Inside-Out Rational Design of Ornithine Cyclodeaminase RlOCD from Rhizobium leguminosarum by a Multiregion Synergy Strategy for Efficient Synthesis of l-Pipecolic Acid

Lysine cyclodeaminase (LCD)-mediated synthesis of l-pipecolic acid (l-PA) from l-lysine (l-Lys) is a promising approach. However, only one LCD has been reported, and its inadequate activity limits industrial applications. To address this problem, a substrate analogue-guided enzyme mining strategy was employed. A novel ornithine cyclodeaminase (OCD) from Rhizobium leguminosarum (RlOCD) was identified in combination with directed macrogenomic approaches. RlOCD displayed a conversion rate of 28% at a substrate loading as high as 1000 mM. A multiregion synergy strategy consisting of pocket reshaping, dynamical cross-correlation matrix-guided coevolutionary design, and surface modification was used to design RlOCD from the inside-out. A quadruple mutant (V93C/L119C/I170T/R90L) designated Mu4 with significantly increased activity was obtained, which showed a 28.46-fold increase in the catalytic efficiency. The conversion of Mu4 was 91% within 10 h at 1000 mM (146.19 g L-1) loading. The space-time yield of 282.1 g L-1 d-1 is the highest level ever reported. Molecular dynamics simulations and interaction analyses revealed that efficient pocket expansion and unique conformational rearrangements increased the affinity for the substrate, resulting in a more catalytically active conformation. This study expands the toolbox for the production of l-PA and demonstrates the effectiveness and potential of Mu4 for its production.

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.

Establishing a coumarin production platform by protein and metabolic engineering

Coumarins are a vast family of natural products with diverse biological activities. Cinnamyl-CoA ortho-hydroxylases (CCHs) catalyze the gateway and rate-limiting step in coumarin biosynthesis. However, engineering CCHs is challenging due to the large size of the substrates and the vague structure-activity relationship. Herein, directed evolution and structure-guided engineering were performed to engineer a CCH (AtF6'H from Arabidopsis thaliana) using a fluorescence-based screening method, yielding the transplantable surface mutations and the substrate-specific pocket mutations with improved activity. Structural analysis and molecular dynamics simulations elucidated the conformational changes that led to increased catalytic efficiency. Applying appropriate variants with the optimized upstream biosynthetic pathways improved the titers of three simple coumarins by 5 to 22-fold. Further introducing glycosylation modules resulted in the production of four coumarin glucosides, among which the titer of aesculin was increased by 15.7-fold and reached 3 g/L in scale-up fermentation. This work unleashed the potential of CCHs and established an Escherichia coli platform for coumarins production.

BenchStab: a tool for automated querying of web-based stability predictors

SUMMARY

Protein design requires information about how mutations affect protein stability. Many web-based predictors are available for this purpose, yet comparing them or using them en masse is difficult. Here, we present BenchStab, a console tool/Python package for easy and quick execution of 19 predictors and result collection on a list of mutants. Moreover, the tool is easily extensible with additional predictors. We created an independent dataset derived from the FireProtDB and evaluated 24 different prediction methods.

AVAILABILITY AND IMPLEMENTATION

BenchStab is an open-source Python package available at https://github.com/loschmidt/BenchStab with a detailed README and example usage at https://loschmidt.chemi.muni.cz/benchstab. The BenchStab dataset is available on Zenodo: https://zenodo.org/records/10637728.

PON-Tm: A Sequence-Based Method for Prediction of Missense Mutation Effects on Protein Thermal Stability Changes

Proteins, as crucial macromolecules performing diverse biological roles, are central to numerous biological processes. The ability to predict changes in protein thermal stability due to mutations is vital for both biomedical research and industrial applications. However, existing experimental methods are often costly and labor-intensive, while structure-based prediction methods demand significant computational resources. In this study, we introduce PON-Tm, a novel sequence-based method for predicting mutation-induced thermal stability variations in proteins. PON-Tm not only incorporates features predicted by a protein language model from protein sequences but also considers environmental factors such as pH and the thermostability of the wild-type protein. To evaluate the effectiveness of PON-Tm, we compared its performance to four well-established methods, and PON-Tm exhibited superior predictive capabilities. Furthermore, to facilitate easy access and utilization, we have developed a web server.

Rational design engineering of a more thermostable Sulfurihydrogenibium yellowstonense carbonic anhydrase for potential application in carbon dioxide capture technologies

Implementing hyperthermostable carbonic anhydrases into CO2 capture and storage technologies in order to increase the rate of CO2 absorption from the industrial flue gases is of great importance from technical and economical points of view. The present study employed a combination of in silico tools to further improve thermostability of a known thermostable carbonic anhydrase from Sulfurihydrogenibium yellowstonense. Experimental results showed that our rationally engineered K100G mutant not only retained the overall structure and catalytic efficiency but also showed a 3 °C increase in the melting temperature and a two-fold improvement in the enzyme half-life at 85 °C. Based on the molecular dynamics simulation results, rearrangement of salt bridges and hydrogen interactions network causes a reduction in local flexibility of the K100G variant. In conclusion, our study demonstrated that thermostability can be improved through imposing local structural rigidity by engineering a single-point mutation on the surface of the enzyme.

Correlation Analysis of Key Residue Sites between Computational-Aided Design Thermostability d-Amino Acid Oxidase and Ancestral Enzymes

The d-amino acid oxidase (DAAO) from Rhodotorula taiwanensis has proven to have great potential for applications due to its excellent catalytic kinetic parameters. However, its poor thermal stability has limited its performance in biocatalysis. Herein, starting from the variant SHVG of RtwDAAO, this study employed a comprehensive computational design approach for protein stability engineering, resulting in positive substitutions at specific sites (A43S, T45M, C234L, E195Y). The generated variant combination, SHVG/SMLY, exhibited a significant synergistic effect, leading to an extension of the half-life and Tmapp. The ancestral sequence reconstruction revealed the conservation of the variant sites. The association of the variant sites with the highly stable ancestral enzyme was further explored. After determining the contribution of the variant sites to thermal stability, it was applied to other homologous sequences and validated. Molecular dynamics simulations indicated that the increased hydrophobicity of the variant SHVG/SMLY was a key factor for the increased stability, with strengthened intersubunit interactions playing an important role. In addition, the physical properties of the amino acids themselves were identified as crucial factors for thermal stability generality in homologous enzymes, which is important for the rapid acquisition of a series of stable enzymes.

