Search

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.

Interactions among biomacromolecules, predominantly noncovalent, underpin biological processes. However, recent advancements in biospecific chemistry have enabled the creation of specific covalent bonds between biomolecules, both in vitro and in vivo. This Review traces the evolution of biospecific chemistry in proteins, emphasizing the role of genetically encoded latent bioreactive amino acids. These amino acids react selectively with adjacent natural groups through proximity-enabled bioreactivity, enabling targeted covalent linkages. We explore various latent bioreactive amino acids designed to target different protein residues, ribonucleic acids, and carbohydrates. We then discuss how these novel covalent linkages can drive challenging protein properties and capture transient protein-protein and protein-RNA interactions in vivo. Additionally, we examine the application of covalent peptides as potential therapeutic agents and site-specific conjugates for native antibodies, highlighting their capacity to form stable linkages with target molecules. A significant focus is placed on proximity-enabled reactive therapeutics (PERx), a pioneering technology in covalent protein therapeutics. We detail its wide-ranging applications in immunotherapy, viral neutralization, and targeted radionuclide therapy. Finally, we present a perspective on the existing challenges within biospecific chemistry and discuss the potential avenues for future exploration and advancement in this rapidly evolving field.

Extracellular vesicles (EVs) play key roles in diverse biological processes, transport biomolecules between cells and have been engineered for therapeutic applications. A useful EV bioengineering strategy is to express engineered proteins on the EV surface to confer targeting, bioactivity and other properties. Measuring how incorporation varies across a population of EVs is important for characterising such materials and understanding their function, yet it remains challenging to quantitatively characterise the absolute number of engineered proteins incorporated at single-EV resolution. To address these needs, we developed a HaloTag-based characterisation platform in which dyes or other synthetic species can be covalently and stoichiometrically attached to engineered proteins on the EV surface. To evaluate this system, we employed several orthogonal quantification methods, including flow cytometry and fluorescence microscopy, and found that HaloTag-mediated quantification is generally robust across EV analysis methods. We compared HaloTag-labelling to antibody-labelling of EVs using single vesicle flow cytometry, enabling us to measure the substantial degree to which antibody labelling can underestimate proteins present on an EV. Finally, we demonstrate the use of HaloTag to compare between protein designs for EV bioengineering. Overall, the HaloTag system is a useful EV characterisation tool which complements and expands existing methods.

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from

The Exabiome project seeks to improve the understanding of microbiomes through the development of methods for accelerating metagenomic science using exascale computing. This article gives an overview of scientific impact of the three components of the project: metagenome assembly, protein family detection, and comparative analysis of metagenomes. Exabiome developed MetaHipMer, the only metagenome assembler capable of scaling to full exascale systems. MetaHipMer has enabled ground-breaking assemblies on the Frontier supercomputer, with many scientific benefits, such as the discovery of rare species and viral genomes. To investigate protein families, Exabiome developed two exascale tools, PASTIS and HipMCL. Together, these can utilize exascale resources to understand the functional diversity of billions of dark matter proteins and novel protein families. For comparative analysis, Exabiome developed kmerprof, a tool that can be used to compare huge metagenomes for many different scientific purposes, for example, grouping human microbiomes according to body location.

The sensitivity and responsiveness of living cells to environmental changes are enabled by dynamic protein structures, inspiring efforts to construct artificial supramolecular protein assemblies. However, despite their sophisticated structures, designed protein assemblies have yet to be incorporated into macroscale devices for real-life applications. We report a 2D crystalline protein assembly of C98/E57/E66L-rhamnulose-1-phosphate aldolase (CEERhuA) that selectively blocks or passes molecular species when exposed to a chemical trigger. CEERhuA crystals are engineered via cobalt(II) coordination bonds to undergo a coherent conformational change from a closed state (pore dimensions

The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm that learns from natural protein sequences to infer evolutionarily plausible mutations and predict enzyme fitness. MODIFY co-optimizes predicted fitness and sequence diversity of starting libraries, prioritizing high-fitness variants while ensuring broad sequence coverage. In silico evaluation shows that MODIFY outperforms state-of-the-art unsupervised methods in zero-shot fitness prediction and enables ML-guided directed evolution with enhanced efficiency. Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation via a new-to-nature carbene transfer mechanism, leading to biocatalysts six mutations away from previously developed enzymes while exhibiting superior or comparable activities. These results demonstrate MODIFY's potential in solving challenging enzyme engineering problems beyond the reach of classic directed evolution.

Dysregulation of DNA methylation is associated with human disease, particularly cancer, and the assessment of aberrant methylation patterns holds great promise for clinical diagnostics. However, DNA polymerases do not effectively discriminate between processing 5‐methylcytosine (5 mC) and unmethylated cytosine, resulting in the silencing of methylation information during amplification or sequencing. As a result, current detection methods require multi‐step DNA conversion treatments or careful analysis of sequencing data to decipher individual 5 mC bases. To overcome these challenges, we propose a novel DNA polymerase‐mediated 5 mC detection approach. Here, we describe the engineering of a thermostable DNA polymerase variant derived from Thermus aquaticus with altered fidelity towards 5 mC. Using a screening‐based evolutionary approach, we have identified a DNA polymerase that exhibits increased misincorporation towards 5 mC during DNA synthesis. This DNA polymerase generates mutation signatures at methylated CpG sites, allowing direct detection of 5 mC by reading an increased error rate after sequencing without prior treatment of the sample DNA. [ABSTRACT FROM AUTHOR]

Triterpenoids have wide applications in the pharmaceutical and agricultural industries. The glycosylation of triterpenoids catalyzed by UDP‐glycosyltransferases (UGTs) is a crucial method for producing valuable derivatives with enhanced functions. However, only a few UDP‐glucosyltransferases have been reported to synthesize the rare triterpenoids with linear‐chain trisaccharide at C3−OH. This study revealed that the UGT91H subfamily primarily contributed to the 2"‐O‐glycosylation of triterpenoids with high regioselectivity, then the substrate scope was further expanded by ancestral sequence reconstruction (ASR). With ancestral enzyme UGT91H_A1 as a model, the sequence‐structure‐function relationship was explored. A RTAS loop (R212/T213/A214/S215) was identified to affect the substrate specificity of UGT91H_A1. Transferring this RTAS loop to the corresponding position of UGT91H enzymes successfully expanded their substrate spectra. The functional role of RTAS loop was further elucidated by molecular dynamics simulation and quantum mechanical computation. UGT91H_A1 was applied to the low‐cost synthesis of terpenoid rhamnosides with a linear trisaccharide in combining with a self‐sufficient UDP‐rhamnose regeneration system. Finally, we developed a phylogeny‐based platform to efficiently mining new UGT91Hs from plant genomic data. This study provided robust biocatalysts for synthesizing various triterpenoid glycosides with a linear trisaccharide and demonstrated ASR as an efficient tool in engineering the function of UDP‐glycosyltransferases. [ABSTRACT FROM AUTHOR]

