Computational design of carboxylase for the synthesis of 4-hydroxyisophthalic acid from p-hydroxybenzoic acid by fixing CO2.

Carbon dioxide (CO2) emissions constitute the primary contribution to global climate change. Synthetic CO2 fixation represents an exceptionally appealing and sustainable method for carbon neutralization. Unlike the limitations of chemical catalysis, biological CO2 fixation displays high selectivity and the ability to operate under mild conditions. The superfamily of amidohydrolases has demonstrated the ability to synthesize a range of aromatic monocarboxylic acids. However, there is a scarcity of reported carboxylases capable of synthesizing aromatic dicarboxylic acids. Among these, 4-hydroxyisophthalic acid holds significant potential for applications across various fields, yet no enzyme has been reported for its synthesis. In this study, we developed for the first time that exhibits starting activity in fixing CO2 to synthesize 4-hydroxyisophthalic acid. Furthermore, we have devised a computational strategy that effectively enhances the catalytic activity of this enzyme. A focused library comprising only 13 variants was generated. Experimental validation confirmed a threefold improvement in the carboxylation activity of the optimal variant (L47M). The computational enzyme design strategy proposed in this paper demonstrates broad applicability in developing carboxylases for synthesizing other aromatic dicarboxylic acids. This lays the groundwork for leveraging biocatalysis in industrial synthesis for CO2 fixation.

Ancestral terpene cyclases: From fundamental science to applications in biosynthesis.

Terpenes constitute one of the largest family of natural products with potent applications as renewable platform chemicals and medicines. The low activity, selectivity and stability displayed by terpene biosynthetic machineries can constitute an obstacle towards achieving expedient biosynthesis of terpenoids in processes that adhere to the 12 principles of green chemistry. Accordingly, engineering of terpene synthase enzymes is a prerequisite for industrial biotechnology applications, but obstructed by their complex catalysis that depend on reactive carbocationic intermediates that are prone to undergo bifurcation mechanisms. Rational redesign of terpene synthases can be tedious and requires high-resolution structural information, which is not always available. Furthermore, it has proven difficult to link sequence space of terpene synthase enzymes to specific product profiles. Herein, the author shows how ancestral sequence reconstruction (ASR) can favorably be used as a protein engineering tool in the redesign of terpene synthases without the need of a structure, and without excessive screening. A detailed workflow of ASR is presented along with associated limitations, with a focus on applying this methodology on terpene synthases. From selected examples of both class I and II enzymes, the author advocates that ancestral terpene cyclases constitute valuable assets to shed light on terpene-synthase catalysis and in enabling accelerated biosynthesis.

Multiple Cofactor Engineering Strategies to Enhance Pyridoxine Production in Escherichia coli.

Pyridoxine, also known as vitamin B6, is an essential cofactor in numerous cellular processes. Its importance in various applications has led to a growing interest in optimizing its production through microbial biosynthesis. However, an imbalance in the net production of NADH disrupts intracellular cofactor levels, thereby limiting the efficient synthesis of pyridoxine. In our study, we focused on multiple cofactor engineering strategies, including the enzyme design involved in NAD+-dependent enzymes and NAD+ regeneration through the introduction of heterologous NADH oxidase (Nox) coupled with the reduction in NADH production during glycolysis. Finally, the engineered E. coli achieved a pyridoxine titer of 676 mg/L in a shake flask within 48 h by enhancing the driving force. Overall, the multiple cofactor engineering strategies utilized in this study serve as a reference for enhancing the efficient biosynthesis of other target products.

Design and Evolution of an Enzyme for the Asymmetric Michael Addition of Cyclic Ketones to Nitroolefins by Enamine Catalysis.

Consistent introduction of novel enzymes is required for developing efficient biocatalysts for challenging biotransformations. Absorbing catalytic modes from organocatalysis may be fruitful for designing new-to-nature enzymes with novel functions. Herein we report a newly designed artificial enzyme harboring a catalytic pyrrolidine residue that catalyzes the asymmetric Michael addition of cyclic ketones to nitroolefins through enamine activation with high efficiency. Diverse chiral γ-nitro cyclic ketones with two stereocenters were efficiently prepared with excellent stereoselectivity (up to 97 % e.e., >20 : 1 d.r.) and good yield (up to 86 %). This work provides an efficient biocatalytic strategy for cyclic ketone functionalization, and highlights the usefulness of artificial enzymes for extending biocatalysis to further non-natural reactions.

