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

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.

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.

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.

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.

Investigation of the molecular and mechanistic basis for the regioselective metabolism of midazolam by cytochrome P450 3A4

Cytochrome P450 3A4 (CYP3A4) is the most important P450 enzyme for drug metabolism and drug-drug interaction, due to it being responsible for the biotransformation of approximately 50% of clinically used drugs. Advance knowledge of the molecular and mechanistic basis of CYP3A4 regioselective metabolism is beneficial for understanding the production of metabolites, and may allow personalized metabolic pathways or designing pathway-specific therapeutics. In this work, we focus on investigating the ligand-receptor interactions, substrate conformational transition, and key factors regulating the specificity of metabolic pathways using midazolam (MDZ) as a probe. Here, three types of substrate-binding conformations related to the diversity of MDZ metabolites are identified. The results also suggest that an allosteric site for MDZ is located near the F'-helix, A-anchor, and C-terminal loop of CYP3A4. The presence of an effector in the allosteric site can accelerate the conformational transition of the substrate via modulating a "sandwich" structure, and may affect the proportion of metabolites at high substrate concentration. We hope that the results can improve the understanding of the CYP3A4 structure and function, and provide a new perspective for drug development.

Insights of conformational dynamics on catalytic activity in the computational stability design of Bacillus subtilis LipA

In protein engineering, the contributions of individual mutations to designed combinatorial mutants are unpredictable. Screening designed mutations that affect enzyme catalytic activity enables evolutions towards efficient activities. Here, Bacillus subtilis LipA (BSLA) was selected as a model protein for thermostabilization designs, and the circular dichroism measurements showed six combinatorial designs with improved stability (from 5.81 °C to 13.61 °C). Based on molecular dynamic simulations, the conformational dynamics of the mutants revealed that mutations alter the populations of conformational states and the increased ensembles of inactive conformations might lead to a reduction in activity. We further demonstrated that the mutations responsible for the reduced enzyme catalytic activity involved a short dynamic correlation path to disturbing the equilibrium conformation of active sites. By removing N82V, which had a close dynamic correlation to the active sites in mutant D3, the redesigned mutant RD3 had an increased activity of 57.6%. By combining computational simulation with experimental verification, this work established that essential sites to counteract the activity-stability trade-off in multipoint combinatorial mutants could be computationally predicted and thus provide a possible strategy by which to indirectly or directly guide protein design.

Successes and challenges in multiscale modelling of artificial metalloenzymes: the case study of POP-Rh2 cyclopropanase

Molecular modelling applications in metalloenzyme design are still scarce due to a series of challenges. On top of that, the simulations of metal-mediated binding and the identification of catalytic competent geometries require both large conformational exploration and simulation of fine electronic properties. Here, we demonstrate how the incorporation of new tools in multiscale strategies, namely substrate diffusion exploration, allows taking a step further. As a showcase, the enantioselective profiles of the most outstanding variants of an artificial Rh₂-based cyclopropanase (GSH, HFF and RFY) developed by Lewis and co-workers (Nat. Commun., 2015, 6, 7789 and Nat. Chem., 2018, 10, 318-324) have been rationalized. DFT calculations on the free-cofactor-mediated process identify the carbene insertion and the cyclopropanoid formation as crucial events, the latter being the enantiodetermining step, which displays up to 8 competitive orientations easily altered by the protein environment. The key intermediates of the reaction were docked into the protein scaffold showing that some mutated residues have direct interaction with the cofactor and/or the co-substrate. These interactions take the form of a direct coordination of Rh in GSH and HFF and a strong hydrophobic patch with the carbene moiety in RFY. Posterior molecular dynamics sustain that the cofactor induces global re-arrangements of the protein. Finally, massive exploration of substrate diffusion, based on the GPathFinder approach, defines this event as the origin of the enantioselectivity in GSH and RFY. For HFF, fine molecular dockings suggest that it is likely related to local interactions upon diffusion. This work shows how modelling of long-range mutations on the catalytic profiles of metalloenzymes may be unavoidable and software simulating substrate diffusion should be applied.

Directed Evolution of a Nonheme Diiron N-oxygenase AzoC for Improving Its Catalytic Efficiency toward Nitrogen Heterocycle Substrates

The azoxy compounds with an intriguing chemical bond [-N=N⁺(-O⁻)-] are known to have broad applications in many industries. Our previous work revealed that a nonheme diiron N-oxygenase AzoC catalyzed the oxidization of amino-group to its nitroso analogue in the formation of azoxy bond in azoxymycins biosynthesis. However, except for the reported pyridine alkaloid azoxy compounds, most azoxy bonds of nitrogen heterocycles have not been biosynthesized so far, and the substrate scope of AzoC is limited to p-aminobenzene-type compounds. Therefore, it is very meaningful to use AzoC to realize the biosynthesis of azoxy nitrogen heterocycles compounds. In this work, we further studied the catalytic potential of AzoC toward nitrogen heterocycle substrates including 5-aminopyrimidine and 5-aminopyridine compounds to form new azoxy compounds through directed evolution. We constructed a double mutant L101I/Q104R via molecular engineering with improved catalytic efficiency toward 2-methoxypyrimidin-5-amine. These mutations also proved to be beneficial for N-oxygenation of methyl 5-aminopyrimidine-2-carboxylate. The structural analysis showed that relatively shorter distance between the substrate and the diiron center and amino acid residues of the active center may be responsible for the improvement of catalytic efficiency in L101I/Q104R. Our results provide a molecular basis for broadening the AzoC catalytic activity and its application in the biosynthesis of azoxy six-membered nitrogen catenation compounds.

Unraveling the mechanism of enantio-controlling switches of an alcohol dehydrogenase toward sterically small ketone

Efficient synthesis of chiral compounds under mild conditions is highly desirable in the chemical and pharmaceutical communities, but it often faces difficulties. Although various enzymes have been harnessed as biocatalysts...

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.