Temperature dependent molecular dynamics simulation study to understand the stabilizing effect of NADES on the protein β-Lactoglobulin

The thermal stability of a protein is an important concern for its practical application in food processing industries. In this study, we have carried out classical molecular dynamics simulations to systematically investigate the effect of NADES (natural deep eutectic solvent) on the stabilization of the protein β-Lactoglobulin (BLG) at different temperatures. This study sheds light on the very aspects of NADES composed of betaine and sorbitol on the stability of the protein. NADES provides better stability to the protein up to a temperature of 400 K than in water. It is observed that the protein starts to unfold above temperature 400 K in spite of the presence of NADES which is quiet evident from the root mean square deviation (RMSD) and radius of gyration (Rg) plots. The decreasing average solvent accessible surface area (SASA) values and increasing intra-protein hydrogen bonds indicate better stability of the protein in NADES medium than in water at temperatures 300 K and 400 K. At high temperatures viz. 450 K and 500 K the number and distribution of solvent species (betaine and sorbitol) around the protein surface show an increment that are evident from the calculations of solvation shell, radial and spatial distribution functions. Increased number of betaine molecules that interact with the protein through electrostatic interaction may lead to destabilization of the protein at these temperatures. This study suggests that NADES could be used as an ideal medium for thermal stability of the protein BLG up to a temperature of 400 K. Beyond this temperature, NADES used for this study fails to exert stabilization effect on the protein.

Structural analysis of wild-type and Val120Thr mutant Candida boidinii formate dehydrogenase by X-ray crystallography

Candida boidinii NAD+-dependent formate dehydrogenase (CbFDH) has gained significant attention for its potential application in the production of biofuels and various industrial chemicals from inorganic carbon dioxide. The present study reports the atomic X-ray crystal structures of wild-type CbFDH at cryogenic and ambient temperatures, as well as that of the Val120Thr mutant at cryogenic temperature, determined at the Turkish Light Source `Turkish DeLight'. The structures reveal new hydrogen bonds between Thr120 and water molecules in the active site of the mutant CbFDH, suggesting increased stability of the active site and more efficient electron transfer during the reaction. Further experimental data is needed to test these hypotheses. Collectively, these findings provide invaluable insights into future protein-engineering efforts that could potentially enhance the efficiency and effectiveness of CbFDH.

Structure-function and engineering of plant UDP-glycosyltransferase

Natural products synthesized by plants have substantial industrial and medicinal values and are therefore attracting increasing interest in various related industries. Among the key enzyme families involved in the biosynthesis of natural products, uridine diphosphate-dependent glycosyltransferases (UGTs) play a crucial role in plants. In recent years, significant efforts have been made to elucidate the catalytic mechanisms and substrate recognition of plant UGTs and to improve them for desired functions. In this review, we presented a comprehensive overview of all currently published structures of plant UGTs, along with in-depth analyses of the corresponding catalytic and substrate recognition mechanisms. In addition, we summarized and evaluated the protein engineering strategies applied to improve the catalytic activities of plant UGTs, with a particular focus on high-throughput screening methods. The primary objective of this review is to provide readers with a comprehensive understanding of plant UGTs and to serve as a valuable reference for the latest techniques used to improve their activities.

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.

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

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

Analyzing Current Trends and Possible Strategies to Improve Sucrose Isomerases' Thermostability

Due to their ability to produce isomaltulose, sucrose isomerases are enzymes that have caught the attention of researchers and entrepreneurs since the 1950s. However, their low activity and stability at temperatures above 40 °C have been a bottleneck for their industrial application. Specifically, the instability of these enzymes has been a challenge when it comes to their use for the synthesis and manufacturing of chemicals on a practical scale. This is because industrial processes often require biocatalysts that can withstand harsh reaction conditions, like high temperatures. Since the 1980s, there have been significant advancements in the thermal stabilization engineering of enzymes. Based on the literature from the past few decades and the latest achievements in protein engineering, this article systematically describes the strategies used to enhance the thermal stability of sucrose isomerases. Additionally, from a theoretical perspective, we discuss other potential mechanisms that could be used for this purpose.

Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning

The prediction of enzyme activity is one of the main challenges in catalysis. With computer-aided methods, it is possible to simulate the reaction mechanism at the atomic level. However, these methods are usually expensive if they are to be used on a large scale, as they are needed for protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein, we test different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase ofIdeonella sakaiensis. As a training data set, we use steered quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulation snapshots and their corresponding pulling work values. We have explored three algorithms together with three chemical representations. As an outcome, our trained models are able to predict pulling works along the steered QM/MM MD simulations with a mean absolute error below 3 kcal mol-1 and a score value above 0.90. More challenging is the prediction of the energy maximum with a single geometry. Whereas the use of the initial snapshot of the QM/MM MD trajectory as input geometry yields a very poor prediction of the reaction energy barrier, the use of an intermediate snapshot of the former trajectory brings the score value above 0.40 with a low mean absolute error (ca. 3 kcal mol-1). Altogether, we have faced in this work some initial challenges of the final goal of getting an efficient workflow for the semiautomatic prediction of enzyme-catalyzed energy barriers and catalytic efficiencies.

Recent Advances in β-Glucosidase Sequence and Structure Engineering: A Brief Review

β-glucosidases (BGLs) play a crucial role in the degradation of lignocellulosic biomass as well as in industrial applications such as pharmaceuticals, foods, and flavors. However, the application of BGLs has been largely hindered by issues such as low enzyme activity, product inhibition, low stability, etc. Many approaches have been developed to engineer BGLs to improve these enzymatic characteristics to facilitate industrial production. In this article, we review the recent advances in BGL engineering in the field, including the efforts from our laboratory. We summarize and discuss the BGL engineering studies according to the targeted functions as well as the specific strategies used for BGL engineering.

