From covalent transition states in chemistry to noncovalent in biology: from ß- to Φ-value analysis of protein folding.

Solving the mechanism of a chemical reaction requires determining the structures of all the ground states on the pathway and the elusive transition states linking them. 2024 is the centenary of Brønsted's landmark paper that introduced the ß-value and structure-activity studies as the only experimental means to infer the structures of transition states. It involves making systematic small changes in the covalent structure of the reactants and analysing changes in activation and equilibrium-free energies. Protein engineering was introduced for an analogous procedure, Φ-value analysis, to analyse the noncovalent interactions in proteins central to biological chemistry. The methodology was developed first by analysing noncovalent interactions in transition states in enzyme catalysis. The mature procedure was then applied to study transition states in the pathway of protein folding - 'part (b) of the protein folding problem'. This review describes the development of Φ-value analysis of transition states and compares and contrasts the interpretation of ß- and Φ-values and their limitations. Φ-analysis afforded the first description of transition states in protein folding at the level of individual residues. It revealed the nucleation-condensation folding mechanism of protein domains with the transition state as an expanded, distorted native structure, containing little fully formed secondary structure but many weak tertiary interactions. A spectrum of transition states with various degrees of structural polarisation was then uncovered that spanned from nucleation-condensation to the framework mechanism of fully formed secondary structure. Φ-analysis revealed how movement of the expanded transition state on an energy landscape accommodates the transition from framework to nucleation-condensation mechanisms with a malleability of structure as a unifying feature of folding mechanisms. Such movement follows the rubric of analysis of classical covalent chemical mechanisms that began with Brønsted. Φ-values are used to benchmark computer simulation, and Φ and simulation combine to describe folding pathways at atomic resolution.

Origin of Enantioselectivity in Engineered Cytochrome c-Catalyzed Carbon-Radical FePP Hydrolysis Revealed Using QM/MM (ABEEM Polarizable Force Field) and MD Simulations.

The origin of highly efficient asymmetric aminohydroxylation of styrene catalyzed by engineered cytochrome c is investigated by the developed Atom-Bond Electronegativity Equalization Method polarizable force field (ABEEM PFF), which is a combined outcome of electronic and steric effects. Model molecules were used to establish the charge parameters of the ABEEM PFF, for which the bond-stretching and angle-bending parameters were obtained by using a combination of modified Seminario and scan methods. The interactions between carbon-radical Fe-porphyrin (FePP) and waters are simulated by molecular dynamics, which shows a clear preference for the pre-R over the pre-S. This preference is attributed to the hydrogen-bond between the mutated 100S and 101P residues as well as van der Waals interactions, enforcing a specific conformation of the carbon-radical FePP complex within the binding pocket. Meanwhile, the hydrogen-bond between water and the nitrogen atom in the active intermediate dictates the stereochemical outcome. Quantum mechanics/molecular mechanics (QM/MM (ABEEM PFF)) and free-energy perturbation calculations elucidate that the 3RTS is characterized by sandwich-like structure among adjacent amino acid residues, which exhibits greater stability than crowed arrangement in 3STS and enables the R enantiomer to form more favorably. Thus, this study provides mechanistic insight into the catalytic reaction of hemoproteins.

Engineering bio-brick protein scaffolds for organizing enzyme assemblies.

Enzyme scaffolding is an emerging approach for enhancing the catalytic efficiency of multi-enzymatic cascades by controlling their spatial organization and stoichiometry. This study introduces a novel family of engineered SCAffolding Bricks, named SCABs, utilizing the consensus tetratricopeptide repeat (CTPR) domain for organized multi-enzyme systems. Two SCAB systems are developed, one employing head-to-tail interactions with reversible covalent disulfide bonds, the other relying on non-covalent metal-driven assembly via engineered metal coordinating interfaces. Enzymes are directly fused to SCAB modules, triggering assembly in a non-reducing environment or by metal presence. A proof-of-concept with formate dehydrogenase (FDH) and L-alanine dehydrogenase (AlaDH) shows enhanced specific productivity by 3.6-fold compared to free enzymes, with the covalent stapling outperforming the metal-driven assembly. This enhancement likely stems from higher-order supramolecular assembly and improved NADH cofactor regeneration, resulting in more efficient cascades. This study underscores the potential of protein engineering to tailor scaffolds, leveraging supramolecular spatial-organizing tools, for more efficient enzymatic cascade reactions.

Graphormer supervised de novo protein design method and function validation.

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

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.

Radical fluorine transfer catalysed by an engineered nonheme iron enzyme.

