Pervasive cooperative mutational effects on multiple catalytic enzyme traits emerge via long-range conformational dynamics

Continuous site mutagenesis of fatty acid photodecarboxylase enables a highly thermostable enzyme for co-solvent-free decarboxylation

Fatty acid photodecarboxylase (FAP) catalyzes the decarboxylation of carboxylic acids and their derivatives with broad substrate specificity, making it a promising biocatalyst for green biofuel production. However, its limited enzymatic stability and the low solubility of fatty acids in aqueous solutions present significant challenges for industrial applications. Moreover, the requirement for organic co-solvents further exacerbates enzyme inactivation. In this study, a critical region encompassing residues 410-429, located near the substrate binding pocket of Chlorella variabilis FAP (CvFAP) was identified. Through continuous site-directed mutagenesis, we designed 35 combinatorial mutants and ultimately obtained a continuous mutagenesis, M8-4, featuring eight mutations. Molecular dynamics analysis revealed that the M8-4 variant formed 13 additional hydrogen bonds, leading to a more stable regional conformation and improved substrate binding affinity. Consequently, the melting temperature of M8-4 increased from 49.24°C to 58.47°C, its half-life was enhanced 7-fold compared to the wild type, and its optimal reaction temperature shifted from 30 °C to 45 °C. At 50 °C, the catalytic efficiency of M8-4 was tripled compared to that of the wild type. Notably, at 55 °C, M8-4 achieved over 80 % conversion efficiency for myristic acid without requiring organic co-solvents. This study significantly improves the thermostability and catalytic efficiency of CvFAP, while reducing its reliance on co-solvents, thereby expanding its potential in biofuel applications.

Termini and substrate cavity engineering of D-Carbamoylase coupled with reduction of ammonium ion inhibition enhanced bioproduction of D-P-Hydroxyphenylglycine

Engineering of D-Carbamoylase is a promising strategy for improving Industrial production of D-Hydroxyphenylglycine (D-p-HPG). Unlike reported strategies employed, we provide a new rational engineering approach aimed at kinetic stabilization of N and C termini, and substrate cavity modifications primarily utilizing an insilico mutant screening pipeline based on changes in Gibbs free energy to further improve activity of a reported thermostable D-Carbamoylase. The best mutants in termini modifications (Q4L, A302C) and cavity modification (T212S, N202D) were combined to form an M4 variant which showed a 1.27-fold increase in activity. When co-expressed with D-Hydantoinase, the whole cells would produce 33.314 ± 0.99 g/l of D-p-HPG in 24 h. Further improvement in productivity was achieved through whole cell Calcium Alginate immobilization that reduced Ammonium ion inhibition on D-Carbamoylase. This resulted in a 4.111-fold titer increase and the highest productivity reported; 32.602 ± 2.549 g/l of D-p-HPG in 12 h. (2.717 g/l/h). Also, immobilized whole cells had operational stability, maintaining 60.68 % of the bioconversion yield of the first cycle after 6 cycles. These results therefore demonstrated a D-Carbamoylase with improved termini stability, N-Carbamoyl D-p-HPG intermediate binding and resistance to Ammonium ion inhibition which could improve industrial bioproduction and yields of D-p-HPG.

Engineering Acidic Resistance in UDP-Galactose 4-Epimerase Enables Efficient UDP-Galactose Synthesis from Sucrose

Uridine diphosphate galactose (UDP-Gal) provides galactosyl units for active carbohydrate biosynthesis; however, limited availability and high costs hamper large-scale applications. In the two-enzyme cascade system of UDP-Gal synthesis, the pH conflict between UDP-galactose 4-epimerase (GALE) and sucrose synthase (Susy) blocks UDP-Gal production. Therefore, surface charge engineering was conducted to obtain a variant (GALEM2) with improved acid resistance. GALEM2 enzyme activity reached 214.26 ± 0.20% that of wild-type GALE at pH 6.5. Its half-life time increased by 2 h at pH 6.5, and the pH resistance range was widened effectively with local surface charge reshaping and a decreased isoelectric point. An improved flexibility of the substrate entrance enhanced the catalytic performance under acidic conditions. Cascading GALEM2 and SusyM6 yielded UDP-Gal (24.5 mM) with a space-time yield of 12 g/L/h within 1.25 h, demonstrating the robust route of short reaction time and high efficiency, for rapid UDP-Gal synthesis from readily available sucrose via cascade catalysis.