Enhanced Thermostability and Catalytic Activity of Streptomyces mobaraenesis Transglutaminase by Rationally Engineering Its Flexible Regions

Streptomyces mobaraenesis transglutaminase can catalyze the cross-linking of proteins, which has been widely used in food processing. In this study, we rationally modified flexible regions to further improve the thermostability of FRAPD-TGm2 (S2P-S23V-Y24N-E28T-S199A-A265P-A287P-K294L), a stable mutant of the transglutaminase constructed in our previous study. First, five flexible regions of FRAPD-TGm2 were identified by molecular dynamics simulations at 330 and 360 K. Second, a script based on Rosetta Cartesian_ddg was developed for virtual saturation mutagenesis within the flexible regions far from the substrate binding pocket, generating the top 18 mutants with remarkable decreases in folding free energy. Third, from the top 18 mutants, we identified two mutants (S116A and S179L) with increased thermostability and activity. Finally, the above favorable mutations were combined to obtain FRAPD-TGm2-S116A-S179L (FRAPD-TGm2A), exhibiting a half-life of 132.38 min at 60 °C (t_1/2(60 °C)) and a specific activity of 79.15 U/mg, 84 and 21% higher than those of FRAPD-TGm2, respectively. Therefore, the current result may benefit the application of S. mobaraenesis transglutaminase at high temperatures.

Simultaneous enhancement of thermostability and catalytic activity of κ-carrageenase from Pseudoalteromonas tetraodonis by rational design

κ-Carrageenase provides an attractive enzymatic approach to preparation of κ-carrageenan oligosaccharides. Pseudoalteromonas tetraodonis κ-carrageenase is active at the alkaline conditions but displays low thermostability. To further improve its enzymatic performance, two mutants of Q42V and I51H exhibiting both improved thermostability and enzyme activity were screened by the PoPMuSiC algorithm. Compared with the wild-type κ-carrageenase (WT), Q42V and I51H increased the enzyme activity by 20.9% and 25.4%, respectively. After treatment at 50 ℃ for 40 min, Q42V and I51H enhanced the residual activity by 31.1% and 25.9%, respectively. The T_m values of Q42V, I51H, and WT determined by differential scanning calorimetry were 58.2 ℃, 54.8 ℃, and 51.2 ℃, respectively. Compared with untreated and HCl-treated κ-carrageenans, Q42V-treated κ-carrageenan exhibited higher pancreatic lipase inhibitory activity. Molecular docking analysis indicated that the additional pi-sigma force and hydrophobic interaction in the enzyme-substrate complex could account for the increased catalytic activity of Q42V and I51H, respectively. Molecular dynamics analysis indicated that the improved thermostability of mutants Q42V and I51H could be attributed to the less structural deviation and the flexible changes of enzyme conformation at high temperature. This study provides new insight into κ-carrageenase performance improvement and identifies good candidates for their industrial applications.

Domain-wise dissection of thermal stability enhancement in multidomain proteins

Stability is critical for the proper functioning of all proteins. Optimization of protein thermostability is a key step in the development of industrial enzymes and biologics. Herein, we demonstrate that multidomain proteins can be stabilized significantly using domain-based engineering followed by the recombination of the optimized domains. Domain-level analysis of designed protein variants with similar structures but different thermal profiles showed that the independent enhancement of the thermostability of a constituent domain improves the overall stability of the whole multidomain protein. The crystal structure and AlphaFold-predicted model of the designed proteins via domain-recombination provided a molecular explanation for domain-based stepwise stabilization. Our study suggests that domain-based modular engineering can minimize the sequence space for calculations in computational design and experimental errors, thereby offering useful guidance for multidomain protein engineering.

Role of distal sites in enzyme engineering

The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Food enzymes have an important role in the improvement of certain food characteristics, such as texture improvement, elimination of toxins and allergens, production of carbohydrates, enhancing flavor/appearance characteristics. Recently, along with the development of artificial meats, food enzymes have been employed to achieve more diverse functions, especially in converting non-edible biomass to delicious foods. Reported food enzyme modifications for specific applications have highlighted the significance of enzyme engineering. However, using direct evolution or rational design showed inherent limitations due to the mutation rates, which made it difficult to satisfy the stability or specific activity needs for certain applications. Generating functional enzymes using de novo design, which highly assembles naturally existing enzymes, provides potential solutions for screening desired enzymes. Here, we describe the functions and applications of food enzymes to introduce the need for food enzymes engineering. To illustrate the possibilities of using de novo design for generating diverse functional proteins, we reviewed protein modelling and de novo design methods and their implementations. The future directions for adding structural data for de novo design model training, acquiring diversified training data, and investigating the relationship between enzyme-substrate binding and activity were highlighted as challenges to overcome for the de novo design of food enzymes.

An Engineered β-Hairpin Peptide Forming Thermostable Complexes with ZnII , NiII , and CuII through a His3 Site

The three-dimensional structure of a peptide, which determines its function, can denature at elevated temperatures, in the presence of chaotropic reagents, or in organic solvents. These factors limit the applicability of peptides. Herein, we present an engineered β-hairpin peptide containing a His₃ site that forms complexes with Zn^II , Ni^II , and Cu^II . Circular dichroism spectroscopy shows that the peptide-metal complexes exhibit melting temperatures up to 80 °C and remain folded in 6 M guanidine hydrochloride as well as in organic solvents. Intrinsic fluorescence titration experiments were used to determine the dissociation constants of metal binding in the nano- to sub-nanomolar range. The coordination geometry of the peptide-Cu^II complex was studied by EPR spectroscopy, and a distorted square planar coordination geometry with weak interactions to axial ligands was revealed. Due to their impressive stability, the presented peptide-metal complexes open up interesting fields of application, such as the development of a new class of peptide-metal catalysts for stereoselective organic synthesis or the directed design of extremophilic β-sheet peptides.

