A Piecewise Design Approach to Engineering a Miniature ACE2 Mimic to Bind SARS-CoV-2.

As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues its global spread, the exploration of novel therapeutic and diagnostic strategies is still needed. The virus enters host cells by binding the angiotensin-converting enzyme 2 (ACE2) receptor through the spike protein. Here, we develop an engineered, small, stable, and catalytically inactive version of ACE2, termed miniature ACE2 (mACE2), designed to bind the spike protein with high affinity. Employing a magnetic nanoparticle-based assay, we harnessed the strong binding affinity of mACE2 to develop a sensitive and specific platform for the detection or neutralization of SARS-CoV-2. Our findings highlight the potential of engineered mACE2 as a valuable tool in the fight against SARS-CoV-2. The success of developing such a small reagent based on a piecewise molecular design serves as a proof-of-concept approach for the rapid deployment of such agents to diagnose and fight other viral diseases.

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.

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.

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.

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.

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.

Tailor-Made α-Glucans by Engineering the Processivity of α-Glucanotransferases via Tunnel-Cleft Active Center Interconversions.

The function of polysaccharides is intimately associated with their size, which is largely determined by the processivity of transferases responsible for their synthesis. A tunnel active center architecture has been recognized as a key factor that governs processivity of several glycoside hydrolases (GHs), e.g., cellulases and chitinases. Similar tunnel architecture is also observed in the Limosilactobacillus reuteri 121 GtfB (Lr121 GtfB) α-glucanotransferase from the GH70 family. The molecular element underpinning processivity of these transglucosylases remains underexplored. Here, we report the synthesis of the smallest (α1 → 4)-α-glucan interspersed with linear and branched (α1 → 6) linkages by a novel 4,6-α-glucanotransferase from L. reuteri N1 (LrN1 GtfB) with an open-clefted active center instead of the tunnel structure. Notably, the loop swapping engineering of LrN1 GtfB and Lr121 GtfB based on their crystal structures clarified the impact of the loop-mediated tunnel/cleft structure at the donor subsites -2 to -3 on processivity of these α-glucanotransferases, enabling the tailoring of both product sizes and substrate preferences. This study provides unprecedented insights into the processivity determinants and evolutionary diversification of GH70 α-glucanotransferases and offers a simple route for engineering starch-converting α-glucanotransferases to generate diverse α-glucans for different biotechnological applications.

Computer-Aided Semi-Rational Design to Enhance the Activity of l-Sorbosone Dehydrogenase from Gluconobacter oxidans WSH-004.

The titer of the microbial fermentation products can be increased by enzyme engineering. l-Sorbosone dehydrogenase (SNDH) is a key enzyme in the production of 2-keto-l-gulonic acid (2-KLG), which is the precursor of vitamin C. Enhancing the activity of SNDH may have a positive impact on 2-KLG production. In this study, a computer-aided semirational design of SNDH was conducted. Based on the analysis of SNDH's substrate pocket and multiple sequence alignment, three modification strategies were established: (1) expanding the entrance of SNDH's substrate pocket, (2) engineering the residues within the substrate pocket, and (3) enhancing the electron transfer of SNDH. Finally, mutants S453A, L460V, and E471D were obtained, whose specific activity was increased by 20, 100, and 10%, respectively. In addition, the ability of Gluconobacter oxidans WSH-004 to synthesize 2-KLG was improved by eliminating H2O2. This study provides mutant enzymes and metabolic engineering strategies for the microbial-fermentation-based production of 2-KLG.

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.

Bioluminescence Imaging of Potassium Ion Using a Sensory Luciferin and an Engineered Luciferase.

Bioluminescent indicators are power tools for studying dynamic biological processes. In this study, we present the generation of novel bioluminescent indicators by modifying the luciferin molecule with an analyte-binding moiety. Specifically, we have successfully developed the first bioluminescent indicator for potassium ions (K+), which are critical electrolytes in biological systems. Our approach involved the design and synthesis of a K+-binding luciferin named potassiorin. Additionally, we engineered a luciferase enzyme called BRIPO (bioluminescent red indicator for potassium) to work synergistically with potassiorin, resulting in optimized K+-dependent bioluminescence responses. Through extensive validation in cell lines, primary neurons, and live mice, we demonstrated the efficacy of this new tool for detecting K+. Our research demonstrates an innovative concept of incorporating sensory moieties into luciferins to modulate luciferase activity. This approach has great potential for developing a wide range of bioluminescent indicators, advancing bioluminescence imaging (BLI), and enabling the study of various analytes in biological systems.

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.

Unleashing the power of antibodies: Engineering for tomorrow's therapy.

Antibodies play a crucial role in host defense against various diseases. Antibody engineering is a multidisciplinary field that seeks to improve the quality of life of humans. In the context of disease, antibodies are highly specialized proteins that form a critical line of defense against pathogens and the disease caused by them. These infections trigger the innate arm of immunity by presenting on antigen-presenting cells such as dendritic cells. This ultimately links to the adaptive arm, where antibody production and maturation occur against that particular antigen. Upon binding with their specific antigens, antibodies trigger various immune responses to eliminate pathogens in a process called complement-dependent cytotoxicity and phagocytosis of invading microorganisms by immune cells or induce antibody-dependent cellular cytotoxicity is done by antibodies. These engineered antibodies are being used for various purposes, such as therapeutics, diagnostics, and biotechnology research. Cutting-edge techniques that include hybridoma technology, transgenic mice, display techniques like phage, yeast and ribosome displays, and next-generation sequencing are ways to engineer antibodies and mass production for the use of humankind. Considering the importance of antibodies in protecting from a diverse array of pathogens, investing in research holds great promise to develop future therapeutic targets to combat various diseases.

Engineering high affinity antigen-binders: Beyond conventional antibodies.

For decades, antibodies have remained the archetypal binding proteins that can be rapidly produced with high affinity and specificity against virtually any target. A conventional antibody is still considered the prototype of a binding molecule. It is therefore not surprising that antibodies are routinely used in basic scientific and biomedical research, analytical workflows, molecular diagnostics etc. and represent the fastest growing sector in the field of biotechnology. However, several limitations associated with conventional antibodies, including stringent requirement of animal immunizations, mammalian cells for expression, issues on stability and aggregation, bulkier size and the overall time and cost of production has propelled evolution of concepts along alternative antigen binders. Rapidly evolving protein engineering approaches and high throughput screening platforms have further complemented the development of myriads of classes of non-conventional protein binders including antibody derived as well as non-antibody based molecular scaffolds. These non-canonical binders are finding use across disciplines of which diagnostics and therapeutics are the most noteworthy.

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.

Improving plastic degrading enzymes via directed evolution.

Plastic degrading enzymes have immense potential for use in industrial applications. Protein engineering efforts over the last decade have resulted in considerable enhancement of many properties of these enzymes. Directed evolution, a protein engineering approach that mimics the natural process of evolution in a laboratory, has been particularly useful in overcoming some of the challenges of structure-based protein engineering. For example, directed evolution has been used to improve the catalytic activity and thermostability of polyethylene terephthalate (PET)-degrading enzymes, although its use for the improvement of other desirable properties, such as solvent tolerance, has been less studied. In this review, we aim to identify some of the knowledge gaps and current challenges, and highlight recent studies related to the directed evolution of plastic-degrading enzymes.

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/.