Computational Investigation of the Role of Metal Center Identity in Cytochrome P450 Enzyme Model Reactivity

Mononuclear Fe enzymes such as heme-containing cytochrome P450 enzymes catalyze a variety of C-H activation reactions under ambient conditions, and they represent an attractive platform for engineering reactivity through changes to the native enzyme. Using density functional theory, we study both native Fe and non-native group 8 (Ru, Os) and group 9 (Ir) metal centers in an active site model of P450. We quantify how changing the metal changes spin state preferences throughout the catalytic cycle. Our calculations reveal an intermediate-spin ground state for all Fe intermediates while the heavier metals prefer low-spin ground states across most intermediates in the reaction cycle. We also study the rate-determining hydrogen atom transfer (HAT) step and the subsequent rebound step. We observe comparable HAT barriers for Fe and Ru, a much higher barrier for Os, and the lowest HAT barrier for Ir. Rebound steps are barrierless for all metals, and the rebound intermediate for Fe is most significantly stabilized. Examination of ground spin states of all intermediates in the reaction cycle reveals spin-allowed pathways for the group 8 metals and spin-forbidden energetics for the group 9 Ir with potential two-state reactivity. Our work highlights the differences between the group 8 metals and the group 9 Ir, and it suggests that engineered P450 enzymes with Ru in particular result in improved enzyme reactivity toward C-H hydroxylation.

Protein representations: Encoding biological information for machine learning in biocatalysis

Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations - primary sequence, 3D structure, and dynamics - to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis.

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

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

Surfactant-tolerance evolution of Bacillus clausii protease for enhancing activity and stability by reshaping the substrate access tunnel

Alkali proteases are crucial in numerous industries, especially in the laundry industry, but their inactivation by surfactants limits their effectiveness. This study employed substrate access tunnel engineering to improve the performance of WT bcPRO in surfactants. By modifying the key residues in the substrate pocket, the best variant N212S showed higher stability and activity in both AES and LAS. Molecular dynamics (MD) simulations provided insights into the enhanced stability and activity. The Asn212Ser mutation weakened the anti-correlation motion, increased the number of hydrogen bonds between amino acid residues, and made the protein structure more compact, contributing to its stability. Additionally, the mutation extended the substrate access tunnel and enabled additional interactions with the substrate, enhancing its catalytic activity in surfactants. This study demonstrates a strategy for reshaping the substrate access tunnel to improve protease stability and activity in surfactant environments, offering a promising protease candidate for the laundry industry.

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.

Rate-Engineered Plasmon-Enhanced Fluorescence for Real-Time Microsecond Dynamics of Single Biomolecules

Single-molecule fluorescence has revealed a wealth of biochemical processes but does not give access to submillisecond dynamics involved in transient interactions and molecular dynamics. Here we overcome this bottleneck and demonstrate record-high photon count rates of >107 photons/s from single plasmon-enhanced fluorophores. This is achieved by combining two conceptual novelties: first, we balance the excitation and decay rate enhancements by the antenna's volume, resulting in maximum fluorescence intensity. Second, we enhance the triplet decay rate using a multicomponent surface chemistry that minimizes microsecond blinking. We demonstrate applications to two exemplary molecular processes: we first reveal transient encounters and hybridization of DNA with a 1 μs temporal resolution. Second, we exploit the field gradient around the nanoparticle as a molecular ruler to reveal microsecond intramolecular dynamics of multivalent complexes. Our results pave the way toward real-time microsecond studies of biochemical processes using an implementation compatible with existing single-molecule fluorescence methods.

Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.

Simultaneously improving the activity and thermostability of hyperthermophillic pullulanase by modifying the active-site tunnel and surface lysine

Pullulanases are important starch-debranching enzymes that mainly hydrolyze the α-1,6-glycosidic linkages in pullulan, starch, and oligosaccharides. Nevertheless, their practical applications are constrained because of their poor activity and low thermostability. Moreover, the trade-off between activity and thermostability makes it challenging to simultaneously improve them. In this study, an engineered pullulanase was developed through reshaping the active-site tunnel and engineering the surface lysine residues using the pullulanase from Pyrococcus yayanosii CH1 (PulPY2). The specific activity of the engineered pullulanase was increased 3.1-fold, and thermostability was enhanced 1.8-fold. Moreover, the engineered pullulanase exhibited 11.4-fold improvement in catalytic efficiency (kcat/Km). Molecular dynamics simulations demonstrated an anti-correlated movement around the entrance of active-site tunnel and stronger interactions between the surface residues in the engineered pullulanase, which would be beneficial to the activity and thermostability improvement, respectively. The strategies used in this study and dynamic evidence for insight into enzyme performance improvement may provide guidance for the activity and thermostability engineering of other enzymes.

