Development of deaminase-free T-to-S base editor and C-to-G base editor by engineered human uracil DNA glycosylase.

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.

Self-driving autonomous machines to engineer novel enzymes.

Can one design and automate a computational and experimental platform such that each platform iteratively guides and drives the other to achieve a pre-determined goal? Rapp and colleagues (2024) describe just this possibility in a paper that details a prototype of a self-driven laboratory that can navigate autonomously to yield an engineered enzyme with a desired attribute. This laboratory, rather, the automated protocol, is referred to by an acronym - SAMPLE. This refers to Self-driving Autonomous Machines for Protein Landscape Exploration. The paper describes a prototype involving the engineering of a glycoside hydrolase for enhanced thermostability. The 'brain', the computational component behind this automated system, was designed to learn protein sequence- function relationships from a curated dataset. These designer proteins were then evaluated by a fully automated robotic system that could synthesize and experimentally characterize the designed protein and provide feedback to the agent, i.e., the computational component, to fine-tune its understanding of the system. The SAMPLE agents were thus designed to continually refine their understanding of the protein landscape by actively acquiring information in the search process. As this intelligent agent learns protein sequence-function relationships from a curated, diverse dataset, this feedback is crucial to refine landscape exploration and the design of new proteins based on the updated hypothesis. In this prototype, four SAMPLE agents were tasked with this goal. The goal of each of these agents was to navigate the glycoside hydrolase landscape and identify enzymes with enhanced thermal tolerance. Differences in the search behavior of individual agents primarily arise from experimental measurement noise. However, despite differences in their search behavior, all four agents could converge on a thermostable glycoside hydrolase - a remarkable feat as it apparently did not need any human intervention.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries.

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Biosynthetic production of anticoagulant heparin polysaccharides through metabolic and sulfotransferases engineering strategies.

Heparin is an important anticoagulant drug, and microbial heparin biosynthesis is a potential alternative to animal-derived heparin production. However, effectively using heparin synthesis enzymes faces challenges, especially with microbial recombinant expression of active heparan sulfate N-deacetylase/N-sulfotransferase. Here, we introduce the monosaccharide N-trifluoroacetylglucosamine into Escherichia coli K5 to facilitate sulfation modification. The Protein Repair One-Stop Service-Focused Rational Iterative Site-specific Mutagenesis (PROSS-FRISM) platform is used to enhance sulfotransferase efficiency, resulting in the engineered NST-M8 enzyme with significantly improved stability (11.32-fold) and activity (2.53-fold) compared to the wild-type N-sulfotransferase. This approach can be applied to engineering various sulfotransferases. The multienzyme cascade reaction enables the production of active heparin from bioengineered heparosan, demonstrating anti-FXa (246.09 IU/mg) and anti-FIIa (48.62 IU/mg) activities. This study offers insights into overcoming challenges in heparin synthesis and modification, paving the way for the future development of animal-free heparins using a cellular system-based semisynthetic strategy.

Automated optimization of the solubility of a hyper-stable α-amylase.

Most successes in computational protein engineering to date have focused on enhancing one biophysical trait, while multi-trait optimization remains a challenge. Different biophysical properties are often conflicting, as mutations that improve one tend to worsen the others. In this study, we explored the potential of an automated computational design strategy, called CamSol Combination, to optimize solubility and stability of enzymes without affecting their activity. Specifically, we focus on Bacillus licheniformis α-amylase (BLA), a hyper-stable enzyme that finds diverse application in industry and biotechnology. We validate the computational predictions by producing 10 BLA variants, including the wild-type (WT) and three designed models harbouring between 6 and 8 mutations each. Our results show that all three models have substantially improved relative solubility over the WT, unaffected catalytic rate and retained hyper-stability, supporting the algorithm's capacity to optimize enzymes. High stability and solubility embody enzymes with superior resilience to chemical and physical stresses, enhance manufacturability and allow for high-concentration formulations characterized by extended shelf lives. This ability to readily optimize solubility and stability of enzymes will enable the rapid and reliable generation of highly robust and versatile reagents, poised to contribute to advancements in diverse scientific and industrial domains.

Engineering a Low-Immunogenic Mirror-Image VHH against Vascular Endothelial Growth Factor.