Non-canonical amino acids as a tool for the thermal stabilization of enzymes

Biocatalysis has become a powerful alternative for green chemistry. Expanding the range of amino acids used in protein biosynthesis can improve industrially appealing properties such as enantioselectivity, activity and stability. This review will specifically delve into the thermal stability improvements that non-canonical amino acids (ncAAs) can confer to enzymes. Methods to achieve this end, such as the use of halogenated ncAAs, selective immobilization and rational design, will be discussed. Additionally, specific enzyme design considerations using ncAAs are discussed along with the benefits and limitations of the various approaches available to enhance the thermal stability of enzymes.

Learning deep representations of enzyme thermal adaptation

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

Enhancement of the polyethylene terephthalate and mono-(2-hydroxyethyl) terephthalate degradation activity of Ideonella sakaiensis PETase by an electrostatic interaction-based strategy

The serious environmental pollution that came up with the continuously growing demand for polyethylene terephthalate (PET) has attracted global concern. The IsPETase which has shown the highest PET degradation activity under ambient temperature is a promising enzyme for PET biodegradation, while poor thermostability limited its practical application. Herein, an electrostatic interaction-based strategy was applied for rational design of IsPETase towards enhanced thermostability. The IsPETaseI139R variant displayed the highest Tm value of 56.4 °C and 3.6-times higher PET degradation activity. Molecular simulations demonstrated that the introduction of salt bridges stabilized the local structures, resulting in robust thermostability. Meanwhile, the IsPETaseS92K/D157E/R251A not only exhibited higher thermostability but also showed a 1.74-fold kcat increase towards mono-(2-hydroxyethyl) terephthalate, which ultimately achieved PET depolymerization to complete monomer TPA. Collectively, the electrostatic interaction-based strategy, together with the derived IsPETase variants, could help promote the bio-recycle of PET, reducing the severe global burden of PET waste.

Thermostability Improvement of L-Asparaginase from Acinetobacter soli via Consensus-Designed Cysteine Residue Substitution

To extend the application range of L-asparaginase in food pre-processing, the thermostability improvement of the enzyme is essential. Herein, two non-conserved cysteine residues with easily oxidized free sulfhydryl groups, Cys8 and Cys283, of Acinetobacter soli L-asparaginase (AsA) were screened out via consensus design. After saturation mutagenesis and combinatorial mutation, the mutant C8Y/C283Q with highly improved thermostability was obtained with a half-life of 361.6 min at 40 °C, an over 34-fold increase compared with that of the wild-type. Its melting temperature (T_m) value reaches 62.3 °C, which is 7.1 °C higher than that of the wild-type. Molecular dynamics simulation and structure analysis revealed the formation of new hydrogen bonds of Gln283 and the aromatic interaction of Tyr8 formed with adjacent residues, resulting in enhanced thermostability. The improvement in the thermostability of L-asparaginase could efficiently enhance its effect on acrylamide inhibition; the contents of acrylamide in potato chips were efficiently reduced by 86.50% after a mutant C8Y/C283Q treatment, which was significantly higher than the 59.05% reduction after the AsA wild-type treatment. In addition, the investigation of the mechanism behind the enhanced thermostability of AsA could further direct the modification of L-asparaginases for expanding their clinical and industrial applications.

Effects of Site-Directed Mutations on the Communicability between Local Segments and Binding Pocket Distortion of Engineered GH11 Xylanases Visualized through Network Topology Analysis

Mutations occurred within the binding pocket of enzymes directly modified the interaction network between an enzyme and its substrate. However, some mutations affecting the catalytic efficiency occurred far from the binding pocket and the explanation regarding mechanisms underlying the transmission of the mechanical signal from the mutated site to the binding pocket was lacking. In this study, network topology analysis was used to characterize and visualize the changes of interaction networks caused by site-directed mutations on a GH11 xylanase from our previous study. For each structure, coordinates from molecular dynamics (MD) trajectory were obtained to create networks of representative atoms from all protein and xylooligosaccharide substrate residues, in which edges were defined between pairs of residues within a cutoff distance. Then, communicability matrices were extracted from the network to provide information on the mechanical signal transmission from the number of possible paths between any residue pairs or local protein segments. The analysis of subgraph centrality and communicability clearly showed that site-direct mutagenesis at non-reducing or reducing ends caused binding pocket distortion close to the opposite ends and created denser interaction networks. However, site-direct mutagenesis at both ends cancelled the binding pocket distortion, while enhancing the thermostability. Therefore, the network topology analysis tool on the atomistic simulations of engineered proteins could play some roles in protein design for the minimization to the correction of binding pocket tilting, which could affect the functionality and efficacy of enzymes.

Engineering functional thermostable proteins using ancestral sequence reconstruction

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

Structural and biophysical studies of new L-asparaginase variants: lessons from random mutagenesis of the prototypic Escherichia coli Ntn-amidohydrolase

This work reports the results of random mutagenesis of the Escherichia coli class 2 L-asparaginase EcAIII belonging to the Ntn-hydrolase family. New variants of EcAIII were studied using structural, biophysical and bioinformatic methods. Activity tests revealed that the L-asparaginase activity is abolished in all analyzed mutants with the absence of Arg207, but some of them retained the ability to undergo the autoproteolytic maturation process. The results of spectroscopic studies and the determined crystal structures showed that the EcAIII fold is flexible enough to accept different types of mutations; however, these mutations may have a diverse impact on the thermal stability of the protein. The conclusions from the experiments are grouped into six lessons focused on (i) the adaptation of the EcAIII fold to new substitutions, (ii) the role of Arg207 in EcAIII activity, (iii) a network of residues necessary for autoprocessing, (iv) the complexity of the autoprocessing reaction, (v) the conformational changes observed in enzymatically inactive variants and (vi) the cooperativity of the EcAIII dimer subunits. Additionally, the structural requirements (pre-maturation checkpoints) that are necessary for the initiation of the autocleavage of Ntn-hydrolases have been classified. The findings reported in this work provide useful hints that should be considered before planning enzyme-engineering experiments aimed at the design of proteins for therapeutic applications. This is especially important for L-asparaginases that can be utilized in leukemia therapy, as alternative therapeutics are urgently needed to circumvent the severe side effects associated with the currently used enzymes.

