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

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.

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.

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.

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.

Consensus design and engineering of an efficient and high-yield peptide asparaginyl ligase for protein cyclization and ligation

Plant legumains are Asn/Asp-specific endopeptidases that have diverse functions in plants. Peptide asparaginyl ligases (PALs) are a special legumain subtype that primarily catalyze peptide bond formation rather than hydrolysis. PALs are versatile protein engineering tools but are rarely found in nature. To overcome this limitation, here we describe a two-step method to design and engineer a high-yield and efficient recombinant PAL based on commonly found asparaginyl endopeptidases. We first constructed a consensus sequence derived from 1500 plant legumains to design the evolutionarily stable legumain conLEG that could be produced in E. coli with 20-fold higher yield relative to that for natural legumains. We then applied the ligase-activity determinant hypothesis to exploit conserved residues in PAL substrate-binding pockets and convert conLEG into conPAL1-3. Functional studies showed that conLEG is primarily a hydrolase, whereas conPALs are ligases. Importantly, conPAL3 is a superefficient and broadly active PAL for protein cyclization and ligation.

Engineering the kinetic stability of a β-trefoil protein by tuning its topological complexity

Kinetic stability, defined as the rate of protein unfolding, is central to determining the functional lifetime of proteins, both in nature and in wide-ranging medical and biotechnological applications. Further, high kinetic stability is generally correlated with high resistance against chemical and thermal denaturation, as well as proteolytic degradation. Despite its significance, specific mechanisms governing kinetic stability remain largely unknown, and few studies address the rational design of kinetic stability. Here, we describe a method for designing protein kinetic stability that uses protein long-range order, absolute contact order, and simulated free energy barriers of unfolding to quantitatively analyze and predict unfolding kinetics. We analyze two β-trefoil proteins: hisactophilin, a quasi-three-fold symmetric natural protein with moderate stability, and ThreeFoil, a designed three-fold symmetric protein with extremely high kinetic stability. The quantitative analysis identifies marked differences in long-range interactions across the protein hydrophobic cores that partially account for the differences in kinetic stability. Swapping the core interactions of ThreeFoil into hisactophilin increases kinetic stability with close agreement between predicted and experimentally measured unfolding rates. These results demonstrate the predictive power of readily applied measures of protein topology for altering kinetic stability and recommend core engineering as a tractable target for rationally designing kinetic stability that may be widely applicable.

Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs

Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate-derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure-based models more suitable for protein design. Specifically, we supplement backbone-coordinate features with TERM-derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM-based and coordinate-based information, and COORDinator, which uses only coordinate-based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine-tuned on experimental data to improve predictions. Our results suggest that using TERM-based and coordinate-based features together may be beneficial for protein design and that structure-based neural models that produce Potts energy tables have utility for flexible applications in protein science.

Rational design of enzyme activity and enantioselectivity

The strategy of rational design to engineer enzymes is to predict the potential mutants based on the understanding of the relationships between protein structure and function, and subsequently introduce the mutations using the site-directed mutagenesis. Rational design methods are universal, relatively fast and have the potential to be developed into algorithms that can quantitatively predict the performance of the designed sequences. Compared to the protein stability, it was more challenging to design an enzyme with improved activity or selectivity, due to the complexity of enzyme molecular structure and inadequate understanding of the relationships between enzyme structures and functions. However, with the development of computational force, advanced algorithm and a deeper understanding of enzyme catalytic mechanisms, rational design could significantly simplify the process of engineering enzyme functions and the number of studies applying rational design strategy has been increasing. Here, we reviewed the recent advances of applying the rational design strategy to engineer enzyme functions including activity and enantioselectivity. Five strategies including multiple sequence alignment, strategy based on steric hindrance, strategy based on remodeling interaction network, strategy based on dynamics modification and computational protein design are discussed and the successful cases using these strategies are introduced.