Descriptor-augmented machine learning for enzyme-chemical interaction predictions.

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.

The shortest path method (SPM) webserver for computational enzyme design.

SPMweb is the online webserver of the Shortest Path Map (SPM) tool for identifying the key conformationally-relevant positions of a given enzyme structure and dynamics. The server is built on top of the DynaComm.py code and enables the calculation and visualization of the SPM pathways. SPMweb is easy-to-use as it only requires three input files: the three-dimensional structure of the protein of interest, and the two matrices (distance and correlation) previously computed from a Molecular Dynamics simulation. We provide in this publication information on how to generate the files for SPM construction even for non-expert users and discuss the most relevant parameters that can be modified. The tool is extremely fast (it takes less than one minute per job), thus allowing the rapid identification of distal positions connected to the active site pocket of the enzyme. SPM applications expand from computational enzyme design, especially if combined with other tools to identify the preferred substitution at the identified position, but also to rationalizing allosteric regulation, and even cryptic pocket identification for drug discovery. The simple user interface and setup make the SPM tool accessible to the whole scientific community. SPMweb is freely available for academia at http://spmosuna.com/.

Carving out a Glycoside Hydrolase Active Site for Incorporation into a New Protein Scaffold Using Deep Network Hallucination.

Enzymes are indispensable biocatalysts for numerous industrial applications, yet stability, selectivity, and restricted substrate recognition present limitations for their use. Despite the importance of enzyme engineering in overcoming these limitations, success is often challenged by the intricate architecture of enzymes derived from natural sources. Recent advances in computational methods have enabled the de novo design of simplified scaffolds with specific functional sites. Such scaffolds may be advantageous as platforms for enzyme engineering. Here, we present a strategy for the de novo design of a simplified scaffold of an endo-α-N-acetylgalactosaminidase active site, a glycoside hydrolase from the GH101 enzyme family. Using a combination of trRosetta hallucination, iterative cycles of deep-learning-based structure prediction, and ProteinMPNN sequence design, we designed proteins with 290 amino acids incorporating the active site while reducing the molecular weight by over 100 kDa compared to the initial endo-α-N-acetylgalactosaminidase. Of 11 tested designs, six were expressed as soluble monomers, displaying similar or increased thermostabilities compared to the natural enzyme. Despite lacking detectable enzymatic activity, the experimentally determined crystal structures of a representative design closely matched the design with a root-mean-square deviation of 1.0 Å, with most catalytically important side chains within 2.0 Å. The results highlight the potential of scaffold hallucination in designing proteins that may serve as a foundation for subsequent enzyme engineering.

De novo design of a polycarbonate hydrolase.

Enzymatic degradation of plastics is currently limited to the use of engineered natural enzymes. As of yet, all engineering approaches applied to plastic degrading enzymes retain the natural $\alpha /\beta $-fold. While mutations can be used to increase thermostability, an inherent maximum likely exists for the $\alpha /\beta $-fold. It is thus of interest to introduce catalytic activity toward plastics in a different protein fold to escape the sequence space of plastic degrading enzymes. Here, a method for designing highly thermostable enzymes that can degrade plastics is described. With the help of Rosetta an active site catalysing the hydrolysis of polycarbonate is introduced into a set of thermostable scaffolds. Through computational evaluation, a potential PCase was selected and produced recombinantly in Escherichia coli. Thermal analysis suggests that the design has a melting temperature of >95$^{\circ }$C. Activity toward polycarbonate was confirmed using atomic force spectroscopy (AFM), proving the successful design of a PCase.

Design of efficient artificial enzymes using crystallographically-enhanced conformational sampling.

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing catalytic efficiency of de novo enzymes to a level comparable to their natural counterparts without relying on directed evolution. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models. Our work shows how computational design can generate efficient artificial enzymes by exploiting the true conformational ensemble to more effectively stabilize the transition state.

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

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

Mechanisms and Opportunities for Rational In Silico Design of Enzymes to Degrade Per- and Polyfluoroalkyl Substances (PFAS).

