De novo design of luciferases using deep learning

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.

Rationally seeded computational protein design of ɑ-helical barrels

Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

Cellular and Biophysical Applications of Genetic Code Expansion

Despite their diverse functions, proteins are inherently constructed from a limited set of building blocks. These compositional constraints pose significant challenges to protein research and its practical applications. Strategically manipulating the cellular protein synthesis system to incorporate novel building blocks has emerged as a critical approach for overcoming these constraints in protein research and application. In the past two decades, the field of genetic code expansion (GCE) has achieved significant advancements, enabling the integration of numerous novel functionalities into proteins across a variety of organisms. This technological evolution has paved the way for the extensive application of genetic code expansion across multiple domains, including protein imaging, the introduction of probes for protein research, analysis of protein-protein interactions, spatiotemporal control of protein function, exploration of proteome changes induced by external stimuli, and the synthesis of proteins endowed with novel functions. In this comprehensive Review, we aim to provide an overview of cellular and biophysical applications that have employed GCE technology over the past two decades.

Exploring the Potential of Arginine to Increase Coelenterazine-Renilla Luciferase Affinity and Enzyme Stability: Kinetic and Molecular Dynamics Studies

Renilla luciferase catalyzes the oxidation of coelenterazine to coelenteramide and results in the emission of a photon of light. Although Renilla luciferase has various applications in biotechnology, its low thermal stability limits the development of its applications. Arginine is a well-known stabilizing amino acid that plays a key role in protein stabilization against inactivation. However, its impact on enzyme properties is unpredictable. This study investigates the impact of arginine on the kinetics and thermal stability of Renilla luciferase. The enzyme's performance was significantly enhanced in the presence of arginine, with catalytic efficiency increasing by 3.31-fold and 3.08-fold when exposed to 0.2 M and 0.3 M arginine, respectively. Additionally, arginine improved the thermal stability of Renilla luciferase. Molecular dynamics simulation showed that the addition of 0.2 M arginine reduced the binding of coelenteramide, the reaction product and an enzyme inhibitor, to the active site of the Renilla luciferase. Therefore, the release of the product was accelerated, and the affinity of Renilla luciferase for coelenterazine increased. Furthermore, Molecular dynamics studies indicated an increased network of water molecules surrounding Renilla luciferase in the presence of 0.2 M arginine. This network potentially enhances the hydrophobic effect on the protein structure, ultimately improving enzyme stability. The findings of this study hold promise for the development of commercial kits incorporating Renilla luciferase.

Generative artificial intelligence for de novo protein design

Engineering new molecules with desirable functions and properties has the potential to extend our ability to engineer proteins beyond what nature has so far evolved. Advances in the so-called 'de novo' design problem have recently been brought forward by developments in artificial intelligence. Generative architectures, such as language models and diffusion processes, seem adept at generating novel, yet realistic proteins that display desirable properties and perform specified functions. State-of-the-art design protocols now achieve experimental success rates nearing 20%, thus widening the access to de novo designed proteins. Despite extensive progress, there are clear field-wide challenges, for example, in determining the best in silico metrics to prioritise designs for experimental testing, and in designing proteins that can undergo large conformational changes or be regulated by post-translational modifications. With an increase in the number of models being developed, this review provides a framework to understand how these tools fit into the overall process of de novo protein design. Throughout, we highlight the power of incorporating biochemical knowledge to improve performance and interpretability.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets