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

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

Structural features, temperature adaptation and industrial applications of microbial lipases from psychrophilic, mesophilic and thermophilic origins

Microbial lipases are very prominent biocatalysts because of their ability to catalyze a wide variety of reactions in aqueous and non-aqueous media. Here microbial lipases from different origins (psychrophiles, mesophiles, and thermophiles) have been reviewed. This review emphasizes an update of structural diversity in temperature adaptation and industrial applications, of psychrophilic, mesophilic, and thermophilic lipases. The microbial origins of lipases are logically dynamic, proficient, and also have an extensive range of industrial uses with the manufacturing of altered molecules. It is therefore of interest to understand the molecular mechanisms of adaptation to temperature in occurring lipases. However, lipases from extremophiles (psychrophiles, and thermophiles) are widely used to design biotransformation reactions with higher yields, fewer byproducts, or useful side products and have been predicted to catalyze those reactions also, which otherwise are not possible with the mesophilic lipases. Lipases as a multipurpose biological catalyst have given a favorable vision in meeting the needs of several industries such as biodiesel, foods, and drinks, leather, textile, detergents, pharmaceuticals, and medicals.

Reconfigurable Peptide Analogs of Apolipoprotein A-I Reveal Tunable Features of Nanodisc Assembly

Nanodisc (ND)-forming membrane scaffold proteins or peptides developed from apolipoprotein A-I (apoA-I) have led to considerable promise in structural biology and therapeutic applications. However, the rationale and regularity characteristics in peptide sequence design remain inconclusive. Here, we proposed a consensus-based normalization approach through the reversed engineering of apoA-IΔ1-45 to design reconfigurable apoA-I peptide analogs (APAs) for tunable ND assembly. We present extensive morphological validations and computational simulation analyses on divergent APA-NDs that are generated by our method. Fifteen divergent APAs were generated accordingly to study the assembly machinery of NDs. We show that APA designs exhibit multifactorial influence in terms of varying APA tandem repeats, sequence composition, and lipid-to-APA ratio to form tunable diameters of NDs. There is a strong positive correlation between DMPC-to-APA ratios and ND diameters. Longer APA with more tandem repeats tends to yield higher particle size homogeneity. Our results also suggest proline is a dispensable residue for the APA-ND formation. Interestingly, proline-rich substitution not only provides an inward-bending effect in forming smaller NDs but also induces the cumulative chain flexibility that enables larger ND formation at higher lipid ratios. Additionally, proline-tryptophan residues in APAs play a dominant role in forming larger NDs. Molecular simulation shows that enriched basic and acidic residues in APAs evoke abundant hydrogen-bond and salt bridge networks to reinforce the structural stability of APA-NDs. Together, our findings provide a rational basis for understanding APA design. The proposed model could be extended to other apolipoproteins for desired ND engineering.

Self-Assembly of Repetitive Segment and Random Segment Polymer Architectures

Recent advances in chemical synthesis have created new methodologies for synthesizing sequence-controlled synthetic polymers, but rational design of monomer sequence for desired properties remains challenging. In this work, we synthesize periodic polymers with repetitive segments using a sequence-controlled ring-opening metathesis polymerization (ROMP) method, which draws inspiration from proteins containing repetitive sequence motifs. The repetitive segment architecture is shown to dramatically affect the self-assembly behavior of these materials. Our results show that polymers with identical repetitive sequences assemble into uniform spherical nanoparticles after thermal annealing, whereas copolymers with random placement of segments with different sequences exhibit disordered assemblies without a well-defined morphology. Overall, these results bring a new understanding to the role of periodic repetitive sequences in polymer assembly.

Logistic Regression-Guided Identification of Cofactor Specificity-Contributing Residues in Enzyme with Sequence Datasets Partitioned by Catalytic Properties

Changing the substrate/cofactor specificity of an enzyme requires multiple mutations at spatially adjacent positions around the substrate pocket. However, this is challenging when solely based on crystal structure information because enzymes undergo dynamic conformational changes during the reaction process. Herein, we proposed a method for estimating the contribution of each amino acid residue to substrate specificity by deploying a phylogenetic analysis with logistic regression. Since this method can estimate the candidate amino acids for mutation by ranking, it is readable and can be used in protein engineering. We demonstrated our concept using redox cofactor conversion of the Escherichia coli malic enzyme as a model, which still lacks crystal structure elucidation. The use of logistic regression with amino acid sequences classified by cofactor specificity showed that the NADP⁺-dependent malic enzyme completely switched cofactor specificity to NAD⁺ dependence without the need for a practical screening step. The model showed that surrounding residues made a greater contribution to cofactor specificity than those in the interior of the substrate pocket. These residues might be difficult to identify from crystal structure observations. We show that a highly accurate and inferential machine learning model was obtained using amino acid sequences of structurally homologous and functionally distinct enzymes as input data.