β‐Branched chiral amines with contiguous stereocenters are valuable building blocks for preparing various biologically active molecules. However, their asymmetric synthesis remains challenging. Herein, we report a highly diastereo‐ and enantioselective biocatalytic approach for preparing a broad range of β‐branched chiral amines starting from their corresponding racemic ketones. This involves a dynamic kinetic resolution‐asymmetric reductive amination process catalyzed using only an imine reductase. Four rounds of protein engineering endowed wild‐type PocIRED with higher reactivity, better stereoselectivity, and a broader substrate scope. Using the engineered enzyme, various chiral amine products were synthesized with up to >99.9 % ee, >99 : 1 dr, and >99 % conversion. The practicability of the developed biocatalytic method was confirmed by producing a key intermediate of tofacitinib in 74 % yield, >99.9 % ee, and 98 : 2 dr at a challenging substrate loading of 110 g L−1. Our study provides a highly capable imine reductase and a protocol for developing an efficient biocatalytic dynamic kinetic resolution‐asymmetric reductive amination reaction system. [ABSTRACT FROM AUTHOR]

Formate dehydrogenases (FDHs) catalyze the oxidation of formate to CO2 while reducing NAD(P)+ to NAD(P)H and are classified into two main classes: metal‐dependent (Mo‐ or W‐containing) and metal‐independent FDHs. The latter are oxygen‐tolerant and relevant as a cofactor regeneration system for various bioprocesses and gained more and more attention due to their ability to catalyze the reverse CO2 reduction. This review gives an overview of metal‐independent FDHs, the recent advances made in this field, and their relevance for future applications in biocatalysis. This includes the exploitation of novel FDHs which have altered co‐substrate specificity as well as enzyme engineering approaches to improve process stability and general performance. [ABSTRACT FROM AUTHOR]

Nanobodies (Nbs) are one of the most promising therapeutics for overcoming immune escape in various diseases, including SARS‐CoV‐2 infection and cancers. However, the small sizes of nanobodies make them prone to renal clearance, thus decreasing circulation half‐life and hindering therapeutic efficacy. Traditional modification technologies, i.e., biotinylation and Fc‐fusion, aim to enhance nanobody pharmacokinetics, but they may introduce heterogeneous products with impaired functions and potentially affect binding to the Fc receptor. Here, a versatile nanobody engineering strategy is presented via molecular modification mediated by an intrinsically disordered protein. The engineered nanobody nano‐formulations retain their high‐affinity binding to the spike protein receptor binding domain and possess submicromolar levels of half‐maximal inhibitory concentration (IC50) against the pseudotyped SARS‐CoV‐2 variants, comparable to the unmodified nanobodies. Notably, the nano‐formulations show elongated half‐lives that are up to ≈15 times higher than those of original nanobodies and superior to other reported modified nanobodies. Furthermore, the in vivo therapeutic efficacy of such nano‐formulation toward breast cancer is significantly enhanced. Therefore, this nanobody engineering strategy offers a convenient and broadly applicable solution to address the suboptimal in vivo performance of nanobodies, holding substantial promise for effectively combating treatment‐tolerant cancers and future pandemics. [ABSTRACT FROM AUTHOR]

Industrial fermentation applications typically require enzymes that exhibit high stability and activity at high temperatures. However, efforts to simultaneously improve these properties are usually limited by a trade‐off between stability and activity. This report describes a computational strategy to enhance both activity and thermal stability of the mesophilic organophosphate‐degrading enzyme, methyl parathion hydrolase (MPH). To predict hotspot mutation sites, we assembled a library of features associated with the target properties for each residue and then prioritized candidate sites by hierarchical clustering. Subsequent in silico screening with multiple algorithms to simulate selective pressures yielded a subset of 23 candidate mutations. Iterative parallel screening of mutations that improved thermal stability and activity yielded, MPHase‐m5b, which exhibited 13.3 °C higher Tm and 4.2 times higher catalytic activity than wild‐type (WT) MPH over a wide temperature range. Systematic analysis of crystal structures, molecular dynamics (MD) simulations, and quantum mechanics/molecular mechanics (QM/MM) calculations revealed a wider entrance to the active site that increased substrate access with an extensive network of interactions outside the active site that reinforced αβ/βα sandwich architecture to improve thermal stability. This study thus provides an advanced, rational design framework to improve efficiency in engineering highly active, thermostable biocatalysts for industrial applications. [ABSTRACT FROM AUTHOR]

Rieske dioxygenase enzymes can perform the cis‐dihydroxylation of aliphatic olefins, representing a potential green alternative to established methods of performing this important transformation. However, the activity of the natural enzymes in this context is low relative to their more well‐known activity in the cis‐dihydroxylation of aromatics. To enable the engineering of dioxygenase enzymes for improved activity in the dihydroxylation of aliphatic olefins, we have developed an assay system to detect the relevant diol metabolites produced from whole‐cell fermentation cultures. Optimization studies were carried out to maximize the sensitivity of the assay system, and its utility in the in vitro screening of enzyme variant libraries was demonstrated. The assay system was utilized in screening studies that identified Rieske dioxygenase variants with significantly improved activity in the dihydroxylation of aliphatic olefins relative to the wild‐type enzyme. [ABSTRACT FROM AUTHOR]