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

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

Unlocking the potential of enzyme engineering via rational computational design strategies

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

Choose Your Own Adventure: A Comprehensive Database of Reactions Catalyzed by Cytochrome P450 BM3 Variants

Cytochrome P450 BM3 monooxygenase is the topic of extensive research as many researchers have evolved this enzyme to generate a variety of products. However, the abundance of information on increasingly diversified variants of P450 BM3 that catalyze a broad array of chemistry is not in a format that enables easy extraction and interpretation. We present a database that categorizes variants by their catalyzed reactions and includes details about substrates to provide reaction context. This database of >1500 P450 BM3 variants is downloadable and machine-readable and includes instructions to maximize ease of gathering information. The database allows rapid identification of commonly reported substitutions, aiding researchers who are unfamiliar with the enzyme in identifying starting points for enzyme engineering. For those actively engaged in engineering P450 BM3, the database, along with this review, provides a powerful and user-friendly platform to understand, predict, and identify the attributes of P450 BM3 variants, encouraging the further engineering of this enzyme.

DNA Framework-Programmed Nanoscale Enzyme Assemblies

Multienzyme assemblies mediated by multivalent interaction play a crucial role in cellular processes. However, the three-dimensional (3D) programming of an enzyme complex with defined enzyme activity in vitro remains unexplored, primarily owing to limitations in precisely controlling the spatial topological configuration. Herein, we introduce a nanoscale 3D enzyme assembly using a tetrahedral DNA framework (TDF), enabling the replication of spatial topological configuration and maintenance of an identical edge-to-edge distance akin to natural enzymes. Our results demonstrate that 3D nanoscale enzyme assemblies in both two-enzyme systems (glucose oxidase (GOx)/horseradish peroxidase (HRP)) and three-enzyme systems (amylglucosidase (AGO)/GOx/HRP) lead to enhanced cascade catalytic activity compared to the low-dimensional structure, resulting in ∼5.9- and ∼7.7-fold enhancements over homogeneous diffusional mixtures of free enzymes, respectively. Furthermore, we demonstrate the enzyme assemblies for the detection of the metabolism biomarkers creatinine and creatine, achieving a low limit of detection, high sensitivity, and broad detection range.

The positive allosteric modulator BPAM344 and L-glutamate introduce an active-like structure of the ligand-binding domain of GluK2

Kainate receptors belong to the family of ionotropic glutamate receptors and contribute to the majority of fast excitatory neurotransmission. Consequently, they also play a role in brain diseases. Therefore, understanding how these receptors can be modulated is of importance. Our study provides a crystal structure of the dimeric ligand-binding domain of the kainate receptor GluK2 in complex with L-glutamate and the small-molecule positive allosteric modulator, BPAM344, in an active-like conformation. The role of Thr535 and Gln786 in modulating GluK2 by BPAM344 was investigated using a calcium-sensitive fluorescence-based assay on transiently transfected cells expressing GluK2 and mutants hereof. This study may aid in the design of compounds targeting kainate receptors, expanding their potential as targets for the treatment of brain diseases.

Verbascum species as a new source of saffron apocarotenoids and molecular tools for the biotechnological production of crocins and picrocrocin

Crocins are glucosylated apocarotenoids present in flowers and fruits of a few plant species, including saffron, gardenia, and Buddleja. The biosynthesis of crocins in these plants has been unraveled, and the enzymes engineered for the production of crocins in heterologous systems. Mullein (Verbascum sp.) has been identified as a new source of crocins and picrocrocin. In this work, we have identified eight enzymes involved in the cleavage of carotenoids in two Verbascum species, V. giganteum and V. sinuatum. Four of them were homologous to the previously identified BdCCD4.1 and BdCCD4.3 from Buddleja, involved in the biosynthesis of crocins. These enzymes were analyzed for apocarotenogenic activity in bacteria and Nicotiana benthamiana plants using a virus-driven system. Metabolic analyses of bacterial extracts and N. benthamiana leaves showed the efficient activity of these enzymes to produce crocins using β-carotene and zeaxanthin as substrates. Accumulations of 0.17% of crocins in N. benthamiana dry leaves were reached in only 2 weeks using a recombinant virus expressing VgCCD4.1, similar to the amounts previously produced using the canonical saffron CsCCD2L. The identification of these enzymes, which display a particularly broad substrate spectrum, opens new avenues for apocarotenoid biotechnological production.