Designing small-molecule-binding proteins, such as enzymes and biosensors, is essential in protein biology and bioengineering. Generating high-fidelity protein pockets-areas where proteins interact with ligand molecules-is challenging due to the complex interactions between ligand molecules and proteins, the flexibility of ligand molecules and amino acid side chains, and intricate sequence-structure dependencies. We introduce PocketGen, a deep generative method that produces the residue sequence and the full-atom structure within the protein pocket region, leveraging sequence-structure consistency. PocketGen comprises a bilevel graph transformer for structural encoding and a sequence refinement module utilizing a protein language model (pLM) for sequence prediction. The bilevel graph transformer captures interactions at multiple granularities (atom-level and residue/ligand-level) and aspects (intra-protein and protein-ligand) through bilevel attention mechanisms. A structural adapter employing cross-attention is integrated into the pLM for sequence refinement to ensure consistency between structure-based and sequence-based prediction. During training, only the adapter is fine-tuned, while the other layers of the pLM remain unchanged. Experiments demonstrate that PocketGen can efficiently generate protein pockets with higher binding affinity and validity than state-of-the-art methods. PocketGen is ten times faster than physics-based methods and achieves a 95% success rate (percentage of generated pockets with higher binding affinity than reference pockets) with an amino acid recovery rate exceeding 64%.

The Development and Opportunities of Predictive Biotechnology

Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.

Selection of a promiscuous minimalist cAMP phosphodiesterase from a library of de novo designed proteins

The ability of unevolved amino acid sequences to become biological catalysts was key to the emergence of life on Earth. However, billions of years of evolution separate complex modern enzymes from their simpler early ancestors. To probe how unevolved sequences can develop new functions, we use ultrahigh-throughput droplet microfluidics to screen for phosphoesterase activity amidst a library of more than one million sequences based on a de novo designed 4-helix bundle. Characterization of hits revealed that acquisition of function involved a large jump in sequence space enriching for truncations that removed >40% of the protein chain. Biophysical characterization of a catalytically active truncated protein revealed that it dimerizes into an α-helical structure, with the gain of function accompanied by increased structural dynamics. The identified phosphodiesterase is a manganese-dependent metalloenzyme that hydrolyses a range of phosphodiesters. It is most active towards cyclic AMP, with a rate acceleration of ~109 and a catalytic proficiency of >1014 M-1, comparable to larger enzymes shaped by billions of years of evolution.

Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications

Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.

Engineering biomaterials for the recovery of rare earth elements

The escalating global demand for rare earth elements (REEs) and the overabundance of REE-containing waste require innovative technologies for REE recovery from waste to achieve a sustainable supply of REEs while reducing the environmental burden. Biosorption mediated by peptides or proteins has emerged as a promising approach for selective REE recovery. To date, multiple peptides and proteins with high REE-binding affinity and selectivity have been discovered, and various strategies are being exploited to engineer robust and reusable biosorptive materials for selective REE recovery. This review highlights recent advances in discovering and engineering peptides and proteins for REE recovery. Future research prospects and challenges are also discussed.

The Damietta Server: a comprehensive protein design toolkit

The growing importance of protein design to various research disciplines motivates the development of integrative computational platforms that enhance the accessibility and interoperability of different design tools. To this end, we describe a web-based toolkit that builds on the Damietta protein design engine, which deploys a tensorized energy calculation framework. The Damietta Server seamlessly integrates different design tools, in addition to other tools such as message-passing neural networks and molecular dynamics routines, allowing the user to perform multiple operations on structural models and forward them across tools. The toolkit can be used for tasks such as core or interface design, symmetric design, mutagenic scanning, or conformational sampling, through an intuitive user interface. With the envisioned integration of more tools, the Damietta Server will provide a central resource for protein design and analysis, benefiting basic and applied biomedical research communities. The toolkit is available with no login requirement through https://damietta.de/.