Per and polyfluoroalkyl substances (PFAS) present a unique challenge to remediation techniques because their strong carbon-fluorine bonds make them difficult to degrade. This review explores the use of in silico enzymatic design as a potential PFAS degradation technique. The scope of the enzymes included is based on currently known PFAS degradation techniques, including chemical redox systems that have been studied for perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) defluorination, such as those that incorporate hydrated electrons, sulfate, peroxide, and metal catalysts. Bioremediation techniques are also discussed, namely the laccase and horseradish peroxidase systems. The redox potential of known reactants and enzymatic radicals/metal-complexes are then considered and compared to potential enzymes for degrading PFAS. The molecular structure and reaction cycle of prospective enzymes are explored. Current knowledge and techniques of enzyme design, particularly radical-generating enzymes, and application are also discussed. Finally, potential routes for bioengineering enzymes to enable or enhance PFAS remediation are considered as well as the future outlook for computational exploration of enzymatic in situ bioremediation routes for these highly persistent and globally distributed contaminants.

Characterization and designing of an SAM riboswitch to establish a high-throughput screening platform for SAM overproduction in Saccharomyces cerevisiae.

S-adenosyl- l-methionine (SAM) is a high-value compound widely used in the treatment of various diseases. SAM can be produced through fermentation, but further enhancing the microbial production of SAM requires novel high-throughput screening methods for rapid detection and screening of mutant libraries. In this work, an SAM-OFF riboswitch capable of responding to the SAM concentration was obtained and a high-throughput platform for screening SAM overproducers was established. SAM synthase was engineered by semirational design and directed evolution, which resulted in the SAM2S203F,W164R,T251S,Y285F,S365R mutant with almost twice higher catalytic activity than the parental enzyme. The best mutant was then introduced into Saccharomyces cerevisiae BY4741, and the resulting strain BSM8 produced a sevenfold higher SAM titer in shake-flask fermentation, reaching 1.25 g L-1 . This work provides a reference for designing biosensors to dynamically detect metabolite concentrations for high-throughput screening and the construction of effective microbial cell factories.

Engineering of Therapeutic and Detoxifying Enzymes.

Therapeutic enzymes present excellent opportunities for the treatment of human disease, modulation of metabolic pathways and system detoxification. However, current use of enzyme therapy in the clinic is limited as naturally occurring enzymes are seldom optimal for such applications and require substantial improvement by protein engineering. Engineering strategies such as design and directed evolution that have been successfully implemented for industrial biocatalysis can significantly advance the field of therapeutic enzymes, leading to biocatalysts with new-to-nature therapeutic activities, high selectivity, and suitability for medical applications. This minireview highlights case studies of how state-of-the-art and emerging methods in protein engineering are explored for the generation of therapeutic enzymes and discusses gaps and future opportunities in the field of enzyme therapy.

Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.

Thermal stability is one of the most important properties of enzymes, which sustains life and determines the potential for the industrial application of biocatalysts. Although traditional methods such as directed evolution and classical rational design contribute greatly to this field, the enormous sequence space of proteins implies costly and arduous experiments. The development of enzyme engineering focuses on automated and efficient strategies because of the breakthrough of high-throughput DNA sequencing and machine learning models. In this review, we propose a data-driven architecture for enzyme thermostability engineering and summarize some widely adopted datasets, as well as machine learning-driven approaches for designing the thermal stability of enzymes. In addition, we present a series of existing challenges while applying machine learning in enzyme thermostability design, such as the data dilemma, model training, and use of the proposed models. Additionally, a few promising directions for enhancing the performance of the models are discussed. We anticipate that the efficient incorporation of machine learning can provide more insights and solutions for the design of enzyme thermostability in the coming years.

A review of enzyme design in catalytic stability by artificial intelligence.

The design of enzyme catalytic stability is of great significance in medicine and industry. However, traditional methods are time-consuming and costly. Hence, a growing number of complementary computational tools have been developed, e.g. ESMFold, AlphaFold2, Rosetta, RosettaFold, FireProt, ProteinMPNN. They are proposed for algorithm-driven and data-driven enzyme design through artificial intelligence (AI) algorithms including natural language processing, machine learning, deep learning, variational autoencoder/generative adversarial network, message passing neural network (MPNN). In addition, the challenges of design of enzyme catalytic stability include insufficient structured data, large sequence search space, inaccurate quantitative prediction, low efficiency in experimental validation and a cumbersome design process. The first principle of the enzyme catalytic stability design is to treat amino acids as the basic element. By designing the sequence of an enzyme, the flexibility and stability of the structure are adjusted, thus controlling the catalytic stability of the enzyme in a specific industrial environment or in an organism. Common indicators of design goals include the change in denaturation energy (ΔΔG), melting temperature (ΔTm), optimal temperature (Topt), optimal pH (pHopt), etc. In this review, we summarized and evaluated the enzyme design in catalytic stability by AI in terms of mechanism, strategy, data, labeling, coding, prediction, testing, unit, integration and prospect.

