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Computational design and directed evolution strategies for optimizing protein stability Qingyun RUAN, Xin HUANG, Zijun MENG, Shu QUAN Synthetic Biology Journal    2023, 4 (1): 5-29.   DOI: 10.12211/2096-8280.2022-038 Abstract （2636）   HTML （343）    PDF（pc） （2169KB）（3547）       Knowledge map    Save Most natural proteins tend to be marginally stable, which allows them to gain flexibility for biological functions. However, marginal stability is often associated with protein misfolding and aggregation under stress conditions, presenting a challenge for protein research and applications such as proteins as biocatalysts and therapeutic agents. In addition, protein instability has been increasingly recognized as one of the major factors causing human diseases. For example, the formation of toxic protein aggregates is the hallmark of many neurodegenerative diseases, including Alzheimer's and Parkinson's diseases. Therefore, optimizing protein folding and maintaining protein homeostasis in cells are long-standing goals for the scientific community. Confronting these challenges, various methods have been developed to stabilize proteins. In this review, we classify and summarize various techniques for engineering protein stability, with a focus on strategies for optimizing protein sequences or cellular folding environments. We first outline the principles of protein folding, and describe factors that affect protein stability. Then, we describe two main approaches for protein stability engineering, namely, computational design and directed evolution. Computational design can be further classified into structure-based, phylogeny-based, folding energy calculation-based and artificial intelligence-assisted methods. We present the principles of several methods under each category, and also introduce easily accessible web-based tools. For directed evolution approaches, we focus on library-based, high-throughput screening or selection techniques, including cellular or cell-free display and stability biosensors, which link protein stability to easily detectable phenotypes. We not only introduce the applications of these techniques in protein sequence optimization, but also highlight their roles in identifying novel folding factors, including molecular chaperones, chemical chaperones, and inhibitors of protein aggregation. Moreover, we demonstrate the applications of protein stability engineering in biomedicine and pharmacotherapeutics, including identifying small molecules to stabilize disease-related, aggregation-prone proteins, obtaining conformation-fixed and stable antigens for vaccine development, and targeting protein stability as a means to control protein homeostasis. Finally, we look forward to the trends and prospects of protein stabilization technologies, and believe that protein stability engineering will lead to a better understanding of protein folding processes to facilitate the development of precision medicine. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Recent development of directed evolution in protein engineering Yanping QI, Jin ZHU, Kai ZHANG, Tong LIU, Yajie WANG Synthetic Biology Journal    2022, 3 (6): 1081-1108.   DOI: 10.12211/2096-8280.2022-025 Abstract （2634）   HTML （288）    PDF（pc） （3627KB）（3353）       Knowledge map    Save Directed evolution aims to accelerate the natural evolution process in vitro or in vivo through iterative cycles of genetic diversification and screening or selection. It has been one of the most solid and widely used tools in protein engineering. This review outlines the representative methods developed in the past 10 years that increase the throughput of directed evolution, including in vitro and in vivo gene diversification methods, high-throughput selection and screening methods, continuous evolution strategies, automation-assisted evolution strategies, and AI-assisted protein engineering. To illustrate the significant applications of directed evolution in protein engineering, this review subsequently discusses some remarkable cases to show how directed evolution was used to improve various properties of enzymes, such as the tolerance to elevated temperature or organic solvent, the activities on non-native substrates, and chemo-, regio-, stereo-, and enantio-selectivities. In addition, directed evolution has also been widely used to expand the biocatalytic repertories by engineering enzymes with abiotic activities. In addition to the native enzymes, directed evolution has also been used to engineer de novo designed enzymes and artificial metalloenzymes with activities comparable to or exceeding the ones of the native enzymes. Finally, this review has pointed out that further improving the efficiency and effectiveness of directed evolution remains challenging. Some advanced continuous evolution and high throughput screening strategies have been succesfully demonstrated in improving the throughput of directed evolutions extensively, but they have been limited to engineering certain protein targets. To resolve those issues, continuously improved computational modeling tools and machine learning strategies can assist us to create a smaller but more accurate library to enhance the probabilities of discovering variants with improved properties. Additionally, laboratorial automation platforms coupled with advanced screening and selection techniques also have great potential to extensively explore the protein fitness landscape by evolving multiple targets continuously in a high throughput manner. Table and Figures | Reference | Related Articles | Metrics

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Artificial intelligence-assisted protein engineering Jiahao BIAN, Guangyu YANG Synthetic Biology Journal    2022, 3 (3): 429-444.   DOI: 10.12211/2096-8280.2021-032 Abstract （3709）   HTML （396）    PDF（pc） （2456KB）（3124）       Knowledge map    Save Protein engineering is one of the important research fields of synthetic biology. However, de novo design of protein functions based on rational design is still challenging, because of the limited understanding on biological fundamentals such as protein folding and the natural evolution mechanism of enzymes. Directed evolution is capable of optimizing protein functions effectively by mimicking the principle of natural evolution in the laboratory without relying on structure and mechanism information. However, directed evolution is highly dependent on high-throughput screening methods, which also limits its applications on proteins which lack high-throughput screening methods. In recent years, artificial intelligence has been developed very rapidly for integrating into multidisciplinary fields. In synthetic biology, artificial intelligence-assisted protein engineering has become an efficient strategy for protein engineering besides rational design and directed evolution, which has shown unique advantages in predicting the structure, function, solubility of proteins and enzymes. Artificial intelligence models can learn the internal properties and relationships from given sequence-function data sets to make predictions on properties for virtual sequences. In this article, we review the application of artificial intelligence-assisted protein engineering. With the basic and process of the strategy introduced, three key points that affect the performance of the predictive model are analyzed: data, molecular descriptors and artificial intelligence algorithms. In order to provide useful tools for researchers who want to take advantage of this strategy, we summarize the main public database, diverse toolkits and web servers of the common molecular descriptors and artificial intelligence algorithms. We also comment on the functions, applications and websites of several artificial intelligence-assisted protein engineering platforms, through which a complete prediction task including protein sequences representation, feature analysis, model construction and output can be completed easily. Finally, we analyze some challenges that need to be solved in the artificial intelligence-assisted protein engineering, such as the lack of high-quality data, deviation in data sets and lacking of the universal models. However, with the development of automated gene annotations, ultra-high-throughput screening technologies and artificial intelligence algorithms, sufficient high-quality data and appropriate algorithms will be developed, which can enhance the performance of artificial intelligence-assisted protein engineering and thus facilitate the development of synthetic biology techniques. Table and Figures | Reference | Related Articles | Metrics