Automated in vivo enzyme engineering accelerates biocatalyst optimization

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Using Protein Design and Directed Evolution to Monomerize a Bright Near-Infrared Fluorescent Protein

The small ultrared fluorescent protein (smURFP) is a bright near-infrared (NIR) fluorescent protein (FP) that forms a dimer and binds its fluorescence chromophore, biliverdin, at its dimer interface. To engineer a monomeric NIR FP based on smURFP potentially more suitable for bioimaging, we employed protein design to extend the protein backbone with a new segment of two helices that shield the original dimer interface while covering the biliverdin binding pocket in place of the second chain in the original dimer. We experimentally characterized 13 designs and obtained a monomeric protein with a weak fluorescence. We enhanced the fluorescence of this designed protein through two rounds of directed evolution and obtained designed monomeric smURFP (DMsmURFP), a bright, stable, and monomeric NIR FP with a molecular weight of 19.6 kDa. We determined the crystal structures of DMsmURFP both in the apo state and in complex with biliverdin, which confirmed the designed structure. The use of DMsmURFP in in vivo imaging of mammalian systems was demonstrated. The backbone design-based strategy used here can also be applied to monomerize other naturally multimeric proteins with intersubunit functional sites.

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 the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. 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, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.

Opportunities and challenges in design and optimization of protein function

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.

Bioluminescence as a functional tool for visualizing and controlling neuronal activity in vivo

The use of bioluminescence as a reporter for physiology in neuroscience is as old as the discovery of the calcium-dependent photon emission of aequorin. Over the years, luciferases have been largely replaced by fluorescent reporters, but recently, the field has seen a renaissance of bioluminescent probes, catalyzed by unique developments in imaging technology, bioengineering, and biochemistry to produce luciferases with previously unseen colors and intensity. This is not surprising as the advantages of bioluminescence make luciferases very attractive for noninvasive, longitudinal in vivo observations without the need of an excitation light source. Here, we review how the development of dedicated and specific sensor-luciferases afforded, among others, transcranial imaging of calcium and neurotransmitters, or cellular metabolites and physical quantities such as forces and membrane voltage. Further, the increased versatility and light output of luciferases have paved the way for a new field of functional bioluminescence optogenetics, in which the photon emission of the luciferase is coupled to the gating of a photosensor, e.g., a channelrhodopsin and we review how they have been successfully used to engineer synthetic neuronal connections. Finally, we provide a primer to consider important factors in setting up functional bioluminescence experiments, with a particular focus on the genetic model Caenorhabditis elegans, and discuss the leading challenges that the field needs to overcome to regain a competitive advantage over fluorescence modalities. Together, our paper caters to experienced users of bioluminescence as well as novices who would like to experience the advantages of luciferases in their own hand.

Recent advances in bioluminescent probes for neurobiology

Bioluminescence is a popular modality for imaging in living organisms. The platform relies on enzymatically (luciferase) generated light via the oxidation of small molecule luciferins. Since no external light is needed for photon production, there are no concerns with background autofluorescence or photobleaching over time-features that have historically limited other optical readouts. Bioluminescence is thus routinely used for longitudinal tracking across whole animals. Applications in the brain, though, have been more challenging due to a lack of sufficiently bioavailable, bright, and easily multiplexed probes. Recent years have seen the development of designer luciferase and luciferin pairs that address these issues, providing more sensitive and real-time readouts of biochemical features relevant to neurobiology. This review highlights many of the advances in bioluminescent probe design, with a focus on the small molecule light emitter, the luciferin. Specific efforts to improve luciferin pharmacokinetics and tissue-penetrant emission are covered, in addition to applications that such probes have enabled. The continued development of improved bioluminescent probes will aid in illuminating critical neurochemical processes in the brain.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Strategies for designing biocatalysts with new functions

The engineering of natural enzymes has led to the availability of a broad range of biocatalysts that can be used for the sustainable manufacturing of a variety of chemicals and pharmaceuticals. However, for many important chemical transformations there are no known enzymes that can serve as starting templates for biocatalyst development. These limitations have fuelled efforts to build entirely new catalytic sites into proteins in order to generate enzymes with functions beyond those found in Nature. This bottom-up approach to enzyme development can also reveal new fundamental insights into the molecular origins of efficient protein catalysis. In this tutorial review, we will survey the different strategies that have been explored for designing new protein catalysts. These methods will be illustrated through key selected examples, which demonstrate how highly proficient and selective biocatalysts can be developed through experimental protein engineering and/or computational design. Given the rapid pace of development in the field, we are optimistic that designer enzymes will begin to play an increasingly prominent role as industrial biocatalysts in the coming years.