Controlling Monoterpene Isomerization by Guiding Challenging Carbocation Rearrangement Reactions in Engineered Squalene-Hopene Cyclases

The interconversion of monoterpenes is facilitated by a complex network of carbocation rearrangement pathways. Controlling these isomerization pathways is challenging when using common Brønsted and Lewis acid catalysts, which often produce product mixtures that are difficult to separate. In contrast, natural monoterpene cyclases exhibit high control over the carbocation rearrangement reactions but are reliant on phosphorylated substrates. In this study, we present engineered squalene-hopene cyclases from Alicyclobacillus acidocaldarius (AacSHC) that catalyze the challenging isomerization of monoterpenes with unprecedented precision. Starting from a promiscuous isomerization of (+)-β-pinene, we first demonstrate noticeable shifts in the product distribution solely by introducing single point mutations. Furthermore, we showcase the tuneable cation steering by enhancing (+)-borneol selectivity from 1 % to >90 % (>99 % de) aided by iterative saturation mutagenesis. Our combined experimental and computational data suggest that the reorganization of key aromatic residues leads to the restructuring of the water network that facilitates the selective termination of the secondary isobornyl cation. This work expands our mechanistic understanding of carbocation rearrangements and sets the stage for target-oriented skeletal reorganization of broadly abundant terpenes.

MD simulations indicate Omicron P132H of SARS-CoV-2 Mpro is a potential allosteric mutant involved in modulating the dynamics of catalytic site entry loop

The SARS-CoV-2 main protease Mpro, essential for viral replication is an important drug target. It plays a critical role in processing viral polyproteins necessary for viral replication assembly. One of the predominant SARS-CoV-2 Mpro mutations of Omicron variant is Pro132His. Structurally, this mutation site is located ∼22 Å away from the catalytic site. The solved crystal structure of this mutant in complex with inhibitors as well as its reported catalytic efficiency did not show any difference with respect to the wild type. Thus, the mutation was concluded to be non-allosteric. Based on microsecond long MD simulation of the Pro132His mutant and wild type, we show that Pro132His mutation affects the conformational equilibrium with more population of conformational substates having open catalytic site, modulated by the dynamics of the catalytic site entry loop, implying the allosteric nature of this mutation. The structural analysis indicates that rearrangement of hydrogen bonds between His132 and adjacent residues enhances the dynamics of the linker, which in turn is augmented by the inherent dynamic flexibility of the catalytic pocket entry site due to the presence of charged residues. The altered dynamics leading to loss of secondary structures corroborate well with the reported compromised thermal stability.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

Modulation of a Loop Region in the Substrate Binding Pocket Affects the Degree of Polymerization of Bacillus subtilis Chitosanase Products

The hydrolytic products of chitosanase from Streptomyces avermitilis (SaCsn46A) were found to be aminoglucose and chitobiose, whereas those of chitosanase from Bacillus subtilis (BsCsn46A) were chitobiose and chitotriose. Therefore, the sequence alignment between SaCsn46A and BsCsn46A was conducted, revealing that the structure of BsCsn46A possesses an extra loop region (194N-200T) at the substrate binding pocket. To clarify the impact of this loop on hydrolytic properties, three mutants, SC, TJN, and TJA, were constructed. Eventually, the experimental results indicated that SC changed the ratio of chitobiose to chitotriose hydrolyzed by chitosanase from 1:1 into 2:3, while TJA resulted in a ratio of 15:7. This experiment combined molecular research to unveil a crucial loop within the substrate binding pocket of chitosanase. It also provides an effective strategy for mutagenesis and a foundation for altering hydrolysate composition and further applications in engineering chitosanase.

Noteworthy Consensus Effects of D/E Residues in Umami Peptides Used for Designing the Novel Umami Peptides

Aspartic acid (D) and glutamic acid (E) play vital roles in the umami peptides. To understand their exact mechanism of action, umami peptides were collected and cut into 1/2/3/4 fragments. Connecting D/E to the N/C-termini of the fragments formed D/E consensus effect groups (DEEGs), and all fragments containing DEEG were summarized according to the ratio and ranking obtained in the above four situations. The interaction patterns between peptides in DEEG and T1R1/T1R3-VFD were compared by statistical analysis and molecular docking, and the most conservative contacts were found to be HdB_277_ARG and HdB_148_SER. The molecular docking score of the effector peptides significantly dropped compared to that of their original peptides (-1.076 ± 0.658 kcal/mol, p value < 0.05). Six types of consensus fingerprints were set according to the Top7 contacts. The exponential of relative umami was linearly correlated with ΔGbind (R2 = 0.961). Under the D/E consensus effect, the electrostatic effect of the umami peptide was improved, and the energy gap between the highest occupied molecular orbital-the least unoccupied molecular orbital (HOMO-LUMO) was decreased. The shortest path map showed that the peptides had similar T1R1-T1R3 recognition pathways. This study helps to reveal umami perception rules and provides support for the efficient screening of umami peptides based on the material richness in D/E sequences.