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Recent advances in photoenzymatic synthesis Yang MING, Bin CHEN, Xiaoqiang HUANG Synthetic Biology Journal    2023, 4 (4): 651-675.   DOI: 10.12211/2096-8280.2022-056 Abstract （2938）   HTML （271）    PDF（pc） （5785KB）（2367）       Knowledge map    Save Biocatalysis has the advantages in terms of sustainability, efficiency, selectivities and evolvability, thus it plays a more and more important role in green and sustainable synthesis, both in industrial production and academic research. However, compared with the well-known privileged chemocatalysts, enzymes suffer from the relatively limited types of reactions it can catalyze, which is unable to meet the future needs of green biomanufacturing. On the other hand, photocatalysis has emerged as one of the most effective strategies for the generation of reactive chemical intermediates under mild conditions, thereby providing a fertile testing ground for inventing new chemistry. However, the light-generated organic intermediates, including radicals, radical ions, ions, as well as excited states, are highly reactive resulting in the difficulties of controlling the chemo- and stereo-selectivities. The integration of biocatalysis and photocatalysis created a cross-disciplinary area, namely photoenzymatic catalysis, which can not only provide a new solution to stereochemical control of photochemical transformations with the exquisite and tunable active site of enzymes, but also open a new avenue to expand the reactivity of enzymes with visible-light-excitation. In addition, photoenzymatic catalysis inherits the inherent advantages of biocatalysis and photocatalysis, such as mild reaction conditions, representing green and sustainable synthetic methods. We have witnessed the booming development of photoenzymatic catalysis during the past several years. In this review paper, the recent advances in this field are highlighted. According to the cooperative modes of photocatalysis and enzymes, this paper is divided into following four parts: photoredox-enabled cofactor regeneration systems, cascade/cooperative reactions combining enzymes with photocatalysts, unnatural transformations with photoactivable oxidoreductase, and artificial photoenzymes. In this paper, we summarize the representative works and emphasize on the catalytic mechanisms of photoenzymatic transformations as well as the strategies for realizing abiological transformations. At the end of this review, by analyzing the challenges of photoenzymatic synthesis, the future directions are prospected. We hope that this review can inspire the discovery of more novel photoenzymatic systems and ultimately spur the applications of photoenzymes in industrial productions of high value-added enantiopure chiral products. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Cell-free protein synthesis: from basic research to engineering applications Jiaqi HOU, Nan JIANG, Lianju MA, Yuan LU Synthetic Biology Journal    2022, 3 (3): 465-486.   DOI: 10.12211/2096-8280.2021-064 Abstract （2655）   HTML （248）    PDF（pc） （2584KB）（2288）       Knowledge map    Save Cell-free protein synthesis (CFPS), also known as in vitro gene expression, is a multifunctional technique used to complement cell-based protein expression, which is at the core of cell-free synthetic biology. Since the CFPS system does not require a living cell, it can simulate the entire cellular transcription and translation process in vitro in a controlled environment, and allows for an in-depth study of individual components and biological networks. Therefore, as a platform technology, it is expected to overcome the loopholes caused by the limitations of cell membranes in the current in vivo manufacturing systems, which has a broad research prospect in fundamental and applied scientific research. The cell-free operation is simple and easy to control, and its advantages over in vivo protein expression include its nature with open systems, eliminating the dependence on living cells and using all system energy for the production of the target proteins. This article reviews the composition of CFPS systems and their development based on different component types, including different biological extracts or purified transcription and translation components. Furthermore, different CFPS reaction patterns are introduced, including batch and continuous exchange modes, and the research progress of CFPS systems in genetic circuits, protein engineering, and the construction of artificial life is described. Among them, the genetic circuit research progress mainly summarizes the latest applications and contributions of cell-free technology in the prototype design, biosensors, and in vitro metabolic engineering. The protein engineering research progress lists the advantages and advances of the CFPS systems for producing membrane proteins, virus-like particles, post-translational modifications, unnatural amino acid incorporation and protein evolution. In the construction of artificial "living systems", the synthesis of bacteriophages and the construction of artificial cells have opened up a novel frontier field. Finally, the opportunities and challenges of the CFPS platforms for future scientific research and industrial applications are highlighted. Table and Figures | Reference | Related Articles | Metrics