Ancestral sequence reconstruction of a cytochrome P450 family involved in chemical defence reveals the functional evolution of a promiscuous, xenobiotic-metabolizing enzyme in vertebrates

The cytochrome P450 family 1 enzymes (CYP1s) are a diverse family of hemoprotein monooxygenases, which metabolize many xenobiotics including numerous environmental carcinogens. However, their historical function and evolution remain largely unstudied. Here we investigate CYP1 evolution via the reconstruction and characterization of the vertebrate CYP1 ancestors. Younger ancestors and extant forms generally demonstrated higher activity toward typical CYP1 xenobiotic and steroid substrates than older ancestors, suggesting significant diversification away from the original CYP1 function. Caffeine metabolism appears to be a recently evolved trait of the CYP1A subfamily, observed in the mammalian CYP1A lineage, and may parallel the recent evolution of caffeine synthesis in multiple separate plant species. Likewise, the aryl hydrocarbon receptor agonist, 6-formylindolo[3,2-b]carbazole (FICZ) was metabolized to a greater extent by certain younger ancestors and extant forms, suggesting that activity toward FICZ increased in specific CYP1 evolutionary branches, a process that may have occurred in parallel to the exploitation of land where UV-exposure was higher than in aquatic environments. As observed with previous reconstructions of P450 enzymes, thermostability correlated with evolutionary age; the oldest ancestor was up to 35 °C more thermostable than the extant forms, with a ¹⁰T₅₀ (temperature at which 50% of the hemoprotein remains intact after 10 min) of 71 °C. This robustness may have facilitated evolutionary diversification of the CYP1s by buffering the destabilizing effects of mutations that conferred novel functions, a phenomenon which may also be useful in exploiting the catalytic versatility of these ancestral enzymes for commercial application as biocatalysts.

Protposer: the web server that readily proposes protein stabilizing mutations with high PPV

Protein stability is a requisite for most biotechnological and medical applications of proteins. As natural proteins tend to suffer from a low conformational stability ex vivo, great efforts have been devoted toward increasing their stability through rational design and engineering of appropriate mutations. Unfortunately, even the best currently used predictors fail to compute the stability of protein variants with sufficient accuracy and their usefulness as tools to guide the rational stabilisation of proteins is limited. We present here Protposer, a protein stabilising tool based on a different approach. Instead of quantifying changes in stability, Protposer uses structure- and sequence-based screening modules to nominate candidate mutations for subsequent evaluation by a logistic regression model, carefully trained to avoid overfitting. Thus, Protposer analyses PDB files in search for stabilization opportunities and provides a ranked list of promising mutations with their estimated success rates (eSR), their probabilities of being stabilising by at least 0.5 kcal/mol. The agreement between eSRs and actual positive predictive values (PPV) on external datasets of mutations is excellent. When Protposer is used with its Optimal kappa selection threshold, its PPV is above 0.7. Even with less stringent thresholds, Protposer largely outperforms FoldX, Rosetta and PoPMusiC. Indicating the PDB file of the protein suffices to obtain a ranked list of mutations, their eSRs and hints on the likely source of the stabilization expected. Protposer is a distinct, straightforward and highly successful tool to design protein stabilising mutations, and it is freely available for academic use at http://webapps.bifi.es/the-protposer.

Glycosyl hydrolase 11 (xynA) gene with xylanase activity from thermophilic bacteria isolated from thermal springs

Background

Hemicellulose is one of the copious polymer in lignocellulosic biomass (LCB). It is primarily composed of xylan linked by β-1,4 glycosidic bonds. Xylanase preferentially cleaves the β-1,4-glycosidic bonds in the xylan backbone resulting in complete hydrolysis of the biomass. Thermostable variants of glycoside hydrolases act as robust catalysts, not only in degradation but also during processing, to obtain specific carbohydrate-containing chemicals and materials (Ramasamy et al. in Madras Agric J 107(special):1. 10.29321/MAJ.2020.000382, 2020).

Results

The xylanase production by two thermophilic bacteria isolated from thermal springs was evaluated. In addition, the gene encoding this industrially vital enzyme was isolated and characterized, and its protein structure was analyzed. The thermophilic bacteria producing xylanases were isolated from augmented sawdust and banana fiber biomass from hot springs of Himachal Pradesh and identified as Bacillus subtilis VSDB5 and Bacillus licheniformis KBFB4 using 16S rRNA gene sequencing. The persistent xylanase activity revealed that the enzyme is secreted extracellularly with the maximum activity of 0.76 IU mL−1 and 1.0 IU mL−1 at 6 h and 12 h of growth by KBFB4 and VSDB5, respectively, under submerged fermentation. Both the strains exhibited the maximum activity at pH 6 and a temperature of 50 °C. The xylanases of KBFB4 and VSDB5 were thermostable and retained 40% of their activity at 60 °C after incubation for 30 min. Xylanase of VSDB5 had wide thermotolerance and retained 20% of its activity from 60 to 80 °C, whereas xylanase of KBFB4 showed wide alkali tolerance and retained 80% of its activity until pH 10. The xylanase (xynA)-encoding gene (650 bp) cloned from both the strains using specific primers showed 98 to 99% homology to β-1,4-endoxylanase gene. Further in silico analysis predicted that the xylanase protein, with a molecular weight of 23 kDa, had a high pI (9.44–9.65), which explained the alkaline nature of the enzyme and greater aliphatic index (56.29). This finding suggested that the protein is thermostable. Multiple sequence alignment and homology modeling of the protein sequence revealed that the gene product belonged to the GH11 family, indicating its possible application in bioconversion.

Conclusion

The strains B. subtilis VSDB5 and B. licheniformis KBFB4 obtained from hot springs of Himachal Pradesh produced potent and alkali-tolerant thermostable xylanases, which belong to the GH11 family. The enzyme can be supplemented in industrial applications for biomass conversion at high temperatures and pH (or in processes involving alkali treatment).