Structure-guided engineering of branched-chain α-keto acid decarboxylase for improved 1,2,4-butanetriol production by in vitro synthetic enzymatic biosystem

Efficient synthetic routes for biomanufacturing chemicals often require the overcoming of pathway bottlenecks by tailoring enzymes to improve the catalytic efficiency or even implement non-native activities. 1,2,4-butanetriol (BTO), a valuable commodity chemical, is currently biosynthesized from D-xylose via a four-enzyme reaction cascade, with the ThDP-dependent α-keto acid decarboxylase (KdcA) identified as the potential bottleneck. Here, to further enhance the catalytic activity of KdcA toward the non-native substrate α-keto-3-deoxy-xylonate (KDX), in silico screening and structure-guided evolution were performed. The best mutants, S286L/G402P and V461K, exhibited a 1.8- and 2.5-fold higher enzymatic activity in the conversion of KDX to 3,4-dihydroxybutanal when compared to KdcA, respectively. MD simulations revealed that the two sets of mutations reshaped the substrate binding pocket, thereby increasing the binding affinity for KDX and promoting interactions between KDX and cofactor ThDP. Then, when the V461K mutant instead of wild type KdcA was integrated into the enzyme cascade, a 1.9-fold increase in BTO titer was observed. After optimization of the reaction conditions, the enzyme cocktail contained V461K converted 60 g/L D-xylose to 22.1 g/L BTO with a yield of 52.1 %. This work illustrated that protein engineering is a powerful tool for modifying the output of metabolic pathway.

Computational redesign of taxane-10β-hydroxylase for de novo biosynthesis of a key paclitaxel intermediate

Paclitaxel (Taxol®) is the most popular anticancer diterpenoid predominantly present in Taxus. The core skeleton of paclitaxel is highly modified, but researches on the cytochrome P450s involved in post-modification process remain exceedingly limited. Herein, the taxane-10β-hydroxylase (T10βH) from Taxus cuspidata, which is the third post-modification enzyme that catalyzes the conversion of taxadiene-5α-yl-acetate (T5OAc) to taxadiene-5α-yl-acetoxy-10β-ol (T10OH), was investigated in Escherichia coli by combining computation-assisted protein engineering and metabolic engineering. The variant of T10βH, M3 (I75F/L226K/S345V), exhibited a remarkable 9.5-fold increase in protein expression, accompanied by respective 1.3-fold and 2.1-fold improvements in turnover frequency (TOF) and total turnover number (TTN). Upon integration into the engineered strain, the variant M3 resulted in a substantial enhancement in T10OH production from 0.97 to 2.23 mg/L. Ultimately, the titer of T10OH reached 3.89 mg/L by fed-batch culture in a 5-L bioreactor, representing the highest level reported so far for the microbial de novo synthesis of this key paclitaxel intermediate. This study can serve as a valuable reference for further investigation of other P450s associated with the artificial biosynthesis of paclitaxel and other terpenoids. KEY POINTS: • The T10βH from T. cuspidata was expressed and engineered in E. coli unprecedentedly. • The expression and activity of T10βH were improved through protein engineering. • De novo biosynthesis of T10OH was achieved in E. coli with a titer of 3.89 mg/L.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

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.

Conformational Switch of the 250s Loop Enables the Efficient Transglycosylation in GH Family 77

Amylomaltases (AMs) play important roles in glycogen and maltose metabolism. However, the molecular mechanism is elusive. Here, we investigated the conformational dynamics of the 250s loop and catalytic mechanism of Thermus aquaticus TaAM using path-metadynamics and QM/MM MD simulations. The results demonstrate that the transition of the 250s loop from an open to closed conformation promotes polysaccharide sliding, leading to the ideal positioning of the acid/base. Furthermore, the conformational dynamics can also modulate the selectivity of hydrolysis and transglycosylation. The closed conformation of the 250s loop enables the tight packing of the active site for transglycosylation, reducing the energy penalty and efficiently preventing the penetration of water into the active site. Conversely, the partially closed conformation for hydrolysis results in a loosely packed active site, destabilizing the transition state. These computational findings guide mutation experiments and enable the identification of mutants with an improved disproportionation/hydrolysis ratio. The present mechanism is in line with experimental data, highlighting the critical role of conformational dynamics in regulating the catalytic reactivity of GHs.