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.

Electrostatics as a Guiding Principle in Understanding and Designing Enzymes

Enzyme design faces challenges related to the implementation of the basic principles that govern the catalytic activity in natural enzymes. In this work, we revisit basic electrostatic concepts that have been shown to explain the origin of enzymatic efficiency like preorganization and reorganization. Using magnitudes such as the electrostatic potential and the electric field generated by the protein, we explain how these concepts work in different enzymes and how they can be used to rationalize the consequences of point mutations. We also discuss examples of protein design in which electrostatic effects have been implemented. For the near future, molecular simulations, coupled with the use of machine learning methods, can be used to implement electrostatics as a guiding principle for enzyme designs.

Recent advances in biocatalytic C-N bond-forming reactions

Molecules containing C-N bonds are of paramount importance in a diverse array of organic-based materials, natural products, pharmaceutical compounds, and agricultural chemicals. Biocatalytic C-N bond-forming reactions represent powerful strategies for producing these valuable targets, and their significance in the field of synthetic chemistry has steadily increased over the past decade. In this review, we provide a concise overview of recent advancements in the development of C-N bond-forming enzymes, with a particular emphasis on the inherent chemistry involved in these enzymatic processes. Overall, these enzymatic systems have proven their potential in addressing long-standing challenges in traditional small-molecule catalysis.

Uncovering the role of sorbitol in Renilla luciferase kinetics: Insights from spectroscopic and molecular dynamics studies

Renilla luciferase catalyzes the oxidation of coelenterazine to coelenteramide, resulting in the emission of a photon of light. This study investigated the impact of sorbitol on the structural and kinetic properties of Renilla luciferase using circular dichroism, fluorescence spectroscopy, and molecular dynamics simulations. Our investigation, carried out using circular dichroism and fluorescence analyses, as well as a thermal stability assay, has revealed that sorbitol induces conformational changes in the enzyme but does not improve its thermal stability. Moreover, through kinetic studies, it has been demonstrated that at a concentration of 0.4 M, sorbitol enhances the catalytic efficiency of Renilla luciferase. However, at higher concentrations, sorbitol results in a decrease in catalytic efficiency. Additionally, molecular dynamics simulations have shown that sorbitol increases the presence of hydrophobic pockets on the enzyme's surface. These simulations have also provided evidence that at a concentration of 0.4 M, sorbitol facilitates substrate access to the active site of the enzyme. Nevertheless, at higher concentrations, sorbitol obstructs substrate trafficking, most likely due to its impact on the gateway to the active site. This study may provide insights into the kinetic changes observed in enzymes with buried active sites, such as those with α/β hydrolase fold.

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.

Lipid vesicle-based molecular robots

A molecular robot, which is a system comprised of one or more molecular machines and computers, can execute sophisticated tasks in many fields that span from nanomedicine to green nanotechnology. The core parts of molecular robots are fairly consistent from system to system and always include (i) a body to encapsulate molecular machines, (ii) sensors to capture signals, (iii) computers to make decisions, and (iv) actuators to perform tasks. This review aims to provide an overview of approaches and considerations to develop molecular robots. We first introduce the basic technologies required for constructing the core parts of molecular robots, describe the recent progress towards achieving higher functionality, and subsequently discuss the current challenges and outlook. We also highlight the applications of molecular robots in sensing biomarkers, signal communications with living cells, and conversion of energy. Although molecular robots are still in their infancy, they will unquestionably initiate massive change in biomedical and environmental technology in the not too distant future.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Bioluminescent Intercalating Dyes for Ratiometric Nucleic Acid Detection