Immunogenicity is a major caveat of protein therapeutics. In particular, the long-term administration of protein therapeutic agents leads to the generation of antidrug antibodies (ADAs), which reduce drug efficacy while eliciting adverse events. One promising solution to this issue is the use of mirror-image proteins consisting of d-amino acids, which are resistant to proteolytic degradation in immune cells. We have recently reported the chemical synthesis of the enantiomeric form of the variable domain of the antibody heavy chain (d-VHH). However, identifying mirror-image antibodies capable of binding to natural ligands remains challenging. In this study, we developed a novel screening platform to identify a d-VHH specific for vascular endothelial growth factor A (VEGF-A). We performed mirror-image screening of two newly constructed synthetic VHH libraries displayed on T7 phage and identified VHH sequences that effectively bound to the mirror-image VEGF-A target (d-VEGF-A). We subsequently synthesized a d-VHH candidate that preferentially bound the native VEGF-A (l-VEGF-A) with submicromolar affinity. Furthermore, immunization studies in mice demonstrated that this d-VHH elicited no ADAs, unlike its corresponding l-VHH. Our findings highlight the utility of this novel d-VHH screening platform in the development of protein therapeutics exhibiting both reduced immunogenicity and improved efficacy.

Diversifying de novo TIM barrels by hallucination.

De novo protein design expands the protein universe by creating new sequences to accomplish tailor-made enzymes in the future. A promising topology to implement diverse enzyme functions is the ubiquitous TIM-barrel fold. Since the initial de novo design of an idealized four-fold symmetric TIM barrel, the family of de novo TIM barrels is expanding rapidly. Despite this and in contrast to natural TIM barrels, these novel proteins lack cavities and structural elements essential for the incorporation of binding sites or enzymatic functions. In this work, we diversified a de novo TIM barrel by extending multiple ßα-loops using constrained hallucination. Experimentally tested designs were found to be soluble upon expression in Escherichia coli and well-behaved. Biochemical characterization and crystal structures revealed successful extensions with defined α-helical structures. These diversified de novo TIM barrels provide a framework to explore a broad spectrum of functions based on the potential of natural TIM barrels.

Enzyme engineering lets us play with new building blocks in non-ribosomal peptide synthesis.

In a recent issue of Nature Chemical Biology, Folger et al. demonstrated a high-throughput approach for engineering peptide bond forming domains from non-ribosomal peptide synthesis. A non-ribosomal peptide synthetase module from surfactin biosynthesis was reprogrammed to accept a fatty acid substrate into peptide biosynthesis, thus illustrating the potential of this approach for generating novel bioactive peptides.

Cytokine/Antibody Fusion Protein Design and Evaluation.

Cytokines constitute a class of secreted proteins that activate transmembrane receptors to coordinate a vast array of physiological processes, particularly those related to immune activity. Due to their vital role in immune regulation, cytokines have garnered great interest as potential therapeutic agents. Unfortunately, the clinical success of cytokine drugs has been limited by their multifunctional activities, which hinder therapeutic performance and lead to harmful toxicities. In addition, the strikingly short circulation half-life of cytokines further hampers their efficacy as drugs. To overcome the translational challenges associated with natural cytokines, significant efforts have focused on engineering cytokines to target their activities and improve their pharmacological properties. One such strategy is the design of fusion proteins that tether a cytokine to an anti-cytokine antibody that selectively biases its functions and extends its serum half-life. These cytokine/antibody fusion proteins (termed immunocytokines) assemble intramolecularly to bias cytokine signaling behavior through multi-layered structural and molecular effects. Here, we present a detailed workflow for the design, production, and functional validation of intramolecularly assembled immunocytokines. In-depth procedures are presented for gene manipulation, mammalian cell-based expression and purification, binding analysis via bio-layer interferometry, and interrogation of cytokine signaling activity on human primary cells. In contrast with immunocytokines in which the tethered cytokine and antibody do not bind one another, intramolecularly assembled immunocytokines require special considerations with respect to their production to avoid oligomerization and/or aggregation. The protocol herein was developed based on experience with immunocytokines that incorporate interleukin-2 (IL-2); however, this modular approach can be extended to any cytokine of interest for a broad range of biomedical applications. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Design and generation of immunocytokine genes Basic Protocol 2: Immunocytokine expression and purification Basic Protocol 3: Validation of immunocytokine assembly and binding by bio-layer interferometry Basic Protocol 4: Analysis of immunocytokine signaling on human primary cells.

