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Advanced design strategies are essential to realize the full potential of engineered living materials, including their biodegradability, manufacturability, sustainability, and ability to tailor functional properties. Toward these goals, we present mechanically engineered living material with compostability, healability, and scalability – a material that integrates these features in the form of a stretchable plastic that is simultaneously flushable, compostable, and exhibits the characteristics of paper. This plastic/paper-like material is produced in scalable quantities (0.5–1 g L−1), directly from cultured bacterial biomass (40%) containing engineered curli protein nanofibers. The elongation at break (1–160%) and Young's modulus (6-450 MPa) is tuned to more than two orders of magnitude. By genetically encoded covalent crosslinking of curli nanofibers, we increase the Young's modulus by two times. The designed engineered living materials biodegrade completely in 15–75 days, while its mechanical properties are comparable to petrochemical plastics and thus may find use as compostable materials for primary packaging. Advanced design strategies are required for increased control of favourable characteristics of Engineered Living Materials. Here, the authors report the development of a material that has plastic-like stretchability and paper-like compostability and manufacturability. [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]

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]

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]

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]

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]

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]

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]

Flavonoids are a class of polyphenolic compounds found in plants that offer extensive health benefits and have applications in the pharmaceutical, cosmetic, and food industries. Currently, flavonoid production largely depends on plant extraction methods, which face limitations owing to low yields and seasonal and environmental impacts. To address these issues, the potential of microbial fermentation, which leverages advances in metabolic engineering and genetic tools, has been discussed as an innovative alternative to overcome these challenges, thus offering an environmentally friendly and sustainable approach to flavonoid production. However, the integration of complex biosynthesis pathways into microbial systems presents challenges such as the inefficient expression of plant-derived genes, metabolic conflicts, and toxicity or feedback inhibition by accumulated flavonoids within the microbial cells. This comprehensive review highlights recent advancements in engineering strategies to address these challenges, focusing on biotransformation, single-strain fermentation, and co-culture systems, each with its own unique characteristics and potential for optimizing flavonoid production in a cost-effective and scalable manner. [ABSTRACT FROM AUTHOR]

Chitosanase plays a pivotal role in the production of chitooligosaccharide. Nevertheless, there is untapped potential for enhancing both its catalytic efficiency and thermostability, which could significantly bolster its therapeutic and biotechnological applications. In this study, two computer-aided protein design methods, namely Fireprot and PROSS, were utilized to pinpoint 6 single Bacillus subtilis chitosanase BsCsn46A mutants (S126A, D191A, K70A, L159I, K104P, and A129L) as well as 4 multiple mutants (K70A/S126A, K70A/S126A/K104P, K70A/S126A/L159I, and K70A/S126A/K104P/L159). The wild-type (WT) and all 10 BsCsn46A mutants displayed robust adaptability across a broad pH range, exhibiting peak activity within the pH spectrum of 5.5 to 9.5. The results demonstrated that, compared to the WT, 9 out of 10 mutants exhibited significantly heightened chitosanase activity, with the sole exception being the D191A mutant, which displayed activity levels nearly identical to the WT. Notably, the A129L displayed an impressive 20% increase in the enzyme activity at elevated temperatures, specifically in the range of 55–80°C. Assessing protein stability, results indicated that all samples maintained stability when incubated at 30°C for 1 h. However, when subjected to a higher temperature of 40°C for 1 h, only the A129L mutant retained stability, which persisted even after an extended incubation period of 3 h at 40°C. Furthermore, a thermal stability analysis revealed noteworthy differences between the WT and the mutants. The WT chitosanase activity diminished by 50% after brief 30-min incubation at 50°C, whereas the K70A/S126A, K70A/S126A/K104P, and A129L mutants maintained 50% of their activity for approximately 2 h under the same conditions. In summary, the study provides valuable insights into the thermostability and catalytic activity of chitosanase, highlighting promising candidates for industrial chitosanase applications. [ABSTRACT FROM AUTHOR]

Clostridioides difficile toxin B (TcdB) is the key virulence factor accounting for C. difficile infection-associated symptoms. Effectively neutralizing different TcdB variants with a universal solution poses a significant challenge. Here we present the de novo design and characterization of pan-specific mini-protein binders against major TcdB subtypes. Our design successfully binds to the first receptor binding interface (RBI-1) of the varied TcdB subtypes, exhibiting affinities ranging from 20 pM to 10 nM. The cryo-electron microscopy (cryo-EM) structures of the mini protein binder in complex with TcdB1 and TcdB4 are consistent with the computational design models. The engineered and evolved variants of the mini-protein binder and chondroitin sulfate proteoglycan 4 (CSPG4), another natural receptor that binds to the second RBI (RBI-2) of TcdB, better neutralize major TcdB variants both in cells and in vivo, as demonstrated by the colon-loop assay using female mice. Our findings provide valuable starting points for the development of therapeutics targeting C. difficile infections (CDI). Xinchen Lv and colleagues design pan-specific protein mini-binders that neutralize Clostridioides difficile toxin B (TcdB) and solve TcdB-binder complex structures via cryo-electron microscopy. [ABSTRACT FROM AUTHOR]