Enzymatic Conjugation of Modified RNA Fragments by Ancestral RNA Ligase AncT4_2

Oligonucleotide therapeutics have great potential as a next-generation approach to treating intractable diseases. Large quantities of modified DNA/RNA containing xenobiotic nucleic acids (XNAs) must be synthesized before clinical application. In this study, the ancestral RNA ligase AncT4_2 was designed by ancestral sequence reconstruction (ASR) to perform the conjugation reaction of modified RNA fragments. AncT4_2 had superior properties to native RNA ligase 2 from T4 phage (T4Rnl2), including high productivity, a >2.5-fold-higher turnover number, and >10°C higher thermostability. One remarkable point is the broad substrate selectivity of AncT4_2; the activity of AncT4_2 toward 17 of the modified RNA fragments was higher than that of T4Rnl2. The activity was estimated by measuring the conjugation reaction of two RNA strands, 3'-OH (12 bp) and 5'-PO₄ (12 bp), in which the terminal and penultimate positions of the 3'-OH fragment and the first and second positions of the 5'-PO₄ fragment were substituted by 2'-fluoro, 2'-O-methyl, 2'-O-methoxyethyl, and 2'-H, respectively. The enzymatic properties of AncT4_2 allowed the enzyme to conjugate large quantities of double-stranded RNA coding for patisiran (>400 μM level), which was formed by four RNA fragments containing 2'-OMe-substituted nucleic acids. Structural analysis of modeled AncT4_2 suggested that protein dynamics were changed by mutation to Gly or indel during ASR and that this may positively impact the conjugation of modified RNA fragments with the enzyme. AncT4_2 is expected to be a key biocatalyst in synthesizing RNA therapeutics by an enzymatic reaction. IMPORTANCE RNA therapeutics is one of the next-generation medicines for treating various diseases. Our designed ancestral RNA ligase AncT4_2 exhibited excellent enzymatic properties, such as high thermal stability, productivity, specific activity, and broad substrate selectivity compared to native enzymes. These advantages create the potential for AncT4_2 to be applied in conjugating the modified RNA fragments containing various xenobiotic nucleic acids. In addition, patisiran, a known polyneuropathy therapeutic, could be synthesized from four fragmented oligonucleotides at a preparative scale. Taken together, these findings indicate AncT4_2 could open the door to synthesizing RNA therapeutics by enzymatic reaction at large-scale production.

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.

Singular value decomposition of protein sequences as a method to visualize sequence and residue space

Singular value decomposition (SVD) of multiple sequence alignments (MSAs) is an important and rigorous method to identify subgroups of sequences within the MSA, and to extract consensus and covariance sequence features that define the alignment and distinguish the subgroups. This information can be correlated to structure, function, stability, and taxonomy. However, the mathematics of SVD is unfamiliar to many in the field of protein science. Here, we attempt to present an intuitive yet comprehensive description of SVD analysis of MSAs. We begin by describing the underlying mathematics of SVD in a way that is both rigorous and accessible. Next, we use SVD to analyze sequences generated with a simplified model in which the extent of sequence conservation and covariance between different positions is controlled, to show how conservation and covariance produce features in the decomposed coordinate system. We then use SVD to analyze alignments of two protein families, the homeodomain and the Ras superfamilies. Both families show clear evidence of sequence clustering when projected into singular value space. We use k-means clustering to group MSA sequences into specific clusters, show how the residues that distinguish these clusters can be identified, and show how these clusters can be related to taxonomy and function. We end by providing a description a set of Python scripts that can be used for SVD analysis of MSAs, displaying results, and identifying and analyzing sequence clusters. These scripts are freely available on GitHub.

Simultaneously improving the specific activity and thermostability of α-amylase BLA by rational design

Higher activity and alkaline α-amylases are desired for textile desizing and detergent additive. Here, rational design was used to improve the specific activity and thermostability of the α-amylase BLA from Bacillus licheniformis. Seventeen mutants of BLA were designed based on sequence consensus analysis and folding free energy calculation, and characterized by measuring their respective activity and thermostability at pH 8.5. Among them, mutant Q360C exhibited nearly threefold improved activity than that of wild-type and retained a higher residual activity (75% vs 59% for wild-type) after preincubation at 70 ℃ for 30 min. The modeled structures and molecular dynamics simulations analysis demonstrated that the enhanced hydrophobic interaction near residue 360 and reduced disturbance to the conformation of catalytic residues are the possible reasons for the improved thermostability and activity of Q360C. The results suggest that 360th of BLA may act as a hotspot for engineering other enzymes in the GH13 superfamily.