Rapid and sensitive DNA detection methods that can be conducted at the point of need may aid in disease diagnosis and monitoring. However, translation of current assays has proven challenging, as they typically require specialized equipment or probe-specific modifications for every new target DNA. Here, we present Luminescent Multivalent Intercalating Dye (LUMID), off-the-shelf bioluminescent sensors consisting of intercalating dyes conjugated to a NanoLuc luciferase, which allow for nonspecific detection of double-stranded DNA through a blue-to-green color change. Through the incorporation of multiple, tandem-arranged dyes separated by positively charged linkers, DNA-binding affinities were improved by over 2 orders of magnitude, detecting nanomolar DNA concentrations with an 8-fold change in green/blue ratio. We show that LUMID is easily combined with loop-mediated isothermal amplification (LAMP), enabling sequence-specific detection of viral DNA with attomolar sensitivity and a smartphone-based readout. With LUMID, we have thus developed a tool for simple and sensitive DNA detection that is particularly attractive for point-of-need applications.

Artificial intelligence-driven design of the assembled major cat allergen Fel d 1 to improve its spatial folding and IgE-reactivity

BACKGROUND

Cat-derived allergens are considered as one of the most common causes of allergic diseases worldwide. Fel d 1 is a major cat allergen and plays an important role in immunoglobulin E (IgE)-reaction diagnosis. However, the two separate chains of Fel d 1 exhibited lower IgE-reactivity than its complete molecule of an assembled form, which makes it difficult to efficiently prepare and limits the application of Fel d 1 in molecular diagnosis of cat allergy.

METHODS

We first applied artificial intelligence (AI) based tool AlphaFold2 to build the 3-dimensional structures of Fel d 1 with different connection modes between two chains, which were evaluated by ERRAT program and were expressed in Escherichia coli. We then calculated the expression ratios of soluble form/inclusion bodies form of optimized Fel d 1. The Circular Dichroism (CD), High Performance Liquid Chromatography-Size Exclusion Chromatography (HPLC-SEC) and reducing/non-reducing SDS-PAGE were performed to characterize the folding status and dimerization of the optimized fusion Fel d 1. The improvement of specific-IgE reactivity to optimized fusion Fel d 1 was investigated by enzyme linked immunosorbent assay (ELISA).

RESULTS

Among several linkers, 2 × GGGGS got the highest scores, with an overall quality factor of 100. The error value of the residues around the junction of 2 × GGGGS was lower than others. It exhibited highest proportion of soluble protein than other Fel d 1 constructs with ERRAT (GGGGS, KK as well as direct fusion Fel d 1). The results of CD and HPLC-SEC showed the consistent folding and dimerization of two fused subunits between the optimized fusion Fel d 1 and previously well-defined direct fusion Fel d 1. The overall IgE-binding absorbance of optimized fusion Fel d 1 tested by ELISA was improved compared with that of the direct fusion Fel d 1.

CONCLUSION

We firstly provided an AI-design strategy to optimize the Fel d 1, which could spontaneously fold into its native-like structure without additional refolding process or eukaryotic folding factors. The improved IgE-binding activity and simplified preparation method could greatly facilitate it to be a robust allergen material for molecular diagnosis of cat allergy.

Molecularly generated light and its biomedical applications

Molecularly generated light, referred to here as "molecular light", mainly includes bioluminescence, chemiluminescence, and Cerenkov luminescence. Molecular light possesses unique dual features of being both a molecule and a source of light. Its molecular nature enables it to be delivered as molecules to regions deep within the body, overcoming the limitations of natural sunlight and physically generated light sources like lasers and LEDs. Simultaneously, its light properties make it valuable for applications such as imaging, photodynamic therapy, photo-oxidative therapy, and photobiomodulation. In this review article, we provide an updated overview of the diverse applications of molecular light and discuss the strengths and weaknesses of molecular light across various domains. Lastly, we present forward-looking perspectives on the potential of molecular light in the realms of molecular imaging, photobiological mechanisms, therapeutic applications, and photobiomodulation. While some of these perspectives may be considered bold and contentious, our intent is to inspire further innovations in the field of molecular light applications.

