysis of the sequence/structure relationship, and supplement the existing protein design strategies.

Protein engineering using mutability landscapes: Controlling site-selectivity of P450-catalyzed steroid hydroxylation

Directed evolution and rational design have been used widely in engineering enzymes for their application in synthetic organic chemistry and biotechnology. With stereoselectivity playing a crucial role in catalysis for the synthesis of valuable chemical and pharmaceutical compounds, rational design has not achieved such wide success in this specific area compared to directed evolution. Nevertheless, one bottleneck of directed evolution is the laborious screening efforts and the observed trade-offs in catalytic profiles. This has motivated researchers to develop more efficient protein engineering methods. As a prime approach, mutability landscaping avoids such trade-offs by providing more information of sequence-function relationships. Here, we describe an application of this efficient protein engineering method to improve the regio-/stereoselectivity and activity of P450BM3 for steroid hydroxylation, while keeping the mutagenesis libraries small so that they will require only minimal screening.

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.

Architectural digest: Thermodynamic stability and domain structure of a consensus monomeric globin

Artificial proteins representing the consensus of a set of homologous sequences have attracted attention for their increased thermodynamic stability and conserved activity. Here, we applied the consensus approach to a b-type heme-binding protein to inspect the contribution of a dissociable cofactor to enhanced stability and the chemical consequences of creating a generic heme environment. We targeted the group 1 truncated hemoglobin (TrHb1) subfamily of proteins for their small size (∼120 residues) and ease of characterization. The primary structure, derived from a curated set of ∼300 representative sequences, yielded a highly soluble consensus globin (cGlbN) enriched in acidic residues. Optical and NMR spectroscopies revealed high-affinity heme binding in the expected site and in two orientations. At neutral pH, proximal and distal iron coordination was achieved with a pair of histidine residues, as observed in some natural TrHb1s, and with labile ligation on the distal side. As opposed to studied TrHb1s, which undergo additional folding upon heme binding, cGlbN displayed the same extent of secondary structure whether the heme was associated with the protein or not. Denaturation required guanidine hydrochloride and showed that apo- and holoprotein unfolded in two transitions-the first (occurring with a midpoint of ∼2 M) was shifted to higher denaturant concentration in the holoprotein (∼3.7 M) and reflected stabilization due to heme binding, while the second transition (∼6.2 M) was common to both forms. Thus, the consensus sequence stabilized the protein but exposed the existence of two separately cooperative subdomains within the globin architecture, masked as one single domain in TrHb1s with typical stabilities. The results suggested ways in which specific chemical or thermodynamic features may be controlled in artificial heme proteins.

Systematic Analyses of the Resistance Potential of Drugs Targeting SARS-CoV-2 Main Protease

Drugs that target the main protease (Mpro) of SARS-CoV-2 are effective therapeutics that have entered clinical use. Wide-scale use of these drugs will apply selection pressure for the evolution of resistance mutations. To understand resistance potential in Mpro, we performed comprehensive surveys of amino acid changes that can cause resistance to nirmatrelvir (Pfizer), and ensitrelvir (Xocova) in a yeast screen. We identified 142 resistance mutations for nirmatrelvir and 177 for ensitrelvir, many of which have not been previously reported. Ninety-nine mutations caused apparent resistance to both inhibitors, suggesting likelihood for the evolution of cross-resistance. The mutation with the strongest drug resistance score against nirmatrelvir in our study (E166V) was the most impactful resistance mutation recently reported in multiple viral passaging studies. Many mutations that exhibited inhibitor-specific resistance were consistent with the distinct interactions of each inhibitor in the substrate binding site. In addition, mutants with strong drug resistance scores tended to have reduced function. Our results indicate that strong pressure from nirmatrelvir or ensitrelvir will select for multiple distinct-resistant lineages that will include both primary resistance mutations that weaken interactions with drug while decreasing enzyme function and compensatory mutations that increase enzyme activity. The comprehensive identification of resistance mutations enables the design of inhibitors with reduced potential of developing resistance and aids in the surveillance of drug resistance in circulating viral populations.

Accelerating antiviral drug discovery: lessons from COVID-19

During the coronavirus disease 2019 (COVID-19) pandemic, a wave of rapid and collaborative drug discovery efforts took place in academia and industry, culminating in several therapeutics being discovered, approved and deployed in a 2-year time frame. This article summarizes the collective experience of several pharmaceutical companies and academic collaborations that were active in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral discovery. We outline our opinions and experiences on key stages in the small-molecule drug discovery process: target selection, medicinal chemistry, antiviral assays, animal efficacy and attempts to pre-empt resistance. We propose strategies that could accelerate future efforts and argue that a key bottleneck is the lack of quality chemical probes around understudied viral targets, which would serve as a starting point for drug discovery. Considering the small size of the viral proteome, comprehensively building an arsenal of probes for proteins in viruses of pandemic concern is a worthwhile and tractable challenge for the community.

Automated optimisation of solubility and conformational stability of antibodies and proteins

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.

Computational Analysis Predicts Correlations among Amino Acids in SARS-CoV-2 Proteomes

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious global challenge requiring urgent and permanent therapeutic solutions. These solutions can only be engineered if the patterns and rate of mutations of the virus can be elucidated. Predicting mutations and the structure of proteins based on these mutations have become necessary for early drug and vaccine design purposes in anticipation of future viral mutations. The amino acid composition (AAC) of proteomes and individual viral proteins provide avenues for exploitation since AACs have been previously used to predict structure, shape and evolutionary rates. Herein, the frequency of amino acid residues found in 1637 complete proteomes belonging to 11 SARS-CoV-2 variants/lineages were analyzed. Leucine is the most abundant amino acid residue in the SARS-CoV-2 with an average AAC of 9.658% while tryptophan had the least abundance of 1.11%. The AAC and ranking of lysine and glycine varied in the proteome. For some variants, glycine had higher frequency and AAC than lysine and vice versa in other variants. Tryptophan was also observed to be the most intolerant to mutation in the various proteomes for the variants used. A correlogram revealed a very strong correlation of 0.999992 between B.1.525 (Eta) and B.1.526 (Iota) variants. Furthermore, isoleucine and threonine were observed to have a very strong negative correlation of -0.912, while cysteine and isoleucine had a very strong positive correlation of 0.835 at p < 0.001. Shapiro-Wilk normality test revealed that AAC values for all the amino acid residues except methionine showed no evidence of non-normality at p < 0.05. Thus, AACs of SARS-CoV-2 variants can be predicted using probability and z-scores. AACs may be beneficial in classifying viral strains, predicting viral disease types, members of protein families, protein interactions and for diagnostic purposes. They may also be used as a feature along with other crucial factors in machine-learning based algorithms to predict viral mutations. These mutation-predicting algorithms may help in developing effective therapeutics and vaccines for SARS-CoV-2.