Erythritol is a natural non-caloric sweetener, which is produced by fermentation and extensively applied in food, medicine and chemical industries. The final step of the erythritol synthesis pathway is involved in erythritol reductase, whose activity and NADPH-dependent become the limiting node of erythritol production efficiency. Herein, we implemented a strategy combining molecular docking and thermal stability screening to construct an ER mutant library. And we successfully obtained a double mutant ERK26N/V295M (ER*) whose catalytic activity was 1.48 times that of wild-type ER. Through structural analysis and MD analysis, we found that the catalytic pocket and the enzyme stability of ER* were both improved. We overexpressed ER* in the engineered strain ΔKU70 to obtain the strain YLE-1. YLE-1 can produce 39.47 g/L of erythritol within 144 h, representing a 35% increase compared to the unmodified strain, and a 10% increase compared to the strain overexpressing wild-type ER. Considering the essentiality of NADPH supply, we further co-expressed ER* with two genes from the oxidative phase of PPP, ZWF1 and GND1. This resulted in the construction of YLE-3, which exhibited a significant increase in production, producing 47.85 g/L of erythritol within 144 h, representing a 63.90% increase compared to the original chassis strain. The productivity and the yield of the engineered strain YLE-3 were 0.33 g/L/h and 0.48 g/g glycerol, respectively. This work provided an ER mutation with excellent performance, and also proved the importance of cofactors in the process of erythritol synthesis, which will promote the industrial production of erythritol by metabolic engineering of Y. lipolytica. [ABSTRACT FROM AUTHOR]

Intracellular delivery of biomolecules is a prerequisite for genetic techniques such as recombinant engineering and genome editing. Realizing the full potential of this technology requires the development of safe and effective methods for delivering protein tools into cells. In this study, we demonstrated the spontaneous internalization of exogenous proteins into intact cells and root tissue of whole plants of Arabidopsis thaliana. We termed this internalization phenomenon as protein Delivery Independent of Vehicles or Equipment (DIVE), which efficiently delivered genome engineering proteins including Cre recombinase and zinc-finger nucleases (ZFN) into plant cells. Using protein DIVE, we achieved less toxic protein delivery than electroporation with up to 94% efficiency in Arabidopsis cell culture and 19% genome modification in Arabidopsis plants that was maintained in regenerated tissue. This work illustrates the potential of protein DIVE for a wide range of applications, including genome engineering in plants. [ABSTRACT FROM AUTHOR]

Molecular imaging holds the potential for noninvasive and accurate grading of liver fibrosis. It is limited by the lack of biomarkers that strongly correlate with liver fibrosis grade. Here, we discover the grading potential of fibroblast activation protein alpha (FAPα) for liver fibrosis through transcriptional analysis and biological assays on clinical liver samples. The protein and mRNA expression of FAPα are linearly correlated with fibrosis grade (R2 = 0.89 and 0.91, respectively). A FAPα-responsive MRI molecular nanoprobe is prepared for quantitatively grading liver fibrosis. The nanoprobe is composed of superparamagnetic amorphous iron nanoparticles (AFeNPs) and paramagnetic gadoteric acid (Gd-DOTA) connected by FAPα-responsive peptide chains (ASGPAGPA). As liver fibrosis worsens, the increased FAPα cut off more ASGPAGPA, restoring a higher T1-MRI signal of Gd-DOTA. Otherwise, the signal remains quenched due to the distance-dependent magnetic resonance tuning (MRET) effect between AFeNPs and Gd-DOTA. The nanoprobe identifies F1, F2, F3, and F4 fibrosis, with area under the curve of 99.8%, 66.7%, 70.4%, and 96.3% in patients' samples, respectively. This strategy exhibits potential in utilizing molecular imaging for the early detection and grading of liver fibrosis in the clinic. Molecular imaging holds promise for liver fibrosis grading but limited by a lack of effective biomarker. Here, the authors discover the grading potential of fibroblast activation protein alpha and design a responsive MRI nanoprobe that achieves fibrosis grading in mice models and clinical samples. [ABSTRACT FROM AUTHOR]

Phosphoethanolamine (pEtN) cellulose is a naturally occurring modified cellulose produced by several Enterobacteriaceae. The minimal components of the E. coli cellulose synthase complex include the catalytically active BcsA enzyme, a hexameric semicircle of the periplasmic BcsB protein, and the outer membrane (OM)-integrated BcsC subunit containing periplasmic tetratricopeptide repeats (TPR). Additional subunits include BcsG, a membrane-anchored periplasmic pEtN transferase associated with BcsA, and BcsZ, a periplasmic cellulase of unknown biological function. While cellulose synthesis and translocation by BcsA are well described, little is known about its pEtN modification and translocation across the cell envelope. We show that the N-terminal cytosolic domain of BcsA positions three BcsG copies near the nascent cellulose polymer. Further, the semicircle's terminal BcsB subunit tethers the N-terminus of a single BcsC protein in a trans-envelope secretion system. BcsC's TPR motifs bind a putative cello-oligosaccharide near the entrance to its OM pore. Additionally, we show that only the hydrolytic activity of BcsZ but not the subunit itself is necessary for cellulose secretion, suggesting a secretion mechanism based on enzymatic removal of translocation incompetent cellulose. Lastly, protein engineering introduces cellulose pEtN modification in orthogonal cellulose biosynthetic systems. These findings advance our understanding of pEtN cellulose modification and secretion. Enterobacteriaceae modify cellulose with lipid-derived pEtN groups to promote biofilm cohesion. Here, using structural and biochemical analyses, the authors provide further insights into the molecular interactions of BcsA, BcsG, BcsB, and BcsC facilitating pEtN modification and secretion of cellulose. [ABSTRACT FROM AUTHOR]

AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added to Tyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model's capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations. [ABSTRACT FROM AUTHOR]