Graphical Abstract

Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides

In silico tools, such as molecular docking, are widely applied to study interactions and binding affinity of biological activity of proteins and peptides. However, restricted sampling of both ligand and receptor conformations and use of approximated scoring functions can produce results that do not correlate with actual experimental binding affinities. Molecular dynamics simulations (MDS) can provide valuable information in deciphering functional mechanisms of proteins/peptides and other biomolecules, overcoming the rigid sampling limitations in docking analysis. This review will discuss the information related to the traditional use of in silico models, such as molecular docking, and its application for studying food proteins and bioactive peptides, followed by an in-depth introduction to the theory of MDS and description of why these molecular simulation techniques are important in the theoretical prediction of structural and functional dynamics of food proteins and bioactive peptides. Applications, limitations, and future prospects of MDS will also be discussed.

Rational-Design Engineering to Improve Enzyme Thermostability

The fundamentals of thermostability engineering need to be carried out for proteins with low thermal stability to expand their utilization. Thus, comprehension of the thermal stability regulating factors of proteins is needful for the engineering of their thermostability. Protein engineering aims to overcome their natural limitations in tough conditions by refining protein stability and activity. Rational-design approach requires a crystal structure dataset along with the biophysical information, protein function, and sequence-based data, especially consensus sequence that is favorable for the protein folding during natural evolution. It can be attained by either single- or multiple-point mutation, by which amino acids are changed. In fact, these mutation approaches show several benefits. For example, the offered mutations are produced after an evaluation and design, which raise the chance to acquire favorable mutations. The rational-design engineering can improve the biochemical properties of enzymes, including the kinetic behaviors, substrate specificity, thermostability, and organic solvent tolerance. Moreover, this approach considerably reduces the library size, so less effort and time can be employed. Here, we apply the computational algorithms and programs with experiments to create thermostable enzymes that will be beneficial for future applications.

Critical assessment of structure-based approaches to improve protein resistance in aqueous ionic liquids by enzyme-wide saturation mutagenesis

Ionic liquids (IL) and aqueous ionic liquids (aIL) are attractive (co-)solvents for green industrial processes involving biocatalysts, but often reduce enzyme activity. Experimental and computational methods are applied to predict favorable substitution sites and, most often, subsequent site-directed surface charge modifications are introduced to enhance enzyme resistance towards aIL. However, almost no studies evaluate the prediction precision with random mutagenesis or the application of simple data-driven filtering processes. Here, we systematically and rigorously evaluated the performance of 22 previously described structure-based approaches to increase enzyme resistance to aIL based on an experimental complete site-saturation mutagenesis library of Bacillus subtilis Lipase A (BsLipA) screened against four aIL. We show that, surprisingly, most of the approaches yield low gain-in-precision (GiP) values, particularly for predicting relevant positions: 14 approaches perform worse than random mutagenesis. Encouragingly, exploiting experimental information on the thermostability of BsLipA or structural weak spots of BsLipA predicted by rigidity theory yields GiP = 3.03 and 2.39 for relevant variants and GiP = 1.61 and 1.41 for relevant positions. Combining five simple-to-compute physicochemical and evolutionary properties substantially increases the precision of predicting relevant variants and positions, yielding GiP = 3.35 and 1.29. Finally, combining these properties with predictions of structural weak spots identified by rigidity theory additionally improves GiP for relevant variants up to 4-fold to ∼10 and sustains or increases GiP for relevant positions, resulting in a prediction precision of ∼90% compared to ∼9% in random mutagenesis. This combination should be applicable to other enzyme systems for guiding protein engineering approaches towards improved aIL resistance.

Simultaneous improvement of the thermostability and activity of lactic dehydrogenase from Lactobacillus rossiae through rational design

d-Phenyllactic acid, is a versatile organic acid with wide application prospects in the food, pharmaceutical and material industries. Wild-type lactate dehydrogenase LrLDH from Lactobacillus rossiae exhibits a high catalytic performance in the production of d-phenyllactic acid from phenylpyruvic acid or sodium phenylpyruvate, but its industrial application is hampered by poor thermostability. Here, computer aided rational design was applied to improve the thermostability of LrLDH. By using HotSpot Wizard 3.0, five hotspot residues (N218, L237, T247, D249 and S301) were identified, after which site-saturation mutagenesis and combined mutagenesis were performed. The double mutant D249A/T247I was screen out as the best variant, with optimum temperature, t_1/2, and T¹⁰₅₀ that were 12 °C, 17.96 min and 19 °C higher than that of wild-type LrLDH, respectively. At the same time, the k_cat/K_m of D249A/T247I was 1.47 s⁻¹ mM⁻¹, which was 3.4 times higher than that of the wild-type enzyme. Thus rational design was successfully applied to simultaneously improve the thermostability and catalytic activity of LrLDH to a significant extent. The results of molecular dynamics simulations and molecular structure analysis could explain the mechanisms for the improved performance of the double mutant. This study shows that computer-aided rational design can greatly improve the thermostability of d-lactate dehydrogenase, offering a reference for the modification of other enzymes.

The d-LDH was engineered using computationally-assisted rational mutagenesis. The two mutants D249A and D249A/T247I showed significantly enhanced thermostability and catalytic activity to sodium phenylpyruvate compared with the wild-type enzyme.

Significantly Improving the Thermostability and Catalytic Efficiency of Streptomyces mobaraenesis Transglutaminase through Combined Rational Design

Streptomyces mobaraenesis transglutaminase has been widely used in food processing. We here significantly improved the catalytic properties of S2P-S23V-Y24N-S199A-K294L (TGm1), a highly stabilized variant of the transglutaminase. First, a virtual proline scan was performed based on folding free energy changes to obtain TGm1 variants with enhanced thermostability. Second, the residues within 15 Å of Cys64 in the enzyme-substrate complex of TGm1 were subjected to virtual saturation mutagenesis to generate the variants with reduced binding free energy and increased activity. After combining the favorable mutations, we obtained the variant FRAPD-TGm1-E28T-A265P-A287P (FRAPD-TGm2), exhibiting 66.9 min of half-life at 60 °C (t_1/2(60 °C)), 67.8 °C of melting temperature (T_m), and 71.8 U/mg of specific activity, which are 2-fold, 2.6 °C, and 43.8% higher than those of FRAPD-TGm1, respectively. At last, to increase the surface negative net charge of FRAPD-TGm2, we introduced the mutations N96E-S144E-N163D-R183E-R208E-K325E, yielding FRAPD-TGm3. The latter's t_1/2(60 °C), T_m, and specific activity were 122.9 min, 68.6 °C, and 83.7 U/mg, which are 83.8%, 0.8 °C, and 16.6% higher than the former, respectively. FRAPD-TGm3 is thus a robust candidate for transglutaminase application.