Machine learning for functional protein design

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelerating the generation of proteins for applications in biotechnology and medicine. To make sense of the exploding diversity of machine learning approaches, we introduce a unifying framework that classifies models on the basis of their use of three core data modalities: sequences, structures and functional labels. We discuss the new capabilities and outstanding challenges for the practical design of enzymes, antibodies, vaccines, nanomachines and more. We then highlight trends shaping the future of this field, from large-scale assays to more robust benchmarks, multimodal foundation models, enhanced sampling strategies and laboratory automation.

Sparks of function by de novo protein design

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels

Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties despite complex folding biophysics. It generates more diverse TMB sequences than Franklin2023 in pore-facing positions. In addition, ProteinMPNN generated TMB sequences that passed state-of-the-art in silico filters for experimental validation, suggesting that the model could be used in de novo design tasks of diverse nanopores for single-molecule sensing and sequencing. Lastly, our results indicate that the low success rate of ProteinMPNN for the design of β-sheet proteins stems from backbone input accuracy rather than software limitations.

Uncovering the role of leucine 59 in Renilla luciferase stability and activity with error-prone PCR: Implications for protein engineering

A new variant of Renilla luciferase, named Met C-SRLuc 8, was obtained from a random mutagenesis library and expressed in Escherichia coli BL21 (DE3) plys and purified. The results of the enzyme's binding affinity, kinetic stability, and bioinformatic studies demonstrated that leucine 59, located within the hot-spot foldon in the N-terminal domain of the protein, plays a significant role in the stability and activity of Renilla luciferase. These findings may facilitate the engineering of different variants of this enzyme to achieve thermally stable versions for various biotechnological applications.

Spirostrain-Accelerated Chemiexcitation of Dioxetanes Yields Unprecedented Detection Sensitivity in Chemiluminescence Bioassays

Chemiluminescence is a fascinating phenomenon that involves the generation of light through chemical reactions. The light emission from adamantyl-phenoxy-1,2-dioxetanes can glow from minutes to hours depending on the specific substituent present on the dioxetane molecule. In order to improve the light emission properties produced by these chemiluminescent luminophores, it is necessary to induce the chemiexcitation rate to a flash mode, wherein the bulk of light is emitted instantly rather than slowly over time. We report the realization of this goal through the incorporation of spirostrain release into the decomposition of 1,2-dioxetane luminophores. DFT computational simulations provided support for the hypothesis that the spiro-cyclobutyl substituent accelerates chemiexcitation as compared to the unstrained adamantyl substituent. Spiro-linking of cyclobutane and oxetane units led to greater than 100-fold and 1000-fold emission enhancement, respectively. This accelerated chemiexcitation rate increases the detection sensitivity for known chemiluminescent probes to the highest signal-to-noise ratio documented to date. A turn-ON probe, containing a spiro-cyclobutyl unit, for detecting the enzyme β-galactosidase exhibited a limit of detection value that is 125-fold more sensitive than that for the previously described adamantyl analogue. This probe was also able to instantly detect and image β-gal activity with enhanced sensitivity in E. coli bacterial assays.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Protein nanoscaffold enables programmable nanobody-luciferase immunoassembly for sensitive and simultaneous detection of aflatoxin B1 and ochratoxin A