Key message: Redesigning the N- and C-capping repeats of the native DARPin G3 significantly improved its stability, and may facilitate its purification from the total soluble proteins of high-temperature dried leaf materials of transplastomic plants. Designed ankyrin repeat proteins (DARPins) constitute a promising class of binding molecules that can overcome the limitations of monoclonal antibodies and enable the development of novel therapeutic approaches. Despite their inherent stability, detailed studies have revealed that the original capping repeats derived from natural ankyrin repeat proteins impair the stability of the initial DARPin design. Consequently, the development of thermodynamically stabilized antibody mimetics may facilitate the development of innovative drugs in the future. In this study, we replaced the original N- and C-capping repeats with improved caps to enhance the thermostability of native DARPin G3. Computational analyses suggested that the redesigned thermostable DARPin G3 structure possessed optimal quality and stability. Molecular dynamics simulations verified the stability of the redesigned thermostable DARPin G3 at high temperatures. The redesigned thermostable DARPin G3 was expressed at high levels in tobacco transplastomic plants and subsequently purified from high-temperature dried leaf materials. Thermal denaturation results revealed that the redesigned thermostable DARPin G3 had a higher Tm value than the native DARPin G3, with a Tm of 35.51 °C greater than that of native DARPin G3. The results of the in vitro bioassays confirmed that the purified thermostable DARPin G3 from high-temperature dried leaf materials maintained its binding activity without any loss of affinity and specifically bound to the HER2 receptor on the cell surface. These findings demonstrate the successful improvement in the thermostability of DARPin G3 without compromising its biological activity. [ABSTRACT FROM AUTHOR]

Bioorthogonal click chemistry has played a transformative role in many research fields, including chemistry, biology, and medicine. Click reactions are crucial to produce increasingly complex bioconjugates, to visualize and manipulate biomolecules in living systems and for various applications in bioengineering and drug delivery. As biological (model) systems grow more complex, researchers have an increasing need for using multiple orthogonal click reactions simultaneously. In this review, we will introduce the most common bioorthogonal reactions and discuss their orthogonal use on the basis of their mechanism and electronic or steric tuning. We provide an overview of strategies to create reaction orthogonality and show recent examples of mutual orthogonal chemistry used for simultaneous biomolecule labeling. We end by discussing some considerations for the type of chemistry needed for labeling biomolecules in a system of choice. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design. [ABSTRACT FROM AUTHOR]

Xylooligosaccharides (XOS) are potential prebiotic ingredients for food industries, mainly obtained after xylan hydrolysis by endoxylanases. Enzyme immobilization offers opportunities for recovery and reuse, while also enhancing its physical and chemical characteristics, such as stability and catalytic efficiency. This work aimed to immobilize the SM2 xylanase derived from the xynA gene from Orpinomyces sp. PC-2 and to evaluate its potential for XOS production. For this, SM2 xylanase was immobilized using the cross-linking methodology. The free and immobilized enzymes were characterized regarding the effect of pH, temperature, and thermostability. The cross-linked enzyme aggregate was evaluated for reuse and storage conditions and used for xylooligosaccharide production. Both free and immobilized SM2 xylanase showed maximal activity at 60 °C. The immobilized enzyme was more active at acidic and neutral conditions, and the free enzyme showed greater activity at basic conditions. The half-life of the free and immobilized xylanase was 30 and 216 h, respectively. In reuse tests, enzymatic activity increased with each cycle, and there was no statistical difference in the activity of SM2 xylanase aggregate stored at 4 and 25 °C. After saccharification, xylobiose (0.895 g/L), xylotriose (0.489 g/L), and xylohexose (0.809 g/L) were detected. As a result, immobilization enhanced thermostability, shifted the pH of maximum activity to 5, facilitated reuse, and eliminated the need for refrigerated packaging. Finally, the xylooligosaccharides produced by the SM2 xylanase are known for their prebiotic role, providing potential application of the immobilized enzyme in the food industry. [ABSTRACT FROM AUTHOR]

Interventions in the upstream production and further processing of recombinant food proteins affect its properties when used for food application. Often the efficiency of particular interventions is evaluated based on molecular purity and yield rather than final functional properties. Yet, the formulation of foods, including the amount of protein required, can be affected when the functional properties have changed. In this explorative study, we exemplify how far we can extend the functionality range of the major whey protein β-lactoglobulin (BLG), in terms of foaming and (heat-set) gelling, through various interventions. Slight changes in the amino acid sequence of BLG affected its functional properties significantly. Foams were up to ten times more stable, when selecting different natural isoforms of BLG (isoform A instead of B) or when inducing targeted cysteine mutations. The isoform B yielded stronger thermally induced gels (+ 40%) compared to isoform A. During downstream processing of recombinantly secreted BLG, limited purification of up to ~ 67 wt% enabled reasonable foaming properties and superior gelation, while a lower purity of ~ 22 wt% resulted in poor performance in both cases. Post-processing allowed conversion of native whey protein into soluble amyloid-like aggregates. These aggregates resulted in better foam stability (i.e., approximately four times longer than non-aggregated protein), but did not improve gelation. The presented study demonstrates that one should consider not only protein yield and purity, but also functional properties when developing recombinant proteins for food application. In turn, these functional properties are a result of the complete upstream and downstream chain. [ABSTRACT FROM AUTHOR]

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search—an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign's high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function. GTalign introduces spatial structure indexing for accelerated and deep superposition search and protein alignment derivation. Its parallel algorithms facilitate massive protein similarity searches, offering speed and accuracy advantages. [ABSTRACT FROM AUTHOR]