The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modeling approach, termed Overcoming Kinetic rate Obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while ensuring specified cell growth. We demonstrate that the application of OKO to enzyme-constrained metabolic models of Escherichia coli and Saccharomyces cerevisiae results in strategies that can at least double the production of over 40 compounds with little penalty to growth. Interestingly, we show that the overproduction of compounds of interest does not entail only an increase in the values of turnover numbers. Lastly, we demonstrate that a refinement of OKO, allowing also for manipulation of enzyme abundance, facilitates the usage of the available compendia and deep learning models of turnover numbers in the design of precise metabolic engineering strategies. Our results expand the usage of genome-scale metabolic models toward the identification of targets for protein engineering, allowing their direct usage in the generation of innovative metabolic engineering designs for various biotechnological applications. Author summary: Enzymes play a crucial role in metabolic processes, and by selecting enzymes with optimal activity, we can enhance the production of desired compounds. However, there has been no computational approach to designing metabolic engineering strategies based on the modification of enzyme activities. In this study, we developed a new computational method called Overcoming Kinetic rate Obstacles (OKO) to increase chemical production by modifying enzyme activities in E. coli and S. cerevisiae. Our method uses growing data on enzyme efficiency from experiments and deep learning models to suggest strategies for metabolic engineering. We applied OKO to increase the production of over 40 different compounds in models of E. coli and S. cerevisiae, and found that it can at least double their production without severely affecting cell growth. Finally, we showed that by refining OKO to account for changes in both enzyme activity and abundance, our method can more effectively use existing data and models to design precise strategies for improving chemical production. Our findings pave the way for advanced metabolic engineering techniques for various biotechnological applications. [ABSTRACT FROM AUTHOR]

Carbon–carbon bond formation represents a key reaction in organic synthesis, resulting in paramount importance for constructing the carbon backbone of organic molecules. However, traditional metal-based catalysis, despite its advantages, often struggles with issues related to efficiency, selectivity, and sustainability. On the other hand, while biocatalysis offers superior selectivity due to an extraordinary recognition process of the substrate, the scope of its applicable reactions remains somewhat limited. In this context, Artificial Metalloenzymes (ArMs) and Metallo Peptides (MPs) offer a promising and not fully explored solution, merging the two fields of transition metal catalysis and biotransformations, by inserting a catalytically active metal cofactor into a customizable protein scaffold or coordinating the metal ion directly to a short and tunable amino acid (Aa) sequence, respectively. As a result, these hybrid catalysts have gained attention as valuable tools for challenging catalytic transformations, providing systems with new-to-nature properties in organic synthesis. This review offers an overview of recent advances in the development of ArMs and MPs, focusing on their application in the asymmetric carbon–carbon bond-forming reactions, such as carbene insertion, Michael additions, Friedel–Crafts and cross-coupling reactions, and cyclopropanation, underscoring the versatility of these systems in synthesizing biologically relevant compounds. [ABSTRACT FROM AUTHOR]

An important role of a particular synonymous codon composition of a gene in its expression level is well known. There are a number of algorithms optimizing codon usage of recombinant genes to maximize their expression in host cells. Nevertheless, the underlying mechanism remains unsolved and is of significant relevance. In the realm of modern biotechnology, directing protein production to a specific level is crucial for metabolic engineering, genome rewriting and a growing number of other applications. In this study, we propose two new simple statistical and empirical methods for predicting the protein expression level from the nucleotide sequence of the corresponding gene: Codon Expression Index Score (CEIS) and Codon Productivity Score (CPS). Both of these methods are based on the influence of each individual codon in the gene on the overall expression level of the encoded protein and the frequencies of isoacceptors in the species. Our predictions achieve a correlation level of up to r = 0.7 with experimentally measured quantitative proteome data of Escherichia coli, which is superior to any previously proposed methods. Our work helps understand how codons determine protein abundances. Based on these methods, it is possible to design proteins optimized for expression in a particular organism. [ABSTRACT FROM AUTHOR]

Severe asthma is a syndromic label assigned to patients based on clinical parameters, yet there are diverse underlying molecular endotypes in severe asthma pathobiology. Immunophenotyping of asthma biospecimens commonly includes a mixture of granulocytes and lymphocytes. Recently, a subset of patients with severe asthma was defined as non-type 2 with neutrophil-enriched inflammation associated with increased Th17 CD4+ T cells and IL-17 levels. Here, we used an allergen-driven mouse model of increased IL-17 and mixed granulocyte lung inflammation to determine the impact of upstream regulation by an Anticalin protein that specifically binds IL-23. Airway administration of the IL-23-binding Anticalin protein (AcIL-23) decreased lung neutrophils, eosinophils, macrophages, lymphocytes, IL-17+ CD4 T cells, mucous cell metaplasia, and methacholine-induced airway hyperresponsiveness. Selective targeting of IL-23 with a monoclonal antibody (IL-23p19; αIL-23) also decreased macrophages, IL-17+ CD4 T cells, and airway hyperresponsiveness. In contrast, a monoclonal antibody against IL-17A (αIL-17A) had no significant effect on airway hyperresponsiveness but did decrease lung neutrophils, macrophages, and IL-17+ CD4 T cells. Targeting the IL-23 pathway did not significantly change IL-5+ or IL-13+ CD4 T cells. Together, these data indicate that airway AcIL-23 mirrored the activity of systemic anti-IL-23 antibody to decrease airway hyperresponsiveness in addition to mixed granulocytic inflammation and that these protective actions were broader than blocking IL-17A or IL-5 alone, which selectively decreased airway neutrophils and eosinophils, respectively. NEW & NOTEWORTHY: This is the first report of an Anticalin protein engineered to neutralize IL-23 (AcIL-23). Airway administration of AcIL-23 in mice regulated allergen-driven airway inflammation, mucous cell metaplasia, and methacholine-induced airway hyperresponsiveness. In mixed granulocytic allergic lung inflammation, immune regulation of IL-23 was broader than neutralization of either IL-17 or IL-5. [ABSTRACT FROM AUTHOR]

Enzyme biocatalysts can be used to improve industrial sustainability. Improvements are necessary to reach sustainability targets and reduce the impacts of climate change, plastic pollution, depleting natural resources, and deforestation. Protein engineering enables the design of enzymes with increased specificity, efficiency, and stability. Engineered enzymes are being developed and implemented for GHG sequestration, biofuel production, plastic waste treatment, and bioremediation. The push for industrial sustainability benefits from the use of enzymes as a replacement for traditional chemistry. Biological catalysts, especially those that have been engineered for increased activity, stability, or novel function, and are often greener than alternative chemical approaches. This Review highlights the role of engineered enzymes (and identifies directions for further engineering efforts) in the application areas of greenhouse gas sequestration, fuel production, bioremediation, and degradation of plastic wastes. [ABSTRACT FROM AUTHOR]