High-activity recombinant human carboxypeptidase B expression in Pichia pastoris through rational protein engineering and enhancing secretion from the Golgi apparatus to the plasma membrane.

Human carboxypeptidase B1 (hCPB1) is vital for recombinant insulin production, holding substantial value in the pharmaceutical industry. Current challenges include limited hCPB1 enzyme activity. In this study, recombinant hCPB1 efficient expression in Pichia pastoris was achieved. To enhance hCPB1 secretion, we conducted signal peptides screening and deleted the Vps10 sortilin domain, reducing vacuolar mis-sorting. Overexpression of Sec4p increased the fusion of secretory vesicles with the plasma membrane and improved hCPB1 secretion by 20%. Rational protein engineering generated twenty-two single-mutation mutants and identified the A178L mutation resulted in a 30% increase in hCPB1 specific activity. However, all combinational mutations that increased specific activities decreased protein expression levels. Therefore, computer-aided global protein design with PROSS was employed for the aim of improving specific activities and preserving good protein expression. Among the six designed mutants, hCPB1-P6 showed a remarkable 114% increase in the catalytic rate constant (kcat), a 137% decrease in the Michaelis constant (Km), and a 490% increase in catalytic efficiency. Most mutations occurred on the surface of hCPB1-P6, with eight sites mutated to proline. In a 5 L fermenter, hCPB1-P6 was produced by the secretion-enhanced P. pastoris chassis to 199.6 ± 20 mg L-1 with a specific activity of 96 ± 0.32 U mg-1, resulting in a total enzyme activity of 19137 ± 1131 U L-1, demonstrating significant potential for industrial applications.

Robust genetic codes enhance protein evolvability.

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

Ser/Leu-swapped cell-free translation system constructed with natural/in vitro transcribed-hybrid tRNA set.

The Ser/Leu-swapped genetic code can act as a genetic firewall, mitigating biohazard risks arising from horizontal gene transfer in genetically modified organisms. Our prior work demonstrated the orthogonality of this swapped code to the standard genetic code using a cell-free translation system comprised of 21 in vitro transcribed tRNAs. In this study, to advance this system for protein engineering, we introduce a natural/in vitro transcribed-hybrid tRNA set. This set combines natural tRNAs from Escherichia coli (excluding Ser, Leu, and Tyr) and in vitro transcribed tRNAs, encompassing anticodon-swapped tRNASerGAG and tRNALeuGGA. This approach reduces the number of in vitro transcribed tRNAs required from 21 to only 4. In this optimized system, the production of a model protein, superfolder green fluorescent protein, increases to 3.5-fold. With this hybrid tRNA set, the Ser/Leu-swapped cell-free translation system will stand as a potent tool for protein production with reduced biohazard concerns in future biological endeavors.

Characterization of a novel format scFv×VHH single-chain biparatopic antibody against metal binding protein MtsA.

Biparatopic antibodies (bpAbs) are engineered antibodies that bind to multiple different epitopes within the same antigens. bpAbs comprise diverse formats, including fragment-based formats, and choosing the appropriate molecular format for a desired function against a target molecule is a challenging task. Moreover, optimizing the design of constructs requires selecting appropriate antibody modalities and adjusting linker length for individual bpAbs. Therefore, it is crucial to understand the characteristics of bpAbs at the molecular level. In this study, we first obtained single-chain variable fragments and camelid heavy-chain variable domains targeting distinct epitopes of the metal binding protein MtsA and then developed a novel format single-chain bpAb connecting these fragment antibodies with various linkers. The physicochemical properties, binding activities, complex formation states with antigen, and functions of the bpAb were analyzed using multiple approaches. Notably, we found that the assembly state of the complexes was controlled by a linker and that longer linkers tended to form more compact complexes. These observations provide detailed molecular information that should be considered in the design of bpAbs.

Engineering G protein-coupled receptors for stabilization.

G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.

A systematic analysis of regression models for protein engineering.

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.