The biocatalytic degradation of poly(ethylene terephthalate) (PET) through enzymatic methods has garnered considerable attention due to its environmentally friendly and non-polluting nature, as well as its high specificity. While previous efforts in enhancing IsPETase performance have focused on amino acid substitutions in protein engineering, we introduced an amino acid insertion strategy in this work. By inserting a negatively charged acidic amino acid, Glu, at the right-angle bend of IsPETase, the binding capability between the enzyme's active pocket and PET was improved. The resulted mutant IsPETase9394insE exhibited enhanced hydrolytic activity towards PET at various temperatures ranging from 30 to 45 ℃ compared with the wild-type IsPETase. Notably, a 10.04-fold increase was observed at 45 ℃. To further enhance PET hydrolysis, different carbohydrate-binding modules (CBMs) were incorporated at the C-terminus of IsPETase9394insE. Among these, the fusion of CBM from Verrucosispora sioxanthis exhibited the highest enhancement, resulting in a 1.82-fold increase in PET hydrolytic activity at 37 ℃ compared with the IsPETase9394insE. Finally, the engineered variant was successfully employed for the degradation of polyester filter cloth, demonstrating its promising hydrolytic capacity. In conclusion, this research presents an alternative enzyme engineering strategy for modifying PETases and enriches the pool of potential candidates for industrial PET degradation. [ABSTRACT FROM AUTHOR]

Objective: Scorpion α-neurotoxins (ɑ-NaTx) inhibit the inactivation of voltage-gated sodium channels (Nav) with variable efficiency between organisms and channel isoforms. Based on our previous results, we hypothesized that the derivative of the ɑ-NaTx called BeM9 with two amino acid residues substituted with glycine (A4G and Y17G; BeM9GG) should be more selective for the mammalian channels. Surprisingly, BeM9GG lost its activity on the cardiac isoform Nav1.5. We provide a possible explanation of this effect, taking into account the published structures of ɑ-NaTx–Nav complexes. Methods: We produced BeM9GG in Escherichia coli as a fusion protein with thioredoxin, which was cleaved by cyanogen bromide. We purified BeM9GG using chromatography and measured its activity on Nav isoforms expressed in Xenopus laevis oocytes by the two-electrode voltage clamp technique. We performed computer modeling of ɑ-NaTx–Nav complexes to figure out the peculiarities of toxin interactions with Nav1.5. Results and Discussion: Using electrophysiology, we tested BeM9GG on Nav, and unexpectedly observed compromised activity on Nav1.5. We compared the structures of available ɑ-NaTx–Nav complexes to explain this effect. In case of Nav1.5 the toxin-binding site is immersed deeper in the membrane. We explain this by sequence variations in two positions of the voltage-sensing domain IV of Nav: in Nav1.5 corresponding residues have shorter side chains, permitting the toxin to sit deeper. At the same time, residue Y17 in BeM9, which is missing in BeM9GG, interacts with the hydrophobic core of the membrane and may play a significant role in its activity against Nav1.5. Conclusions: The role of the membrane in ɑ-NaTx interactions with Nav has been overlooked. It is especially important in case of Nav1.5, where the tripartite toxin–ion channel– membrane complex is formed. Our study will be of help for the future design of selective Nav ligands. [ABSTRACT FROM AUTHOR]

C13-apocarotenoids are volatile organic compounds naturally derived from the oxidative cleavage of carotenoids. These small molecules form unique aromas in flowers, fruits, and plants. They are highly valued compounds in the flavor and fragrance industry. The microbial production of C13-apocarotenoids, such as β-ionone, α-ionone, and pseudoionone, is an emerging and promising approach with inherent advantageousness of scalable output to reach the goals as stated in the United Nations Sustainable Development Goals. Many engineering efforts have been implemented continuously but very few of them proved to be successful in achieving product titer at the grams-per-liter level with the least accumulated amount of precursor carotenoids and byproducts. The efficiency of oxidative cleavage of carotenoids conducted by carotenoid cleavage dioxygenases is suggested to be the critical metabolic node to reconstruct an economically viable C13-apocarotenoids biosynthetic pathway. In this regard, we review recent advances in improving microbial biosynthesis of C13-apocarotenoids by protein and metabolic engineering. The potential strategies that could be implemented further to achieve efficient C13-apocarotenoid production are also discussed. [ABSTRACT FROM AUTHOR]

With the increasing world population and vegan diet, there has been increasing consumer demand for alternative protein sources. A substitute for animal proteins is the plant protein, for instance, leaves. The Pereskia aculeata, known as ora-pro-nóbis, is undoubtedly a leafy vegetable with great potential due to its relatively high protein content (17 to 28%). This study aimed to produce ora-pro-nóbis protein concentrate (OPNPC) from ora-pro-nóbis leaves flour (OPNF) by isoelectric precipitation at three different pH's (3.5, 4.0, and 4.5). The protein extraction by precipitation in different pHs produced OPNPC with protein content and extraction yield ranging from 52 to 55% and 1–4%, respectively. Given the highest yield, the concentrate obtained at pH 3.5 (OPNPC3.5) was selected for further investigation and comparison to OPNF. The differences in color, techno-functional properties, in vitro protein digestibility (IVPD), and structural properties were evaluated. Most techno-functional properties were statistically higher in OPNPC3.5 than in OPNF. These included its water solubility, oil holding capacity, foam capacity and stability, and emulsifying activity and stability. OPNPC3.5 had a higher IVPD (80%) than flour (77%). Scanning electron microscopy and Fourier transform infrared spectroscopy confirmed distinct compositions of materials, which can explain the difference in techno-functional properties. The findings indicate controlling protein extraction conditions as a useful technique to maximize the yield of protein concentrate obtained from ora-pro-nóbis, which was more nutritious and had better techno-functional properties than flour. This demonstrates its potential as an alternative plant-based protein to design healthy and sustainable food products. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks' capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models' extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. We also find that implementing a simple ensemble of convolutional neural networks enables robust design of high-performing variants in the local landscape. Our findings highlight how each architecture's inductive biases prime them to learn different aspects of the protein fitness landscape and how a simple ensembling approach makes protein engineering more robust. Machine learning accelerates protein engineering by predicting sequence-function relationships. Here, authors evaluate neural network architectures' ability to extrapolate beyond training data, finding simpler models excel in local design while convolutional models explore deeper sequence spaces. [ABSTRACT FROM AUTHOR]