The paper presents the incorporation of in silico missenses and studies the effect of missenses to understand its effect on the ligand–protein interactions, of COVID-19 protein. In silico protein–ligand interaction, studies are being used to understand and investigate the drug-likeness of various molecules. 19 novel COVID-19 proteins are designed by inducing in silico missenses by mutating N691 amino acid residue in 7bv2 protein, the only residue forming H-bond with the ligand molecule in the parent protein. The work illustrates the effects of in silico-induced mutation on various interactions such as H-Bond, VDW, π-alkyl interactions, and changes in the number and type of surrounding amino acid residues. The results have suggested a common pattern of behaviour on mutation with T, V, W, and Y. Further, it is observed that the number and type of amino acid residues increase on mutation, suggesting the effect of mutation on the ligand–protein binding. [ABSTRACT FROM AUTHOR]

The production of bioethanol from lignocellulosic biomass requires the efficient conversion of glucose and xylose to ethanol, a process that depends on the ability of microorganisms to internalize these sugars. Although glucose transporters exist in several species, xylose transporters are less common. Several types of transporters have been identified in diverse microorganisms, including members of the Major Facilitator Superfamily (MFS) and Sugars Will Eventually be Exported Transporter (SWEET) families. Considering that Saccharomyces cerevisiae lacks an effective xylose transport system, engineered yeast strains capable of efficiently consuming this sugar are critical for obtaining high ethanol yields. This article reviews the structure-function relationship of sugar transporters from the MFS and SWEET families. It provides information on several tools and approaches used to identify and characterize them to optimize xylose consumption and, consequently, second-generation ethanol production. [ABSTRACT FROM AUTHOR]

Copyright of Nachrichten aus der Chemie is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Catechol 1,2 dioxygenase is a versatile enzyme with several potential applications. However, due to its low thermostability, its industrial potential is not being met. In this study, the thermostability of a mesophilic catechol 1,2 dioxygenase from the species Rhodococcus opacus was enhanced via the introduction of disulphide bonds into its structure. Engineered designs (56) were obtained using computational prediction applications, with a set of hypothesized selection criteria narrowing the list to 9. Following recombinant production and purification, several of the designs demonstrated substantially improved protein thermostability. Notably, variant K96C‐D278C yielded improvements including a 4.6°C increase in T50, a 725% increase in half‐life, a 5.5°C increase in Tm, and a >10‐fold increase in total turnover number compared to wild type. Stacking of best designs was not productive. Overall, current state‐of‐the‐art prediction algorithms were effective for design of disulfide‐thermostabilized catechol 1,2 dioxygenase. [ABSTRACT FROM AUTHOR]

Biodiesel generated from oils and fats composed of fatty acid alkyl esters is a potential substitution for fossil fuels, gaining attention as the demand for sustainable energy grows. Enzymatic transesterification has emerged as a promising method for biodiesel production due to the low energy required, easy glycerol recovery and compatibility with various feedstocks. This method also offers high specificity and operates under mild reaction conditions. However, limitations such as enzyme production cost, low product yield, and low stability under harsh conditions, have hindered the progress of biocatalysis. Protein engineering has emerged as an important strategy to overcome these limitations, particularly through rational design. Rational design approaches are advantageous in lipase engineering, due to accessible experimental tools and existing knowledge of lipase structure and function. Site-directed mutagenesis has been applied to improve stability, substrate specificity, methanol tolerance, and catalytic efficiency of lipase by introducing molecular interactions like hydrogen bonds and disulfide bridges as well as improved lid flexibility by mutations at the lid or lid swapping. This targeted approach allows for the precise modification of amino acid residues, optimizing lipase for better performance, which is crucial for efficient biodiesel production. [ABSTRACT FROM AUTHOR]

Stimuli‐responsive hydrogels that leverage protein conformational changes are of significant interest in the design of dynamic materials applicable in a myriad of fields, such as drug delivery, actuators, biosensors, and microfluidics. The small calcium binding protein calmodulin (CaM), which undergoes three‐stage conformational changes upon successive binding with Ca2+ and specific ligands, offers a mechanism to create dynamic hydrogels with three distinct physical states. In this work, a CaM‐based recombinant protein hydrogel is engineered using [Ru(bpy)3]2+‐mediated photo‐crosslinking. This hydrogel displays the ability to reversibly increase its Young's modulus by 1.5‐fold and ∼7‐fold, respectively, upon binding with Ca2+ and subsequent interaction with trifluoperazine. The magnitude of stiffness changes is tunable by adjusting the length proportion of dynamic and static domains and modifying protein content. This tunable and reversible control over hydrogel mechanics is further utilized to engineer shape‐morphing materials, highlighting the versatile potential of this CaM‐based protein hydrogel for diverse applications. [ABSTRACT FROM AUTHOR]

Machine learning (ML)-based design approaches have advanced the field of de novo protein design, with diffusion-based generative methods increasingly dominating protein design pipelines. Here, we report a "hallucination"-based protein design approach that functions in relaxed sequence space, enabling the efficient design of high-quality protein backbones over multiple scales and with broad scope of application without the need for any form of retraining. We experimentally produced and characterized more than 100 proteins. Three high-resolution crystal structures and two cryo-electron microscopy density maps of designed single-chain proteins comprising up to 1000 amino acids validate the accuracy of the method. Our pipeline can also be used to design synthetic protein-protein interactions, as validated experimentally by a set of protein heterodimers. Relaxed sequence optimization offers attractive performance with respect to designability, scope of applicability for different design problems, and scalability across protein sizes. [ABSTRACT FROM AUTHOR]

Protein engineering with non-canonical amino acids (ncAAs) holds great promises for diverse applications, however, there are still limitations in the implementation of this technology at manufacturing scale. The know-how to efficiently produce ncAA-incorporated proteins in a scalable manner is still very limited. In the present study, we incorporated the ncAA N6-[(2-azidoethoxy)carbonyl]-L-lysine (Azk) into an antigen binding fragment (Fab) in Escherichia coli. We used the orthogonal pyrrolysyl-tRNA synthetase/suppressor tRNACUAPyl pair from Methanosarcina mazei to incorporate Azk site-specifically. We characterized Azk uptake and Fab production at bench-scale under different fermentation conditions, varying timing and mode of Azk addition, Azk-to-cell ratio and induction time. Our results indicate that Azk uptake is comparatively efficient in the batch phase. We discovered that the time between Azk uptake and inducing its incorporation into the Fab must be kept short, which suggests that intracellular Azk is consumed and/or degraded. The results obtained in this study are an important step towards the development of efficient production methods for Azk-incorporated proteins in E. coli. The developed process is scalable and provides excellent yields of 2.95 mg functionalized Fab per g CDM, which corresponds to 80% of yield obtained with the wild type Fab. We also identified the cellular uptake of Azk being dependent on the physiological state of the cell as a potential bottleneck in production. [ABSTRACT FROM AUTHOR]