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Rewiring and application of Yarrowia lipolytica chassis cell Meili SUN, Kaifeng WANG, Ran LU, Xiaojun JI Synthetic Biology Journal    2023, 4 (4): 779-807.   DOI: 10.12211/2096-8280.2022-060 Abstract （1265）   HTML （179）    PDF（pc） （2749KB）（2222）       Knowledge map    Save Engineering microbial chassis cells to efficiently synthesize high value-added products has received increasing attention. This biomanufacturing mode based on excellent performance microbial chassis cells has become the research frontier in the field of synthetic biology. Yarrowia lipolytica, an unconventional oleaginous yeast, is emerging as one of the popular microbial chassis cells in the field of advanced and green biomanufacturing. This is due to its unique physiological and biochemical characteristics, such as the inherent mevalonate pathway, adequate acetyl-CoA supply, broad substrate spectrum, and high tolerance to multiple extreme environments. These characteristics make Y. lipolytica a superior chassis candidate for the advanced and green biomanufacturing. In recent years, the researches and applications on the rewiring of Y. lipolytica chassis cell for biomanufacturing have gradually increased, which promoted the further upgrading of Y. lipolytica chassis cells. This review firstly describes the development of the genetic elements for rewiring Y. lipolytica chassis cell, including promoters, terminators, and selecting markers. Then, this review summarizes the expression modes and integration methods for endogenous and heterogenous genes, including gene expression based on episomal plasmid, genomic integration based on homologous recombination (HR) and non-homologous end joining (NHEJ). This review further summarizes the research progress of various synthetic biology tools developed for Y. lipolytica, including various gene overexpression methods, biosensor-based dynamic regulation strategies, CRISPR/Cas-based gene expression regulation methods, and the emerging strategies such as genome-scale metabolic modelling, genome-wide mutational screening, etc. This review also introduces the achievements of rewiring Y. lipolytica chassis cell for the synthesis of different high value-added products, including proteins, organic acids, terpenes, functional sugars and sugar alcohols, fatty acids and their derivatives, flavonoids and polyketides, and amino acid derivatives. In addition, the prospects of Y. lipolytica chassis cell-based biomanufacturing are discussed in light of the current progresses, challenges, and trends in this field. Finally, guidelines for building next-generation Y. lipolytica chassis cell for production of the aforementioned products are also emphasized. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Enzyme engineering in the age of artificial intelligence Liqi KANG, Pan TAN, Liang HONG Synthetic Biology Journal    2023, 4 (3): 524-534.   DOI: 10.12211/2096-8280.2023-009 Abstract （2771）   HTML （303）    PDF（pc） （1310KB）（2082）       Knowledge map    Save Enzymes have garnered significant attention in both research and industry due to their unparalleled specificity and functionality, and thus opportunities remain for enhancing their physichemical properties and fitness to improve catalytic performance. The primary objective of enzyme engineering is to optimize the fitness of targeted enzymes through various strategies for their modifications, even redesigning. This review provides a comprehensive overview for progress made in enzyme engineering, with a focus on artificial intelligence (AI)-guided design methodology. Several key strategies have been employed in enzyme engineering, including rational design, directed evolution, semi-rational design, and AI-guided design. Rational design relies on an extensive knowledge based on encompassing protein structures and catalytic mechanisms, allowing for purposeful manipulations of enzyme properties. Directed evolution, on the other hand, involves the generation of a library of random variants for subsequent high-throughput screening to identify beneficial mutations. Semi-rational design combines rational design and directed evolution, resulting in a smaller, yet more targeted, library of variants, which mitigates high cost associated with extensive screening of large libraries developed through directed evolution. In recent years, AI technologies, particularly deep neural networks, have emerged as a promising approach for enzyme engineering, and AI-guided methods leverage a vast amount of information regarding protein sequences, multiple sequence alignments, and protein structures to learn key features for correlations. These learned features can then be applied to various downstream tasks in enzyme engineering, such as predicting mutations with beneficial effect, optimizing protein stability, and enhancing catalytic activity. Herewith, we delves into advancements and successes in each of these strategies for enzyme engineering, highlighting the growing impact of AI-guided design on the process. By offering a detailed examination of the current state of enzyme engineering, we aim at providing valuable insight for researchers and engineers to further advance the development and optimization of enzymes for more applications. Table and Figures | Reference | Related Articles | Metrics