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.

Reversing the Enantioselectivity of Enzymatic Carbene N-H Insertion Through Mechanism-Guided Protein Engineering

We report a computationally driven approach to access enantiodivergent enzymatic carbene N-H insertions catalyzed by P411 enzymes. Computational modeling was employed to rationally guide engineering efforts to control the accessible conformations of a key lactone-carbene (LAC) intermediate in the enzyme active site by installing a new H-bond anchoring point. This H-bonding interaction controls the relative orientation of the reactive carbene intermediate, orienting it for an enantioselective N-nucleophilic attack by the amine substrate. By combining MD simulations and site-saturation mutagenesis and screening targeted to only two key residues, we were able to reverse the stereoselectivity of previously engineered S-selective P411 enzymes. The resulting variant, L5_FL-B3, accepts a broad scope of amine substrates for N-H insertion with excellent yields (up to >99 %), high efficiency (up to 12 300 TTN), and good enantiocontrol (up to 7 : 93 er).

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Regulation of IL-24/IL-20R2 complex formation using photocaged tyrosines and UV light

Human interleukin 24 (IL-24) is a multifunctional cytokine that represents an important target for autoimmune diseases and cancer. Since the biological functions of IL-24 depend on interactions with membrane receptors, on-demand regulation of the affinity between IL-24 and its cognate partners offers exciting possibilities in basic research and may have applications in therapy. As a proof-of-concept, we developed a strategy based on recombinant soluble protein variants and genetic code expansion technology to photocontrol the binding between IL-24 and one of its receptors, IL-20R2. Screening of non-canonical ortho-nitrobenzyl-tyrosine (NBY) residues introduced at several positions in both partners was done by a combination of biophysical and cell signaling assays. We identified one position for installing NBY, tyrosine70 of IL-20R2, which results in clear impairment of heterocomplex assembly in the dark. Irradiation with 365-nm light leads to decaging and reconstitutes the native tyrosine of the receptor that can then associate with IL-24. Photocaged IL-20R2 may be useful for the spatiotemporal control of the JAK/STAT phosphorylation cascade.

Counteraction of stability-activity trade-off of Nattokinase through flexible region shifting

The widespread trade-off between stability and activity severely limits enzyme evolution. Although some progresses have been made to overcome this limitation, the counteraction mechanism for enzyme stability-activity trade-off remains obscure. Here, we clarified the counteraction mechanism of the Nattokinase stability-activity trade-off. A combinatorial mutant M4 was obtained by multi-strategy engineering, exhibiting a 20.7-fold improved half-life; meanwhile, the catalytic efficiency was doubled. Molecular dynamics simulation revealed that an obvious flexible region shifting in the structure of mutant M4 was occurred. The flexible region shifting which contributed to maintain the global structural flexibility, was considered to be the key factor for counteracting the stability-activity trade-off. Further analysis illustrated that the flexible region shifting was driven by region dynamical networks reshaping. This work provided deep insight into the counteraction mechanism of enzyme stability-activity trade-off, suggesting that flexible region shifting would be an effective strategy for enzyme evolution through computational protein engineering.

Tuning Selectivity in CalA Lipase: Beyond Tunnel Engineering

Engineering studies of Candida (Pseudozyma) antarctica lipase A (CalA) have demonstrated the potential of this enzyme in the selective hydrolysis of fatty acid esters of different chain lengths. CalA has been shown to bind substrates preferentially through an acyl-chain binding tunnel accessed via the hydrolytic active site; it has also been shown that selectivity for substrates of longer or shorter chain length can be tuned, for instance by modulating steric hindrance within the tunnel. Here we demonstrate that, whereas the tunnel region is certainly of paramount importance for substrate recognition, residues in distal regions of the enzyme can also modulate substrate selectivity. To this end, we investigate variants that carry one or more substitutions within the substrate tunnel as well as in distal regions. Combining experimental determination of the substrate selectivity using natural and synthetic substrates with computational characterization of protein dynamics and of tunnels, we deconvolute the effect of key substitutions and demonstrate that epistatic interactions contribute to procuring selectivity toward either long-chain or short/medium-chain fatty acid esters. We demonstrate that various mechanisms contribute to the diverse selectivity profiles, ranging from reshaping tunnel morphology and tunnel stabilization to obstructing the main substrate-binding tunnel, highlighting the dynamic nature of the substrate-binding region. This work provides important insights into the versatility of this robust lipase toward diverse applications.