In the past 20 years, unspecific peroxygenases (UPOs) have emerged as promising biocatalysts for various organic transformations. Particularly, we have witnessed great attention being paid to the screening of new enzymes and expansion of the substrates/products. However, challenges such as enzyme stability, low turnover numbers, and substrate specificity hinder their widespread utilization in practical organic synthesis. This review article provides a concrete and mini-overview of the challenges associated with using UPOs in organic synthesis and discusses strategies for enzyme engineering to overcome these limitations. The article highlights recent advancements in UPO research and presents potential solutions to enhance their catalytic efficiency, stability, substrate specificity, and regioselectivity. Additionally, the review outlines the current methodologies employed for directed evolution and protein engineering of UPOs, along with computational modeling approaches for rational enzyme design. By addressing the challenges and exploring avenues for enzyme engineering, this review aims to shed light on the prospects of UPOs in organic synthesis. [ABSTRACT FROM AUTHOR]

Engineering proteins with desired functions and biochemical properties is pivotal for biotechnology and drug discovery. While computational methods based on evolutionary information are reducing the experimental burden by designing targeted libraries of functional variants, they still have a low success rate when the desired protein has few or very remote homologous sequences. Here we propose an autoregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the mathematical properties of the Dirichlet distribution and temporal convolution to efficiently learn high-order information from a functionally related, possibly remotely similar, set of sequences. TDVAE is highly accurate in predicting the effects of amino acid mutations, while being significantly 90% smaller than the other state-of-the-art models. We then use TDVAE to design variants of the human alpha galactosidase enzymes as potential treatment for Fabry disease. Our model builds a library of diverse variants which retain sequence, biochemical and structural properties of the wildtype protein, suggesting they could be suitable for enzyme replacement therapy. Taken together, our results show the importance of accurate sequence modelling and the potential of autoregressive models as protein engineering and analysis tools. Engineering therapeutic proteins is crucial yet challenging. Here, authors present TDVAE, a generative model that combines Dirichlet distribution and temporal convolution. TDVAE effectively predicts mutations and designs new alpha-galactosidase variants, showing promise for Fabry disease therapies. [ABSTRACT FROM AUTHOR]

Genetically encoded fluorescent biosensors became indispensable tools for biological research, enabling real-time observation of physiological processes in live cells. Recent protein engineering efforts have resulted in the generation of a large variety of fluorescent biosensors for a wide range of biologically relevant processes, from small ions to enzymatic activity and signaling pathways. However, biosensors for imaging sulfate ions, the fourth most abundant physiological anion, in mammalian cells are still lacking. Here, we report the development and characterization of a green fluorescent biosensor for sulfate named Thyone. Thyone, derived through structure-guided design from bright green fluorescent protein mNeonGreen, exhibited a large negative fluorescence response upon subsecond association with sulfate anion with an affinity of 11 mM in mammalian cells. By integrating mutagenesis analyses with molecular dynamics simulations, we elucidated the molecular mechanism of sulfate binding and revealed key amino acid residues responsible for sulfate sensitivity. High anion selectivity and sensitivity of Thyone allowed for imaging of sulfate anion transients mediated by sulfate transporter heterologously expressed in cultured mammalian cells. We believe that Thyone will find a broad application for assaying the sulfate transport in mammalian cells via anion transporters and exchangers. A genetically encoded green fluorescent sensor, Thyone, enables real-time imaging of sulfate anion dynamics in mammalian cells to probe the sulfate transport activity of the SLC26A2 protein. [ABSTRACT FROM AUTHOR]

Interfacial electron transfer between electroactive microorganisms (EAMs) and electrodes underlies a wide range of bio‐electrochemical systems with diverse applications. However, the electron transfer rate at the biotic‐electrode interface remains low due to high transmembrane and cell‐electrode interfacial electron transfer resistance. Herein, a modular engineering strategy is adopted to construct a Shewanella oneidensis‐carbon felt biohybrid electrode decorated with bacterial cellulose aerogel‐electropolymerized anthraquinone to boost cell‐electrode interfacial electron transfer. First, a heterologous riboflavin synthesis and secretion pathway is constructed to increase flavin‐mediated transmembrane electron transfer. Second, outer membrane c‐Cyts OmcF is screened and optimized via protein engineering strategy to accelerate contacted‐based transmembrane electron transfer. Third, a S. oneidensis‐carbon felt biohybrid electrode decorated with bacterial cellulose aerogel and electropolymerized anthraquinone is constructed to boost the interfacial electron transfer. As a result, the internal resistance decreased to 42 Ω, 480.8‐fold lower than that of the wild‐type (WT) S. oneidensis MR‐1. The maximum power density reached 4286.6 ± 202.1 mW m−2, 72.8‐fold higher than that of WT. Lastly, the engineered biohybrid electrode exhibited superior abilities for bioelectricity harvest, Cr6+ reduction, and CO2 reduction. This study showed that enhancing transmembrane and cell‐electrode interfacial electron transfer is a promising way to increase the extracellular electron transfer of EAMs. [ABSTRACT FROM AUTHOR]

Small GTPases comprise a superfamily of over 167 proteins in the human genome and are critical regulators of a variety of pathways including cell migration and proliferation. Despite the importance of these proteins in cell signaling, a standardized approach for controlling small GTPase activation within living cells is lacking. Herein, we report a split‐protein‐based approach to directly activate small GTPase signaling in living cells. Importantly, our fragmentation site can be applied across the small GTPase superfamily. We highlight the utility of these standardized parts by demonstrating the ability to directly modulate the activity of four different small GTPases with user‐defined inputs, providing the first plug and play system for direct activation of small GTPases in living cells. [ABSTRACT FROM AUTHOR]

