Transfer RNAs have been extensively explored as the molecules that translate the genetic code into proteins. At this interface of genetics and biochemistry, tRNAs direct the efficiency of every major step of translation by interacting with a multitude of binding partners. However, due to the variability of tRNA sequences and the abundance of diverse post-transcriptional modifications, a guidebook linking tRNA sequences to specific translational outcomes has yet to be elucidated. Here, we review substantial efforts that have collectively uncovered tRNA engineering principles that can be used as a guide for the tuning of translation fidelity. These principles have allowed for the development of basic research, expansion of the genetic code with non-canonical amino acids, and tRNA therapeutics.

A major challenge in the fields of biological imaging and synthetic biology is noninvasively visualizing the functions of natural and engineered cells inside opaque samples such as living animals. One promising technology that addresses this limitation is ultrasound (US), with its penetration depth of several cm and spatial resolution on the order of 100 μm. Within the past decade, reporter genes for US have been introduced and engineered to link cellular functions to US signals via heterologous expression in commensal bacteria and mammalian cells. These acoustic reporter genes (ARGs) represent a novel class of genetically encoded US contrast agent, and are based on air-filled protein nanostructures called gas vesicles (GVs). Just as the discovery of fluorescent proteins was followed by the improvement and diversification of their optical properties through directed evolution, here we describe the evolution of GVs as acoustic reporters. To accomplish this task, we establish high-throughput, semiautomated acoustic screening of ARGs in bacterial cultures and use it to screen mutant libraries for variants with increased nonlinear US scattering. Starting with scanning site saturation libraries for two homologues of the primary GV structural protein, GvpA/B, two rounds of evolution resulted in GV variants with 5- and 14-fold stronger acoustic signals than the parent proteins. We anticipate that this and similar approaches will help high-throughput protein engineering play as large a role in the development of acoustic biomolecules as it has for their fluorescent counterparts.

Adeno-associated viruses 2 (AAV2) are minute viruses renowned for their capacity to infect human cells and akin organisms. They have recently emerged as prominent candidates in the field of gene therapy, primarily attributed to their inherent non-pathogenic nature in humans and the safety associated with their manipulation. The efficacy of AAV2 as gene therapy vectors hinges on their ability to infiltrate host cells, a phenomenon reliant on their competence to construct a capsid capable of breaching the nucleus of the target cell. To enhance their infection potential, researchers have extensively scrutinized various combinatorial libraries by introducing mutations into the capsid, aiming to boost their effectiveness. The emergence of high-throughput experimental techniques, like deep mutational scanning (DMS), has made it feasible to experimentally assess the fitness of these libraries for their intended purpose. Notably, machine learning is starting to demonstrate its potential in addressing predictions within the mutational landscape from sequence data. In this context, we introduce a biophysically-inspired model designed to predict the viability of genetic variants in DMS experiments. This model is tailored to a specific segment of the CAP region within AAV2\'s capsid protein. To evaluate its effectiveness, we conduct model training with diverse datasets, each tailored to explore different aspects of the mutational landscape influenced by the selection process. Our assessment of the biophysical model centers on two primary objectives: (i) providing quantitative forecasts for the log-selectivity of variants and (ii) deploying it as a binary classifier to categorize sequences into viable and non-viable classes.

The development of immune cell engagers (ICEs) can be limited by logistical and functional restrictions associated with fusion protein designs, thus limiting immune cell recruitment to solid tumors. Herein, a high affinity superantigen-based multivalent ICE is developed for simultaneous activation and recruitment of NK and T cells for tumor treatment. Yeast library-based directed evolution is adopted to identify superantigen variants possessing enhanced binding affinity to immunoreceptors expressed on human T cells and NK cells. High-affinity superantigens exhibiting improved immune-stimulatory activities are then incorporated into a superantigen-based tri-functional yeast-display-enhanced multivalent immune cell engager (STYMIE), which is functionalized with a nanobody, a Neo-2/15 cytokine, and an Fc domain for tumor targeting, immune stimulation, and prolonged circulation, respectively. Intravenous administration of STYMIE enhances NK and T cell recruitment into solid tumors, leading to enhanced inhibition in multiple tumor models. The study offers design principles for multifunctional ICEs.

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

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.

Nature exhibits an enormous diversity of organisms that thrive in extreme environments. From snow algae that reproduce at sub-zero temperatures to radiotrophic fungi that thrive in nuclear radiation at Chernobyl, extreme organisms raise many questions about the limits of life. Is there any environment where life could not \"find a way\"? Although many individual extremophilic organisms have been identified and studied, there remain outstanding questions about the limits of life and the extent to which extreme properties can be enhanced, combined or transferred to new organisms. In this review, we compile the current knowledge on the bioengineering of extremophile microbes. We summarize what is known about the basic mechanisms of extreme adaptations, compile synthetic biology\'s efforts to engineer extremophile organisms beyond what is found in nature, and highlight which adaptations can be combined. The basic science of extremophiles can be applied to engineered organisms tailored to specific biomanufacturing needs, such as growth in high temperatures or in the presence of unusual solvents.

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. As part of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was established and refined during the past 15 years. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. Later, this phenomenon was shown to be general. 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 more mechanistic insight than 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.

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.