Expanding the horizons of levan: from microbial biosynthesis to applications and advanced detection methods.

Levan, a ß-(2,6)-linked fructose polymer, exhibits diverse properties that impart versatility, rendering it a highly sought-after biopolymer with various industrial applications. Levan can be produced by various microorganisms using sucrose, food industry byproducts and agricultural wastes. Microbial levan represents the most potent cost-effective process for commercial-scale levan production. This study reviews the optimization of levan production by understanding its biosynthesis, physicochemical properties and the fermentation process. In addition, genetic and protein engineering for its increased production and emerging methods for its detection are introduced and discussed. All of these comprehensive studies could serve as powerful tools to optimize levan production and broaden its applications across various industries.

Rationalization design, soluble expression and PEG modification of highly active recombinant human-porcine uricase mutant protein.

Uric acid is the end product of purine metabolism in humans due to inactivation of the uricase determined by the mutated uricase gene. Uricase catalyzes the conversion of uric acid into water-soluble allantoin that is easily excreted by the kidneys. Hyperuricemia occurs when the serum concentration of uric acid exceeds its solubility (7 mg/dL). However, modifications to improve the uricase activity is under development for treating the hyperuricemia. Here we designed 7 types of human-porcine chimeric uricase by multiple sequence comparisons and targeted mutagenesis. An optimal human-porcine chimeric uricase mutant (uricase-10) with both high activity (6.33 U/mg) and high homology (91.45 %) was determined by enzyme activity measurement. The engineering uricase was further modified with PEGylation to improve the stability of recombinant protein drugs and reduce immunogenicity, uricase-10 could be more suitable for the treatment of gout and hyperuricemia theoretically.

Enhancing the stability of a novel D-allulose 3-epimerase from Ruminococcus sp. CAG55 by interface interaction engineering and terminally attached a self-assembling peptide.

D-allulose, a highly desirable sugar substitute, is primarily produced using the D-allulose 3-epimerase (DAE). However, the availability of usable DAE enzymes is limited. In this study, we discovered and engineered a novel DAE Rum55, derived from a human gut bacterium Ruminococcus sp. CAG55. The activity of Rum55 was strictly dependent on the presence of Co2+, and it exhibited an equilibrium conversion rate of 30.6 % and a half-life of 4.5 h at 50 °C. To enhance its performance, we engineered the interface interaction of Rum55 to stabilize its tetramer structure, and the best variant E268R was then attached with a self-assembling peptide to form active enzyme aggregates as carrier-free immobilization. The half-life of the best variant E268R-EKL16 at 50 °C was dramatically increased 30-fold to 135.3 h, and it maintained 90 % of its activity after 13 consecutive reaction cycles. Additionally, we identified that metal ions played a key role in stabilizing the tetramer structure of Rum55, and the dependence on metal ions for E268R-EKL16 was significantly reduced. This study provides a useful route for improving the thermostability of DAEs, opening up new possibilities for the industrial production of D-allulose.

Rational design to improve the catalytic efficiency and stability of arginine deiminase.

Arginine deiminase (ADI) has garnered significant interest because of its ability to objectively eradicate cancer cells and produce L-citrulline. To meet the production demands, this study focused on enhancing the enzyme activity and thermal stability of ADI. In this study, 24 ADI mutants were obtained through computer aid site-specific mutation in the ADI of Enterobacter faecalis. Notably, the specific enzyme activities of F44W, N163P, E220I, E220L, N318E, A336G, T340I, and N382F increased, reaching 1.33-2.53 times that of the original enzyme. This study confirmed that site-specific mutations are critical for optimizing enzyme function. Additionally, the F44W, N163P, E220I, T340I, and A336G mutants demonstrated good thermal stability. The optimal pH for mutant F44W increased to 8, whereas mutants E220I, I244V, A336G, T340I, and N328F maintained an optimal pH of 7.5. Conversely, the M109L, N163P, E220L, I244L, and N318E mutants shad an optimal pH of 7. This study revealed that mutant enzymes with increased activity were more likely to contain mutation sites situated near the four loops associated with catalytic residues, whereas mutations at the dimer junction sites had a higher tendency to enhance enzyme stability. These findings contribute to the development of ADI industrial applications and its modifications.