Mycotoxins produced by fungi can contaminate various foods and pose significant health risks. Ensuring food safety demands rapid, highly sensitive analytical techniques. One-step Bioluminescent Enzyme Immunoassays (BLEIAs) employing nanobody-nanoluciferase fusion proteins have recently garnered attention for operational simplicity and heightened sensitivity. Nevertheless, fixed nanobody:nanoluciferase ratios in fusion proteins restrict the customization and sensitivity of traditional BLEIAs. In this study, we present a Scaffold Assembly-based BLEIA (SA-BLEIA) that overcomes these limitations through the programmable conjugation of nanobodies and luciferases onto 60-meric protein nanoscaffolds using SpyTag/SpyCatcher linkages. These nanoscaffolds facilitate the adjustable coupling of anti-aflatoxin B1 and anti-ochratoxin A nanobodies with luciferases, optimizing nanobody/luciferase ratios and diversifying specificities. Compared to conventional methods, SA-BLEIA demonstrates considerably elevated sensitivity for detecting both toxins. The elevated local concentration of luciferase significantly amplifies bioluminescence intensity, permitting reduced substrate consumption and cost-effective detection. The usage of dual-nanobody conjugates facilitates the quantification or simultaneous detection of both mycotoxins in a single test with shared reagents. The assay exhibits exceptional recovery rates in spiked cereal samples, strongly correlating with outcomes from commercial ELISA kits. Overall, this adaptable, highly sensitive, cost-effective, and multiplexed immunoassay underscores the potential of tunable scaffold assembly as a promising avenue for advancing bioanalytical diagnostic tools.

Extensive antibody search with whole spectrum black-box optimization

In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from. In our approach, multiple prediction models are trained by subsampling the training set and the multi-objective optimization problem, where one objective is the average activity and the other is the standard deviation, is solved. The Pareto front represents a list of sequences with the whole spectrum of activity and stability. Using this method, we designed VHH (Variable domain of Heavy chain of Heavy chain) antibodies based on the dataset obtained from deep mutational screening. To solve multi-objective optimization, we employed our sequence design software MOQA that uses quantum annealing. By applying several selection criteria to 19,778 designed sequences, five sequences were selected for wet-lab validation. One sequence, 16 mutations away from the closest training sequence, was successfully expressed and found to possess desired binding specificity. Our whole spectrum approach provides a balanced way of dealing with the prediction uncertainty, and can possibly be applied to extensive search of functional sequences.

Artificial carbon assimilation: From unnatural reactions and pathways to synthetic autotrophic systems

Synthetic biology is being increasingly used to establish novel carbon assimilation pathways and artificial autotrophic strains that can be used in low-carbon biomanufacturing. Currently, artificial pathway design has made significant progress from advocacy to practice within a relatively short span of just over ten years. However, there is still huge scope for exploration of pathway diversity, operational efficiency, and host suitability. The accelerated research process will bring greater opportunities and challenges. In this paper, we provide a comprehensive summary and interpretation of representative one-carbon assimilation pathway designs and artificial autotrophic strain construction work. In addition, we propose some feasible design solutions based on existing research results and patterns to promote the development and application of artificial autotrophy.

Current advances in the use of bioluminescence assays for drug discovery: an update of the last ten years

INTRODUCTION

Bioluminescence is a well-established optical detection technique widely used in several bioanalytical applications, including high-throughput and high-content screenings. Thanks to advances in synthetic biology techniques and deep learning, a wide portfolio of luciferases is now available with tuned emission wavelengths, kinetics, and high stability. These luciferases can be implemented in the drug discovery and development pipeline, allowing high sensitivity and multiplexing capability.

AREAS COVERED

This review summarizes the latest advancements of bioluminescent systems as toolsets in drug discovery programs for in vitro applications. Particular attention is paid to the most advanced bioluminescence-based technologies for drug screening over the past 10 years (from 2013 to 2023) such as cell-free assays, cell-based assays based on genetically modified cells, bioluminescence resonance energy transfer, and protein complementation assays in 2D and 3D cell models.