Illustrative Tutorials for ProThermDB: Thermodynamic Database for Proteins and Mutants

ProThermDB (https://web.iitm.ac.in/bioinfo2/prothermdb/index.html) is a primary resource for protein stability, which contains experimentally determined thermodynamic data for proteins and their mutants. The most recent version of ProThermDB accumulates the data obtained from both high- and low-throughput experimental biophysical methods. It includes comprehensive information at four different levels, i.e.: (i) protein sequence and structure; (ii) experimental conditions; (iii) thermodynamic parameters such as Gibbs free energy, melting temperature, enthalpy, etc.; and (iv) literature. In the following protocols, we present detailed tutorials for retrieving data using different search, display and sorting options, interpretation of search results, description of each entry-level information category, data upload and download, cross-links with other databases, and visualization options. This protocol consists of six pictorial exercises, which are useful for biologists/users to understand the contents and organization of data in ProThermDB. Further, potential applications of ProThermDB in protein engineering are discussed. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Retrieval of experimental thermodynamic data for wild-type and mutants of a specific protein using a simple query Basic Protocol 2: Retrieval of stabilizing point mutations, which are located at the interior of α-helical regions, and obtaining data by thermal denaturation methods Basic Protocol 3: Retrieval of destabilizing point mutations, which are in β-sheets of exposed regions, and obtaining data by chemical denaturation methods (urea and GdnHCl) Basic Protocol 4: Retrieval of stabilizing and destabilizing point mutations in a range of physiological conditions (pH: 6-9 and T: 20°C-25°C) and publication years (2010-2020) Support Protocol: Downloading the entire data of the database for academic research purposes and submission of new data in ProThermDB.

Engineering Leifsonia Alcohol Dehydrogenase for Thermostability and Catalytic Efficiency by Enhancing Subunit Interactions

Leifsonia alcohol dehydrogenase (LnADH) is a promising biocatalyst for the synthesis of chiral alcohols. However, limitations of wild-type LnADH observed for practical application include low activity and poor stability. In this work, protein engineering was employed to improve its thermostability and catalytic efficiency by altering the subunit interfaces. Residues T100 and S148 were identified to be significant for thermostability and activity, and the melting temperature (ΔT_m ) and catalytic efficiency of the mutant T100R/S148I toward ketone substrates was improved by 18.7 °C and 1.8-5.5-fold. Solving the crystal structures of the wild-type enzyme and T100R/S148L revealed beneficial effects of mutations on stability and catalytic activity. The most robust mutant T100R/S148I is promising for industrial applications and can produce 200 g liter^-1  day^-1 chiral alcohols at 50 °C by only a 1 : 500 ratio of enzyme to substrate.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new S^transitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Overview of strategies for developing high thermostability industrial enzymes: Discovery, mechanism, modification and challenges

Biocatalysts such as enzymes are environmentally friendly and have substrate specificity, which are preferred in the production of various industrial products. However, the strict reaction conditions in industry including high temperature, organic solvents, strong acids and bases and other harsh environments often destabilize enzymes, and thus substantially compromise their catalytic functions, and greatly restrict their applications in food, pharmaceutical, textile, bio-refining and feed industries. Therefore, developing industrial enzymes with high thermostability becomes very important in industry as thermozymes have more advantages under high temperature. Discovering new thermostable enzymes using genome sequencing, metagenomics and sample isolation from extreme environments, or performing molecular modification of the existing enzymes with poor thermostability using emerging protein engineering technology have become an effective means of obtaining thermozymes. Based on the thermozymes as biocatalytic chips in industry, this review systematically analyzes the ways to discover thermostable enzymes from extreme environment, clarifies various interaction forces that will affect thermal stability of enzymes, and proposes different strategies to improve enzymes' thermostability. Furthermore, latest development in the thermal stability modification of industrial enzymes through rational design strategies is comprehensively introduced from structure-activity relationship point of view. Challenges and future research perspectives are put forward as well.

Synergistic stabilization of a double mutant in chymotrypsin inhibitor 2 from a library screen in E. coli

Most single point mutations destabilize folded proteins. Mutations that stabilize a protein typically only have a small effect and multiple mutations are often needed to substantially increase the stability. Multiple point mutations may act synergistically on the stability, and it is often not straightforward to predict their combined effect from the individual contributions. Here, we have applied an efficient in-cell assay in E. coli to select variants of the barley chymotrypsin inhibitor 2 with increased stability. We find two variants that are more than 3.8 kJ mol-1 more stable than the wild-type. In one case, the increased stability is the effect of the single substitution D55G. The other case is a double mutant, L49I/I57V, which is 5.1 kJ mol-1 more stable than the sum of the effects of the individual mutations. In addition to demonstrating the strength of our selection system for finding stabilizing mutations, our work also demonstrate how subtle conformational effects may modulate stability.

Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures

Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the limitations of prediction methods and of the datasets used to train them, which result in low reliability for actual applications despite globally capturing trends. Here, we review these and other issues by analyzing one of the most detailed, carefully curated datasets of melting temperature change (ΔTm) upon mutation for proteins with high-resolution structures. After examining the composition of this dataset to discuss imbalances and biases, we inspect several of its entries assisted by an online app for data navigation and structure display and aided by a neural network that predicts ΔTm with accuracy close to that of programs available to this end. We pose that the ΔTm predictions of our network, and also likely those of other programs, account only for a baseline-like general effect of each type of amino acid substitution which then requires substantial corrections to reproduce the actual stability changes. The corrections are very different for each specific case and arise from fine structural details which are not well represented in the dataset and which, despite appearing reasonable upon visual inspection of the structures, are hard to encode and parametrize. Based on these observations, additional analyses, and a review of recent literature, we propose recommendations for developers of stability prediction methods and for efforts aimed at improving the datasets used for training. We leave our interactive interface for analysis available online at http://lucianoabriata.altervista.org/papersdata/proteinstability2021/s1626navigation.html so that users can further explore the dataset and baseline predictions, possibly serving as a tool useful in the context of structural biology and protein biotechnology research and as material for education in protein biophysics.