Expanding the protein fold space beyond linear chains is of fundamental significance, yet remains largely unexplored. Herein, we report the creation of seven topological isoforms (i.e., linear, cyclic, knot, lasso, pseudorotaxane, and catenane) from a single protein fold precursor by rewiring the connectivity of secondary structure elements of the SpyTag-SpyCatcher complex and mutating the reactive residue on SpyTag to abolish the isopeptide bonding. These topological isoforms can be directly expressed in cells. Their topologies were confirmed by combined techniques of proteolytic digestion, fluorescence correlation spectroscopy (FCS), size-exclusion chromatography (SEC), and topological transformation. To study the effects of topology on their structures and properties, their biophysical properties were characterized by differential scanning calorimetry (DSC), heteronuclear single quantum coherence nuclear magnetic resonance spectroscopy (HSQC-NMR), and circular dichroism (CD) spectroscopy. Molecular dynamics (MD) simulations were further performed to reveal the atomic details of structural changes upon unfolding. Both experimental and simulation results suggest that they share a similar, well-folded hydrophobic core but exhibit distinct folding/unfolding dynamic behaviors. These results shed light onto the folding landscape of topological isoforms derived from the same protein fold. As a model system, this work improves our understanding of protein structure and dynamics beyond linear chains and suggests that protein folds are highly amenable to topological variation. [ABSTRACT FROM AUTHOR]

Phytopathogens represent an ongoing threat to crop production and a significant impediment to global food security. During the infection process, these pathogens spatiotemporally deploy a large array of effectors to sabotage host defense machinery and/or manipulate cellular pathways, thereby facilitating colonization and infection. However, besides their pivotal roles in pathogenesis, certain effectors, known as avirulence (AVR) effectors, can be directly or indirectly perceived by plant resistance (R) proteins, leading to race-specific resistance. An in-depth understanding of the intricate AVR-R interactions is instrumental for genetic improvement of crops and safeguarding them from diseases. Magnaporthe oryzae (M. oryzae), the causative agent of rice blast disease, is an exceptionally virulent and devastating fungal pathogen that induces blast disease on over 50 monocot plant species, including economically important crops. Rice-M. oryzae pathosystem serves as a prime model for functional dissection of AVR effectors and their interactions with R proteins and other target proteins in rice due to its scientific advantages and economic importance. Significant progress has been made in elucidating the potential roles of AVR effectors in the interaction between rice and M. oryzae over the past two decades. This review comprehensively discusses recent advancements in the field of M. oryzae AVR effectors, with a specific focus on their multifaceted roles through interactions with corresponding R/target proteins in rice during infection. Furthermore, we deliberated on the emerging strategies for engineering R proteins by leveraging the structural insights gained from M. oryzae AVR effectors. [ABSTRACT FROM AUTHOR]

Protein aggregation causes a wide range of neurodegenerative diseases. Targeting and removing aggregates, but not the functional protein, is a considerable therapeutic challenge. Here, we describe a therapeutic strategy called "RING-Bait," which employs an aggregating protein sequence combined with an E3 ubiquitin ligase. RING-Bait is recruited into aggregates, whereupon clustering dimerizes the RING domain and activates its E3 function, resulting in the degradation of the aggregate complex. We exemplify this concept by demonstrating the specific degradation of tau aggregates while sparing soluble tau. Unlike immunotherapy, RING-Bait is effective against both seeded and cell-autonomous aggregation. RING-Bait removed tau aggregates seeded from Alzheimer's disease (AD) and progressive supranuclear palsy (PSP) brain extracts and was also effective in primary neurons. We used a brain-penetrant adeno-associated virus (AAV) to treat P301S tau transgenic mice, reducing tau pathology and improving motor function. A RING-Bait strategy could be applied to other neurodegenerative proteinopathies by replacing the Bait sequence to match the target aggregate. [Display omitted] • RING-Bait technology turns aggregate sequences into potent aggregate degraders • RING-Bait degraders remove pre-existing aggregates and prevent new ones from forming • RING-Bait degraders prevent seeded aggregation from human AD and PSP tau aggregates • RING-Bait degraders reduce tau pathology and improve motor function in vivo RING-Bait technology hijacks the process of templated aggregation to recruit ubiquitination machinery and selectively degrade pathogenic tau assemblies without generating a specific binder, thereby reducing tau pathology and improving motor function in vivo. [ABSTRACT FROM AUTHOR]

The engineered TadA variants used in cytosine base editors (CBEs) present distinctive advantages, including a smaller size and fewer off-target effects compared to cytosine base editors that rely on natural deaminases. However, the current TadA variants demonstrate a preference for base editing in DNA with specific motif sequences and possess dual deaminase activity, acting on both cytosine and adenosine in adjacent positions, limiting their application scope. To address these issues, we employ TadA orthologs screening and multi sequence alignment (MSA)-guided protein engineering techniques to create a highly effective cytosine base editor (aTdCBE) without motif and adenosine deaminase activity limitations. Notably, the delivery of aTdCBE to a humanized mouse model of Duchenne muscular dystrophy (DMD) mice achieves robust exon 55 skipping and restoration of dystrophin expression. Our advancement in engineering TadA ortholog for cytosine editing enriches the base editing toolkits for gene-editing therapy and other potential applications. Engineered TadA variants in cytosine base editors (CBEs) offer advantages like smaller size and reduced off-target effects. Here, authors develop an advanced CBE (aTdCBE) through ortholog screening and protein engineering, successfully enabling exon skipping in Duchenne muscular dystrophy model. [ABSTRACT FROM AUTHOR]

In mammalian cells, protein condensates underlie diverse cell functions. Intensive synthetic biological research has been devoted to fabricating liquid droplets using de novo peptides/proteins designed from scratch in test tubes or bacterial cells. However, the development of de novo sequences for synthetic droplets forming in eukaryotes is challenging. Here, we report YK peptides, comprising 9–15 residues of alternating repeats of tyrosine and lysine, which form reversible amyloid-like fibrils accompanied by binding with poly-anion species such as ATP. By genetically tagging the YK peptide, superfolder GFPs assemble into artificial liquid-like droplets in living cells. Rational design of the YK system allows fine-tuning of the fluidity and construction of multi-component droplets. The YK system not only facilitates intracellular reconstitution of simplified models for natural protein condensates, but it also provides a toolbox for the systematic creation of droplets with different dynamics and composition for in situ evaluation. Protein condensates underlie various cellular events. Here, the authors developed short peptide tags that form reversible amyloids, which enable the fabrication of artificial protein condensates with different fluidities or multi-component properties in mammalian cells. [ABSTRACT FROM AUTHOR]

Proteins are the foundation of life, and designing functional proteins remains a key challenge in biotechnology. Before the development of AlphaFold2, the focus of design was primarily on structure-centric approaches such as using the well-known open-source software Rosetta3. Following the development of AlphaFold2, deep-learning techniques for protein design gained prominence. This study proposes a new method to generate functional proteins using the diffusion model and ESM2 protein language model. Diffusion models, which are widely used in image and natural language generation, are used here for protein design, facilitating the controlled generation of new sequences. The ESM2 model, trained on the basis of large-scale protein sequence data, provides a deep understanding of the context of the sequence, thus improving the model's ability to generate biologically relevant proteins. In this study, we used the Protein A-like peptide as a model study object, combined the diffusion model and the ESM2 model to generate new peptide sequences from minimal input data, and verified their biological activities through experiments such as the BLI affinity test. In conclusion, we developed a new method for protein design that provides a novel strategy to meet the challenges of generic protein generation. [ABSTRACT FROM AUTHOR]