Accurate Prediction of Enzyme Thermostabilization with Rosetta Using AlphaFold Ensembles

Thermostability enhancement is a fundamental aspect of protein engineering as a biocatalyst's half-life is key for its industrial and biotechnological application, particularly at high temperatures and under harsh conditions. Thermostability changes upon mutation originate from modifications of the free energy of unfolding (ΔG_u), making thermostabilization extremely challenging to predict with computational methods. In this contribution, we combine global conformational sampling with energy prediction using AlphaFold and Rosetta to develop a new computational protocol for the quantitative prediction of thermostability changes upon laboratory evolution of acyltransferase LovD and lipase LipA. We highlight how using an ensemble of protein conformations rather than a single three-dimensional model is mandatory for accurate thermostability predictions. By comparing our approaches with existing ones, we show that ensembles based on AlphaFold models provide more accurate and robust calculated thermostability trends than ensembles based solely on crystallographic structures as the latter introduce a strong distortion (scaffold bias) in computed thermostabilities. Eliminating this bias is critical for computer-guided enzyme design and evaluating the effect of multiple mutations on protein stability.

DHFR Mutants Modulate Their Synchronized Dynamics with the Substrate by Shifting Hydrogen Bond Occupancies

Antibiotic resistance is a global health problem in which mutations occurring in functional proteins render drugs ineffective. The working mechanisms of the arising mutants are seldom apparent; a methodology to decipher these mechanisms systematically would render devising therapies to control the arising mutational pathways possible. Here we utilize C_α-C_β bond vector relaxations obtained from moderate length MD trajectories to determine conduits for functionality of the resistance conferring mutants of Escherichia coli dihydrofolate reductase. We find that the whole enzyme is synchronized to the motions of the substrate, irrespective of the mutation introducing gain-of-function or loss-of function. The total coordination of the motions suggests changes in the hydrogen bond dynamics with respect to the wild type as a possible route to determine and classify the mode-of-action of individual mutants. As a result, nine trimethoprim-resistant point mutations arising frequently in evolution experiments are categorized. One group of mutants that display the largest occurrence (L28R, W30G) work directly by modifying the dihydrofolate binding region. Conversely, W30R works indirectly by the formation of the E139-R30 salt bridge which releases energy resulting from tight binding by distorting the binding cavity. A third group (D27E, F153S, I94L) arising as single, resistance invoking mutants in evolution experiment trajectories allosterically and dynamically affects a hydrogen bonding motif formed at residues 59-69-71 which in turn modifies the binding site dynamics. The final group (I5F, A26T, R98P) consists of those mutants that have properties most similar to the wild type; these only appear after one of the other mutants is fixed on the protein structure and therefore display clear epistasis. Thus, we show that the binding event is governed by the entire enzyme dynamics while the binding site residues play gating roles. The adjustments made in the total enzyme in response to point mutations are what make quantifying and pinpointing their effect a hard problem. Here, we show that hydrogen bond dynamics recorded on sub-μs time scales provide the necessary fingerprints to decipher the various mechanisms at play.

Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin

CYP105AS1 is a cytochrome P450 from Amycolatopsis orientalis that catalyzes monooxygenation of compactin to 6-epi-pravastatin. For fermentative production of the cholesterol-lowering drug pravastatin, the stereoselectivity of the enzyme needs to be inverted, which has been partially achieved by error-prone PCR mutagenesis and screening. In the current study, we report further optimization of the stereoselectivity by a computationally aided approach. Using the CoupledMoves protocol of Rosetta, a virtual library of mutants was designed to bind compactin in a pro-pravastatin orientation. By examining the frequency of occurrence of beneficial substitutions and rational inspection of their interactions, a small set of eight mutants was predicted to show the desired selectivity and these variants were tested experimentally. The best CYP105AS1 variant gave >99% stereoselective hydroxylation of compactin to pravastatin, with complete elimination of the unwanted 6-epi-pravastatin diastereomer. The enzyme-substrate complexes were also examined by ultrashort molecular dynamics simulations of 50 × 100 ps and 5 × 22 ns, which revealed that the frequency of occurrence of near-attack conformations agreed with the experimentally observed stereoselectivity. These results show that a combination of computational methods and rational inspection could improve CYP105AS1 stereoselectivity beyond what was obtained by directed evolution. Moreover, the work lays out a general in silico framework for specificity engineering of enzymes of known structure.