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 <1 nm) to an ajar state (pore dimensions ~4 nm) when exposed to an HCN(g) trigger. When layered onto a mesoporous silicon (pSi) photonic crystal optical sensor configured to detect HCN(g), the 2D CEERhuA crystal layer effectively blocks interferents that would otherwise result in a false positive signal. The 2D CEERhuA crystal layer opens in selective response to low-ppm levels of HCN(g), allowing analyte penetration into the pSi sensor layer for detection. These findings illustrate that designed protein assemblies can function as dynamic components of solid-state devices in non-aqueous environments. Vijayakumar and coworkers report the integration of a designed, 2D crystalline protein assembly as a dynamic gatekeeper into mesoporous silicon photonic crystals for the selective sensing of hydrogen cyanide [ABSTRACT FROM AUTHOR]

The prefusion conformation of human metapneumovirus fusion protein (hMPV Pre-F) is critical for eliciting the most potent neutralizing antibodies and is the preferred immunogen for an efficacious vaccine against hMPV respiratory infections. Here we show that an additional cleavage event in the F protein allows closure and correct folding of the trimer. We therefore engineered the F protein to undergo double cleavage, which enabled screening for Pre-F stabilizing substitutions at the natively folded protomer interfaces. To identify these substitutions, we developed an AI convolutional classifier that successfully predicts complex polar interactions often overlooked by physics-based methods and visual inspection. The combination of additional processing, stabilization of interface regions and stabilization of the membrane-proximal stem, resulted in a Pre-F protein vaccine candidate without the need for a heterologous trimerization domain that exhibited high expression yields and thermostability. Cryo-EM analysis shows the complete ectodomain structure, including the stem, and a specific interaction of the newly identified cleaved C-terminus with the adjacent protomer. Importantly, the protein induces high and cross-neutralizing antibody responses resulting in near complete protection against hMPV challenge in cotton rats, making the highly stable, double-cleaved hMPV Pre-F trimer an attractive vaccine candidate. The authors report an efficacious vaccine for human metapneumovirus based on a prefusion-stabilized fusion protein trimer that does not require a trimerization domain, designed using an AI convolutional classifier trained to predict optimized complex polar interactions. [ABSTRACT FROM AUTHOR]

Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. To validate the method, we show that it can produce highly thermostable, catalytically active enzymes with high success rates. This concept is anticipated to improve the versatility of protein design pipelines for crafting desired functions. Advances in deep learning are transforming protein design. Here, authors introduce a method using geometric transformers to predict protein sequences, resulting in highly thermostable and catalytically active enzymes with high success rates. [ABSTRACT FROM AUTHOR]

Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM's generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub. Geometric deep learning methods have the advantage of being expressive, while denoising diffusion probabilistic models have great generative power. Here, the authors introduce a geometry-complete diffusion model for effective 3D molecule generation for specific protein pockets that can also consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity [ABSTRACT FROM AUTHOR]

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering. In this work, the authors proposed a few-shot learning approach that can efficiently optimize protein language models for fitness prediction. It combines the techniques of meta-transfer learning, learning to rank, and parameter-efficient fine-tuning. [ABSTRACT FROM AUTHOR]

Zeolitic imidazolate frameworks (ZIFs) are an important subclass of metal-organic frameworks (MOFs) with zeolite-type topology, which can be fabricated under ambient synthesis conditions. However, the applications of ZIFs are commonly limited due to the weak hydrostability of their metal–ligand coordination bonds, particularly under humid and aqueous conditions. In this work, as an example, the hydrolysis behaviours of ZIF-L with a special focus on ZIF-L coatings were tested at aqueous conditions with a wide range of pHs to systematically study and fundamentally understand their structural stability and degradation mechanism. Pristine ZIF-L powder and ZIF-L coatings were severely damaged after only 24 h in aqueous media. Interestingly, the ZIF-L coatings showed two distinct hydrolyzation pathways regardless of pH conditions, exhibiting either a ring-shaped etching or unfolding behaviours. While the ZIF-L powders were hydrolyzed almost identically across all pH conditions. With this new understanding, a facile silk fibroin (SF) protein modification method was developed to enhance the hydrostability of ZIF-L coatings in aqueous media. The effect of protein concentration on surface coating was systemically studied. ZIF-L coating retained its surface morphology after soaking in water and demonstrated switchable super wetting properties and superior separation performance for oil/water mixture. As a result, the quick SF protein modification significantly enhanced the stability of ZIF-L coatings under various pHs, while retaining their switchable wetting property and excellent separation performance. [ABSTRACT FROM AUTHOR]

The deep-learning protein structure prediction method AlphaFold2 has garnered enormous attention beyond the realm of structural biology, for its groundbreaking contribution to solving the "protein folding problem". In this perspective, we explore the connection between protein structure studies and environmental research, delving into the potential for addressing specific environmental challenges. Proteins are promising for environmental applications because of the functional diversity endowed by their structural complexity. However, structural studies on proteins with environmental significance remain scarce. Here, we present the opportunity to study proteins by advancing experimental determination and deep-learning prediction methods. Specifically, the latest progress in environmental research via cryogenic electron microscopy is highlighted. It allows us to determine the structure of protein complexes in their native state within cells at molecular resolution, revealing environmentally-associated structural dynamics. With the remarkable advancements in computational power and experimental resolution, the study of protein structure and dynamics has reached unprecedented depth and accuracy. These advancements will undoubtedly accelerate the establishment of comprehensive environmental protein structural and functional databases. Tremendous opportunities for protein engineering exist to enable innovative solutions for environmental applications, such as the degradation of persistent contaminants, and the recovery of valuable metals as well as rare earth elements. [ABSTRACT FROM AUTHOR]