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Design of synthetic biology components based on artificial intelligence and computational biology Sheng WANG, Zechen WANG, Weihua CHEN, Ke CHEN, Xiangda PENG, Fafen OU, Liangzhen ZHENG, Jinyuan SUN, Tao SHEN, Guoping ZHAO Synthetic Biology Journal    2023, 4 (3): 422-443.   DOI: 10.12211/2096-8280.2023-004 Abstract （2539）   HTML （356）    PDF（pc） （1930KB）（1977）       Knowledge map    Save The primary objective of synthetic biology is to conceptualize, engineer, and construct novel biological components, devices, and systems based on established principles and extant information or to reconfigure existing natural biological systems. The core concept of synthetic biology encompasses the design, modification, reconstruction, or fabrication of biological components, reaction systems, metabolic pathways and processes, and even the creation of cells and organisms with functions or living characteristics. This burgeoning field offers innovative technologies to address challenges with sustainable development in environment, resource, energy, and so on. Undeniably, synthetic biology has yielded significant progress in numerous fields, ranging from DNA recombination to gene circuit design, yet its full potential remains insufficiently explored, but the emergence and application of artificial intelligence (AI) definitely can facilitate the development of synthetic biology for more applications. From a synthetic biology perspective, essence for life is rooted in digitalization and designability. This article reviews current advances in computational biology, particularly AI for synthetic biology to be more efficient and effective, focusing on the development of biocatalysts, regulators, and sensors. De novo enzyme design has been successfully implemented by using Rosetta software, as AI exhibiting significant potential for generating innovative structures and protein sequences with diverse functions. Also, the reprogramming of natural enzymes for specific purposes is crucial for synthetic biology applications. By employing various force fields and sampling techniques, promiscuity and thermal stability can be modified to accommodate specific requirements rather than those with natural hosts. AI can be integrated into the life-cycle of synthetic biology through an active learning paradigm, which enables alterations in enzyme specificity, and demonstrates potential for accurately and rapidly predicting mutation effects, surpassing force-field-based methods. The rapidly decreasing cost of sequencing has facilitated the characterization of cis-regulators, primarily DNA and RNA, with high-throughput. Concurrently, more trans-regulators have been identified in sequenced genomes. The expanding wealth in big data serves as a driving force for AI. AI models have successfully predicted the strength of promoters, ribosome binding sites (RBSs), and enhancers, and generated artificial protomers and RBSs. Recent progress in RNA structure prediction is expected to aid the design of RNA elements. Sensors, vital for genetic circuits and other applications such as toxin detection, typically involve interactions among various molecules, including nucleic acids, proteins, small organic molecules, and metal ions. Consequently, sensor design necessitates the integration of diverse computational biology tools to balance accuracy and computational cost. As the pool of data keeps growing, we anticipate that AI will be increasingly applied to the design of more bio-parts. Table and Figures | Reference | Related Articles | Metrics

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Research progress of artificial intelligence in desiging protein structures Zhihang CHEN, Menglin JI, Yifei QI Synthetic Biology Journal    2023, 4 (3): 464-487.   DOI: 10.12211/2096-8280.2023-008 Abstract （1869）   HTML （225）    PDF（pc） （3481KB）（1931）       Knowledge map    Save Proteins are essential to life as they carry out a great variety of biological functions. Protein sequences determine their three-dimensional structures, and therefore physiological functions. Proteins with specific functions have important applications in many fields such as biomedicine, where they are utilized in drug design and delivery. In the past, protein engineering and directed evolution are commonly used to improve the activity and stability of proteins. These methods, however, are both complex and expensive, as they require a large number of biological experiments for validation. Computational protein design (CPD) allows the design of amino acid sequences based on desired protein functions and structures, and more intriguingly, generation of proteins even not found in nature. Conventional CPD uses energy function and optimization algorithm to design protein sequences. In recent years, with the rapid development of artificial intelligence (AI) technique, the accumulation of big data and the development of high speed computing, AI has made great progresses in learning, and been successfully applied in CPD. In this review, based on the input constraints and sampling space size, we present a systematic overview of recent applications of AI in protein design from three aspects: fixed-backbone design, flexible-backbone design, and sequence structure generation. We focus on algorithms and protein feature encoding, present the effect of dataset size and architectural improvements on model performance in prediction, and showcase several enzymes, antibodies, and binding proteins that were successfully designed using these models. The advantages of AI compared with traditional CPD methods are also discussed. Finally, we highlight challenges in AI-aided protein design, and propose some strategies for solutions. Table and Figures | Reference | Related Articles | Metrics

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Application of deep learning in protein function prediction Yidong SONG, Qianmu YUAN, Yuedong YANG Synthetic Biology Journal    2023, 4 (3): 488-506.   DOI: 10.12211/2096-8280.2022-078 Abstract （1633）   HTML （164）    PDF（pc） （1457KB）（1922）       Knowledge map    Save Protein function prediction is essential for bioinformatics analysis, which benefits a wide range of biological studies such as understanding the functions of metagenomes, uncovering mechanism underlying diseases, and finding new drug targets. With the rapid development of high-throughput sequencing technology, protein sequence data have been increased quickly, but functions of most proteins have not yet been identified. Since traditional biochemical experiments to determine protein functions are usually expensive, time-consuming, and less efficient, developing more efficient and effective computational methods for protein function prediction is of great significance. Deep learning technology has made breakthroughs in many fields, including image recognition, natural language processing, genomic analysis and drug discovery. In this review, we address applications of deep learning in protein function prediction, which can be divided into residue-level binding site prediction and protein-level gene ontology (GO) prediction. Protein binding sites are regions that bind to specific ligands, which play an important role in signal transduction, metabolism, revealing molecular mechanisms underlying diseases, and designing new drugs. Gene ontology is a standard function classification system for genes, which provides a set of annotations to comprehensively describe the properties of genes and gene products. Firstly, we introduce commonly used large-scale protein structure and function databases. Secondly, discriminative protein sequence and structure features are described. Thirdly, we summarize the latest protein function prediction methods: in terms of the prediction of binding sites, we introduce the latest methods based on the ligand type, including protein, peptide, nucleic acid and small molecule as well as ion ligand, and in the aspect of GO prediction, we highlight the latest sequence-based, structure-based, and protein interaction network-based methods developed with protein information. Finally, we comment the advantages and disadvantages of the current protein function prediction methods, and discuss the future development in this field. Table and Figures | Reference | Related Articles | Metrics