Natural evolution endows some insects and marine organisms with a special class of protein-based elastic tissues that possess energy feedback characteristics, providing them with the foundation for jumping and flying, and protecting them from the damage caused by movements or waves. However, the design and fabrication of such protein-based elastomeric materials that can function in human society through biomimetic strategies still remains challenging. Recombinant proteins designed by synthetic biology can mimic the advantageous structures in natural proteins and can be biosynthesized without the requirements for harsh conditions such as high temperatures and cytotoxic agents, which provides a great opportunity to prepare protein-based elastomeric materials. In this review, starting from the design of protein molecules, we highlight an overview of the synthesis of elastomeric materials based on recombinant resilin, recombinant elastin-like proteins and other recombinant folded proteins, etc., and then demonstrate their application progress in the fields of biomedicine and high technology. Finally, the challenges and prospects for the future development of protein-based elastomeric materials are envisioned to provide insights into the design and synthesis of the next generation of protein-based elastomeric materials. [ABSTRACT FROM AUTHOR]

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme–ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75% of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50% of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles. Scientific contributions The pipeline introduced in this work allows for the detailed analysis of a large set of protein–ligand complexes, focusing on transport pathways. We are introducing a novel predictor for determining the relevance of binding pockets for tunnel calculation. For the first time in the field, we present a high-throughput energetic analysis of ligand binding and unbinding, showing that approximate methods for these simulations can identify additional mutagenesis hotspots in enzymes compared to purely geometrical methods. The predictor is included in the supplementary material and can also be accessed at https://github.com/Faranehhad/Large-Scale-Pocket-Tunnel-Annotation.git. The tunnel data calculated in this study has been made publicly available as part of the ChannelsDB 2.0 database, accessible at https://channelsdb2.biodata.ceitec.cz/. [ABSTRACT FROM AUTHOR]

Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies. Author summary: A great challenge in protein science is to relate sequences to physical properties, both to predict physical properties from sequences, and to design sequences with desired phenotypes. A promising solution lies in integrating large-scale selection experiments, high-throughput sequencing, and machine learning techniques. However, existing models often focus solely on the property under selection ("fitness"), lacking interpretability. This limits our fundamental understanding of proteins and our ability to engineer them for desired properties, especially those not directly selectable in experiments. Previous studies have shown that incorporating biophysical constraints into models can offer quantitative insights, particularly in transcription factors Here, we demonstrate that when coupled with extensive experiments, such modeling can not only predict physical features but also design new proteins with specific properties. Our demonstration involves a problem of primary biotechnological and biomedical interest: the design of antibodies with defined specificity profiles. We focus on one of the most challenging tasks in the field, designing antibodies capable of discriminating between structurally and chemically similar ligands. This approach has applications for creating antibodies with both specific and cross-specific binding properties and for mitigating experimental artifacts and biases in selection experiments. The combination of biophysics-informed modeling and extensive selection experiments holds broad applicability beyond antibodies, offering a powerful toolset for designing proteins with desired physical properties. [ABSTRACT FROM AUTHOR]

Prediction of protein fitness from computational modeling is an area of active research in rational protein design. Here, we investigated whether protein fluctuations computed from molecular dynamics simulations can be used to predict the expression levels of SARS‐CoV‐2 receptor binding domain (RBD) mutants determined in the deep mutational scanning experiment of Starr et al. [Science (New York, N.Y.) 2022, 377, 420] Specifically, we performed more than 0.7 milliseconds of molecular dynamics (MD) simulations of 557 mutant RBDs in triplicate to achieve statistical significance under various simulation conditions. Our results show modest but significant anticorrelation in the range [−0.4, −0.3] between expression and RBD protein flexibility. A simple linear regression machine learning model achieved correlation coefficients in the range [0.7, 0.8], thus outperforming MD‐based models, but required about 25 mutations at each residue position for training. [ABSTRACT FROM AUTHOR]

Rechargeable aqueous zinc‐ion (Zn‐ion) batteries are widely regarded as important candidates for next‐generation energy storage systems for low‐cost renewable energy storage. However, the development of Zn‐ion batteries is currently facing significant challenges due to uncontrollable Zn dendrite growth and severe parasitic reactions on Zn metal anodes. Herein, we report an effective strategy to improve the performance of aqueous Zn‐ion batteries by leveraging the self‐assembly of bovine serum albumin (BSA) into a bilayer configuration on Zn metal anodes. BSA′s hydrophilic and hydrophobic fragments form unique and intelligent ion channels, which regulate the migration of Zn ions and facilitate their desolvation process, significantly diminishing parasitic reactions on Zn anodes and leading to a uniform Zn deposition along the Zn (002) plane. Notably, the Zn||Zn symmetric cell with BSA as the electrolyte additive demonstrated a stable cycling performance for up to 2400 hours at a high current density of 10 mA cm−2. This work demonstrates the pivotal role of self‐assembled protein bilayer structures in improving the durability of Zn anodes in aqueous Zn‐ion batteries. [ABSTRACT FROM AUTHOR]

Deep learning tools for enzyme design are rapidly emerging, and there is a critical need to evaluate their effectiveness in engineering workflows. Here we show that the deep learning‐based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially relevant non‐native functions. This superfamily has diverse catalytic functions and could provide a rich new source of biocatalysts for synthesis and industrial processes. Through systematic comparisons of directed evolution trajectories for a non‐native, remote C(sp3)−H hydroxylation reaction, we demonstrate that the stabilized redesign can be evolved more efficiently than the wild‐type enzyme. After three rounds of directed evolution, we obtained a 6‐fold activity increase from the wild‐type parent and an 80‐fold increase from the stabilized variant. To generate the initial stabilized variant, we identified multiple structural and sequence constraints to preserve catalytic function. We applied these criteria to produce stabilized, catalytically active variants of a second Fe(II)/αKG enzyme, suggesting that the approach is generalizable to additional members of the Fe(II)/αKG superfamily. ProteinMPNN is user‐friendly and widely accessible, and our results provide a framework for the routine implementation of deep learning‐based protein stabilization tools in directed evolution workflows for novel biocatalysts. [ABSTRACT FROM AUTHOR]