Nonheme iron enzymes stand out as one of the most versatile biocatalysts for molecular functionalization. They facilitate a wide array of chemical transformations within biological processes, including hydroxylation, chlorination, epimerization, desaturation, cyclization, and more. Beyond their native biological functions, these enzymes possess substantial potential as powerful biocatalytic platforms for achieving abiological metal-catalyzed reactions, owing to their functional and structural diversity and high evolvability. To this end, our group has recently engineered a series of nonheme iron enzymes to employ non-natural radical-relay mechanisms for abiological radical transformations not previously known in biology. Notably, we have demonstrated that a nonheme iron enzyme, (S)-2-hydroxypropylphosphonate epoxidase from Streptomyces viridochromogenes (SvHppE), can be repurposed into an efficient and selective biocatalyst for radical fluorine transfer reactions. This marks the first known instance of a redox enzymatic process for C(sp3)F bond formation. This chapter outlines the detailed experimental protocol for engineering SvHPPE for fluorination reactions. Furthermore, the provided protocol could serve as a general guideline that might facilitate other engineering endeavors targeting nonheme iron enzymes for novel catalytic functions.

Semirational Design Based on Consensus Sequences to Balance the Enzyme Activity-Stability Trade-Off.

In this study, the phenomenon of the stability-activity trade-off, which is increasingly recognized in enzyme engineering, was explored. Typically, enhanced stability in enzymes correlates with diminished activity. Utilizing Rosa roxburghii copper-zinc superoxide dismutase (RrCuZnSOD) as a model, single-site mutations were introduced based on a semirational design derived from consensus sequences. The initial set of mutants was selected based on activity, followed by combinatorial mutation. This approach yielded two double-site mutants, D25/A115T (18,688 ± 206 U/mg) and A115T/S135P (18,095 ± 1556 U/mg), exhibiting superior enzymatic properties due to additive and synergistic effects. These mutants demonstrated increased half-lives (T1/2) at 80 °C by 1.2- and 1.6-fold, respectively, and their melting temperatures (Tm) rose by 3.4 and 2.5 °C, respectively, without any loss in activity relative to the wild type. Via an integration of structural analysis and molecular dynamics simulations, we elucidated the underlying mechanism facilitating the concurrent enhancement of both thermostability and enzymatic activity.

Excelzyme: A Swiss University-Industry Collaboration for Accelerated Biocatalyst Development.

Excelzyme, an enzyme engineering platform located at the Zurich University of Applied Sciences, is dedicated to accelerating the development of tailored biocatalysts for large-scale industrial applications. Leveraging automation and advanced computational techniques, including machine learning, efficient biocatalysts can be generated in short timeframes. Toward this goal, Excelzyme systematically selects suitable protein scaffolds as the foundation for constructing complex enzyme libraries, thereby enhancing sequence and structural biocatalyst diversity. Here, we describe applied workflows and technologies as well as an industrial case study that exemplifies the successful application of the workflow.

GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes.

Michaelis constant (KM) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of KM are difficult and time-consuming, prediction of the KM values from machine and deep learning models would increase the pace of the enzymes kinetics studies. Existing machine and deep learning models are limited to the specific enzymes, i.e., a minority of enzymes or wildtype enzymes. Here, we used a deep learning framework PaddlePaddle to implement a machine and deep learning approach (GraphKM) for KM prediction of wildtype and mutant enzymes. GraphKM is composed by graph neural networks (GNN), fully connected layers and gradient boosting framework. We represented the substrates through molecular graph and the enzymes through a pretrained transformer-based language model to construct the model inputs. We compared the difference of the model results made by the different GNN (GIN, GAT, GCN, and GAT-GCN). The GAT-GCN-based model generally outperformed. To evaluate the prediction performance of the GraphKM and other reported KM prediction models, we collected an independent KM dataset (HXKm) from literatures.

Diverse models of cavity engineering in enzyme modification: Creation, filling, and reshaping.

Most enzyme modification strategies focus on designing the active sites or their surrounding structures. Interestingly, a large portion of the enzymes (60%) feature active sites located within spacious cavities. Despite recent discoveries, cavity-mediated enzyme engineering remains crucial for enhancing enzyme properties and unraveling folding-unfolding mechanisms. Cavity engineering influences enzyme stability, catalytic activity, specificity, substrate recognition, and docking. This article provides a comprehensive review of various cavity engineering models for enzyme modification, including cavity creation, filling, and reshaping. Additionally, it also discusses feasible tools for geometric analysis, functional assessment, and modification of cavities, and explores potential future research directions in this field. Furthermore, a promising universal modification strategy for cavity engineering that leverages state-of-the-art technologies and methodologies to tailor cavities according to the specific requirements of industrial production conditions is proposed.

Directed evolution of the fluorescent protein CGP with in situ biosynthesized noncanonical amino acids.