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Assessment on the pre-reaction state of enzyme: could we understand catalytic activity with near transition-state molecular dynamic simulation?-a review Byuri SIM, Yilei ZHAO Synthetic Biology Journal    2022, 3 (3): 567-586.   DOI: 10.12211/2096-8280.2021-013 Abstract （1487）   HTML （85）    PDF（pc） （1963KB）（1921）       Knowledge map    Save The bottleneck of enzyme design for biosynthetic elements lies in the incompetence of the limited computing resources with demanding for an in-depth computation on complicated potential energy surfaces of catalytic reactions. However, two unprecedented achievements are expected to expand artificial intelligence machine learning in protein engineering-one is a variety of high-efficient mutants brought by high-throughput directed evolution experiments, and the other is the high-quality molecular simulation of all-atom with femtosecond precision revealed by ab initio quantum mechanics calculation and three-dimensional structural information. This work briefly describes the basic concept and application of the pre-reaction state (PRS) model from the perspectives of the fundamental enzyme theories, the near-attack conformation of Michealis complex, and the control points of the catalytic cycle efficiency. The pre-reaction state model tries to use the intrinsic features of biochemical reactions with low activation energy in which transition state and pre-reaction states share similar physiochemical stability, flexibly selects the rate-determining transition states related to the evolutional goal of the catalytic element, and employs classical molecular dynamics simulations to understand the relationship of active conformation population with distal mutations, substrate spectrum, and experimental conditions. The general pre-reaction state protocol is: first, the near-transition state structural features are extracted from the high-level quantum-mechanical calculation on the rate-determining transition structures; then the PRS molecular dynamic simulations are collected from the restrained to the free state, which is used to study the adaptability between mutants and substrates. The population in the PRS trajectory is used as a semi-quantitative correlation coefficient of “pre-reaction state-enzyme activity” （PRS-EA）, and the adaptation map of enzyme and substrate is mined from the pre-reaction state stability. Although the mechanism-based pre-reaction state analysis provides an insightful rationale at atom levels as a post-NAC approach, the quantitative relationship between the PRS structure and enzymatic reaction cannot be fully illustrated owing to the ambiguity of the PRS constraint, the repeatability of molecular dynamics simulation, and the arbitrariness of reactive population. The high throughput quantum calculation for transition state samplings and machine learning and artificial intelligence could be integrated to unveil the quantitative structure-activity relationship, paving a way for the practical applications of pre-reaction state in protein engineering. Table and Figures | Reference | Related Articles | Metrics

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Recent advances in heterologous production of natural products using Aspergillus oryzae Jiayu DONG, Min LI, Zonghua XIAO, Ming HU, Yudai MATSUDA, Weiguang WANG Synthetic Biology Journal    2022, 3 (6): 1126-1149.   DOI: 10.12211/2096-8280.2022-007 Abstract （2189）   HTML （155）    PDF（pc） （3318KB）（1868）       Knowledge map    Save Organic molecules produced by living organisms, generally termed as natural products, are rich sources of pharmaceutical drugs and biopesticides, and fungi are one of the most prolific producers of medicinally important natural products, as represented by penicillins, lovastatin, and cyclosporins. Heterologous expression is a commonly used approach to study the function of biosynthetic genes of natural products, and a number of heterologous hosts have been developed and utilized over the last decades. The filamentous fungus Aspergillus oryzae has long been utilized for the production of fermented food and drinks in East Asia, and intensive genetic and molecular biological studies on the fungus have allowed for its genetic engineering in an efficient manner. Importantly, A. oryzae is known to possess a relatively clean metabolic background with a low level of secondary metabolite production, providing an attractive feature as a heterologous host. Furthermore, unlike prokaryotic and yeast hosts, most coding sequences of fungal biosynthetic proteins can be directly introduced into A. oryzae in their intact form without removing introns, which simplifies the transformation procedures. Collectively, A. oryzae is a robust platform for heterologous production of natural products, which not only facilitates the elucidation of the biosynthetic pathway of a given natural product but also allows the activation of silent or cryptic biosynthetic gene clusters. Thus, the A. oryzae host has been widely utilized for biosynthetic studies, genome mining, and synthetic biology of fungal natural products. It should be noted that more than ten biosynthetic genes can readily be introduced into the fungus, indicating that the majority of fungal biosynthetic gene clusters can be easily transferred to the A. oryzae host. This review first provides the general transformation procedure of A. oryzae and the molecular biological tools available for the fungus. Next, recent successful applications of this fungal host for the heterologous production of natural products are summarized. With the recent rapid advance in molecular biology, such as the development of genome editing tools, we believe that the heterologous expression of biosynthetic genes in A. oryzae will be performed in a much faster and more versatile manner in the near future, which would ultimately lead to the discovery of useful natural products for drug leads and other applications. Table and Figures | Reference | Related Articles | Metrics