Thermostability Engineering of a Class II Pyruvate Aldolase from Escherichia coli by in Vivo Folding Interference

The use of enzymes in industrial processes is often limited by the unavailability of biocatalysts with prolonged stability. Thermostable enzymes allow increased process temperature and thus higher substrate and product solubility, reuse of expensive biocatalysts, resistance against organic solvents, and better "evolvability" of enzymes. In this work, we have used an activity-independent method for the selection of thermostable variants of any protein in Thermus thermophilus through folding interference at high temperature of a thermostable antibiotic reporter protein at the C-terminus of a fusion protein. To generate a monomeric folding reporter, we have increased the thermostability of the moderately thermostable Hph5 variant of the hygromycin B phosphotransferase from Escherichia coli to meet the method requirements. The final Hph17 variant showed 1.5 °C higher melting temperature (T _m) and 3-fold longer half-life at 65 °C compared to parental Hph5, with no changes in the steady-state kinetic parameters. Additionally, we demonstrate the validity of the reporter by stabilizing the 2-keto-3-deoxy-l-rhamnonate aldolase from E. coli (YfaU). The most thermostable multiple-mutated variants thus obtained, YfaU99 and YfaU103, showed increases of 2 and 2.9 °C in T _m compared to the wild-type enzyme but severely lower retro-aldol activities (150- and 120-fold, respectively). After segregation of the mutations, the most thermostable single variant, Q107R, showed a T _m 8.9 °C higher, a 16-fold improvement in half-life at 60 °C and higher operational stability than the wild-type, without substantial modification of the kinetic parameters.

An effective computational-screening strategy for simultaneously improving both catalytic activity and thermostability of α-l-rhamnosidase

Catalytic efficiency and thermostability are the two most important characteristics of enzymes. However, it is always tough to improve both catalytic efficiency and thermostability of enzymes simultaneously. In the present study, a computational strategy with double-screening steps was proposed to simultaneously improve both catalysis efficiency and thermostability of enzymes; and a fungal α-l-rhamnosidase was used to validate the strategy. As the result, by molecular docking and sequence alignment analysis within the binding pocket, seven mutant candidates were predicted with better catalytic efficiency. By energy variety analysis, A355N, S356Y, and D525N among the seven mutant candidates were predicted with better thermostability. The expression and characterization results showed the mutant D525N had significant improvements in both enzyme activity and thermostability. Molecular dynamics simulations indicated that the mutations located within the 5 Å range of the catalytic domain, which could improve root mean squared deviation, electrostatic, Van der Waal interaction, and polar salvation values, and formed water bridge between the substrate and the enzyme. The study indicated that the computational strategy based on the binding energy, conservation degree and mutation energy analyses was effective to develop enzymes with better catalysis and thermostability, providing practical approach for developing industrial enzymes.

DiRect: Site-directed mutagenesis method for protein engineering by rational design

Site-directed mutagenesis (SDM), an indispensable method in molecular biology and protein engineering, is rather time-consuming and laborious. Protein engineering, especially that of enzymes, nowadays increasingly relies on rational design approaches in which both SDM and protein expression are the bottlenecks because they are generally based on the recombinant DNA technology. Here, we developed a new PCR-based mutagenesis method, DiRect, that achieves high performance in product quality (≥99% substitution) without recombinant DNA technology. We applied DiRect in combination with a cell-free protein expression system to an industrially relevant enzyme, nicotinamide adenine dinucleotide phosphate-dependent 3-quinuclidinone reductase from Rhodotorula rubra. In a single round of screening, 90 newly designed mutant proteins were produced within two days, and an unreported mutant (Q135I) exhibiting much higher thermostability than the wild-type enzyme was successfully identified within one extra day. Thus, DiRect is a simple, efficient, and potentially scalable SDM method.

Tailoring an aldo-keto reductase KmAKR for robust thermostability and catalytic efficiency by stepwise evolution and structure-guided consensus engineering

t-Butyl 6-cyano-(3R,5R)-dihydroxyhexanoate ((3R,5R)-2) is an advanced chiral diol intermediate of the cholesterol-lowering drug atorvastatin. KmAKR_M5 (W297H/Y296W/K29H/Y28A/T63M) constructed in our previous work, displayed good biocatalytic performance on (3R,5R)-2. In the present work, stepwise evolution was applied to further enhance the thermostability and activity of KmAKR_M5. For thermostability enhancement, N109 and S196 located far from the active site were picked out by structure-guided consensus engineering, and mutated by site-directed mutagenesis (SDM). For catalytic efficiency improvement, the residues A30 and T302 adjacent to the substrate-binding pocket were subjected to site-saturation mutagenesis (SSM). As a result, the "best" mutant KmAKR_M9 (W297H/Y296W/K29H/Y28A/T63M/A30P/T302S/N109K/S196C) was developed, of which T₅₀¹⁵ and T_m were 5.0 °C and 8.2 °C higher than those of KmAKR_M5. Moreover, compared to KmAKR_M5, KmAKR_M9 displayed a 1.9-fold (846 vs 2436 min) and 6.7-fold (126 vs 972 min) longer half-lives at 40 and 50 °C, respectively. Structural analysis suggested that beneficial mutations introduced additional hydrophobic interactions and hydrogen bonds, contributing rigidification of the flexible loops and the increase of internal forces, hence increasing the thermostability and activity. 5 g DCW (dry cell weight) L^-1KmAKR_M9 completely reduced 350 g L^-1t-butyl 6-cyano-(5R)-hydroxy-3-oxo-hexanoate ((5R)-1), within 3.7 h at 40 °C, yielding optically pure (3R,5R)-2 (d.e._p > 99.5%) with a space-time yield (STY) of 1.82 kg L^-1 d^-1. Hence, KmAKR_M9 is a robust biocatalyst for the synthesis of (3R,5R)-2.