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.

Natural enzymes are often difficult to meet the needs of application and research in terms of activity, enantiomer selectivity or thermal stability. Therefore, it is an important task of enzyme engineering to explore efficient molecular modification technologies to improve the properties of such enzymes. The molecular modification technologies of enzymes mainly include rational design, directed evolution, and artificial intelligence-assisted design. Directed evolution and rational design are experiment-driven molecular modification approaches of enzymes and have been successfully applied to enzyme engineering. However, due to the huge space sizes of protein sequences and the lack of experimental data, the current modification methods still face major challenges. With the development of next-generation sequencing, high-throughput screening, protein databases, and artificial intelligence (AI), data-driven enzyme engineering is emerging as a promising solution to these challenges. The AI-assisted statistical learning method has been used to establish a model for predicting the sequence/structure-properties of enzymes in a data-driven manner. Excellent mutant enzymes can be selected according to the prediction results, which greatly improve the efficiency of molecular modification. Considering the application requirements of molecular modification of enzymes, this paper reviews the data acquisition methods and application examples of AI-assisted molecular modification of enzymes, with focuses on the convolutional neural network method for predicting protein thermostability, aiming to provide reference for researchers in this field.

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.

UNASSIGNED: Organophosphorus compounds, widely used in agriculture and industry, pose a serious threat to human health due to their acute neurotoxicity. Although traditional interventions for organophosphate poisoning are effective, they often come with significant side effects.
UNASSIGNED: This paper aims to evaluate the potential of enzymes within biological organisms as organophosphorus bioclearing agents. It analyses the technical challenges in current enzyme research, such as substrate specificity, stereoselectivity, and immunogenicity, while exploring recent advancements in the field.
UNASSIGNED: A comprehensive review of literature related to detoxifying enzymes or proteins was conducted. Existing studies on organophosphorus bioclearing agents were summarised, elucidating the biological detoxification mechanisms, with a particular focus on advancements in protein engineering and novel delivery methods.
UNASSIGNED: Current bioclearing agents can be categorised into stoichiometric and catalytic bioclearing agents, both of which have shown some success in preventing organophosphate poisoning. Technological advancements have significantly improved various properties of bioclearing agents, yet challenges remain, particularly in substrate specificity, stereoselectivity, and immunogenicity. Future research will focus on expanding the substrate spectrum, enhancing catalytic efficiency, prolonging in vivo half-life, and developing convenient administration methods.
UNASSIGNED: With the progression of clinical trials, bioclearing agents are expected to become widely used as a new generation of therapeutic organophosphate detoxifiers.

The development of new or improved single fluorescent protein (FP)-based biosensors (SFPBs), particularly those with excitation and emission at near-infrared wavelengths, is important for the continued advancement of biological imaging applications. In an effort to accelerate the development of new SFPBs, we report modified transposons for the transposase-based creation of libraries of FPs randomly inserted into analyte binding domains, or vice versa. These modified transposons feature ends that are optimized to minimize the length of the linkers that connect the FP to the analyte binding domain. We rationalized that shorter linkers between the domains should result in more effective allosteric coupling between the analyte binding-dependent conformational change in the binding domain and the fluorescence modulation of the chromophore of the FP domain. As a proof of concept, we employed end-modified Mu transposons for the discovery of SFPB prototypes based on the insertion of two circularly permuted red FPs (mApple and FusionRed) into binding proteins for l-lactate and spermidine. Using an analogous approach, we discovered calcium ion (Ca2+)-specific SFPBs by random insertion of calmodulin (CaM)-RS20 into miRFP680, a particularly bright near-infrared (NIR) FP based on a biliverdin (BV)-binding fluorescent protein. Starting from an miRFP680-based Ca2+ biosensor prototype, we performed extensive directed evolution, including under BV-deficient conditions, to create highly optimized biosensors designated the NIR-GECO3 series. We have extensively characterized the NIR-GECO3 series and explored their utility for biological Ca2+ imaging. The methods described in this work will serve to accelerate SFPB development and open avenues for further exploration and optimization of SFPBs across a spectrum of biological applications.