The incorporation of noncanonical amino acids (ncAAs) into proteins can enhance their function beyond the abilities of canonical amino acids and even generate new functions. However, the ncAAs used for such research are usually chemically synthesized, which is expensive and hinders their application on large industrial scales. We believe that the biosynthesis of ncAAs using metabolic engineering and their employment in situ in target protein engineering with genetic code expansion could overcome these limitations. As a proof of principle, we biosynthesized four ncAAs, O-L-methyltyrosine, 3,4-dihydroxy-L-phenylalanine, 5-hydroxytryptophan, and 5-chloro-L-tryptophan using metabolic engineering and directly evolved the fluorescent consensus green protein (CGP) by combination with nine other exogenous ncAAs in Escherichia coli. After screening a TAG scanning library expressing 13 ncAAs, several variants with enhanced fluorescence and stability were identified. The variants CGPV3pMeoF/K190pMeoF and CGPG20pMeoF/K190pMeoF expressed with biosynthetic O-L-methyltyrosine showed an approximately 1.4-fold improvement in fluorescence compared to the original level, and a 2.5-fold improvement in residual fluorescence after heat treatment. Our results demonstrated the feasibility of integrating metabolic engineering, genetic code expansion, and directed evolution in engineered cells to employ biosynthetic ncAAs in protein engineering. These results could further promote the application of ncAAs in protein engineering and enzyme evolution. IMPORTANCE: Noncanonical amino acids (ncAAs) have shown great potential in protein engineering and enzyme evolution through genetic code expansion. However, in most cases, ncAAs must be provided exogenously during protein expression, which hinders their application, especially when they are expensive or have poor cell membrane penetration. Engineering cells with artificial metabolic pathways to biosynthesize ncAAs and employing them in situ for protein engineering and enzyme evolution could facilitate their application and reduce costs. Here, we attempted to evolve the fluorescent consensus green protein (CGP) with biosynthesized ncAAs. Our results demonstrated the feasibility of using biosynthesized ncAAs in protein engineering, which could further stimulate the application of ncAAs in bioengineering and biomedicine.

Fc engineered anti-virus therapeutic human IgG1 expressed in plants with altered binding to the neonatal Fc receptor.

Production of therapeutic monoclonal antibody (mAb) in transgenic plants has several advantages such as large-scale production and the absence of pathogenic animal contaminants. However, mAb with high mannose (HM) type glycans has shown a faster clearance compared to antibodies produced in animal cells. The neonatal Fc receptor (FcRn) regulates the persistence of immunoglobulin G (IgG) by the FcRn-mediated recycling pathway, which salvages IgG from lysosomal degradation within cells. In this study, Fc-engineering of antirabies virus therapeutic mAb SO57 with the endoplasmic reticulum (ER)-retention peptide signal (Lys-Asp-Glu-Leu; KDEL) (mAbpK SO57) in plant cell was conducted to enhance its binding activity to human neonatal Fc receptor (hFcRn), consequently improve its serum half-life. Enzyme-linked immunosorbent assay (ELISA) and Surface plasmon resonance assay showed altered binding affinity of the Fc region of three different mAbpK SO57 variants [M252Y/S254T/T256E (MST), M428L/N434S (MN), H433K/N434F (HN)] to hFcRn compared to wild type (WT) of mAbpK SO57. Molecular modeling data visualized the structural alterations in these mAbpK SO57. All of the mAbpK SO57 variants had HM type glycan structures similar to the WT mAbpK SO57. In addition, the neutralizing activity of the three variants against the rabies virus CVS-11 was effective as the WT mAbpK SO57. These results indicate that the binding affinity of mAbpK SO57 variants to hFcRn can be modified without alteration of N-glycan structure and neutralization activity. Taken together, this study suggests that Fc-engineering of antirabies virus mAb can be applied to enhance the efficacy of therapeutic mAbs in plant expression systems.

Enhancing thermostability of tryptophan hydroxylase via protein engineering and its application in 5-hydroxytryptophan production.

5-Hydroxytryptophan (5-HTP), as the precursor of serotonin and melatonin in animals, can regulate mood, sleep, and behavior, which is widely used in pharmaceutical and health products industry. The enzymatic production of 5-hydroxytryptophan (5-HTP) from L-tryptophan (L-Trp) using tryptophan hydroxylase (TPH) show huge potential in application due to its advantages, such as mild reaction conditions, avoidance of protection/deprotection processes, excellent regioselectivity and considerable catalytic efficiency, compared with chemical synthesis and natural extraction. However, the low thermostability of TPH restricted its hydroxylation efficiency toward L-Trp. In this study, we aimed to improve the thermostability of TPH via semi-rational design guided by (folding free energy) ΔΔG fold calculation. After two rounds of evolution, two beneficial mutants M1 (S422V) and M30 (V275L/I412K) were obtained. Thermostability evaluation showed that M1 and M30 possessed 5.66-fold and 6.32-fold half-lives (t1/2) at 37 °C, and 4.2 °C and 6.0 °C higher melting temperature (Tm) than the WT, respectively. The mechanism behind thermostability improvement was elucidated with molecular dynamics simulation. Furthermore, biotransformation of 5-HTP from L-Trp was performed, M1 and M30 displayed 1.80-fold and 2.30-fold than that of WT, respectively. This work provides important insights into the thermostability enhancement of TPH and generate key mutants that could be robust candidates for practical production of 5-HTP.