The production of bioethanol from lignocellulosic biomass requires the efficient conversion of glucose and xylose to ethanol, a process that depends on the ability of microorganisms to internalize these sugars. Although glucose transporters exist in several species, xylose transporters are less common. Several types of transporters have been identified in diverse microorganisms, including members of the Major Facilitator Superfamily (MFS) and Sugars Will Eventually be Exported Transporter (SWEET) families. Considering that Saccharomyces cerevisiae lacks an effective xylose transport system, engineered yeast strains capable of efficiently consuming this sugar are critical for obtaining high ethanol yields. This article reviews the structure–function relationship of sugar transporters from the MFS and SWEET families. It provides information on several tools and approaches used to identify and characterize them to optimize xylose consumption and, consequently, second-generation ethanol production. [ABSTRACT FROM AUTHOR]

Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research. Machine learning has been used to reveal the relationship between protein sequences and biocatalytic enantioselectivity, but the establishment of substrate fitness space is challenging. Herein, the authors build random forest classification models for predicting the enantioselectivity of amidase toward different substrates, adopting chemistry and geometry descriptors. [ABSTRACT FROM AUTHOR]

Fusicoccadiene synthase from Phomopsis amygdala (PaFS) is a bifunctional terpene synthase. It contains a prenyltransferase (PT) domain that generates geranylgeranyl diphosphate (GGPP) from dimethylallyl diphosphate and three equivalents of isopentenyl diphosphate, and a cyclase domain that converts GGPP into fusicoccadiene, a precursor of the diterpene glycoside Fusicoccin A. The two catalytic domains are connected by a flexible 69-residue linker. The PT domain mediates oligomerization to form predominantly octamers, with cyclase domains randomly splayed out around the PT core. Surprisingly, despite the random positioning of cyclase domains, substrate channeling is operative in catalysis since most of the GGPP generated by the PT remains on the enzyme for cyclization. Here, we demonstrate that covalent linkage of the PT and cyclase domains is not required for GGPP channeling, although covalent linkage may improve channeling efficiency. Moreover, GGPP competition experiments with other diterpene cyclases indicate that the PaFS PT and cyclase domains are preferential partners regardless of whether they are covalently linked or not. The cryoelectron microscopy structure of the 600-kD "linkerless" construct, in which the 69-residue linker is spliced out and replaced with the tripeptide PTQ, reveals that cyclase pairs associate with all four sides of the PT octamer and exhibit fascinating quaternary structural flexibility. These results suggest that optimal substrate channeling is achieved when a cyclase domain associates with the side of the PT octamer, regardless of whether the two domains are covalently linked and regardless of whether this interaction is transient or locked in place. [ABSTRACT FROM AUTHOR]

Continuous enzymatic synthesis has gained increasing prominence, extending its applicability to the chemical industry. A practical approach for integrating enzymes into continuously operated reactors is their entrapment within polymer‐based hydrogels. In this study, we present a novel approach to enhance enzyme retention within agarose hydrogels, employing a carbohydrate‐binding module sourced from the Microbulbifer thermotolerans thermostable β‐agarase I (MtCBM) as a versatile agarose‐binding tag. Among the four tested carbohydrate‐binding modules, MtCBM exhibited superior binding affinity to solid agarose and fusion proteins incorporating MtCBM demonstrated significantly enhanced retention within agarose hydrogels. Employing a newly developed fluidic flat‐bed agarose reactor, we demonstrate that, due to increased retention in agarose hydrogels, a phenacrylate decarboxylase tagged with MtCBM yielded nearly six times more product compared to a similarly sized fusion enzyme lacking the MtCBM‐tag. The higher leaching rate of the unlabeled enzyme could not be compensated even by tripling the hydrogel layer thickness, clearly documenting the effectiveness of MtCBM labeling for retention in the gel matrix. In addition, we performed theoretical modelling of the reactor, which led to a deeper understanding of the retention kinetics. The results suggest that this innovative approach is promising for improving stability and reusability of enzymes in agarose‐based carriers. [ABSTRACT FROM AUTHOR]

The PP2A-B55 phosphatase regulates a plethora of signaling pathways throughout eukaryotes. How PP2A-B55 selects its substrates presents a severe knowledge gap. By integrating AlphaFold modeling with comprehensive high-resolution mutational scanning, we show that a helices in substrates bind B55 through an evolutionary conserved mechanism. Despite a large diversity in sequence and composition, these a helices share key amino acid determinants that engage discrete hydrophobic and electrostatic patches. Using deep learning protein design, we generate a specific and potent competitive peptide inhibitor of PP2A-B55 substrate interactions. With this inhibitor, we uncover that PP2A-B55 regulates the nuclear exosome targeting (NEXT) complex by binding to an α-helical recruitment module in the RNA binding protein 7 (RBM7), a component of the NEXT complex. Collectively, our findings provide a framework for the understanding and interrogation of PP2A-B55 function in health and disease. [ABSTRACT FROM AUTHOR]

Introduction: Bacterial surface display is a valuable biotechnology technique for presenting proteins and molecules on the outer surface of bacterial cells. However, it has limitations, including potential toxicity to host bacteria and variability in display efficiency. To address these issues, we investigated the removal of abundant non-essential outer membrane proteins (OMPs) in E. coli as a new strategy to improve the surface display of recombinant proteins. Methods: We targeted OmpA, a highly prevalent OMP in E. coli, using the lambda red method. We successfully knocked out ompA in two E. coli strains, K-12 MG1655 and E. coli BL-21, which have broad research and therapeutic applications. We then combined ompA knockout strains and two OMPs with three therapeutic proteins including an anti-toxin enzyme (ClbS), interleukin 18 (IL-18) for activating cytotoxic T cells and an anti- CTLA4 nanobody (αCTLA4) for immune checkpoint blockade. Results: A total of six different display constructs were tested for their display levels by flow cytometry, showing that the ompA knockout strains increased the percentage as well as the levels of display in bacteria compared to those of isogenic wild-type strains. Conclusions: By removing non-essential, highly abundant surface proteins, we develop an efficient platform for displaying enzymes and antibodies, with potential industrial and therapeutic applications. Additionally, the enhanced therapeutic efficacy opens possibilities for live bacteria-based therapeutics, expanding the technology's relevance in the field. [ABSTRACT FROM AUTHOR]