Xenobiology is an emerging field that focuses on the extension and redesign of biological systems through the use of laboratory-derived xenomolecules, which are molecules that are new to the metabolism of the cell. Despite the enormous potential of using xenomolecules in living organisms, most noncanonical building blocks still need to be supplied externally, and often poor uptake into cells limits wider applicability. To improve the cytosolic availability of noncanonical molecules, a synthetic transport system based on portage transport was developed, in which molecules of interest \"cargo\" are linked to a synthetic transport vector that enables piggyback transport through the alkylsulfonate transporter (SsuABC) of Escherichia coli. Upon cytosolic delivery, the vector-cargo conjugate is enzymatically cleaved by GGTxe, leading to the release of the cargo molecule. To deepen our understanding of the synthetic transport system, we focused on the characterization and further development of the enzymatic cargo release step. Hence, the substrate scope of GGTxe was characterized using a library of structurally diverse vector-cargo conjugates and MS/MS-based quantification of hydrolysis products in a kinetic manner. The resulting substrate tolerance characterization revealed that vector-amino acid conjugates were significantly unfavored. To overcome this shortcoming, a selection system based on metabolic auxotrophy complementation and directed evolution of GGTxe was established. In a directed evolution campaign, we improved the enzymatic activity of GGTxe for vector-amino acid conjugates and revealed the importance of residue D386 in the cargo unloading step.

The crucial molecular factors that shape the interfaces of lipid-binding proteins with their target ligands and surfaces remain unknown due to the complex makeup of biological membranes. Cholesterol, the major modulator of bilayer structure in mammalian cell membranes, is recognised by various proteins, including the well-studied cholesterol-dependent cytolysins (CDCs). Here, we use in vitro evolution to investigate the molecular adaptations that preserve the cholesterol specificity of perfringolysin O (PFO), the prototypical CDC from Clostridium perfringens. We identify variants with altered membrane-binding interfaces whose cholesterol-specific activity exceeds that of the wild-type PFO. These novel variants represent alternative evolutionary outcomes and have mutations at conserved positions that can only accumulate when epistatic constraints are alleviated. Our results improve the current understanding of the biochemical malleability of the surface of a lipid-binding protein.

Protein-based therapeutics, including antibodies and antibody-like-proteins, have increasingly attracted attention due to their high specificity compared to small-molecular drugs. The Gγ recruitment system, one of the in vivo yeast two-hybrid systems for detecting protein-protein interactions, has been previously developed using yeast signal transduction machinery. In this study, we modified the Gγ recruitment system to screen the protein mutants that efficiently bind to the intracellular domain of the epidermal growth factor receptor L858R mutant (cytoEGFRL858R). Using the modified platform, we performed in vivo directed evolution for growth factor receptor-bound protein 2 (Grb2) and its truncated variant containing only the Src-homology 2 (SH2) domain, successfully identifying several mutants that more strongly bound to cytoEGFRL858R than their parental proteins. Some of them contained novel beneficial mutations (F108Y and Q144H) and specifically bound to the recombinant cytosolic phosphorylated EGFR in vitro, highlighting the utility of the evolutionary platform.

Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine learning or deep learning methods have shown some success in this task, but their outcomes were not satisfactory enough, primarily due to the neglect of evolutionary information. In this paper, we introduce the Chaos-Attention net for Promoter Evolution (CAPE) to address the limitations of existing methods. We comprehensively extract evolutionary information within promoters using merged chaos game representation and process the overall information with modified DenseNet and Transformer structures. Our model achieves state-of-the-art results on two kinds of distinct tasks related to prokaryotic promoter strength prediction. The incorporation of evolutionary information enhances the model\'s accuracy, with transfer learning further extending its adaptability. Furthermore, experimental results confirm CAPE\'s efficacy in simulating in silico directed evolution of promoters, marking a significant advancement in predictive modeling for prokaryotic promoter strength. Our paper also presents a user-friendly website for the practical implementation of in silico directed evolution on promoters. The source code implemented in this study and the instructions on accessing the website can be found in our GitHub repository https://github.com/BobYHY/CAPE.

O-Glycosylflavonoids exhibit diverse biological activities but their low content in plants is difficult to extract and isolate, and chemical synthesis steps are cumbersome, which are harmful to the environment. Therefore, the biosynthesis of O-glycosylflavonoids represents a green and sustainable alternative strategy, with glycosyltransferases playing a crucial role in this process. However, there are few studies on flavone 5-O-glycosyltransferases, which limits the synthesis of rare flavone 5-O glycosides by microorganisms. In this study, we characterized a highly regioselectivity flavone 5-O glycosyltransferase from Panicum hallii. Site-directed mutagenesis at residue P141 switches glucosylation to xylosylation. Using a combinatorial strategy of metabolic engineering, we generated a series of Escherichia coli recombinant strains to biocatalyze glycosylation of the typical flavone apigenin. Ultimately, further optimization of transformation conditions, apigenin-5-O-glucoside and apigenin-5-O-xyloside were biosynthesized for the first time so far, and the yields were 1490 mg/L and 1210 mg/L, respectively. This study provides a biotechnological component for the biosynthesis of flavone-5-O-glycosides, and established a green and sustainable approach for the industrial production of high-value O-glycosylflavones by engineering, which lays a foundation for their further development and application in food and pharmaceutical fields.

Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the protein sequence landscape and the epistatic mutational effects across residues. To address this challenge, we introduce MLSmut, a deep learning-based approach that leverages multi-level structural features of proteins. MLSmut extracts salient information from protein co-evolution, sequence semantics, and geometric features to predict the mutational effect. Extensive benchmark evaluations on 10 single-site and two multi-site deep mutation scanning datasets demonstrate that MLSmut surpasses existing methods in predicting mutational outcomes. To overcome the limited training data availability, we employ a two-stage training strategy: initial coarse-tuning on a large corpus of unlabeled protein data followed by fine-tuning on a curated dataset of 40-100 experimental measurements. This approach enables our model to achieve satisfactory performance on downstream protein prediction tasks. Importantly, our model holds the potential to predict the mutational effects of any protein sequence. Collectively, these findings suggest that our approach can substantially reduce the reliance on laborious wet lab experiments and deepen our understanding of the intricate relationships between mutations and protein function.

Gene therapy aims to add, replace or turn off genes to help treat disease. To date, the US Food and Drug Administration (FDA) has approved 14 gene therapy products. With the increasing interest in gene therapy, feasible gene delivery vectors are necessary for inserting new genes into cells. There are different kinds of gene delivery vectors including viral vectors like lentivirus, adenovirus, retrovirus, adeno-associated virus et al, and non-viral vectors like naked DNA, lipid vectors, polymer nanoparticles, exosomes et al, with viruses being the most commonly used. Among them, the most concerned vector is adeno-associated virus (AAV) because of its safety, natural ability to efficiently deliver gene into cells and sustained transgene expression in multiple tissues. In addition, the AAV genome can be engineered to generate recombinant AAV (rAAV) containing transgene sequences of interest and has been proven to be a safe gene vector. Recently, rAAV vectors have been approved for the treatment of various rare diseases. Despite these approvals, some major limitations of rAAV remain, namely nonspecific tissue targeting and host immune response. Additional problems include neutralizing antibodies that block transgene delivery, a finite transgene packaging capacity, high viral titer used for per dose and high cost. To deal with these challenges, several techniques have been developed. Based on differences in engineering methods, this review proposes three strategies: gene engineering-based capsid modification (capsid modification), capsid surface tethering through chemical conjugation (surface tethering), and other formulations loaded with AAV (virus load). In addition, the major advantages and limitations encountered in rAAV engineering strategies are summarized.

In vitro RNA synthesis technologies are crucial in developing therapeutic RNA drugs, such as mRNA vaccines and RNA interference (RNAi) therapies. Enzymatic RNA synthesis, recognized for its sustainability and efficiency, enables the production of extensive RNA sequences under mild conditions. Among the enzymes utilized, T7 RNA polymerase is distinguished by its exceptional catalytic efficiency, enabling the precise and rapid transcription of RNA from DNA templates by recognizing the specific T7 promoter sequence. With the advancement in clinical applications of RNA-based drugs, there is an increasing demand for the synthesis of chemically modified RNAs that are stable and resistant to nuclease degradation. To this end, researchers have applied directed evolution to broaden the enzyme\'s substrate scope, enhancing its compatibility with non-canonical substrates and reducing the formation of by-products. This review summarizes the progress in engineering T7 RNA polymerase for these purposes and explores prospective developments in the field.

Enzymatic DNA writing technologies based on the template-independent DNA polymerase terminal deoxynucleotidyl transferase (TdT) have the potential to advance DNA information storage. TdT is unique in its ability to synthesize single-stranded DNA de novo but has limitations, including catalytic inhibition by ribonucleotide presence and slower incorporation rates compared to replicative polymerases. We anticipate that protein engineering can improve, modulate, and tailor the enzyme\'s properties, but there is limited information on TdT sequence-structure-function relationships to facilitate rational approaches. Therefore, we developed an easily modifiable screening assay that can measure the TdT activity in high-throughput to evaluate large TdT mutant libraries. We demonstrated the assay\'s capabilities by engineering TdT mutants that exhibit both improved catalytic efficiency and improved activity in the presence of an inhibitor. We screened for and identified TdT variants with greater catalytic efficiency in both selectively incorporating deoxyribonucleotides and in the presence of deoxyribonucleotide/ribonucleotide mixes. Using this information from the screening assay, we rationally engineered other TdT homologues with the same properties. The emulsion-based assay we developed is, to the best of our knowledge, the first high-throughput screening assay that can measure TdT activity quantitatively and without the need for protein purification.

Herbicides play a crucial role in boosting crop yields, yet the emergence of herbicide-resistant weeds and the susceptibility of crops to herbicides have posed significant challenges to their efficacy. β-triketone herbicides specifically target the enzyme 4-Hydroxyphenylpyruvate dioxygenase (HPPD) essential for plant growth. Remarkably, few resistant weeds have been identified against these herbicides. In this study, we aimed to identify mutations within the cotton HPPD gene that confer resistance to mesotrione, a widely used triketone herbicide. Through the establishment of a high-throughput mutant screening system in E. coli, we identified four single nucleotide changes leading to amino acid substitutions in HPPD, resulting in mesotrione resistance while preserving native enzymatic activity. Various combinations of these mutations displayed synergistic effects on herbicide resistance. Additionally, the HPPD variants were able to complement the Arabidopsis athppd mutant, indicating their retention of sufficient native activity crucial for plant growth and development. Expression of these cotton HPPD variants in Arabidopsis resulted in heightened herbicide resistance. These findings offer critical insights into the target amino acids of HPPD for gene editing, paving the way for the development of herbicide-resistant cotton in the future.