Structural and Mechanistic Studies on Substrate and Stereoselectivity of the Indole Monooxygenase VpIndA1: New Avenues for Biocatalytic Epoxidations and Sulfoxidations.

Flavoprotein monooxygenases are a versatile group of enzymes for biocatalytic transformations. Among these, group E monooxygenases (GEMs) catalyze enantioselective epoxidation and sulfoxidation reactions. Here, we describe the crystal structure of an indole monooxygenase from the bacterium Variovorax paradoxus EPS, a GEM designated as VpIndA1. Complex structures with substrates reveal productive binding modes that, in conjunction with force-field calculations and rapid mixing kinetics, reveal the structural basis of substrate and stereoselectivity. Structure-based redesign of the substrate cavity yielded variants with new substrate selectivity (for sulfoxidation of benzyl phenyl sulfide) or with greatly enhanced stereoselectivity (from 35.1 % to 99.8 % ee for production of (1S,2R)-indene oxide). This first determination of the substrate binding mode of GEMs combined with structure-function relationships opens the door for structure-based design of these powerful biocatalysts.

Frankenstein Cas9: engineering improved gene editing systems.

The discovery of CRISPR-Cas9 and its widespread use has revolutionised and propelled research in biological sciences. Although the ability to target Cas9's nuclease activity to specific sites via an easily designed guide RNA (gRNA) has made it an adaptable gene editing system, it has many characteristics that could be improved for use in biotechnology. Cas9 exhibits significant off-target activity and low on-target nuclease activity in certain contexts. Scientists have undertaken ambitious protein engineering campaigns to bypass these limitations, producing several promising variants of Cas9. Cas9 variants with improved and alternative activities provide exciting new tools to expand the scope and fidelity of future CRISPR applications.

Design and optimization of enzymatic activity in a de novo ß-barrel scaffold.

While native scaffolds offer a large diversity of shapes and topologies for enzyme engineering, their often unpredictable behavior in response to sequence modification makes de novo generated scaffolds an exciting alternative. Here we explore the customization of the backbone and sequence of a de novo designed eight stranded ß-barrel protein to create catalysts for a retro-aldolase model reaction. We show that active and specific catalysts can be designed in this fold and use directed evolution to further optimize activity and stereoselectivity. Our results support previous suggestions that different folds have different inherent amenability to evolution and this property could account, in part, for the distribution of natural enzymes among different folds.

Estimating conformational heterogeneity of tryptophan synthase with a template-based Alphafold2 approach.

The three-dimensional structure of the enzymes provides very relevant information on the arrangement of the catalytic machinery and structural elements gating the active site pocket. The recent success of the neural network Alphafold2 in predicting the folded structure of proteins from the primary sequence with high levels of accuracy has revolutionized the protein design field. However, the application of Alphafold2 for understanding and engineering function directly from the obtained single static picture is not straightforward. Indeed, understanding enzymatic function requires the exploration of the ensemble of thermally accessible conformations that enzymes adopt in solution. In the present study, we evaluate the potential of Alphafold2 in assessing the effect of the mutations on the conformational landscape of the beta subunit of tryptophan synthase (TrpB). Specifically, we develop a template-based Alphafold2 approach for estimating the conformational heterogeneity of several TrpB enzymes, which is needed for enhanced stand-alone activity. Our results show the potential of Alphafold2, especially if combined with molecular dynamics simulations, for elucidating the changes induced by mutation in the conformational landscapes at a rather reduced computational cost, thus revealing its plausible application in computational enzyme design.

Efficient Base-Catalyzed Kemp Elimination in an Engineered Ancestral Enzyme.

The routine generation of enzymes with completely new active sites is a major unsolved problem in protein engineering. Advances in this field have thus far been modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral ß-lactamase with a new active site that is capable of efficiently catalyzing Kemp elimination. The engineering of our Kemp eliminase involved minimalist design based on a single function-generating mutation, inclusion of an extra polypeptide segment at a position close to the de novo active site, and sharply focused, low-throughput library screening. Nevertheless, its catalytic parameters (kcat/KM~2·105 M-1 s-1, kcat~635 s-1) compare favorably with the average modern natural enzyme and match the best proton-abstraction de novo Kemp eliminases that are reported in the literature. The general implications of our results for de novo enzyme engineering are discussed.