Phospholipase A1 (PLA1) is a kind of specific phospholipid hydrolase widely used in food, medical, textile. However, limitations in its expression and enzymatic activity have prompted the investigation of the phospholipase-assisting protein PlaS. In this study, we elucidate the role of PlaS in enhancing the expression and activity of PlaA1 through N-terminal truncation. Our research demonstrates that truncating the N-terminal region of PlaS effectively overcomes its inhibitory effect on host cells, resulting in improved cell growth and increased protein solubility of the protein. The yeast two-hybrid assay confirms the interaction between PlaA1 and N-terminal truncated PlaS (∆N27 PlaS), highlighting their binding capabilities. Furthermore, in vitro studies using Biacore analysis reveal a concentration-dependent and specific binding between PlaA1 and ∆N27 PlaS, exhibiting high affinity. Molecular docking analysis provides insights into the hydrogen bond interactions between ∆N27 PlaS and PlaA1, identifying key amino acid residues crucial for their binding. Finally, the enzyme activity of PLA1 was boost to 8.4 U/mL by orthogonal test. Study significantly contributes to the understanding of the interaction mechanism between PlaS and PlaA1, offering potential strategies for enhancing PlaA1 activity through protein engineering approaches. [ABSTRACT FROM AUTHOR]

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β-lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications. A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Here the authors use the evolutionary model EVcouplings to computationally design highly mutated variants of TEM-1 ß-lactamase, and characterise these designs experimentally. [ABSTRACT FROM AUTHOR]

Targeted gene delivery to the brain is a critical tool for neuroscience research and has significant potential to treat human disease. However, the site-specific delivery of common gene vectors such as adeno-associated viruses (AAVs) is typically performed via invasive injections, which limit its applicable scope of research and clinical applications. Alternatively, focused ultrasound blood-brain-barrier opening (FUS-BBBO), performed noninvasively, enables the site-specific entry of AAVs into the brain from systemic circulation. However, when used in conjunction with natural AAV serotypes, this approach has limited transduction efficiency and results in substantial undesirable transduction of peripheral organs. Here, we use high throughput in vivo selection to engineer new AAV vectors specifically designed for local neuronal transduction at the site of FUS-BBBO. The resulting vectors substantially enhance ultrasound-targeted gene delivery and neuronal tropism while reducing peripheral transduction, providing a more than ten-fold improvement in targeting specificity in two tested mouse strains. In addition to enhancing the only known approach to noninvasively target gene delivery to specific brain regions, these results establish the ability of AAV vectors to be evolved for specific physical delivery mechanisms. Targeted gene delivery to the brain is a critical tool for neuroscience research and has significant potential to treat human disease. Here the authors engineer the protein shell of a common gene therapy vector for enhanced efficiency and specificity of brain delivery in ultrasound-targeted brain regions. [ABSTRACT FROM AUTHOR]

DNA base editors enable direct editing of adenine (A), cytosine (C), or guanine (G), but there is no base editor for direct thymine (T) editing currently. Here we develop two deaminase-free glycosylase-based base editors for direct T editing (gTBE) and C editing (gCBE) by fusing Cas9 nickase (nCas9) with engineered human uracil DNA glycosylase (UNG) variants. By several rounds of structure-informed rational mutagenesis on UNG in cultured human cells, we obtain gTBE and gCBE with high activity of T-to-S (i.e., T-to-C or T-to-G) and C-to-G conversions, respectively. Furthermore, we conduct parallel comparison of gTBE/gCBE with those recently developed using other protein engineering strategies, and find gTBE/gCBE show the outperformance. Thus, we provide several base editors, gTBEs and gCBEs, with corresponding engineered UNG variants, broadening the targeting scope of base editors. Efficient base editors for direct thymine (T) editing are highly desirable. Here, authors develop two deaminase-free glycosylase-based base editors for direct T editing (gTBE) and C editing (gCBE) by rounds of structure-informed mutagenesis on human DNA glycosylase UNG and further engineering. [ABSTRACT FROM AUTHOR]

Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins, including splicing variants, derived from the UniProt reference proteome UP000005640. To ensure high quality and comparability, the dataset was generated by combining state-of-the-art modeling-tools AlphaFold 2, OpenFold, and ESMFold, provided within NVIDIA's BioNeMo platform, as well as homology modeling using Innophore's CavitomiX platform. Our dataset is offered in both unedited and edited formats for diverse research requirements. The unedited version contains structures as generated by the different prediction methods, whereas the edited version contains refinements, including a dataset of structures without low prediction-confidence regions and structures in complex with predicted ligands based on homologs in the PDB. We are confident that this dataset represents the most comprehensive collection of human protein structures available today, facilitating diverse applications such as structure-based drug design and the prediction of protein function and interactions. [ABSTRACT FROM AUTHOR]