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Application of imine reductase in the synthesis of chiral amines Lu YANG, Xudong QU Synthetic Biology Journal    2022, 3 (3): 516-529.   DOI: 10.12211/2096-8280.2021-054 Abstract （2289）   HTML （139）    PDF（pc） （2499KB）（1845）       Knowledge map    Save Chiral amines with bioactivities are important chiral auxiliaries, and also key intermediates for the synthesis of many natural products and chiral drugs. Among the top 200 drugs for market revenues in 2019, more than 30% contain chiral amine structures. Therefore, the development of efficient and effective methods to synthesize chiral amine compounds is of interest for research. Due to its high efficiency, environmental friendliness and economic competitiveness, more attention has been paid on the enzyme-catalyzed production of chiral amines by academia and industry. Imine reductases (IREDs) reviewed in this article are a class of NAD(P) H-dependent oxidoreductases that catalyze asymmetric reduction of imines to chiral amines. The reduction of C\underset{\text{??????}}{?}N bonds constitutes a physiological reaction present in a number of biosynthetic pathways, leading to a variety of metabolites. The imine reductases have excellent characteristics such as high catalytic efficiency, strong regioselectivity and stereoselectivity, etc., which stand out among many other methods for the synthesis of chiral amines, and attract attention and enthusiasm of many researchers. In the past decade, with the rapid development of bioinformatics, structural biology, high-throughput screening approaches and the continuous expansion of the gene databases, many imine reductases with different functions have been identified. Significant achievements have been made in the discovery of IREDs, protein engineering and multi-enzyme cascade applications, among which some successful modification cases have industrial application potentials. This review summarizes the structural characteristics, catalytic mechanisms and applications of IREDs, with emphasis on their protein engineering and applications in multi-enzyme cascade reactions, as well as the bottleneck, breakthrough and progress in asymmetric catalytic chiral amine biosynthesis. In addition, the challenges and potentials of the enzymatic synthesis of chiral amine compounds for industrial production, and the importance of novel artificial biosynthesis pathway design to overcome these challenges are highlighted. Table and Figures | Reference | Related Articles | Metrics

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Rewiring and application of Pichia pastoris chassis cell Qi LIU, Zhilan QIAN, Lili SONG, Chaoying YAO, Mingqiang XU, Yanna REN, Menghao CAI Synthetic Biology Journal    2022, 3 (6): 1150-1173.   DOI: 10.12211/2096-8280.2022-039 Abstract （2050）   HTML （254）    PDF（pc） （2981KB）（1828）       Knowledge map    Save Microbial chassis hosts are important platforms for green and sustainable biomanufacturing. Pichia pastoris has served as a preferred chassis for heterologous protein expression and fermentation production, which is attributed to its numerous advantages in expression capacity, post-translational modification, high cell density culture, and extracellular product purification. Moreover, as an industrial methylotrophic yeast, P. pastoris effectively utilizes cheap and widely sourced methanol as the sole carbon source, making it a potential biotransformation platform for C1 compounds. Recently, scientists have endowed this nonconventional yeast as an efficient microbial cell factory for biosynthesis of small molecule products beyond its traditional role of a protein expression workhorse. The growing of synthetic biology and biopharmaceutical technology has promoted the rapid development on the genetic rewiring of P. pastoris chassis host. A series of engineering strategies have been developed to break the restrictions and bottlenecks of P. pastoris in both academic and industrial applications. This allowed the updated chassis versions adapting to diversified application scenarios. In this review, we briefly introduce the advances and current status of P. pastoris. We describe the development and application of this chassis from the genetic manipulation technology, regulation of gene expression, and metabolic engineering. We summarize the establishment and characterization of synthetic biological techniques, regulatory parts and devices, novel expression platform, and bioconversion system in P. pastoris. We emphasize the CRISPR-mediated gene editing, transcription regulation, rewiring of natural transcription system, and the design of artificial biosystems. Then the production of glycoprotein and the synthesis of natural products based on alcohols are concisely summarized. Also, the advantages and limitations of this host in practical application are analyzed and discussed. Finally, we propose the research directions for further updating versions of P. pastoris and provide a perspective on their future application scenarios. Table and Figures | Reference | Related Articles | Metrics

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Design, optimization and application of whole-cell microbial biosensors with engineered genetic circuits Lu YANG, Nan WU, Rongrong BAI, Weiliang DONG, Jie ZHOU, Min JIANG Synthetic Biology Journal    2022, 3 (6): 1061-1080.   DOI: 10.12211/2096-8280.2021-021 Abstract （2662）   HTML （301）    PDF（pc） （2989KB）（1825）       Knowledge map    Save Whole-cell microbial biosensors with engineered genetic circuits constructed based on the concept of synthetic biology is an important branch of the biosensor. Whole-cell microbial biosensor is mainly composed of the sensing module, the computing module and the output actuating module. It can sense the concentration of specific substances in the environment and then transfer it to specific signal outputs in time according to certain rules, which shows great potential in bioengineering process control, environmental monitoring, food safety, environmental quality monitoring and disease diagnosis and control, etc. With the improvement of various technologies in synthetic biology and the enrichment of genetic elements, more and more whole-cell microbial sensors based on different response mechanisms, different logic gates and logic circuits have been developed. However, the design and construction of genetically engineered whole-cell microbial biosensor still mainly rely on the empirical method of trial-and error-learning. Therefore, how to design and construct high performance genetically engineered microbial whole-cell biosensors and how to tune its response curves by optimizing genetic elements or circuits to meet the detection requirements of different practical application scenarios is the new and important challenge. Here we reviewed the principle, classification and development process of genetically engineered whole-cell biosensor. We also focused on the design and construction of genetic circuit based on transcription factors and riboswitches, discussed optimization strategies for improving biosensor detection performance including dynamic range, specificity and working range, and then summarized its application progress in different detection fields. The optimization strategies are mainly involved in changing the expression level of genetic elements, adjusting the binding affinity between metabolites and genetic elements, restructuring the position of functional domains, etc. Finally, some challenges, such as biological safety, cumbersome design and construction, and inconvenience to enter the sensor market were discussed. It is expected that emerging technologies such as artificial intelligence, synthetic biology, and droplet microfluidics, will accelerate the development of genetic regulatory elements and the design and construction of novel biosensors. Table and Figures | Reference | Related Articles | Metrics