Thermostability engineering of industrial enzymes through structure modification

Thermostability is an essential requirement of enzymes in the industrial processes to catalyze the reactions at high temperatures; thus, enzyme engineering through directed evolution, semi-rational design and rational design are commonly employed to construct desired thermostable mutants. Several strategies are implemented to fulfill enzymes' thermostability demand including decreasing the entropy of the unfolded state through substitutions Gly → Xxx or Xxx → Pro, hydrogen bond, salt bridge, introducing two different simultaneous interactions through single mutant, hydrophobic interaction, filling the hydrophobic cavity core, decreasing surface hydrophobicity, truncating loop, aromatic-aromatic interaction and introducing positively charged residues to enzyme surface. In the current review, horizons about compatibility between secondary structures and substitutions at preferable structural positions to generate the most desirable thermostability in industrial enzymes are broadened. KEY POINTS: • Protein engineering is a powerful tool for generating thermostable industrial enzymes. • Directed evolution and rational design are practical approaches in enzyme engineering. • Substitutions in preferable structural positions can increase thermostability.

Extending the lore of curcumin as dipteran Butyrylcholine esterase (BChE) inhibitor: A holistic molecular interplay assessment

Since its origin, the emergence of vector-borne infections has taken a toll on incalculable human lives. The use of chemical insecticides is one of the early known methods of vector control and although their use is still a prevalent way to combat insect population sadly the perils of insects related transmission still persists. Most commonly, the existing insecticides face the wrath of getting resisted repeatedly, paying way to develop resilient, efficient, and cost-effective natural insecticides. In this study, computational screening was performed using homology modelling, E-pharmacophore feature mapping, molecular docking, Density Function Theory (DFT) assessment, Molecular mechanics generalized Born surface area (MM-GBSA) based binding free energy calculations and Molecular Dynamics (MD) simulation to identify a potential lead phytochemical out of a manually curated library from published literature. The protein target used under this study is insect Butyrylcholine esterase (BChE). Additionally, in vitro insect (Aedes aegypti) BChE inhibition assay was also performed with the top phytochemical identified from in silico assessments. Our research highlights that curcumin leads to inhibition of enzyme BChE of Ae. aegypti. The identified mode of action of curcumin as an insect BChE inhibitor indicates the possibility of its use as an environment friendly and natural futuristic insecticide.

Thermoadaptation in an Ancestral Diterpene Cyclase by Altered Loop Stability

Thermostability is the key to maintain the structural integrity and catalytic activity of enzymes in industrial biotechnological processes, such as terpene cyclase-mediated generation of medicines, chiral synthons, and fine chemicals. However, affording a large increase in the thermostability of enzymes through site-directed protein engineering techniques can constitute a challenge. In this paper, we used ancestral sequence reconstruction to create a hyperstable variant of the ent-copalyl diphosphate synthase PtmT2, a terpene cyclase involved in the assembly of antibiotics. Molecular dynamics simulations on the μs timescale were performed to shed light on possible molecular mechanisms contributing to activity at an elevated temperature and the large 40 °C increase in melting temperature observed for an ancestral variant of PtmT2. In silico analysis revealed key differences in the flexibility of a loop capping the active site, between extant and ancestral proteins. For the modern enzyme, the loop collapses into the active site at elevated temperatures, thus preventing biocatalysis, whereas the loop remains in a productive conformation both at ambient and high temperatures in the ancestral variant. Restoring a Pro loop residue introduced in the ancestral variant to the corresponding Gly observed in the extant protein led to reduced catalytic activity at high temperatures, with only moderate effects on the melting temperature, supporting the importance of the flexibility of the capping loop in thermoadaptation. Conversely, the inverse Gly to Pro loop mutation in the modern enzyme resulted in a 3-fold increase in the catalytic rate. Despite an overall decrease in maximal activity of ancestor compared to wild type, its increased thermostability provides a robust backbone amenable for further enzyme engineering. Our work cements the importance of loops in enzyme catalysis and provides a molecular mechanism contributing to thermoadaptation in an ancestral enzyme.