Bacteriophage endolysins are potential alternatives to conventional antibiotics for treating multidrug-resistant gram-negative bacterial infections. However, their structure–function relationships are poorly understood, hindering their optimization and application. In this study, we focused on the individual functionality of the C-terminal muramidase domain of Gp127, a modular endolysin from E. coli O157:H7 bacteriophage PhaxI. This domain is responsible for the enzymatic activity, whereas the N-terminal domain binds to the bacterial cell wall. Through protein modeling, docking experiments, and molecular dynamics simulations, we investigated the activity, stability, and interactions of the isolated C-terminal domain with its ligand. We also assessed its expression, solubility, toxicity, and lytic activity using the experimental data. Our results revealed that the C-terminal domain exhibits high activity and toxicity when tested individually, and its expression is regulated in different hosts to prevent self-destruction. Furthermore, we validated the muralytic activity of the purified refolded protein by zymography and standardized assays. These findings challenge the need for the N-terminal binding domain to arrange the active site and adjust the gap between crucial residues for peptidoglycan cleavage. Our study shed light on the three-dimensional structure and functionality of muramidase endolysins, thereby enriching the existing knowledge pool and laying a foundation for accurate in silico modeling and the informed design of next-generation enzybiotic treatments. [ABSTRACT FROM AUTHOR]

Plastic pollution is becoming a major health issue due to the recent discovery of microplastics and nanoplastics in living organisms and the environment, calling for advanced technologies to remove plastic waste. Here we review enzymes that degrade plastics with focus on plastic properties, protein engineering and polymers such as poly(ethylene terephthalate), poly(butylene adipate-co-terephthalate), poly(lactic acid), polyamide and polyurethane. The mechanism of action of natural and engineered enzymes has been probed by experimental and computation approaches. The performance of polyester-degrading enzymes has been improved via directed evolution, structure-guided rational design and machine learning-aided strategies. The improved enzymes display higher stability at elevated temperatures, and tailored substrate-binding sites. [ABSTRACT FROM AUTHOR]

Calcitonin is a 32-amino acid peptide, which causes a decrease in blood calcium levels and bone resorption in the human body. It is produced in different species. Salmon calcitonin is used as a medicine for diseases such as osteoporosis, Paget's disease, and hypercalcemia. However, the salmon calcitonin peptide used as a medicine, could induce immune responses in humans. Decreasing the antigenicity of salmon calcitonin could improve this molecule for pharmaceutical usage. In this study, improving physicochemical properties and reducing allergenicity and especially the antigenicity of salmon calcitonin were followed. The calcitonin sequences of different species were evaluated, and those with better properties were considered as a guide for peptide engineering. In silico methods were utilized to characterize the properties of the reviewed calcitonin sequences, and the best sequences (the calcitonin of sheep, dog, rat, and human) were used as a template to decrease the antigenicity of salmon calcitonin. The epitopic parts, i.e., amino acids 16 to 29, were identified by different servers. Hot spot residues including Y22, N26, T27, and S29 were characterized based on the main criteria of being a B-cell epitope including convexity index, hydrophilicity, and surface accessibility. These residues were replaced with lower antigenic counterparts. Results showed the final selected mutant (T27V/S29V) had a lower antigenicity and higher solubility and stability than salmon calcitonin. Thus, our suggested mutant could be a potential alternative candidate to salmon calcitonin. However, future in vitro and in vivo evaluations are needed to confirm its suitability for clinical usage. [ABSTRACT FROM AUTHOR]

In view of the extensive potential applications of chitinase (ChiA) in various fields such as agriculture, environmental protection, medicine, and biotechnology, the development of a high-yielding strain capable of producing chitinase with enhanced activity holds significant importance. The objective of this study was to utilize the extracellular chitinase from Bacillus thuringiensis as the target, and Bacillus licheniformis as the expression host to achieve heterologous expression of ChiA with enhanced activity. Initially, through structural analysis and molecular dynamics simulation, we identified key amino acids to improve the enzymatic performance of chitinase, and the specific activity of chitinase mutant D116N/E118N was 48% higher than that of the natural enzyme, with concomitant enhancements in thermostability and pH stability. Subsequently, the expression elements of ChiA(D116N/E118N) were screened and modified in Bacillus licheniformis, resulting in extracellular ChiA activity reached 89.31 U/mL. Further efforts involved the successful knockout of extracellular protease genes aprE, bprA and epr, along with the gene clusters involved in the synthesis of by-products such as bacitracin and lichenin from Bacillus licheniformis. This led to the development of a recombinant strain, DW2△abelA, which exhibited a remarkable improvement in chitinase activity, reaching 145.56 U/mL. To further improve chitinase activity, a chitinase expression frame was integrated into the genome of DW2△abelA, resulting in a significant increas to 180.26 U/mL. Optimization of fermentation conditions and medium components further boosted shake flask enzyme activity shake flask enzyme activity, achieving 200.28 U/mL, while scale-up fermentation experiments yielded an impressive enzyme activity of 338.79 U/mL. Through host genetic modification, expression optimization and fermentation optimization, a high-yielding ChiA strain was successfully constructed, which will provide a solid foundation for the extracellular production of ChiA. [ABSTRACT FROM AUTHOR]

Immune checkpoint blockade (ICB) therapies function by alleviating immunosuppression on tumor-infiltrating lymphocytes (TILs) but are often insufficient to fully reactivate these dysfunctional TILs. Although interleukin 12 (IL-12) has been used in combination with ICB to improve efficacy, this remains limited by severe toxicity associated with systemic administration of this cytokine. Here, we engineer a fusion protein composed of an anti-PD-1 antibody and a mouse low-affinity IL-12 mutant-2 (αPD1-mIL12mut2). Systemic administration of αPD1-mIL12mut2 displays robust antitumor activities with undetectable toxicity. Mechanistically, αPD1-mIL12mut2 preferentially activates tumor-infiltrating PD-1+CD8+T cells via high-affinity αPD-1 mediated cis-binding of low-affinity IL-12. Additionally, αPD1-mIL12mut2 treatment exerts an abscopal effect to suppress distal tumors, as well as metastasis. Collectively, αPD1-mIL12mut2 treatment induces robust systemic antitumor responses with reduced side effects. IL-12 has been shown to enhance the efficacy of anti-PD-1 therapy, but this has been hampered by issues with toxicity and poor delivery to the tumour site. In this study, the authors generate an anti-PD-1 antibody/IL-12 fusion protein for specific targeting of IL-12 to tumour sites, resulting in potent anti-tumour immunity with limited toxicity. [ABSTRACT FROM AUTHOR]