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Biosynthesis of elastin-like polypeptides and their applications in drug delivery Zhaoying YANG, Fan ZHANG, Jianwen GUO, Weiping GAO Synthetic Biology Journal    2022, 3 (4): 728-747.   DOI: 10.12211/2096-8280.2021-094 Abstract （2284）   HTML （138）    PDF（pc） （4210KB）（1809）       Knowledge map    Save Elastin-like polypeptides (ELPs) are artificially synthetic peptide polymers inspired by human elastin. ELPs are composed of repeat units of a Val-Pro-Gly-X-Gly, where X can be any amino acid except proline, and they can exhibit different biological functions along with X residue changes. ELPs are thermally responsive and demonstrate lower critical solution temperature phase behavior. They are soluble at temperatures below a characteristic transition temperature (Tt) and reversibly phase separate into an insoluble, coacervate phase above the Tt. Moreover, the phase behavior is retained when the ELP is either genetically fused to peptides or covalently conjugated to small molecules, and this phase behavior can be adjusted through changing X residue and chain length of ELPs. As ELPs are typically produced from synthetic genes, the structure and function of ELPs can be accurately regulated through genetic engineering. The amino acids or peptides with reactive side chains can be incorporated into ELPs through recombination synthesis as well. This precision control over ELP is unmatched by synthetic polymers. Based on these properties, ELPs can be engineered to assemble into unique architecture and used as soluble macromolecular carriers, therapeutic drug depots, hyperthermia-targeted drug carriers and self-assembled micelles. Lastly, as ELPs are derived from natural protein sequences, they show desirable biological properties including excellent biocompatibility, low immunogenicity, and non-toxic effects. Due to these attributes, ELPs have been widely used in biomedical fields including protein expression and purification, in vitro diagnosis, drug delivery and tissue engineering. By focusing primarily on applications of ELPs in drug delivery, this review introduces the design principles, physicochemical properties, biosynthetic methods of ELPs and ELP conjugates, as well as exemplifies representative applications of ELPs in drug delivery, as extending the half-life of drugs, tumor targeted delivery, local delivery, hyperthermia-targeted delivery and sustained released of the drugs in vivo. Challenges and problems faced in this emerging field are discussed at the end of this review. Table and Figures | Reference | Related Articles | Metrics

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Biosynthesis of antimicrobial peptides and its medical application Daixu WEI, Hailun GONG, Xuwei ZHANG Synthetic Biology Journal    2022, 3 (4): 709-727.   DOI: 10.12211/2096-8280.2022-001 Abstract （2171）   HTML （202）    PDF（pc） （1946KB）（1782）       Knowledge map    Save Due to their broad-spectrum antibacterial activity and low incidence of drug resistance, natural antimicrobial peptides have become a potential alternative to antibiotics. In addition to being able to control pathogenic bacteria and fungi, antimicrobial peptides also have many other biological effects, such as anticancer, antiviral, antiparasitic and immunomodulatory activity, exhibiting broad biomedical application prospects. This review introduces the distribution and mechanisms of antimicrobial peptides, and summarizes the biosynthesis methods of antimicrobial peptides. We further compare and analyze the advantages and disadvantages of various antimicrobial peptide biosynthesis approaches relying on microbial expression systems and introduce new interdisciplinary peptide-design strategies based on synthetic biology. In addition, we also briefly summarize the applications of antimicrobial peptides. The application prospects of antimicrobial peptides can be classified into seven medical fields, including antiinflammatory drugs, antiviral drugs, antiparasitic drugs, anticancer drugs, medical tissue engineering, drug delivery systems, skin care and cosmetology. Furthermore, we also identify potential problems such as low expression yield, difficulty in extraction, high process cost, poor stability and insufficient biosafety of existing antimicrobial peptides. To solve these issues, computational prediction and directed gene editing technology can be used to create new antimicrobial peptides with improved antibacterial properties and reduced toxicity. It is also important to improve the industrial infrastructure of antibacterial peptide biosynthesis and develop strategies for rapid recovery of high-purity antibacterial peptides. Antimicrobial peptides can also be combined with existing antibiotics to prevent bacterial resistance to traditional antibiotics. Finally, antimicrobial peptides can be combined with new biomaterials to reduce their toxicity to tissues and organs in vivo. Table and Figures | Reference | Related Articles | Metrics