Engineering metalloproteinase inhibitors: tissue inhibitors of metalloproteinases or antibodies, that is the question.

Targeting metalloproteinases (MPs) has been the center of attention for developing therapeutics due to their contribution to a wide range of diseases, including cancer, cardiovascular, neurodegenerative disease, and preterm labor. Protein-based MP inhibitors offer higher stability and selectivity, which is critical for developing efficient therapeutics with low off-target effects. Tissue inhibitors of metalloproteinases (TIMPs), natural inhibitors of MPs, and antibodies provide excellent protein scaffolds for engineering selective or multispecific MP inhibitors. Advances in protein engineering and design techniques, such as rational design and directed evolution using yeast display to develop potent MP inhibitors, are discussed, including but not limited to loop grafting, swapping, and counterselective selection.

Evolution-inspired engineering of nonribosomal peptide synthetases.

Many clinically used drugs are derived from or inspired by bacterial natural products that often are produced through nonribosomal peptide synthetases (NRPSs), megasynthetases that activate and join individual amino acids in an assembly line fashion. In this work, we describe a detailed phylogenetic analysis of several bacterial NRPSs that led to the identification of yet undescribed recombination sites within the thiolation (T) domain that can be used for NRPS engineering. We then developed an evolution-inspired "eXchange Unit between T domains" (XUT) approach, which allows the assembly of NRPS fragments over a broad range of GC contents, protein similarities, and extender unit specificities, as demonstrated for the specific production of a proteasome inhibitor designed and assembled from five different NRPS fragments.

Design of functional intrinsically disordered proteins.

Many proteins do not fold into a fixed three-dimensional structure, but rather function in a highly disordered state. These intrinsically disordered proteins pose a unique challenge to protein engineering and design: How can proteins be designed de novo if not by tailoring their structure? Here, we will review the nascent field of design of intrinsically disordered proteins with focus on applications in biotechnology and medicine. The design goals should not necessarily be the same as for de novo design of folded proteins as disordered proteins have unique functional strengths and limitations. We focus on functions where intrinsically disordered proteins are uniquely suited including disordered linkers, desiccation chaperones, sensors of the chemical environment, delivery of pharmaceuticals, and constituents of biomolecular condensates. Design of functional intrinsically disordered proteins relies on a combination of computational tools and heuristics gleaned from sequence-function studies. There are few cases where intrinsically disordered proteins have made it into industrial applications. However, we argue that disordered proteins can perform many roles currently performed by organic polymers, and that these proteins might be more designable due to their modularity.

Practical Approaches to Genetic Code Expansion with Aminoacyl-tRNA Synthetase/tRNA Pairs.

Genetic code expansion (GCE) can enable the site-selective incorporation of non-canonical amino acids (ncAAs) into proteins. GCE has advanced tremendously in the last decade and can be used to create biorthogonal handles, monitor and control proteins inside cells, study post-translational modifications, and engineer new protein functions. Since establishing our laboratory, our research has focused on applications of GCE in protein and enzyme engineering using aminoacyl-tRNA synthetase/tRNA (aaRS/tRNA) pairs. This topic has been reviewed extensively, leaving little doubt that GCE is a powerful tool for engineering proteins and enzymes. Therefore, for this young faculty issue, we wanted to provide a more technical look into the methods we use and the challenges we think about in our laboratory. Since starting the laboratory, we have successfully engineered over a dozen novel aaRS/tRNA pairs tailored for various GCE applications. However, we acknowledge that the field can pose challenges even for experts. Thus, herein, we provide a review of methodologies in ncAA incorporation with some practical commentary and a focus on challenges, emerging solutions, and exciting developments.

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

Multidomain chimeric enzymes as a promising alternative for biocatalysts improvement: a minireview.

Searching for new and better biocatalysts is an area of study in constant development. In nature, mechanisms generally occurring in evolution, such as genetic duplication, recombination, and natural selection processes, produce various enzymes with different architectures and properties. The recombination of genes that code proteins produces multidomain chimeric enzymes that contain two or more domains that sometimes enhance their catalytic properties. Protein engineering has mimicked this process to enhance catalytic activity and the global stability of enzymes, searching for new and better biocatalysts. Here, we present and discuss examples from both natural and synthetic multidomain chimeric enzymes and how additional domains heighten their stability and catalytic activity. Moreover, we also describe progress in developing new biocatalysts using synthetic fusion enzymes and revise some methodological strategies to improve their biological fitness.