BioKIT: a versatile toolkit for processing and analyzing diverse types of sequence data

Bioinformatic analysis-such as genome assembly quality assessment, alignment summary statistics, relative synonymous codon usage, file format conversion, and processing and analysis-is integrated into diverse disciplines in the biological sciences. Several command-line pieces of software have been developed to conduct some of these individual analyses, but unified toolkits that conduct all these analyses are lacking. To address this gap, we introduce BioKIT, a versatile command line toolkit that has, upon publication, 42 functions, several of which were community-sourced, that conduct routine and novel processing and analysis of genome assemblies, multiple sequence alignments, coding sequences, sequencing data, and more. To demonstrate the utility of BioKIT, we conducted a comprehensive examination of relative synonymous codon usage across 171 fungal genomes that use alternative genetic codes, showed that the novel metric of gene-wise relative synonymous codon usage can accurately estimate gene-wise codon optimization, evaluated the quality and characteristics of 901 eukaryotic genome assemblies, and calculated alignment summary statistics for 10 phylogenomic data matrices. BioKIT will be helpful in facilitating and streamlining sequence analysis workflows. BioKIT is freely available under the MIT license from GitHub (https://github.com/JLSteenwyk/BioKIT), PyPi (https://pypi.org/project/jlsteenwyk-biokit/), and the Anaconda Cloud (https://anaconda.org/jlsteenwyk/jlsteenwyk-biokit). Documentation, user tutorials, and instructions for requesting new features are available online (https://jlsteenwyk.com/BioKIT).

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.

Making Enzymes Suitable for Organic Chemistry by Rational Protein Design

This review outlines recent developments in protein engineering of stereo‐ and regioselective enzymes, which are of prime interest in organic and pharmaceutical chemistry as well as biotechnology. The widespread application of enzymes was hampered for decades due to limited enantio‐, diastereo‐ and regioselectivity, which was the reason why most organic chemists were not interested in biocatalysis. This attitude began to change with the advent of semi‐rational directed evolution methods based on focused saturation mutagenesis at sites lining the binding pocket. Screening constitutes the labor‐intensive step (bottleneck), which is the reason why various research groups are continuing to develop techniques for the generation of small and smart mutant libraries. Rational enzyme design, traditionally an alternative to directed evolution, provides small collections of mutants which require minimal screening. This approach first focused on thermostabilization, and did not enter the field of stereoselectivity until later. Computational guides such as the Rosetta algorithms, HotSpot Wizard metric, and machine learning (ML) contribute significantly to decision making. The newest advancements show that semi‐rational directed evolution such as CAST/ISM and rational enzyme design no longer develop on separate tracks, instead, they have started to merge. Indeed, researchers utilizing the two approaches have learned from each other. Today, the toolbox of organic chemists includes enzymes, primarily because the possibility of controlling stereoselectivity by protein engineering has ensured reliability when facing synthetic challenges. This review was also written with the hope that undergraduate and graduate education will include enzymes more so than in the past.

Whole-cell Biotransformation of Penicillin G by a Three-enzyme Co-expression System with Engineered Deacetoxycephalosporin C Synthase

Deacetoxycephalosporin C synthase (DAOCS) catalyzes the tranformation of penicillin G to phenylacetyl-7-aminodeacetoxycephalosporanic acid (G-7-ADCA) in dependence on 2-oxoglutarate (2OG). However, the low activity of DAOCS and the expense of 2OG restricted the practical application in the production of G-7-ADCA. Herein, a rational design campaign was performed on a DAOCS from Streptomyces clavuligerus (scDAOCS) in the quest to construct novel expandases. The resulting mutants showed 25~58% increase in activity compared to the template. The dominant DAOCS variants were then embeded into a three-enzyme co-expression system, consisting of a catalase and a L-glutamic oxidase for the generation of 2OG, to convert penicillin G into G-7-ADCA in E. coli . The engineered whole-cell enzyme cascade was applied on an up scaled reaction, exhibiting a yield of G-7-ADCA up to 39.21 mM (14.6 g·L -1 ) with a conversion of 78.42 mol%. This work highlights the potential of the integrated whole-cell system that may inspire further research on green and efficient production of 7-ADCA.