EXPERT OPINION

The availability of tuned bioluminescent proteins with improved emission and stability properties is vital for the development of bioluminescence assays for drug discovery, spanning from reporter gene technology to protein-protein techniques. Further studies, combining machine learning with synthetic biology, will be necessary to obtain new tools for sustainable and highly predictive bioluminescent drug discovery platforms.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2 ) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Machine Learning-Supported Enzyme Engineering toward Improved CO2-Fixation of Glycolyl-CoA Carboxylase

Glycolyl-CoA carboxylase (GCC) is a new-to-nature enzyme that catalyzes the key reaction in the tartronyl-CoA (TaCo) pathway, a synthetic photorespiration bypass that was recently designed to improve photosynthetic CO2 fixation. GCC was created from propionyl-CoA carboxylase (PCC) through five mutations. However, despite reaching activities of naturally evolved biotin-dependent carboxylases, the quintuple substitution variant GCC M5 still lags behind 4-fold in catalytic efficiency compared to its template PCC and suffers from futile ATP hydrolysis during CO2 fixation. To further improve upon GCC M5, we developed a machine learning-supported workflow that reduces screening efforts for identifying improved enzymes. Using this workflow, we present two novel GCC variants with 2-fold increased carboxylation rate and 60% reduced energy demand, respectively, which are able to address kinetic and thermodynamic limitations of the TaCo pathway. Our work highlights the potential of combining machine learning and directed evolution strategies to reduce screening efforts in enzyme engineering.

Soil microbiome engineering for sustainability in a changing environment

Recent advances in microbial ecology and synthetic biology have the potential to mitigate damage caused by anthropogenic activities that are deleteriously impacting Earth's soil ecosystems. Here, we discuss challenges and opportunities for harnessing natural and synthetic soil microbial communities, focusing on plant growth promotion under different scenarios. We explore current needs for microbial solutions in soil ecosystems, how these solutions are being developed and applied, and the potential for new biotechnology breakthroughs to tailor and target microbial products for specific applications. We highlight several scientific and technological advances in soil microbiome engineering, including characterization of microbes that impact soil ecosystems, directing how microbes assemble to interact in soil environments, and the developing suite of gene-engineering approaches. This Review underscores the need for an interdisciplinary approach to understand the composition, dynamics and deployment of beneficial soil microbiomes to drive efforts to mitigate or reverse environmental damage by restoring and protecting healthy soil ecosystems.

Recent advances in systems metabolic engineering

Systems metabolic engineering, which integrates metabolic engineering with systems biology, synthetic biology, and evolutionary engineering, has revolutionized the sustainable production of fuels and materials through the creation of efficient microbial cell factories. Recent advancements in systems metabolic engineering targeting different biological components of the host cell have enabled the creation of highly productive microbial cell factories. This article provides a review of the recent tools and strategies used for enzyme-, genetic module-, pathway-, flux-, genome-, and cell-level engineering, supported by illustrative examples. Furthermore, we highlight recent trends in systems metabolic engineering, which involve the application of multiple tools discussed in this review. Finally, the paper addresses the challenges and perspectives of transitioning academic-level metabolic engineering studies to commercial-scale production.

Glow-type conversion and characterization of a minimal luciferase via mutational analyses

Luciferases are widely used as reporter proteins in various fields. Recently, we developed a minimal bright luciferase, picALuc, via partial deletion of the artificial luciferase (ALuc) derived from copepods luciferases. However, the structures of copepod luciferases in the substrate-bound state remain unknown. Moreover, as suggested by structural modeling, picALuc has a larger active site cavity, unlike that in other copepod luciferases. Here, to explore the bioluminescence mechanism of picALuc and its luminescence properties, we conducted multiple mutational analyses, and identified residues and regions important for catalysis and bioluminescence. Mutations of residues likely involved in catalysis (S33, H34, and D55) markedly reduced bioluminescence, whereas that of residue (E50) (near the substrate in the structural model) enhanced luminescence intensity. Furthermore, deletion mutants (Δ70-Δ78) in the loop region (around I73) exhibited longer luminescence lifetimes (~ 30 min) and were reactivated multiple times upon re-addition of the substrate. Due to the high thermostability of picALuc, one of its representative mutant (Δ74), was able to be reused, that is, luminescence recycling, for day-scale time at room temperature. These findings provide important insights into picALuc bioluminescence mechanism and copepod luciferases and may help with sustained observations in a variety of applications.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.