Polyethylene terephthalate (PET) is one of the most marketed aromatic polyesters in the world with an annual demand in 2022 of approximately 29 million metric tons, expected to increase by 40% by 2030. The escalating volume of PET waste and the current inadequacy of recycling methods have led to an accumulation of PET in the terrestrial ecosystem, thereby posing significant global health risks. The pressing global energy and environmental issues associated with PET underscore the urgent need for "upcycling" technologies. These technologies aim to transform reclaimed PET into higher-value products, addressing both energy concerns and environmental sustainability. Enzyme-mediated biocatalytic depolymerization has emerged as a potentially bio-sustainable method for treating and recycling plastics. Numerous plastic-degrading enzymes have been identified from microbial origins, and advancements in protein engineering have been employed to modify and enhance these enzymes. Microbial metabolic engineering allows for the development of modified microbial chassis capable of degrading PET substrates and converting their derived monomers into industrial relevant products. In this review, we describe several engineering approaches aiming at enhancing the performances of PET-degrading enzymes and we present the current metabolic engineering strategies adopted to bio-upcycle PET into high-value molecules. [ABSTRACT FROM AUTHOR]

The work by Hassabis and Jumper on protein structure prediction together with Baker's supremacy in de novo protein design set the stage for a future where AI not only deciphers biology at the atomic level but also designs new molecules for biotechnology, medicine, and beyond. I provide an overview of the recent past, the present, and the future of AI in structural biology, from how it all started with the Critical Assessment of Structure Prediction (CASP) experiments and a protein engineering lab, to how the field could further evolve with AI models that eventually "understand" biology holistically. A Comment on the transformative progress of artificial intelligence for structural and protein biology, referencing the 2024 Nobel Prize in Chemistry. [ABSTRACT FROM AUTHOR]

Fucosyl-oligosaccharides (FUS) provide many health benefits to breastfed infants, but they are almost completely absent from bovine milk, which is the basis of infant formula. Therefore, there is a growing interest in the development of enzymatic transfucosylation strategies for the production of FUS. In this work, the α-L-fucosidases Fuc2358 and Fuc5372, previously isolated from the intestinal bacterial metagenome of breastfed infants, were used to synthesize fucosyllactose (FL) by transfucosylation reactions using p-nitrophenyl-α-L-fucopyranoside (pNP-Fuc) as donor and lactose as acceptor. Fuc2358 efficiently synthesized the major fucosylated human milk oligosaccharide (HMO) 2′-fucosyllactose (2′FL) with a 35% yield. Fuc2358 also produced the non-HMO FL isomer 3′-fucosyllactose (3′FL) and traces of non-reducing 1-fucosyllactose (1FL). Fuc5372 showed a lower transfucosylation activity compared to Fuc2358, producing several FL isomers, including 2′FL, 3′FL, and 1FL, with a higher proportion of 3′FL. Site-directed mutagenesis using rational design was performed to increase FUS yields in both α-L-fucosidases, based on structural models and sequence identity analysis. Mutants Fuc2358-F184H, Fuc2358-K286R, and Fuc5372-R230K showed a significantly higher ratio between 2′FL yields and hydrolyzed pNP-Fuc than their respective wild-type enzymes after 4 h of transfucosylation. The results with the Fuc2358-F184W and Fuc5372-W151F mutants showed that the residues F184 of Fuc2358 and W151 of Fuc5372 could have an effect on transfucosylation regioselectivity. Interestingly, phenylalanine increases the selectivity for α-1,2 linkages and tryptophan for α-1,3 linkages. These results give insight into the functionality of the active site amino acids in the transfucosylation activity of the GH29 α-L-fucosidases Fuc2358 and Fuc5372. Key points: Two α-L-fucosidases from infant gut bacterial microbiomes can fucosylate glycans Transfucosylation efficacy improved by tailored point-mutations in the active site F184 of Fuc2358 and W151 of Fuc5372 seem to steer transglycosylation regioselectivity [ABSTRACT FROM AUTHOR]

The delivery of CRISPR ribonucleoproteins (RNPs) for genome editing in vitro and in vivo has important advantages over other delivery methods, including reduced off-target and immunogenic effects. However, effective delivery of RNPs remains challenging in certain cell types due to low efficiency and cell toxicity. To address these issues, we engineer self-deliverable RNPs that can promote efficient cellular uptake and carry out robust genome editing without the need for helper materials or biomolecules. Screening of cell-penetrating peptides (CPPs) fused to CRISPR-Cas9 protein identifies potent constructs capable of efficient genome editing of neural progenitor cells. Further engineering of these fusion proteins establishes a C-terminal Cas9 fusion with three copies of A22p, a peptide derived from human semaphorin-3a, that exhibits substantially improved editing efficacy compared to other constructs. We find that self-deliverable Cas9 RNPs generate robust genome edits in clinically relevant genes when injected directly into the mouse striatum. Overall, self-deliverable Cas9 proteins provide a facile and effective platform for genome editing in vitro and in vivo. The delivery of CRISPR RNPs has potential advantages over other genome editing approaches, including reduced off-target editing and reduced immunogenicity. Here the authors report self-deliverable Cas9 RNPs capable of robustly editing cultured cells in vitro and the mouse brain upon direct injections. [ABSTRACT FROM AUTHOR]