Molecular Insights into the Heterotropic Allosteric Mechanism in Cytochrome P450 3A4-Mediated Midazolam Metabolism

Cytochrome P450 3A4 (CYP3A4) is the main P450 enzyme for drug metabolism and drug-drug interactions (DDIs), as it is involved in the metabolic process of approximately 50% of drugs. A detailed mechanistic elucidation of DDIs mediated by CYP3A4 is commonly believed to be critical for drug optimization and rational use. Here, two typical probes, midazolam (MDZ, substrate) and testosterone (TST, allosteric effector), are used to investigate the molecular mechanism of CYP3A4-mediated heterotropic allosteric interactions, through conventional molecular dynamics (cMD) and well-tempered metadynamics (WT-MTD) simulations. Distance monitoring shows that TST can stably bind in two potential peripheral sites (Site 1 and Site 2) of CYP3A4. The binding of TST at these two sites can induce conformational changes in CYP3A4 flexible loops on the basis of conformational analysis, thereby promoting the transition of the MDZ binding mode and affecting the ratio of MDZ metabolites. According to the results of the residue interaction network, multiple allosteric communication pathways are identified that can provide vivid and applicable insights into the heterotropic allostery of TST on MDZ metabolism. Comparing the regulatory effects and the communication pathways, the allosteric effect caused by TST binding in Site 2 seems to be more pronounced than in Site 1. Our findings could provide a deeper understanding of CYP3A4-mediated heterotropic allostery at the atomic level and would be helpful for rational drug use as well as the design of new allosteric modulators.

NMR-guided directed evolution

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

Energy landscape reshaped by strain-specific mutations underlies epistasis in NS1 evolution of influenza A virus

Elucidating how individual mutations affect the protein energy landscape is crucial for understanding how proteins evolve. However, predicting mutational effects remains challenging because of epistasis—the nonadditive interactions between mutations. Here, we investigate the biophysical mechanism of strain-specific epistasis in the nonstructural protein 1 (NS1) of influenza A viruses (IAVs). We integrate structural, kinetic, thermodynamic, and conformational dynamics analyses of four NS1s of influenza strains that emerged between 1918 and 2004. Although functionally near-neutral, strain-specific NS1 mutations exhibit long-range epistatic interactions with residues at the p85β-binding interface. We reveal that strain-specific mutations reshaped the NS1 energy landscape during evolution. Using NMR spin dynamics, we find that the strain-specific mutations altered the conformational dynamics of the hidden network of tightly packed residues, underlying the evolution of long-range epistasis. This work shows how near-neutral mutations silently alter the biophysical energy landscapes, resulting in diverse background effects during molecular evolution.

Influenza A virus (IAV) nonstructural protein 1 (NS1) is a multifunctional virulence factor that interacts with several host factors such as phosphatidylinositol-3-kinase (PI3K). NS1 binds specifically to the p85β regulatory subunit of PI3K and subsequently activates PI3K signaling. Here, Kim et al. show that functionally near-neutral, strain-specific NS1 mutations lead to variations in binding kinetics to p85β exhibit long-range epistatic interactions. Applying NMR they provide evidence that the structural dynamics of the NS1 hydrophobic core have evolved over time and contributed to epistasis.

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.

Venus: Elucidating the Impact of Amino Acid Variants on Protein Function Beyond Structure Destabilisation

Exploring the functional effect of a non-synonymous coding variant at the protein level requires multiple pieces of information to be interpreted appropriately. This is particularly important when embarking on the study of a potentially pathogenic variant linked to a rare or monogenic disease. Whereas accurate protein stability predictions alone are generally informative, other effects, such as disruption of post-translational modifications or weakened ligand binding, may also contribute to the disease phenotype. Furthermore, consideration of nearby variants that are found in the healthy population may strengthen or refute a given mechanistic hypothesis. Whilst there are several bioinformatics tools available that score a genetic variant in terms of deleteriousness, there is no single tool that assembles multiple effects of a variant on the encoded protein, beyond structural stability, and presents them on the structure for inspection. Venus is a web application which, given a protein substitution, rapidly estimates the predicted effect on protein stability of the variant, flags if the variant affects a post-translational modification site, a predicted linear motif or known annotation, and determines the effect on protein stability of variants which affect nearby residues and have been identified in healthy populations. Venus is built upon Michelanglo and the results can be exported to it, allowing them to be annotated and shared with other researchers. Venus is freely accessible at https://venus.cmd.ox.ac.uk and its source code is openly available at https://github.com/CMD-Oxford/Michelanglo-and-Venus.