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Advances and applications of droplet-based microfluidics in evolution and screening of engineered microbial strains Ran TU, Shixin LI, Haoni LI, Meng WANG Synthetic Biology Journal    2023, 4 (1): 165-184.   DOI: 10.12211/2096-8280.2021-105 Abstract （2180）   HTML （188）    PDF（pc） （2702KB）（1774）       Knowledge map    Save Microbial strains are perquisites for biomanufacting through microbial culture and fermentation. However, most strains usually need to be engineered to improve their performances for industrial applications. Therefore how to efficiently screen and isolate robust strains is a critical step of strain engineering. As an advanced high-throughput screening technology, droplet-based microfluidics developed with micro-chips can generate highly independent and uniform micro- or nano-liter droplets, in which single cells can be encapsulated, inoculated, detected, and analyzed for strain engineering. It is especially useful in the evolution of microbial strains for producing extracellular products. In this review, we first introduce the basic components of the droplet-based microfluidic system and the main steps involved in the strain screening. We then summarize key factors for the application of the droplet-based microfluidic technology in strain engineering, such as the signal sources of droplet detection, the difficulties of handling droplet screening, and the scopes of droplet sorting instruments. Based on the instruments used for the droplet sorting, we group the application cases into two types either via fluorescence-activated droplet sorting (FADS) using microfluidic equipment or via fluorescence-activated cell sorting (FACS) using flow cytometry instrument. While FADS using single-layer water-in-oil droplet can be further classified into cellular signature, fluorescent reporter protein, and substrate-based reaction according to the signal sources, FACS can be divided into double-layers water-in-oil-in-water (W/O/W) droplet or microgel droplet according to the droplet property. Finally, we outline challenges and prospects for the droplet microfluidic technology, and provide some guidelines for its applications in synthetic biology. Compared with traditional screening methods such as shaking flask or microplate with a throughput of hundreds to thousands of samples per day in milli- or micro-liter volume, the droplet-based microfluidic technology can achieve millions of samples per day in pico- or nano-liter volume, resulting in an increase of thousand-folds in screening speed and cost-saving for million-folds. By integrating with an automated station, the droplet-based microfluidic technology can be further improved for its screening efficiencies and application potentials in microbial synthetic biology. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Advances in optogenetics for biomedical research Yuanhuan YU, Yang ZHOU, Xinyi WANG, Deqiang KONG, Haifeng YE Synthetic Biology Journal    2023, 4 (1): 102-140.   DOI: 10.12211/2096-8280.2022-030 Abstract （1550）   HTML （164）    PDF（pc） （5941KB）（1753）       Knowledge map    Save Synthetic biology enables rational design of regulatory molecules and circuits to reprogram cellular behaviors, and its applications to human cells could lead to powerful gene- and cell-based therapies, which are well recognized as central pillars of next-generation medicines. However, the safety of these therapies remains to be assessed, and controllability is a critical issue affecting their safety and limiting their clinical applications. In recent years, optogenetic technologies have been widely used in biomedical applications, which provides new insights for treating intractable diseases due to their distinguishing features of non-invasiveness, reversibility, and spatiotemporal resolution. Light is an ideal inducer to control gene expression, enabling precise and spatiotemporal manipulation of gene expression and cell behaviors by illuminating with light of appropriate intensity and wavelength as a triggering signal to achieve pinpoint spatiotemporal control of cellular activities. With the development of optogenetic toolkits, optogenetics has recently been developed for therapeutic applications. In this review, we summarize various optogenetic tools responsive to different wavelengths and their applications for precise treatment of neurological diseases, tumors, cardiovascular diseases, diabetes, enteric diseases as well as for the optogenetic control of gene transcription, gene editing, gene recombination and organelle movement. At the same time, we introduce recent research progress in portable bioelectronic medicine and artificial intelligence-assisted diagnosis and treatment systems, which are based on optogenetic techniques and the intelligent electronic devices. The rapid development of optogenetics has enormously extended the scope of traditional bioelectronic medicine, and the remote-controllability, reversibility, and negligible toxicity of optical control systems provide a solid foundation for the application of optogenetics in biomedicine. The success of these approaches would have an impact on precision medicine in the future practice. Finally, we also discuss the shortcomings of existing optogenetic tools and the challenges that would be faced in the future clinical applications as well as the prospects of their development. {L-End} Table and Figures | Reference | Related Articles | Metrics

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Mining, engineering and functional expansion of CRISPR/Cas systems Ke LIU, Guihong LIN, Kun LIU, Wei ZHOU, Fengqing WANG, Dongzhi WEI Synthetic Biology Journal    2023, 4 (1): 47-66.   DOI: 10.12211/2096-8280.2021-022 Abstract （2177）   HTML （209）    PDF（pc） （2351KB）（1736）       Knowledge map    Save The clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) were derived from an acquired immune system in microbes. Since their functions on gene editing have been reported, they have been rapidly used to enhance our ability to edit, regulate, annotate, detect, and image DNA and RNA fragments of various organisms, which consequently have faciliated fundamental research in life science, medicine, bioengineering and so on, and drived the development of synthetic biology and other disciplines. However, CRISPR/Cas systems also have some inherent drawbacks, such as off-target effect, constraint of protospacer-adjacent motif (PAM) on the target, and controllability of the gene editing activity, which substantially compromise their applications in highly precise and controllable gene editing. In order to overcome these challenges, two important strategies have been employed to develop enhanced CRISPR/Cas systems and expand the CRISPR toolbox, including modifying the Cas proteins by protein engineering and mining novel CRISPR/Cas systems with bioinformatics. In the review, focusing on the most widely used the type II CRISPR/Cas systems, we mainly introduce the basic structures and functions of three representative systems, including CRISPR/Cas9, CRISPR/Cas12a and CRISPR/Cas13a, as well as recent progress in their structural modifications and functional expansion. Thereinto, engineering strategies for CRISPR/Cas systems have been systematically commented, which include modification methods for Cas proteins and the way to expand the function of CRISPR/Cas systems by coupling specfic proteins with Cas. In addition, we also review some novel CRISPR/Cas systems with important characteristics and potential applications that have been discovered in recently years, such as CRISPR/CasФ and CRISPR/Cas12k. These engineering modifications and mining work have greatly addressed the inherent problems of the CRISPR/Cas systems, and effectively expanded their functions and applicability, which will further promote the applications of the CRISPR/Cas systems in many fields. {L-End} Table and Figures | Reference | Related Articles | Metrics